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Expanding Photovoltaic Penetration with Residential Distributed Generation from Hybrid Solar Photovoltaic Combined Heat and Power Systems[edit | edit source]

This paper mainly focuses on the potential of taking into action a distributed PV and CHP hybrid system and how it can help in order to increase PV penetration level in the U.S. The installation of such a hybridized section will reduce the energy waste and will also increase the share of Solar PV to be expanded by a factor of 5.

Technical Limitation to PV penetration in the current grid[edit | edit source]

At high PV penetration (eg>20%) the cost saved by intermittent load will increase instead of decreasing. The variation in PV power that create this problem are 1)day/night cycle 2)yearly cycle 3)fluctuating cloud condition.

Electrical and heat requirements of representative U.S. single family[edit | edit source]

The average annual electricity demand per house in the U.S. is 10654kWh. If we observe the solar flux and electricity demand at various hot and cold region just installing PV system cannot met all the demands. When the PV system is not providing enough power the CHP system will turn on and will maintain a constant load. A CHP+PV system in hotter region can give maximum efficiency if the heat generated by the CHP unit is used for cooling( using absorption chiller) such a system is called CCHP.

Design of Solar PV and CHP hybrid system[edit | edit source]

The PV+CHP system consists of 3 technologies:-1)PV array 2)a natural gas engine generator 3)Advanced warm air heating system. Technology Evolution of CHP units:- 0th generation:-1.2kWe CHP and advanced thermal comfort for variable thermal loads.(no electric loads)--Already Available in market. 1st generation:-1.2kWe CHP + 0.6 kWe PV and advanced thermal comfort for variable thermal loads-fixed input for generator heat dumping-load following in backup mode. PV panel converts 20% of the sunlight incident on them rest is wasted. This type of system is 84% efficient and can be compared to 35% efficient conventional power plant for burning same natural gas. 2nd generation:-1.2kWe CHP + 1.2 kWe PV and advanced thermal comfort for variable thermal loads-fixed input for generator heat dumping-load following in backup mode. Future generations:-Adding a Absorption chiller to the system to utilize the CHP produce heat for cooling. Even trying to reduce the wasted energy from the sun this an be done by adding a solar thermal system.

Sizing of Solar PV and CHP hybrid system[edit | edit source]

The amount of electricity and heat generated considering CHP system gives full backup to PV system( certain assumptions are made) for 2nd generation PV+CHP system is around 10512kWh per year which meets the electricity demand per household. The PV+CHp system produces 76 Mbtu and 96 Mbtu per year at peak sun hours. In 2nd generation CHP will produce a far more than required amount of heat for certain residential but in 3rd generation this problem will be solved by installing a absorption chiller.

PV penetration level-Percentage of PV generated electricity[edit | edit source]

Using various equations and observing average electricity demand at each hour 25% of the total demand can be supplied by PV system in hotter regions an d remaining 75% comes from the CHP system with no storage element included. This will for sure increase the PV penetration in U.S. I order to avoid hourly variation it is better to install a energy storage unit as the response time of CHP unit is not so fast.

Institutional scale operational symbiosis of photovoltaic and cogeneration energy systems[edit | edit source]

1 M. Mostofi; 2 A. H. Nosrat; 2 J. M. Pearce* Department of Mechanical Engineering, Islamic Azad University, East Tehran Branch, Tehran, Iran Department of Mechanical and Materials Engineering, Queen's University, Kingston, Ontario, Canada

The paper mainly focuses on Three design scenarios using only existing technologies for such a hybrid system are considered here:1) single cogeneration + photovoltaic, 2) double cogeneration + photovoltaic, 3) single cogeneration + photovoltaic + storage. Numerical simulations for photovoltaic and cogeneration performance. The paper even shows total amount of natural gas required to provide for the hospitals needs could be lowered from the current status by 55 % for scenario 1 and 62 % for both scenarios 2 and 3, respectively. This significant improvement in natural gas consumption illustrates the potential of hybridizing solar photovoltaic systems and cogeneration systems on a large scale.

Materials and Methods[edit | edit source]

In this section the raw data which is important for installing a PV+CHP hybrid system at a location is discussed. In this paper they have considered example of a Hospital. The solar PV component is simulated using Solar flux and temperature data of that location. The CHP unit is simulated using thermal and electric load demand of the hospital in Tehran (Iran).

Proposed CHP system[edit | edit source]

CHP unit uses heat exchanger to utilize the waste heat. Thus, we get a overall efficiency of more than 85%. Fuel consumption is very less in CHP system.The total efficiency for a CHP system is given by:η= (Q+E)/Q0 Where Q and E represent utilized thermal and electrical energy, respectively and Q0 shows the heat content of the fuel. Hourly data of thermal and electric load is taken. Then base load of the location is considered. Here the base load for hospital was 300kW. Even the peak hours are identified and here the peak hours are 18-24 hrs where load is 600kW. CHP units consisting of natural gas as source has electrical efficiency of around 30-40%. Out of the wasted heat is almost 90% recovered. Even the paper provide formula to calculate the recovered heat. Along wit this the block diagram of CHP system is also given.

Solar PV system[edit | edit source]

The output of solar PV is in the form of Direct current and this cannot be directly connected to grid. An inverter is connected to convert this DC to AC. The PV panels were installed at the roof of hospital in series. Important thing here is the tilt angle of system kept is 10 degrees to ensure self cleaning with rains without penalties. Around 0.5m space was maintained even each panel to ease cleaning. PV technologies works very well where there is good solar irradiance along with it less continuous monthly cloudy days. Such scenario can be seen at borders of Iran.

Design scenario PV+CHP[edit | edit source]

It is very important to consider energy efficiency first. Thermal energy consumption can be reduced by installing heat control mechanism which can improve this is done by the CHP system. Electric energy consumption can be reduced by using CFLs. Installing just a PV system can full upto 23% of the electric energy requirement in summer and upto 20% in winter. Design scenario 1: Single CHP + PV

This scenario incorporates a CHP engine capable of matching the 300 kW base load of the hospital.It is expected that the CHP engine will operate at limited capacity during PV electric generation times while operating at full capacity during nighttime. If suppose some extra electrical energy is generated by the PV+CHP system then required, then the excess energy is fed back to the grid. This scenario will suffice for covering the base load of hospital load. Such an arrangement fulfills about 76% of the requirements of the hospital.

Design scenario 2: Double CHP + PV

This scenario incorporates 2 CHP engine each having 300 kW base load of the hospital. This type of arrangement ismore effective and is also complicated. Such an arrangement fulfills about 93% of the electrical energy requirements of the hospital. The remaining energy is fed into the grid this is mainly during the month April and May. The thermal energy requirement of the hospital still are only 56% met.

Design scenario 3: Single CHP + PV + Storage

This scenario incorporates 1 CHP system having base load of 300kW of the hospital. This arrangement is cheaper as compared to scenario 2 and is results are almost similar to result of scenario 2. However, the controls and batteries necessary to smooth out the electrical load are considerable. The only shortcoming of this design is thermal energy requirement of the hospital is not fulfilled completely.

Results and Discussion[edit | edit source]

The CHP unit is assumed to work entirely on Natural gas for producing both electrical and thermal energy. Producing energy on site is far more efficient then producing energy at power plant which involves transmission losses of around 26%. The power plant efficiency is less then the CHP unit electrical efficiency which is around 35%. The percentage saving on fuel for all the three above mentioned scenarios can be calculated. For scenario 1 the saving is around 44% and for 2 ,3 it is around 38%.

Optimizing design of household scale hybrid solar photovoltaic + combined heat and power systems for Ontario[edit | edit source]

P. Derewonko and J. M. Pearce' Department of Mechanical and Materials Engineering Queen's University, 60 Union Street,Kingston, ON K7L 3N6 Canada • Corresponding author: phone 613-533-3369 fax 613-533-6610

This paper focuses on the feasibility of implementing a hybrid solar photovoltaic (PV) + combined heat and power (CHP) and battery bank system for a residential application to generate reliable base load power to the grid in Ontario. Majority of Solar fluctuations are small in magnitude and the one which are major they can be accommodated by installing Energy storage units such as batteries. This paper provides analysis for a preliminary base line system.

Introduction[edit | edit source]

The inherent power supply intermittent makes PV alone unable to fully replace a new power plant operated in base load. Development of small scale CHP units has given the opportunity for in-house power backup of residential scale PV arrays. Such an hybrid arrangement also increase the PV penetration level without any drawback. In order to explore this solution for Ontario, this study begins the investigation of the feasibility of implementing a hybrid solar photovoltaic + combined heat and power (CHP) + battery bank system to supply the grid with base load power.

Background[edit | edit source]

Penetration level of PV generation is limited below 5% to avoid inherent power supply intermittent. Currently, PV penetration level is <1%. This problems are mainly due to i) diurnal cycle, ii) yearly cycle, and iii) fluctuating cloud conditions The fluctuating cloud conditions is a more challenging problem and it can be partially solved by installing solar PV for large geographical regions.

Data Collection and Analysis[edit | edit source]

The data required for this project was collected from Queens University, where solar panels where installed titled at angle 70 degrees and were capable of generating 20kW. This angle is even selected keeping the snow loading on panels almost 0. A Vantage pro solar sensor is installed in order to determine the solar availability which was tilted at same angle 70 degrees as the panels, this data can be accessed using a data-link and PI process-book. Annual solar irradiation data with minimum shutdown days was considered (year 2007). All the available one second solar energy data recorded for the PV array and pyranometer over a year was analyzed to determine change in energy available per second as a function of time step, time of day, and time of year. A Matlab program was made in order to determine maximum measured irradiance, total amount of measured energy and histogram data for change in PV generation. Then, monthly datasets are viewed graphically to find a day where solar energy distribution is maximum with least power fluctuations.A Matlab program was created to remove this minimum power fluctuations. The program gave a bell like shaped showing which determines a maximum solar irradance value and at what time they took place. The area under this curve gives information of the solar irradiance at every cloudless day in a month. Using this data Solar energy Lost due to cloud cover was determined.

Hybrid PV+CHP+Battery design for a residential system[edit | edit source]

Selecting a CHP system which produced electric energy same as the electric power generated by the solar PV (in this case 1.2kW). The datasheet of various CHP available in market is prepared, which mainly focuses on Electric power , thermal power output, duty cycle of CHP, Cost of the unit, Efficiency and its compatibility.During hours of high solar flux, the instantaneous PV energy is the primary energy source, and the CHP unit is turned off. However, the CHP unit runs continuously during the non-solar hours of the day and during an additional specified overlap time with the low irradiance hours of the day (morning and evening), generating a base load of 1.2 kW using natural gas as a fuel. The heat generated during this process can be used for heating space or water or even can be used by absorption chiller(cooling). The excess electrical energy generated by can be stored in the battery. This energy stored in battery can be utilized when the PV is not able to meet electric load requirements and CHP unit is off.Using hybrid system for residential use in Ontario, depending on the complexity of the system and economics the PV installing angle is determined.

Results and Discussion[edit | edit source]

From the data for the average eletric energy generated by the PV over an year (monthly data), Total Cloud loss estimated over an year (monthly data), it was seen that the average amount of solar energy so generated in an year was same as the amount of cloud loss over an year in Ontario. Thus 50% of solar irradiance is wasted due to cloud covering.Hence significant battery backup is also necessary.

It can also be seen that in Ontario without the CHP unit, the PV array can generate approximately 11% of the base load requirement. Using the CHP unit only during non-solar hours of the day, the CHP and PV account for 60% of the annual energy requirement. Adding an overlap of CHP with PV generation the system is capable of providing 100% of the base load energy requirement. The key factors affecting the overlap time are: the PV array tilt angle, size of the PV array, size of the battery bank, and the base load power requirement.

Optimal sizing of hybrid solar micro-CHP systems for the household sector[edit | edit source]

Caterina Brandoni a, *, Massimiliano Renzi b a Centre for Sustainable Technologies, School of Built Environment, University of Ulster, Newtownabbey, Belfast BT370QB, UK b Libera Universit�a di Bolzano, Facolt�a di Scienze e Tecnologie, Piazza Universit�a 5, 39100 Bolzano, Italy

The paper mainly focuses on the importance of optimal sizing hybrid microgeneration systems for dwelling applications. Indeed, the parameters, the constraints and the criteria which must be considered in the sizing phase are several: i) energy prices, ii) ambient conditions, iii) energy demand, iv) units' characteristics, v) electricity grid constraints. Results point out the importance of optimal sizing hybrid renewable energy systems, in particular the micro-CHP unit, in order to maximize the economic and the energy savings with respect to conventional generation. Furthermore results point out the critical nature of electricity grid constraints, which can halve the achievable energy savings.

Overview[edit | edit source]

Distributed generation devices can be fed by renewable or fossil fuels, and can also be operated in combined heat and power production, providing important results in terms of energy savings and emission reduction.Over the last few years, due to Government funding and supporting schemes, the PV market has experienced a rapid expansion, for instance, the cost of a 3-10 kWp PV system, thanks to both improvements in research and economies of scale, has decreased from 14,000 V/kWp in 1990 down to 1800 V/kWp in 2014. The main problem related to the integration of solar electrical systems into the national electricity grid comes from its intermittency and unpredictable nature.This can be mitigated by introduction of hybrid systems, consisting of coupling solar systems with micro-CHP units fueled by natural gas. Indeed developing hybrid PV systems with CHP devices enables additional PV deployment above what is possible with a conventional centralized electric generation system. The high cost in terms of investment in the technologies involved requires the optimization of the system size in order to be competitive with conventional generation. When dealing with hybrid and, in general, poly-generation systems, identifying the optimal sizing of the energy conversion systems is a tough issue due to several parameters that must be taken into account in the analysis, such as electricity and fuel price, energy loads and weather conditions. The present paper addresses the optimal sizing of hybrid micro-CHP systems defined on the basis of linear programming techniques, with the aim of taking advantage of rapid calculations even in the presence of a high number of variables.

Energy system modeling[edit | edit source]

Notes:- 1)Meteorological Year database for determining the yield of Solar system depending on solar radiation and ambient conditions. 2)The hourly values of the following quantities are used: the Direct Normal Irradiation (DNI); the global solar irradiation over a south-oriented 30degree tilted surface; the ambient temperature. 3)The efficiency of a PV panel is strongly dependent on the ambient conditions, the most influential being the available solar radiation and the solar cell temperature figures.

Micro-CHP modeling[edit | edit source]

Notes:- 1)All the micro-CHP units were modelled on the basis of the main characteristic parameters, such as electrical efficiency and power to heat ratio. 2)The electrical efficiency of the system has been considered constant in order to take advantage of linear programming techniques. 3)Micro-generation technologies are characterised by an electrical output lower than 50 kW, as defined by the EU Cogeneration Directive. 4)The technologies considered in this workare four: ICE, Stirling engine, microturbine and fuel cell. Table is given in paper which shows comparison for all those ways of which fuel cell technique is most efficient as it has power to heat ratio =1. But it has a drawback that is its cost.

Sizing of system[edit | edit source]

Notes:- 1)Conceptual lay-out of the hybrid solar micro-CHP system was designed for providing the highest flexibility (see Fig. 1). Electricity needs can be satisfied by: i) the solar electrical system (PV/HCPV), ii) the micro-CHP unit and iii) the electricity bought from the grid (if needed), with the solar electrical system having the priority. 2)Certain assumptions are to be considered.

Objective function[edit | edit source]

Notes:- 1)Minimum annualized cost derived by implementation of such a hybrid system is given by sum of annualized capital cost of all the devices and annual operating cost of them. 2)The capital cost of each device depends on its life time and capacity recovery factor with an interest rate of 3%. 3) Operating cost can be determined by depends on fuel cost of running CHP unit, operating and maintenance cost of the CHP unit, Cost of purchasing electric energy from grid if needed, the revenue coming from generating electric energy from solar and CHP unit.

Case study for residential in Rome[edit | edit source]

Notes:- 1)The thermal, electricity and cooling demand has been calculated where the inputs are geographical location, electrical peak load,maximum thermal power for heating and domestic water, and the maximum cooling power in summer. 2)Even consider Techno-economic parameters. 3)The achievable Primary Energy Savings and the CO2 Emissions Reduction can be calculated.

Sensitivity Analysis[edit | edit source]

Notes:- 1)First, it has been assumed, respectively, a 15% increase and reduction in the natural gas price, shows that a lower NG price promotes the use of micro-CHP technology, with a consequent increase in the size and CO2 emission reduction achievable with respect to the reference case. 2) As in the previous case,the effect of a variation in the electricity purchase a 15% increase and reduction in the price has been considered. Results show that a reduction in the electricity price largely rules out the use of micro-CHP technologies. 3)But an increase in the electricity price helps fuel cell technology to be chosen by the algorithm, providing a further CO2 emission reduction with respect to the single application of PV technology. 4)A 25% reduction in the investment cost, promotes the introduction of such micro-CHP systems. 5)If a lower capital cost of the unit is assumed, the algorithm activates the storage unit for both ICE and fuel cell, increasing the CO2 emission reduction achievable.

.Simulations of greenhouse gas emission reductions from low-cost hybrid solar photovoltaic and cogeneration systems for new communities[edit | edit source]

Amir H. Nosrata, , Lukas G. Swanb, , Joshua M. Pearcec,

The papers mainly focuses on an optimization model of PV+CHP hybrid system using multiobjective genetic algorithms called the Photovoltaic-Trigeneration Optimization Model (PVTOM).In this paper, PVTOM is applied to emission-intensive and rapidly growing communities of Calgary, Canada. Results consistently show decreases in emissions necessary to provide both electrical and thermal energy for individual homes of all types. The savings range from 3000–9000 kg CO2e/year, which represents a reduction of 21–62% based on the type of loads in the residential household for the lowest economic cost hybrid system. These results indicate that hybrid PV–CHP technologies may serve as replacements for conventional energy systems for new communities attempting to gain access to emission-intensive grids.

Overview[edit | edit source]

Recent work has shown that small-scale CHP and PV technologies have symbiotic relationships, which enable coverage of technical weaknesses while providing the potential of significant emission reductions at the residential level. Of these technologies the additional coupling of trigeneration (or combined cooling, heat and power (CCHP) was found to be the most effective in most applications. In 2010 alone, residential buildings were responsible for 41 Mt of CO2e. PVTOM is applied to newly developed Calgary, Alberta communities,Canada as case study for various reasons.

Methodology[edit | edit source]

Notes:- 1)PVTOM was developed to simulate and optimize hybrid photovoltaic and trigeneration energy systems based on technical, economic,and emissions performance. 2)PVTOM incorporates multi-objective genetic algorithms to minimize both the life cycle costs and GHG emissions. 3)Presently, PVTOM uses the annual average GHG emission intensity of the local electricity grid. 4)Inputs required are

        1. Hourly solar global and diffuse irradiation.
        2. Hourly ambient temperature.
        3. Actual or representative hourly data for household’s appliance and lighting (AL) load.
        4. Actual or representative hourly data for household’s domestic hot water (DHW) load.
        5. Actual or representative hourly data for household’s space heating (SH) load.

5)The first two inputs for PVTOM have been obtained from the Meteonorm database via PVSYST 4.37. The last three inputs were obtained by the Canadian Hybrid Residential End-use Energy and Emissions Model (CHREM). 6)The optimizer operates with eight variables that configure the system size and specifications. The variables are

        1. Selection of CHP technology (from a database of CHP units).
        2. Selection of PV panel technology (from a database of PV panels).
        3. Selection of battery technology (from a database of battery modules).
        4. Number of CHP units.
        5. Number of PV panels connected in series. 
        6. Number of PV strings connected in parallel.
        7. Number of battery units connected in series.
        8. Number of battery strings connected in parallel.

7)The life cycle costs can be calculated and it depends on the initial capital costs, the discounted operational costs, and the replacement costs of the different components of the system across a 20-year lifespan and penalty function and 20-year discount factor, respectively. 8)The annual GHG emission can be calculate by using the carbon dioxide and nitrous oxide emission intensity of the CHP unit (expressed in g/Wh), annual electric output of the CHP unit in Wh, emission intensity of the electric grid, amount of electricity the electric grid has provided in Wh in the event of system failure, emission intensity of natural gas heating, and the amount of thermal Wh the system failed to meet.

Data selection[edit | edit source]

Notes:- 1)Energy data for the required area including all standalone houses. 2)Histogram including AL and SH to understand distribution of consumption. 3)From data above a minimum, most common and maximum AL and SH required are calculated. 4)House meeting those requirements are identified. 5)Some sample houses satisfying above criteria of AL and SH are selected and a matrix is generated which is used for optimization in PVTOM.

Results and Discussion[edit | edit source]

Notes:- 1)Technical summary of optimized PV–CHP systems for selected data. 2)Costs and emissions directly compete against each other and therefore generate a set of solutions that range across both costs and emissions. 3)Efficient reduction in annual GHG(CO2) emission.

Techno-economic Analysis of an Off-Grid Photovoltaic Natural Gas Power System for a University[edit | edit source]

P. Sunderan1* , B. Singh2 , N.M.Mohamed2 , N.S. Husain1 1 Department of Electrical & Electronic Engineering, 2 Department of Fundamental & Applied Sciences Universiti Teknologi PETRONAS Tronoh, 31750 Perak, Malaysia

This paper mainly focus on to determine the technical and economical feasibility of a PV-natural gas hybrid power system to supply electricity and energy for a university in Malaysia. Hybrid Optimization Model for Electric Renewable (HOMER) software was used to size, simulate and evaluate the hybrid power system in this analysis. The simulations provide some insights into the monthly electricity generated by the photovoltaic-natural gas system, net present cost (NPC) and cost of energy (COE) of the system, renewable fraction (RF) and greenhouse gas emissions of the system. With the inclusion of PV, the amount of natural gas burned in the hybrid system was reduced.

Overview[edit | edit source]

1)The objective of this study is to determine the technical and economical feasibility of a PV-natural gas hybrid power system to supply electricity and energy for a university. This analysis is conducted with the goal of reducing the natural gas consumption of the existing non-renewable energy source system with a keen eye on the cost effectiveness of a hybrid system. The reduced usage of natural gas is also set to be beneficial as it promises to reduce the greenhouse gas emissions by the system.For the purpose of this study, the hybrid system considered is a 2 MW PV generator and two 4.2 MW gas generators, and is used for electrifying the university as well as powering the air conditioning system for the campus. The solar irradiance data for the region is graphed. 2)Moreover, the Electric load graphs( hourly data and monthly data) depending on the average peak hours have been noted. This average peak hours also depends on office time, on season, off-season as university closes during vacation is also considered. 3)The economic feasibility analysis of the hybrid system is done by using HOMER software. The input data that is needed are as the following: solar resource data, electrical load data, economic constraints, technical specifications, cost constraints, types of components or equipment's, controls, emissions constraints etc. 4)Once the required data are available, the simulation can be run where calculations are performed to determine if the available renewable resources is able to meet the load demand. When the renewable resource is not sufficient to meet the load demand, the generator system or grid connection is considered. 5)The total costs of installing, operating and maintaining all the different configurations such as a hybrid system, stand alone renewable energy system, generator only system or grid integrated system is listed for the respective simulation inputs. 6)HOMER calculates the net present cost of the system/ life cycle cost of the system and even the LCOE. The COE gives an idea of the cost of electrical energy produced by the system.

System configuration in HOMER[edit | edit source]

1)The gas generators and load are connected directly to the AC bus whereas the PV generator is connected to the DC bus. Both these buses are then linked through a converter since this system only supplies AC load. 2)Determine the investment for gas generator it includes initial cost, operation and maintenance cost, price of natural gas. Even the life time of generator is to be considered. 3)PV generation the installing cost is considered. There is no maintenance cost involved as its life time is around 25 years. 4)The inverter cost and its replacement cost is considered which are same. The life time of the inverter is 15 years with 97% efficiency. 5)Economic constraints is interest rate with is considered to be 4%.

Simulation Results by HOMER[edit | edit source]

1)Electricity generated by the PV system as well as by the generator is presented given in monthly form. It even provides the contribution by the PV system and the generators. If excessive electrical energy is being generated then being utilized small storage units can be added to avoid wastage of electricity. 2)The simulation also provided categorized results that are also ranked according to the NPC but more specifically the lowest cost for each type of system.

Results[edit | edit source]

The result shows that the LCOE of the hybrid system is less as compared to only generators. 3)The hybrid system not only increases PV penetration but also reduces the cost of the total system compared to just the generators. 4)Importantly even if the initial cost of the solar PV system is more then the generator arrangement but if we consider the facts like maintenance, replacement, fuel cost. It show that the 97% of the total cost of the hybrid system is due to generator arrangement. 5)Emission also reduces by a considerable amount.

Photovoltaics energy: Improved modeling and analysis of the levelized cost of energy (LCOE) and grid parity – Egypt case study[edit | edit source]

M. Saida, M. EL-Shimyb, , , , , M.A. Abdelraheemb

This paper presents improved modeling and analysis of the levelized cost of energy (LCOE) associated with photovoltaic (PV) power plants.

Points covered[edit | edit source]

1)The presented model considers the effective lifetime of various PV technologies rather than the usual use of the financial lifetime. Parametric and sensitivity studies are also presented for overcoming the uncertainties in the input data and for searching of the significant options for LCOE reduction. 2)The salient outcome of this paper is that the effective lifetime has a significant impact on both the LCOE and the lifetime energy production.

Introduction[edit | edit source]

1)There are 3 main SOlar technologies:- Solar PV Thermal PV Concentrated Solar power 2)Paper mainly focuses on direct Solar conversion (Solar PV) There are 3 geenration of Solar PV:- i)Crystalline PV (80%of market) ii)Amorphous PV(10-12%market) iii)Concentrating PV cell(R&D) 3)Only 0.2% of the gloabal market utilize the solar PV for generation of electricity, the main reason is its cost. 4)The LCOE is sensitive to small changes in the input variables and assumptions. The main input variables are the discount rate, average system cost, financing method and incentives, average system lifetime, and degradation of energy generation over the lifetime. 5)The Grid parity and Break even cost of the Solar PV are basically the point at which the cost of Solar generated electricity is equal to the cost of the electricity purchased by the grid.

MOdel LCOE and grid parity[edit | edit source]

1)The LCOE captures capital costs, ongoing system-related costs and fuel costs – along with the amount of electricity produced– and converts them into a common metric: $/kWh.2 2)the sum of the present value of the LCOE multiplied by the energy generated should be equal to the net present value of costs. 3)Consequently, the LCOE is usually determined as the average cost of energy over the lifetime of the project such that the net present value (NPV) becomes zero in the discounted cash flow (DCF) analysis. 4)In general, the efficiency of power plants is reduced with time; the time-dependent reduction in the efficiency is called output degradation. As any power plant, PV generators exhibit output degradation too. The energy generated in a given year (Et) is then equals to the rated energy output per year (Eo) multiplied by the degradation factor(1-d)^t. 5)The net annual cost of the project include all the cost paid at the beginning of the project- initial cost, maintenance and operation cost and even rate of interest of the year. In this paper no incentives have been considered. 6)For this paper SAM has been used to determine the energy production and the LCOE. It uses the NREL meteorological irradiance data for analysis. 7)In the SAM, two important factors should be taken into consideration. These two factors are Analysis Period and Loan Term. Analysis period is defined as the number of years covered by the analysis and determines the number of years in the project cash flow while the loan term defines as the number of years required to repay a loan. 8)There are two life times for a PV financial and effective. Financial life time is the duration the PV works. And effective life time evn consdiers the degradation of PV. 9)In the SAM, two important factors should be taken into consideration. These two factors are Analysis Period=effective life time and Loan Term=financial life time. 10)Thus depending on whether the effective life time is greater then the financial life time we get two equation for LCOE 11)Even the inverter replacement , operation and maintenance cost should be considered while calculating the LCOE. 12)Determining grid parity depends on various factors local price of electricity, solar PV price that depends on size and supplier, geographical region.

Case study results[edit | edit source]

1)The study shows that the region where the cost is maximum is base of system cost, maintenance and operation cost, installation cost respectively. 2)If effective life time is not considered the data regarding the LCOE and the energy generation estimated by the software greatly varies. In other words including effective time we get improved performance of PV system in terms of LCOE na d Energy generation. 3)Sensitivity analysis shows that the BOS and loan rate shows significant impact on LCOE.

Assumptions and the Levelized Cost of Energy for Photovoltaics[edit | edit source]

Seth B. Darling*a, Fengqi You b, Thomas Veselka c, and Alfonso Velosa d Received (in XXX, XXX) Xth XXXXXXXXX 200X, Accepted Xth XXXXXXXXX 200X First published on the web Xth XXXXXXXXX 200X 5 DOI: 10.1039/b000000x

Generally, LCOE is treated as a definite number and the assumptions lying beneath that result are rarely reported or even understood. In this paper MOnte-Carlo simulation is used to do LCOE calculation.

Introduction[edit | edit source]

For PV to attain deep market penetration, its costs must be comparable to those from fossil fuels, though it should be noted that there are substantial hidden costs associated with fossil fuels that are generally not accounted for such as pollution and climate change. The cost of conventional electricity is rising while the cost of solar electricity is dropping, so wide-scale grid parity is likely at some point in the future.

LCOE[edit | edit source]

1)It is an assessment of the economic lifetime energy cost and lifetime energy production. 2)The financial cost not only includes system cost but also maintenance, operations, insurance, different type of depreciation schedules, tax, subsidies and other compelxity.

Assumptions associated with energy production[edit | edit source]

1)The key is to use the best available data, but more importantly to understand the source of the data and the uncertainty associated with it. 2)Assumptions to be made are- Subsidies by government Insurance cost Inflation rate federal tax rate state tax rate lifetime span

Solar insolation data[edit | edit source]

The time series method to forecast the annual solar insolation in the coming 30 years based on the historial montly solar insolation data.

Power conversion efficiency[edit | edit source]

Many analysis consider LCOE as a constant but this not the case. It is a varying factor which should be consider during LCOE analysis.When the sunlight is less not only will a PV panel produce less power in many cases it will also do so less efficiently. Moreover, the dependence of efficiency on insolation is not necessarily linear. Other factors affecting the efficiency are the partial shadowing from clouds, temperature and so on.

System Degradation rate[edit | edit source]

As with the previous parameters, system degradation rate is generally treated as a single value in LCOE calculations despite the fact that it is known that even within a single PV installation individual panels will degrade with substantially different rates.

Assumption associated with Cost[edit | edit source]

Real discount rate[edit | edit source]

In addition to risks assocaited with solar insolation levels and the preformance of PV technologies at a specific location, there is also financial uncertainty in terms of the time value of money. It Depends on future interest rate falls or rise with respect to the inflation rate.

Maintenance and operation cost[edit | edit source]

Upkeep of a utility-scale PV system will vary widely depending on the local conditions.

Carbon market or Tax[edit | edit source]

A carbon tax is an alternative maket-based approach that directly taxes emissions and thereby provides an incentive to reduce pollution.

Subsidies and tax rate[edit | edit source]

As with inputs such as solar insolation, taxes and incentives for promoting solar energy also vary widely by location. For the United States, there is a valuable online database that compiles the various state, local, utility, and federal incentives and policies.

Analyzing the influence of assumptions[edit | edit source]

1)Monte Carlo simulation performs uncertainty analysis by building models of possible results through the substitution of a range of values—a probability distribution—for any factor that has inherent uncertainty. By using probability distributions, variables can have different probabilities of different outcomes occurring. 2)Monte-carlo simulation provide number of advantages- Probabilistic result, sensitivity analysis, correlation of input. 3)The results of Monte-carlo simulation gives broad distributions, which emphasizes the shortcomings of calculations that use singular input parameters. 4)Using Monte-Carlo simulation at different location to calculate LCOE of Solar PV module. The LCOE comparison can be done for the different location. 5)This sort of information is of tremendous potential value to investors, utility companies, insurers, and other stakeholders who need to ascertain the risk associated with a new installation.

Profitability of PV electricity in Sweden [edit | edit source]

Bengt Stridh1 , Stefan Yard2, David Larsson3, and Björn Karlsson4 1ABB Corporate Research, 2 Lund University, 1,3,4Mälardalen University, 3 Solkompaniet, SE-721 78 Västerås, SWEDEN,

The paper mainly focus on detailed study of profitability of PV electricity. Levelized cost of Energy(LCOE) and payback period are presented for a PV system that is installed to replace retail electricity with PV electricity.

Overview[edit | edit source]

Even after the subsidy was pass to for connecting PV system to grid still only 43.1 MW PV power was generated by 2013 in Sweden. Lack of sun-shine is not the reason for the limited PV power installed. The reason is The production cost of PV is today higher than the production cost of other electricity production technologies used in Sweden. However, the PV installation price has reduced considerable in 2012-2013. Therefore, there is a growing interest to install PV to replace retail electricity.

LCOE[edit | edit source]

The basis of profitability depends on LCOE. The parameters that were shown to have the most effect on the LCOE were investment cost, life, and yield, system degradation, discount rate and lifetime. A private residential system has to pay 25% VAT with investment price. A residential private system was recorded to pay 2.5 kUSD/kW including VAT, whereas, a non private was recorded to pay minimum 1.5 kUSD/kW. A fixed operation and maintenance real cost 15USD/kW each year was applied. yield of about 800–1100 kWh/kW per year can be expected during a year with typical solar irradiation for systems with reasonable good azimuth, tilt and without major shading effects. A degradation rate of 0.5% per year. The discount rate varies depending on the type of investor. Therefore, a span of 3-6% in discount rate can be found for other applications than private households. A common guarantee on PV module is 80% of the rated power after 20-25 years.

Profitability[edit | edit source]

To calculate the profitability it is needed to predict the PV electricity value over the life time of the system. One part is the PV electricity that will replace bought electricity, with a value equal to the retail electricity price. The other part is the value of excess electricity that is fed into the grid.

Retail Electricity Price[edit | edit source]

The PV electricity used for self-consumption will have the same value as retail electricity price, excluding the fixed subscription costs. There are two prices one transfer of electricity from distribution operator ans second is buying electricity from the company. For the present case it is considered to be 14-15 US¢/kWh, excluding VAT. A significant difficulty is to predict the retail price during such a long period as the life time of the PV system i.e. 30 years. It was considered to be 18 US¢/kWh for a duration of 30 years.

Self consumption[edit | edit source]

The consumption of PV power varies a-lot especially in residential area depending on the amount of power required for the house. The size of the panel also matter. A typical 50% consumption for a house is considered.

Credits from distributor[edit | edit source]

When excessive electricity is fed into grid the Distributor is suppose to pay you for that. This compensations payed by DSO are not much and also varies from DSO to DSO and area you live in. Typically for this case it is 0.6-1.1 US¢/kWh.

Other factors[edit | edit source]

1)Tax deduction 2)Electricity trading price 3)Electricity certificates 4)Certificate of origin 5)Electricity mean present value

Results[edit | edit source]

1)Electricity yield varies depending on investment and discount rate. 2)A payback period can be calculated by a formula presented in the paper. 3)The payback period as function of the mean present value of PV electricity and investment cost. 4)For other investors then private investors the payback years increases as the discount increases. 5)As electricity mean value increases the pay back period decreases.

The cost of storage - how to calculate the levelized cost of stored energy (LCOE) and applications to renewable energy generation [edit | edit source]

Ilja Pawel, ,

The paper mainly provides a new framework for the calculation of levelized cost of stored energy. The framework is based on the relations for photovoltaics amended by new parameters.The framework allows for comparisons between different storage technologies. The newly developed framework model is applied to derive the LCOE for a PV and storage combined power plant. In general, the combined levelized cost of energy lies between the LCOE of PV and LCOE of storage.

Overview[edit | edit source]

As the investment cost for renewable energy as deceasing steady it is not far that the subsidies will shutdown. This paper outlines the methodology to calculate the levelized cost of energy for combined PV and storage power plants. However, the methodology is applicable to other scenarios as well.

LCOE[edit | edit source]

The Levelized Cost of Energy (LCOE) is defined as the total lifetime cost of an investment divided by the cumulated generated energy by this investment. The LCOE is the (average) internal price at which the energy is to be sold in order to achieve a zero NPV(net present value).

LCOE of PV[edit | edit source]

This can be calculated using a given formula in the paper where it is considered only some amount of energy is being utilized of what is total being produced.

LCOE of Storage system[edit | edit source]

The levelized cost of energy for storage systems is calculated in a similar manner as for PV generation. The total cost of ownership over the investment period is divided by the delivered energy.The cost consists of a term similar to PV, in which total cost during lifetime is divided by the cumulated energy delivered by the system. Due to the fact, that no energy is generated a second term exists that models the energy purchase from generation plants or from the grid. The energy input into the storage system will be a certain amount of the total generated energy output. The energy output of the storage system is the energy input reduced by the average energy roundtrip efficiency ηSt of the storage system over the lifetime. Sometimes it is more convenient to consider the output energy of the storage system.

LCOE of PV+Storage system[edit | edit source]

1)The total lifetime cost is the sum of the cost of PV energy generation and the cost of storage. The energy output of the PP is the sum of directly used energy from PV and the amount that is taken from PV to the storage system and then released to the output of the PP. 3)The energy that can be used directly should be used directly, minimizing the energy storage. So design energy storage as small as possible else it will be uneconomical. 2)The usable PV output energy is energy generated at the output of PV excluding the one fed into the grid system. Of this usable energy only some part is stored into the energy storage system. 3)The total cost of power plant is sum of cost of PV generation + the cost of storage.

Break-Even Cost for Residential Photovoltaics in the United States: Key Drivers and Sensitivities [edit | edit source]

Paul Denholm, Robert M. Margolis, Sean Ong, and Billy Roberts

The Technical report mainly focuses on break even cost of solar PV with electricity being purchased from the grid. Break even cost is the point at which the cost of the electricity generated by solar PV is equals to electricity being purchased from the grid. It is even called as 'grid parity'. The break even function depends on many variables solar resources, local electricity prices, and other incentives, this factors vary regionally and so the break even cost will also vary. The break even cost is the cost at which the net present cost of the PV is equal to the net present benefit from the system. This can be used to determine the installed system cost required for electricity price($/W) or the price of electricity ($/kWh) required for a given installed system cost.

A review of solar photovoltaic levelized cost of electricity [edit | edit source]

K. Brankera, M.J.M. Pathaka, J.M. Pearcea, b, ,

The paper mainly focus on the methodology of properly calculating the LCOE for solar PVe. Then a template is provided for better reporting of LCOE results for PV needed to influence policy mandates or make invest decisions. A numerical example is provided with variable ranges to test sensitivity, allowing for conclusions to be drawn on the most important variables.

Overview[edit | edit source]

A tipping point for solar adoption will be when the grid parity is achieved. In simple words the PV installation or the investment in PV world will increase when PV generated electricity cost becomes equal to the cost of the conventional electriicty purchased from the grid. Here the accuracy depends on the lifetime generation cost of the solar PV electricity. Levelized cost of energy is mainly considered for emerging technologies such as PV. Different levels of cost inclusion and sweeping assumptions across different technologies result in different costs estimated for even the same location.Reporting the wrong LCOE values for technologies can result in not only sub-optimal decisions for a specific project, but can also misguide policy initiatives at the local and global scale.

Review of Cost of electricity and LCOE[edit | edit source]

1)The final electricity price payed by the consumer is different from the cost of its production. 2)The method considers the lifetime generated energy and costs to estimate a price per unit energy generated. 3)The method usually does not include risks and different actual financing methods available for the different technologies. 4)Economic and financial systems have a large impact on the price of electricity, although the quality of electricity rarely changes, which is often not reflected by the LCOE. 5)Improvements to the LCOE for solar PV can be made once realistic assumptions and justifications are given, real financing variability is considered, and consideration is made for technological and geographical variability. 6)Understanding the true costs, energy production and system specifications would improve the capabilities of LCOE software like the Solar Advisor Model.

Methodology to calculate LCOE[edit | edit source]

1)Calculating the LCOE requires considering the cost of the energy generating system and the energy generated over its lifetime to provide a cost in $/kWh (or $/MWh or cents/kWh). 2)The net cost of the system must include cash outflow like the initial investment, interest payment,operation and maintenance cost, cash inflow such as govt. incentives. also with this it should even include all cost required transmission and connection fees and be dynamic for future project. 3)The LCOE is very dependent on the financing methods available and manufacturing cost reductions.

Results[edit | edit source]

1) The LCOE decreases with increasing interest rate, increasing discount rate and and increasing loan duration. 2)Discount rate has very small affect on LCOE 3)LCOE decreases as the energy generation price decreases and system duration increases. 4)LCOE decreases with decreased installation cost and increased energy output. 5)LCOE is less with less degradation rate.

Economics of Solar Photovoltaic system [edit | edit source]

K. Brankera, M.J.M. Pathaka, J.M. Pearcea, b, ,

Notes:- 1)Solar photovoaltaic vary greatly in size and cost. Calculating the economics of the solar system gives us the idea about whether the solar Pv system is right for us. 2) Prices of solar Panels are declining which in turn is improving the economics of the Solar PV. 3)Find total cost of solar panel, inverter, mounting panel and installation. 4)Determining the payback period of the solar PV which in other words is the duration required to recover your investment. 5)Other factors that will pay important role in determining pay back period are the price of electricity per kWh and its inflation rate. 6)Net metering and incentives also pays an important role and it adds for benefit to the renewable system.

Economic optimization and sensitivity analysis of photovoltaic system in residential buildings[edit | edit source]

K. Brankera, M.J.M. Pathaka, J.M. Pearcea, b, ,

The objective is to minimize the annual energy cost of a given customer, including PV investment cost, maintenance cost, utility electricity cost, subtracting the revenue from selling the excess electricity.

Overview[edit | edit source]

The power output of a PV system depends on the irradiance on the PV cells, the efficiency of PV cells used and the effective area of the PV cells. Therefore, it is necessary to choose the optimal size of PV cell in accordance with the application. An economic optimization design tool for optimal PV size based on technology information and current tariffs and policy has been developed.

Model overview[edit | edit source]

This model required input:- 1)Hourly irradiamce data 2)Hourly electricity load profile 3)Efficiency of PV 4) Capital cost, interest rate, electricity tariff, subsidies.

Result at output:- 1)Economical optimal PV installation 2)System performance characteristics.

The objective function is to reduce annual energy cost of given customer. This depends on system investment cost (excluding subsidies), cost for maintenance of the system, cost for buying electricity power from the spot market, income from selling electricity back into the grid.

Results[edit | edit source]

1)The cost of electricity purchase from the grid decreases as the PV capacity is increased. 2)Increasing PV capacity does not always led to cost saving, this is because the investment cost increases. 3)The CO2 emission can be reduced considerable by increasing the capacity of PV.

Sensitivity analysis of LCOE[edit | edit source]

1)The increase of both capital cost and interest rate leads to a linear increase of levelized cost; contrarily, a higher efficiency brings on a lower levelized cost. 2)the simple payback period is greatly effected by capital cost, efficiency and electricity sale price.

Economic viability of captive off-grid solar photovoltaic and diesel hybrid energy systems for the Nigerian private sector[edit | edit source]

Abstract[edit | edit source]

It is well established that lack of both electric supply capacity and reliability weaken the Nigerian economy. Recently, the reduction in solar photovoltaic (PV) costs along with the technical potential to couple PV to hybrid battery and diesel generators provides Nigerian businesses with an opportunity to reduce operating costs while defecting from the grid. This study investigates the potential of using off-grid hybrid energy systems for private industries within and near Lagos state currently with relatively high daily electricity demands that are met with supply through captive diesel generation. The results based on simulations of six industry sector load profiles developed from surveys found solar PV and diesel hybrid energy systems are economically viable for a wide array of industries in the Nigerian private sector including real estate, education, banking, automobile, hospitality and production. Five of the six sectors had discounted payback times for the systems under a year and ROIs >100%. The results established that the levelized cost of electricity is lower for every sector analysed with inclusion of solar PV, lower still with coupling of batteries and more reliable than the current grid-provided electricity. Nigeria as a whole will also benefit from widespread adoption of solar hybrid systems, as it will assist the balance of trade by reducing refined petroleum imports. In conclusion, the results of this study make it clear that every scale of Nigerian businesses could increase profitability with the use of solar hybrid systems.

Economical and environmental analysis of grid connected photovoltaic systems in Spain[edit | edit source]

José L. Bernal-Agustín, , Rodolfo Dufo-López

In this paper an economical study on PV installed is performed depending on different scenario depending on interest rates and energy tariff are considered. The following parameters are used to determine the profitability of a PV installation: the Net Present Value and the Pay-Back Period.

Economic valuation of Investment[edit | edit source]

Notes:- 1) The initial cost of the grid connected PV system This includes the cost of the generator Cgen, the cost of the inverter Cinv, the costs of the installation Cinst (including supporting structures, wiring, protective elements, engineering,etc.). The sum of these costs is the system cost Csystem. Likewise, Csub is the possible quantity of financial subsidy on the initial cost. 2)Net flow cash is the difference between the cash input generated by the investment and the payment or cash output the investment requires. 3)The income and expenditure vary from year to year due to inflation. 4)The Pay-Back Time (P), is the number of years needed to make the NPV of the cash flow, up to the present moment, to be equal to the initial outlay of the investment.

Case study[edit | edit source]

Notes:- 1)For the present case the data being considered is the annual irradiation at the place, the panel inclination and orientation, irradiation Losses. All this data can be used to determine the kWh for the PV system. 2)Assumptions are no shadow is there, and total energy generated by PV is sold. 3)For analysis various values for the subsidies are considered. 4)The economical analysis has shown that with the current prices, investment in a grid connected PV systems is generally profitable. 5)An increase in the sale price of the energy, with this remaining constant throughout the service life of the installations, would make shorter return times possible, thus attracting investors and so producing a mass investment in PV installation.

Re-considering the economics of photovoltaic power[edit | edit source]

Morgan Baziliana, b, Ijeoma Onyejia, c, , , , Michael Liebreichd, Ian MacGille, Jennifer Chased, Jigar Shahf, Dolf Gieleng, Doug Arenth, Doug Landfeari, Shi Zhengrongj

This paper mainly focus on This paper briefly considers the recent dramatic reductions in the underlying costs and market prices of solar photovoltaic (PV) systems, and their implications for decision-makers. In many cases, current PV costs and the associated market and technological shifts witnessed in the industry have not been fully noted by decision-makers. The perception persists that PV is prohibitively expensive, and still has not reached ‘competitiveness’.

PV power generation has long been acknowledged as a clean energy technology with vast potential, assuming its economics can be significantly improved. Inspite of having highly attractive benefits and proven technical feasibility, the high costs of PV in comparison with other electricity generation options have until now prevented widespread commercial deployment. The economics of PV depends on 3 metrices:-1)Price per watt( Capital cost of PV),2)LCOE,3)Grid parity LCOE and ‘grid parity’ are of special relevance to government stakeholders but require a wider set of assumptions. There is a clear requirement for greater transparency in presenting metrics so that they can be usefully compared or used in further analysis.

Price per watt[edit | edit source]

Notes:- 1)The most fundamental metric for considering the costs of PV is the price-per-watt of the modules. PV module factory prices have historically decreased at a rate (price experience factor) of 15-24%. 2)modules had a share of around 60% of the total PV system cost [20], but due to the extraordinary decline in module prices since 2008, its share in the total installed system cost has since decreased and now the BOS is the majority share in capital cost of PV installation. 3)Reducing the price/watt of the inverter being used will reduces the BOS of the system and hence the capital cost for PV installation.

LCOE[edit | edit source]

Notes:- 1)LCOE is most commonly used by policy makers as a long term guide to the competitiveness of technologies. 2)LCOE analysis considers costs distributed over the project lifetime. 3)The LCOE mainly depends on locations nd financing assumptions. 4)Variation in LCOE presented in various papers is due to certain assumptions, moreover, the lifetime of PV module is considered to 25 years in many papers but it is 40 years as the researcher suggests. 5)The variation in M&O of the PV module may be due to difference in scope of service provided. 6)Other variation are due to the type of project owner, the nature and stability of regulatory regimes, and regional differences in cost of capital. 7)The LCOE levelized costs of power generated by PV exhibit a particularly high sensitivity to load factor variations, followed by variations in construction costs and discount rate. 8)Uncertainty in LCOE is due to financial uncertainties (e.g., variation of discount rate) are a major determining factor of LCOE, followed by system performance (including geographical insolation variation), which equally represents a major contributor to the uncertainty in LCOE.

Grid parity[edit | edit source]

Notes:- 1)As noted with LCOE, however, behind the relatively simple concept of grid parity lies considerable complexity and ambiguity. 2)Many papers present that in few countries grid parity is already been achieved and in few they say it will be achieved in coming future. 3)This shows that how difficult is the concept to communicate.

Modelling the Italian household sector at the municipal scale: Micro-CHP, renewables and energy efficiency[edit | edit source]

Morgan Baziliana, b, Ijeoma Onyejia, c, , , , Michael Liebreichd, Ian MacGille, Jennifer Chased, Jigar Shahf, Dolf Gieleng, Doug Arenth, Doug Landfeari, Shi Zhengrongj

This paper mainly focuses on the potential of energy efficiency, renewables, and micro-cogeneration to reduce household consumption in a medium Italian town and analyses the scope for municipal local policies. The study also investigates the effects of tourist flows on town’s energy consumption by modelling energy scenarios for permanent and summer homes. Two long-term energy scenarios (to 2030) were modeled using the MarkAL-TIMES generator model: BAU (business as usual), which is the reference scenario, and EHS (exemplary household sector), which involves targets of penetration for renewables and micro-cogeneration. Because most of household energy demand is ascribable to space-heating or hot water production, this study finds that micro-CHP technologies with lower power-to-heat ratios (mainly, Stirling engines and microturbines) show a higher diffusion.

Methodology[edit | edit source]

1)Data base of energy consumption (Thermal as well as electrical) for residential has been recorded. More precisely, the share by electrical appliances at residential sector were divided in residential sector. 2)Examination of the data regarding permanent and summer homes allowed the estimation of the electricity and natural gas consumption of summer households: in the summer, natural gas is used for hot water production and cooking, not for heating, resulting in distinctive patterns for the different household types by season. 3)Shares of electricity and natural gas consumption by type of appliance in permanent and summer homes. 4)Price of natural gas and electricity now and in future coming years were noted.

Micro-CHP unit[edit | edit source]

Notes 1)Only for space heating 2)4 CHP technologies were considered MGT (micro-gas turbine),ICE(Internal combustion engine) ,FC (Fuel cell) and stirling engines. 3)Techno-economical parameters to be considered for each technology is Efficiency, investment cost, fixed O&M, variable O&M, cogeneration ratio and life time.

Solar Panel unit[edit | edit source]

Notes: 1)Techno-economical parameters to be considered are Efficiency, life time and AF (availability factor), indicates the percent working hours over the year.

Photovoltaic unit[edit | edit source]

Notes: 1)Techno-economical parameters to be considered are AF data reflect the overall yearly production of the PVs in the town, investment cost, cost for M&O, life time, efficiency.

Results[edit | edit source]

Notes:- 1)Primary energy saving by introducing these technologies. 2)Co2 emission has reduced considerably. 3)Important municipal policies to be adopted:- a)Information campaign to tell people about the benifits of the PV-CHP technology( incentive mechanisms, lower energy bills, short payback times for the installation of energy efficient products); b)bureaucratic simplification for the installation of PV and solar thermal plants; c)local tax breaks, especially to boost micro-CHP. Indeed, because local taxes considerably affect the price of natural gas.

Economic and environmental evaluation of micro CHP systems with different operating modes for residential buildings in Japan[edit | edit source]

Hongbo Ren, , Weijun Gao

In this paper, two typical micro CHP alternatives, namely, gas engine and fuel cell for residential buildings, are analyzed. For each facility, two different operating modes including minimum-cost operation and minimum-emission operation are taken into consideration by employing a plan and evaluation model for residential micro CHP systems.

Introduction[edit | edit source]

Besides the grid-connected photovoltaic (PV) system, another timely example of the distributed residential energy supply technology is small-scale combined heat and power (micro CHP) generation, with a maximum electrical output capacity between roughly 1 kW and 10 kW. However, in order to achieve a widespread implementation of micro CHP systems, an interdisciplinary effort including technology, finance and policy etc., is necessary. It is believed that micro CHP offers significant benefits to energy suppliers, to households and to the society as a whole.In this paper, for an optimal efficiency of residential facilities, a study of various alternative micro CHP systems, including gas engine and fuel cell, is undertaken. In order to understand the tradeoff between economical and environmental potentials of the micro CHP systems, two different operating strategies are studied: to minimize annual energy cost and to minimize annual CO2 emissions.

Modelling of Residential CHP unit[edit | edit source]

The system consists of a CHP plant, a storage tank and a back-up burner. The CHP plant which is driven by city gas is used to meet part of the electrical demand (including cooling load with the use of air conditioning), the deficiency is served by the utility grid. As to the thermal load, the recovered heat from micro CHP plant is used for heating and hot water requirements. If the heating does not completely satisfy the application needs, a supplementary burner can be used. The role of storage tank is to store thermal energy during periods of low-thermal energy demand and to supply thermal energy during high demand.

Description of plan and evaluation model[edit | edit source]

1.In order to introduce and operate residential micro CHP system in an effective way, it is necessary to take into full consideration of local conditions, energy requirements, as well as technical and financial information. Based on the investigated electricity and thermal loads, energy (both electricity and city gas) prices, as well as micro CHP performance characteristics, a pre developed plan and evaluation model is employed. 2.Lingo software package has been used for the analysis in this paper.

Economic assessment index[edit | edit source]

1.The economic assessment provides information on how the economic resources (investments, fuels, etc.) are used to meet the customer requirements. 2.Micro CHP system has usually higher initial investment and lower running cost compared with the conventional energy supply system, which serves the electricity load by utility grid and thermal load by gas boiler. 3.Cost saving ratio, important index for the CHP economic assessment. It is given as rate of total energy cost difference between the micro CHP system and the conventional system to the annual energy cost of the conventional system.

Studied Case[edit | edit source]

Energy Demand[edit | edit source]

1.The energy demand in the residence can be divided into electrical demand and thermal demand, which consists of space heating, hot water and cooling load. It should be noted that the cooling load for air conditioning is also an electrical demand. 2.Generally, the above energy consumptions can be obtained through direct no-site measurement or simulation with some building energy simulation software, for example, EnergyPlus, Flexsim, DOE-2, and so on. 3.various hourly load demands of 8760 h, hourly peak load and annual total load for the assumed residential building can be assessed.

Utility Demand[edit | edit source]

1.Utility electricity and gas tariffs are key factors determining the economic benefits of the micro CHP installation.

Micro-CHP alternative[edit | edit source]

1.Commercially available micro CHP technologies include internal combustion engine, Stirling engine, micro-turbine and fuel cell. 2.Micro-gas Engine in MIcro-CHP is the most promising technology which has been introduced in the market and is manufactured by Honda etc. 3.Characteristics of the CHP unit includes capital cost of the unit, Electrical as well as thermal efficinecy and life time. 4. CHP unit with fuell cell Manufacturers (Panasonic, Ebara Ballard, Sanyo and Toshiba) supplying these units are striving to increase the lifetime and reliability and reduce the initial costs.

Simulation Results[edit | edit source]

Micro-CHP with gas engine[edit | edit source]

1. Hourly Electrical and Thermal demand is noted. 2.Compared with the results of gas engine plant, it can be deduced that the fuel cell system supplies more electricity but less thermal energy.

Economic assessment[edit | edit source]

1.In this section, the economic aspects of each scenario illustrated above have been evaluated and the results have been compared with a conventional system 2.It can be found that the introduction of both two micro CHP alternatives with different operation modes leads to considerable cost reductions. 3.The fuel cell system with minimum-cost operation has the least annual energy cost, which is about 26% less than the conventional energy system. 4.Furthermore, Fuel cell CHP system has a relatively higher economic efficiency than the gas engine system, although with a larger investment cost.

Technical and economic feasibility study of using Micro CHP in the different climate zones of Iran[edit | edit source]

Fatemeh TeymouriHamzehkolaei , Sourena Sattari,

In this paper, technical and economic studies for the use of Micro CHP in the different climate zones of Iran are executed. These zones are categorized in to five; Tehran, Rasht, Bandar Abbas, Ardebil and Yazd, based on weather conditions. Later on using an economic model, both annual energy savings and percentage of system profitability in each zone are calculated as well as reduction in annual emissions. It should be mentioned that, for economic calculations, gas and electricity price are determined using a sensitivity analysis. This analysis indicated that profitability of Micro CHP systems are sensitive to energy prices, as well as hours needed for heating room in each climate zones.

Introduction[edit | edit source]

Notes:- 1)Detailed knowledge of energy end use loads is important for selecting an appropriate residential CHP system depending on electrical as well as thermal energy demand. 2)Should have knowledge about the climatic conditions of the area where CHP unit is to be installed.

Model Description[edit | edit source]

1)The system is designed in such a manner that thermal and electrical energy generated by the CHP should satisfy the demand of the families. If excess thermal energy is generated it is stored so that it can be used for future. If excess electric energy is generated it is fed into the grid back.

Equation description[edit | edit source]

1)In order to introduce and operate residential Micro CHP system in an effective way, it is necessary to take into full consideration of local conditions, energy requirements, as well as technical and financial information. 2)The economic model is used to calculate annual energy saving and annual CO2 emissions in different zones. 3)Where the average thermal power output of Micro CHP system, is the ratio of annual heat load for space heating and hot water divided by the hours needed for heating and hot water. 4)Total annual hours to supply household’s heating and also hot water demands are determined in each zone, separately. 5)The total amount of electricity generated during the year by CHP system is calculated with electrical rated capacity of CHP plant multiplied by the annual operation hours. 6)Thermal capacity of Micro CHP system is related to electrical capacity, by the electrical and thermal efficiencies of CHP plant. 7)Cost saving ratio is calculated depending on annual energy cost on coventional system and micro-CHP system. 8)Then annual running cost the CHP unit is calculated which depends on fuel as well as maintenance cost. 9)The total cost of electricity purchase has been calculated depending on the demand of electricity by the house as well as the electricity rate purchased from the retailer. 10)If the electrical energy so generated by the CHp system is greater then the required demand then the excess energy is fed into the grid. This also considered while calculating the cost of saving( included in annual energy cost by CHP ) 11)On the other hand if the demand is more then what is being generated by the CHP unit. This is also included in calculations of annual energy cost by CHP. 12)Finally, the payback period can be determined by using initial investment for the CHP unit divided by the amount of cost saving by the CHP unit.

Sensitivity Analysis[edit | edit source]

1)Sensitivity analysis improves understanding the influence of key parameters on the decision to adopt Micro CHP systems. Gas and electricity price are significant factors that show economic benefits of installing Micro CHP systems in buildings. 2)The sensitivity of profitability index to changes in natural gas prices has been analyzed and was reported as the gas price reduces the profitability index increases.Thus, reducing natural gas price is an effective way to stimulate the adoption of Micro CHP system because of the reduced running cost. 3)Another factor that influence the profitability index is electricity price, which also has an important effect on the adoption of residential Micro CHP systems. The intuitive result that Micro CHP economic feasibility is quite sensitive to electricity prices. The sensitivity of profitability index to electricity price in lower prices is much more than higher prices; the analysis shows that if the electricity price there is increase in profitability for CHP system. 4)From the sensitivity analysis for simultaneous changes in electricity price and the gas price it can be observed that, given a fixed capital cost, the profitability index would begin to rise when gas price is reduced and electricity price is increased. It is not surprising that increases in electricity price and decreases in gas price result in corresponding increase in profitability index and decrease in payback period. Furthermore, it can be found that greater profitability result in larger Micro CHP systems installation. 5)Other factors that effect on the profitability index and payback period are building’s area and annual hours needed for space heating.Profitability index and payback period varies with change in the thermal load demand, thus even change in area also changes the profitability index.

Micro-CHP systems for residential applications[edit | edit source]

Michel De Paepea, , , Peter D’Herdta, David Mertensb

In this paper, a thorough analysis is made of the operational parameters of 3 types of micro-CHP systems for residential use. For each building type, the energy demands for electricity and heat are dynamically determined. Using these load profiles, several CHP systems are designed for each building type. All CHP systems, if well sized, result in a reduction of primary energy use, though different technologies have very different impacts.

Introduction[edit | edit source]

A large amount of energy in the world is being utilized for heating purpose. Using CHP unit will not only provide heat to the building but will also provide electricity.

Methodology[edit | edit source]

CHP technology[edit | edit source]

Notes: 1.In this paper, five micro-CHP systems (<5 kW) are evaluated for use in residential applications. 2.Most important step is to determine the load demands of the families (Electrical as well as thermal). In the present case DOE 2.5 was used for doing this analysis. The requisite data for the simulation include: building dimensions, building materials, installed equipment and lighting, usage time profiles (schedules), data concerning the heating system and the ventilation rates. 3.CHP technologies being used- a) TWo CHP units using gas engine, running on natural gas.-- b) Two CHP units using sterling engine, running on any fuel. c) CHP unit using Fuel cell, running on hydrogen gas.

Primary Energy[edit | edit source]

Notes: 1.For each configuration, the primary energy savings, CO2 emission reduction and the financial savings are calculated. 2.The cases consider for comparison- a)Electricity coming from gas/steam turbine power plant, whose efficiency is 50% b)Electricity coming from fossil fuel power plant, whose efficiency is 42% c)Electricity coming from nuclear power plant, whose efficiency is 37% 3.The CHP unit running strategy is determined by the heat requirement at the output side. The CHP tries to fulfill all demand but is the requirement is greater then the CHP produces then the excess is taken from grid. Thus, a external boiler is connected along with the CHP to provide excess required demand which cant be fulfilled by the CHP unit.

Electrical equipment and Lightning[edit | edit source]

Notes:- 1.For each room in the houses, the installed equipment and lighting power were listed. 2.The central heating system was present in all houses, temperature demands are highly dependent on the users of the building. The central heating system is controlled by an on/off thermostat with time programming. This data for individual house was noted.

Energy saving[edit | edit source]

Notes:- 1.The simulation results depending on all above taken data gives the total amount of electrical as well as thermal demand. 2.The energy consumption by the CHP each CHP technology is recognized with respect to a base case i.e. gas fired combined cycle power plant. 3.Using the data obtained from above the maximum energy saving by various CHP modules can be determined.

Economics[edit | edit source]

1.Investment and Maintenance cost for each CHP module is considered. 2. Electricity purchasing and selling price. Even gas purchase price is considered. 3.Annual Profit:- The annual profits are the sum of: • The avoided costs for purchasing electricity, equalling the purchasing price. • The money received for the electricity which is sold to the grid. These profits are decreased with • The maintenance costs. • The extra cost due to the extra amount of gas used.

Micro combined heat and power (MCHP) technologies and applications[edit | edit source]

Maryam Mohammadi Maghankia, Barat Ghobadiana, , , , Gholamhassan Najafia, Reza Janzadeh Galogahb

This paper mainly focus on Micro-cogeneration systems have the potential to reduce energy demand of the residential sector for space heating, domestic hot water heating,and electricity. The reduced green house gas emissions and reduced reliance upon central electrical generation, transmission, and distribution systems are the possible benefits. Also in the present paper,a comparison has been made between the MCHP technology and the other ones such as primemover, electrical and thermal power,efficiency and emissions.

Layout of CHP unit[edit | edit source]

Notes:- 1)Layout of CHP intergarted system to supply residential electrical and thermal demand is presented. 2)Here the output of prime-mover is electricity which is if generated in excess can be fed into grid. If the electricity demand is not fulfilled the excess required can be taken from grid. 3)The CHP block consists of a heat exchanger, auxiliary boiler and thermal storage unit. This are needed for thermal demands. If the heat so generated is in excess by CHP then it is stored in the Thermal storage unit. 4)The efficiency of the CHP unit varies depending on the technology being used and also the fuel/gas source employed. 5)It can seen that the conventional electricity system has an efficiency of 48% and gas boiler installed seperately has an efficiency of 80%.Hence overall conventional thermal and electrical system efficiency goes up to 60%. 6)Moreover, on an average the CHP units gives an total efficiency of about 76.5% with less fuel consumption.

Global CHP status[edit | edit source]

Notes:- 1)The paper presents the CHP share of total national power generation in different countries and even gives the approximate capacity of CHP unit installed in different countries (report from IEA data analysis.) 2)The paper even provide the expected rise in the share of CHP power generated in national power generation by 2030.

CHP Technologies[edit | edit source]

Notes:- 1)ICE,SE,MGT,MRC are 4 types of Primemover Technologies. 2)The overall efficiency of a CHP unit is combined thermal and electrical efficiency. This is different for different CHP technologies. 3)ICE internal combustion engine has a potential to provide efficiency up to 90% 4)MRC Micro rankine cycle has a potential to provide efficiency of more than 90% 5)The paper even gives a list of various companies and the efficiency of there CHP modules.

Advantages[edit | edit source]

1)CHP units are used for space heating and warm water. Along with this it generates electricity with same fuel intake at good efficiency. 2)The GHG emission is very less as compared to the conventional system used. 3)It can seen from paper that CHP units are able to satisfy 80% of the thermal demand and less 85% electrical demand. 4)The prime mover saving is depending on prime mover technology its in the range(20-28%).

Microgeneration model in energy hybrid system - cogeneration and PV panels[edit | edit source]

Galvão, J. ; ESTG, Leiria Polythecnic Inst., Leiria, Portugal ; Leitão, S. ; Malheiro, S. ; Gaio, T.

This paper focus on the development of a hybrid and autonomous energy model with solar PV panels and a small CHP (combined heating and power production) system whose primary energy source is the biomass. Also it will be presented several rules for to achieve new energy efficiency levels of a service building.

INTRODUCTION[edit | edit source]

The paper present a hybrid system model where the co-generation unit (CHP) contributes for power generation electrical as well as thermal power. The electrical power is also provided by the PV solar process. The gasification used in this paper for the CHP is biomass with IC engine technique. PV solar process includes PV panels, batteries, transformers,inverters.


1)The accurate diagnosis of the hotel gave the data for the electrical, thermal consumption. 2)The sources being consumed by the hotel are fuel, electricity and gas; data for this sources from 2002-2008 is presented in graphical form. 3)The note of hotel high consumption electrical appliances is made. 4)The time and amount of electricity being used is note including the peak electricity consumption and its timings. 5)A note of electricity consumption during summer time is made and which sector requires the maximum electricity is noted. 6)Along with electrical demand the variation in thermal needs during winter day is noted.


1)The main characteristic of this production mode is to obtain electricity at low cost and with low-emissions. 2)The software SOLTERM and PVIS European Communities provides the data about the the solar radiation within one year. 3)The solar PV installed with a proper inclination in this region is able to fulfill 7.22% of the electrical demand. During summer the supply of electricity by PV is greater then 8.23%. 4)The additional electrical demand is fulfilled by the CHP unit.


1)The tariff rate plays an important role. Because after satisfying the electrical needs with the hybrid system the excess of electricity is fed into the grid. 2)Simulating the production cost of the hybrid system, the payback period can be determined.

ADVANTAGES[edit | edit source]

1)Hybrid energy system increases the efficiency and also helps in increasing the PV penetration level. 2)It is economically and technically viable hybrid renewable energy model, which is eco-friendly and provides an autonomous environment in terms of electricity use, heating and cooling, with economic viability.

Feasibility Study for Self-Sustained Wastewater Treatment Plants—Using Biogas CHP Fuel Cell, Micro-Turbine, PV and Wind Turbine Systems[edit | edit source]

Ahmed Helal, Walid Ghoneim, Ahmed Halaby

The primary objective is to provide an entirely renewable standalone power system, which satisfies lowest possible emissions with the minimum lifecycle cost.HOMER software was used to simulate the hybrid system composed of combined-heat-and-power units, wind turbines and photovoltaic systems. Simulation results gave the best system configuration and optimum size of each component beside the detailed electrical and cost analysis of the model.

INTRODUCTION[edit | edit source]

Renewable energy conversion devices like photovoltaic (PV), micro-turbines (MT), fuel cells (FC) and storage devices are expected to play an important role in future electricity supply and low carbon economy.Combining renewable energy to form standalone hybrid systems is considered as one of the most promising ways to handle the electrical requirements.Other factors which spots light on use of renewable energy system are GHG emission, rising temperature. Using Renewable energy resources to power waste water power plant reduces its operating cost considerably.


1)The digester gas so generated can be used to power a CHP unit which in turn is used to generate thermal and electrical energy. 2)The selection of CHP technology depends on the application and the requirements. 3)In the present case Fuel cell CHP unit is considered as it has low emission and high efficiency but it has high cost, able to work with great variety of fuels, low demand for cleaning, high operating temperature. 4)The efficiency of solid oxide Fuel cell (SOFC) CHP is around 46% and thermal efficiency of about 40%. 5)For the present case as the thermal load demand is less then what is being generated by the SOFC CHP unit. A micro-turbine is connected which can consume this excess heat(thermal energy) and can convert it into electricity. 6)Microturbine which is connected to use the waste heat from SOFC has electrical efficiency of 22%. 7)Thus, at the output we get electrical energy genrated by both Microturbine and SOFC.

PLANT LOAD STUDY[edit | edit source]

Electrical Load demand by the plant[edit | edit source]

1)Plant loads according to its design & installation were grouped, and distributed throughout their working hours to form the load profile. 2)The minimum and maximum electrical peaks are noted. 3)The average power requirement (Base load demand) of the plant can be noted from the graphical data obtained. 4)Thus, from the power generated from the combination of SOFC and microturbine arrangement and power demand of the plant. It can be seen for the present case 69% of the electric power demand is fulfilled.

Thermal Load demand by the plant[edit | edit source]

1)For the Waste water power plant the heating or thermal load is very less as there is no space heating required. 2)The present SOFC and Micro-turbine arrangement is able to satisfy the thermal demand efficiently.

System Modeling[edit | edit source]

1)Even if the combine SOFC and MT is able to fulfill thermal demands but still electrical demand is yet to satisfy. 2)Additional Solar PV and wind energy resources are connected for reaching the electrical demands of the water waste power plant.

Resources[edit | edit source]

1) The SOFC works on the biogas (digester gas) generated by the plant. 2)The resources data i.e. the solar irradiance data and even wind speed data is for 12 months has been obtained.

SOFC modeling[edit | edit source]

1)The total cost includes capital cost, installation cost and O &M cost. 2)It is estimated that the capital cost and O&M cost will reduce in future. 3)The unit can work for >50,000 hrs, thus its life time is expected to be more then 20 years.

Microturbine modeling[edit | edit source]

1)The total cost include capital cost, installation cost and O&M cost.

Wind turbine and solar PV modeling[edit | edit source]

1)As the rated power of the new wind turbine machines are increasing it corresponding capital cost are reducing. 2)The total cost is the capital cost for the wind turbine along with its O&M cost. 3)For simulation purpose technical data and the cost data are put entered in to get accurate results same is the case with Solar PV.

Other required componenets[edit | edit source]

1)Inverters, battery bank whose cost depends on the commercial market. Moreover the battery bank cost depends on the type and size of the battery also.

Results[edit | edit source]

1)The optimum system is defined as the system combination which satisfies the user defined constraints at the lowest life cycle cost or net present cost (NPC). 2)The cashflow summary states that SOFC unit holds the maximum share followed by batteries, wind , PV , MT and then converter unit. 3)The power generated contribution by units is as follows SOFC, Wind, Solar, Microturbine.

Are we there yet? Improving solar PV economics and power planning in developing countries: The case of Kenya[edit | edit source]

Janosch Ondraczeka, b, ,

This paper mainly focus on calculating LCOE of PV system and to see whether it is competitive to the cost of Electricity.This paper might help the policy developers to invest more in PV electricty then using traditional generators with contributes more grid electricity in KEnya.

Notes:- 1)LCOE is a common metric which is used in order to compare various technologies generating electrical output. 2)LCOE is a simple division of all cost incurred for the technology by the project lifetime. 3)Calculating the LCOE for the installed PV system 4)Doing sensitivity analysis on the LCOE valu determined. 5)In sensitivity analysis one parameter is changed at a time and its corresponding effect on the LCOE value for PV system is observed. 6)The value which were changed for sensitivty analysis in the paper where, life time, investment cost, scrap cost, O&M cost, discount rate, location and degradation factor. 7)The investment cost, changing the locations,discount rate affects the LCOE rate for PV system considerably.

Modeling and Performance Analysis of an Integrated System: Variable Speed Operated Internal Combustion Engine Combined Heat and Power Unit–Photovoltaic Array[edit | edit source]

Robert Radu1, Diego Micheli, Stefano Alessandrini, Iosto Casula and Bogdan Radu

The paper presents the model of a combined heat and power (CHP) unit, based on a variable speed internal combustion engine (ICE) interfaced with a photovoltaic (PV) system. This model is validated by means of experimental data obtained on an 85 kWe CHP unit fueled with natural gas and a PV system with a rated power of 17.9 kW. Starting from daily load profiles, the model is applied to investigate the primary energy saving (PES) of the integrated CHP+PV system in several operating conditions and for different sizes of PV array.

INTRODUCTION[edit | edit source]

NOTES:- 1)Using CHP reduces the fuel consumption by 20-30% with respect to conventional plant. 2)The CHP technology being used is ICE with variable speed, ICE being selected because of its operational advantage. 3)Variable speed speed technique is being considered as it increases the electrical efficiency by around 28%. 4)Solar PV system is being used as it produced ZERO GHG emission. 5)Making single system operation in PV+CHP hybrid system this will lead to alot of energy saving. 6)The above hybrid system fits good to satisfy residential energy demands.

SYSTEM MODELING[edit | edit source]

1)System modeling is done in MAtlab-simulink. 2)Depending on the inputs given the model calculates the operating parameters of hybrid system. 3)Models main output are PV and CHP operating efficiency and primary energy saving. 4)The CHP unit consists of 3 blocks 1 ICE nad 2 heat exchangers.

SYSTEM PERFORMANCE[edit | edit source]

1)The primary energy saving of the CHP unit can be calculated using a simple formula provided in the paper. 2)The data required is the thermal as well as electrical efficiencies of the CHP unit and some reference for both of them.

RESULTS[edit | edit source]

1)It can be observed from the graphs and numerical data the CHP unit along has no doubt good energy saving by hybrid system energy saving is much more. 2)HYbrid system is efficient.

Cost-benefit analysis of a photovoltaic power plant[edit | edit source]

C.R. Sanchez Reinosoa, b, , , , M. De Paulaa, c, R.H. Buitragoa

In this paper the energy generated by photovoltaic generators with different mounting angles to the horizontal plane, and the optimum angle is estimated. Other aspects considered are the costs and legal framework associated with installing a photovoltaic power plant.After having done a cost-benefit analysis under different scenarios, results showing the feasibility of building a photovoltaic power plant were obtained.

COST ANALYSIS[edit | edit source]

NOTES:- 1)Determine the elctric power generated by the PV depending on the cost and its service life. 2)The cost of PV is composed of two groups:-a)photovoltaic module b)Balance of system(HARDWARE AND NO-HARDWARE COST). 3)Hardware cost includes-inverter, wires, junction box, structure etc. 4)NON hardware cost includes-planning and building the plan 5)The balance of system cost also depends on where the system has been installed (location). 6)Modules with different materials also affects the cost of balance of system. 7)Cost of balance of system contributes a lot for the total cost of PV.

EVALUATION OF GENERATION[edit | edit source]

NOTES:- 1)The generation energy of the module greatly depends on the installation angle(this depends on the weather conditions). 2)The report from NASA gives the opportunity to analyse the solar irradiation data both direct and diffuse for different angle at certain location.

FEASIBILITY OF POWER PLANT[edit | edit source]

NOTES:- 1)Incentive rates greatly affects

Improved performance of hybrid photovoltaic-trigeneration systems over photovoltaic-cogen systems including effects of battery storage[edit | edit source]

Amir H. Nosrata, , Lukas G. Swanb, , Joshua M. Pearcec, d, ,

This paper mainly focus on the hybrid model of CHP(Combined Heat and Power)with PV(Photovoltaic)and CCHP(Combined Cooling Heat and Power)with PV. The paper discuss about the several advantages of using such hybrid systems over conventional power generation systems.

CHP UNIT[edit | edit source]

NOTES:- 1)The CHP unit generally generates thermal output twice/ trice that of electrical output. 2)The CHP unit efficiency depends on the electrical as well as thermal efficiency.

PV UNIT[edit | edit source]

NOTES:- 1)In Solar PV effciency is between 6-20%. 2)The PV technology is combined with CHP unit to produce sufficient electrical energy to meet requirements. 3)Such hybrid system will surely help in increasing PV penetration level.

BATTERY STORAGE UNIT[edit | edit source]

NOTES:- 1)Used to store excessive energy from PV or CHP unit. 2)Lifetime of battery is around 4 years, so it adds up to total O&M cost as it requires replacemnets.

HYBRID SYSTEM[edit | edit source]

NOTES:- 1)Electricity generated by the PV and Cogeneration unit is used to meet electric requirements. The waste heat is harnessed by heat exchanger to provide hot water and space heating. 2)Whenever, excess electricity is generated it is stored in the batteries which is used to supply electricity when PV+CHP unit fails to meet requirements. 3)Even after utilizing the heat for thermal load, still there is some waste heat. This heat can further be used by installing absorption chillers which can use this waste heat to give space-cooling. Such type of CHP unit with absorption chiller installed are called as C-CHP units.

ADVANTAGES[edit | edit source]

NOTES:- 1.)GHG emission reduces considerabely (PV+CHP<PV+CCHP) 2.)High efficiency (PV+CHP<PV+CCHP)

The prospects for cost competitive solar PV power[edit | edit source]

Stefan Reichelsteina, , , Michael Yorstonb,

The paper mainly focus on cost competitiveness of power generated from solar PV. Depending on the data available from 2011 it can be seen that utility-scale PV installation is not yet cost competitive as compared to normal fossil fuel. But commercial-scale PV installation has achieved cost parity.

LCOE[edit | edit source]

The Levelized Cost of Electricity (LCOE) is a life-cycle cost concept which seeks to account for all physical assets and resources required to deliver one unit of electricity output. In simple words it is just the ratio of life time cost to the life time electricity generated. 1) The discount and interest rate must also be considered. The paper provides a formula for the same. 2) If there is no operating or no income tax- then LCOE depends on following terms:- a)Life duration b)Acquisition cost of capacity c)Degradation rate

3)Capacity factor is also to be considered, as it is part of the theoretical power generating facility. The equation to calculate cost of capacity for 1kWh is given in paper. 4)Variable operating cost is also to be included which includes fuel, labor and other cost conversion costs. 5)Fixed operating cost is also included for calculating LCOE. 6)Thus LCOE is summation of time-fixed operating cost, time-variable operating cost and cost of capacity(marked by tax factor).

CURRENT LCOE OF SOLAR PV[edit | edit source]

1) The system price means the capital cost of installing solar PV includes price of module and balance of system price. 2)BOS cost includes all external cost involved other then module cost including wiring, labor, inverter cost ..etc.. 3)The capacity factor for PV solar depends on various factors such as the major being the solar inrradiance followed by weather condition, location, tilt and orientation of the PV module . 4)Annual operating cost is negligible for PV modules. 5)From the LCOE calculation for utility power and commercial power; it can be seen the utility power is 30-40% higher then comparable base load rates. 6)Use of trackers on utility scale can reduce the LCOE by around 15%.

SENSITIVITY ANALYSIS[edit | edit source]

1)The factor which affects the LCOE of solar PV most- a)Capacity factor. b)System price c)Discount rate

Levelised Cost of Energy[edit | edit source]

Tarn YatesBradley Hibberd

This article mainly dicuss about whta is LCOE, how to calculate LCOE. It uses this technique to calculate LCOE for PV system.

LCOE Definition[edit | edit source]

  • It is a Manual for the Economic Evaluation of Energy Efficiency and Renewable Energy Technologies, LCOE is determined by dividing the project’s total

cost of operation by the energy generated.

  • LCOE is a metric that describes the cost of every unit of energy generated by a project in $/kWh.
  • Specifically, it is used to rank options and determine the most cost-effective energy source.
  • This will be benificial for policy maker in which energy generation to support in future.
  • Similarly, LCOE could be used to identify areas where cost-savings research would be most valuable.

Determining LCOE[edit | edit source]

  • The LCOE—the cost of every unit of energy generated by the project—multiplied by the total units of energy generated by the project is equal to the total cost of operation for the project.
  • Cash flow, for the purposes of the LCOE calculation, shows the amount of money either spent or received each year over the life of the project.
  • PV project cash flow would include the capital cost of installing the system and any up-front investment or capacity-based incentives.
  • For the purpose of including net value cost it is very important to include the concept of discount factor.
  • Renewable technologies often require a large up-front investment and incur little cost over the project lifetime, whereas traditional sources of energy often have a lower up-front cost but require continuing significant investment in fuel costs.
  • The value of the energy produced each year that is discounted rather than the energy itself.
  • The total life cycle cost should include construction or capital cost and the operation costs, including fuel and maintenance.
  • In determining the LCOE of a PV system, the following factors should be considered:

Costs Initial investment or capital cost O&M and operating expenses Financing costs Insurance costs State and federal income taxes Property taxes Required return on investment Decommissioning or removal Incentives Federal tax credit Accelerated depreciation (MACRS) Incentive revenue Energy Estimated Year 1 production Annual degradation System availability

  • If we assume that variables other than performance, capital and operating costs in the LCOE equation remain proportional to the capital cost or system size, then the following can be shown: If a change to a project increases the energy production by a greater percentage than it increases the cost, then that will decrease the LCOE.

Different Generating Technology LCOE[edit | edit source]

  • One of the most widespread uses of LCOE has been in comparing the cost of energy delivered from different sources, such as conventional fossil fuel, nuclear and renewable materials. These different energy sources have very different cost structures and performance characteristics. For example, coal plants have significant capital and operating costs and a consistent generation profile, as evidenced by a high capacity factor (the ratio of a power plant’s actual output over time to its potential output based on its nameplate capacity). In contrast, PV systems are characterized by high capital costs, low operating expenses and a low capacity factor, due to the nature of the solar resource. The LCOE metric takes these differences into account and enables direct comparison change decreases the LCOE.

Grid Parity[edit | edit source]

  • Grid parity is a metric regularly used in evaluating the viability of renewable energy sources.
  • For a retail customer, grid parity is achieved when the cost of power from an energy project is equal to or less than the retail price of power from the


  • The result is that grid parity occurs at different project costs for different regions and at a higher rate for residential customers, followed by commercial and industrial customers.
  • The LCOE includes projections about future inflation and fuel cost changes, but that’s not what you see in a single point value like electricity price. To make an effective comparison, you need to take the LCOE of future projected electricity prices into account.

LCOE and Location[edit | edit source]

  • Location selection can have a major impact on a project’s feasibility.
  • The weather conditions at a project site and its geographical location have implications for construction costs due to labor rates or building costs associated with land preparation or terrain, interconnection costs, or simply the cost of land.

LCOE Sensitivity[edit | edit source]

  • It is important to know which factors have the greatest influence on the LCOE equation.
  • A change in the debt fraction or in the assumed discount rate can have nearly as large an impact on LCOE as a significant change in the module cost.
  • In addition, the degradation rate has a significantly larger impact on LCOE.
  • However, a significant change in irradiance can have as large an effect on LCOE.

The present and future of residential refrigeration, power generation and energy storage[edit | edit source]

R.Z. Wang, , , X. Yu, T.S. Ge, T.X. Li

Based upon the fast development of energy efficiency, energy safety and use of renewable and sustainable energy, various energy systems related to residential refrigeration, power generation and storage have been developing. In this paper the current status of such various integrated system are summarized.

NOTES[edit | edit source]

  • Compared to the conventional energy supply the integrated PV+CHP unit is extremely efficient.
  • Adding an absorption chiller unit in the circuit will increase the efficiency more of the system. Such an arrangement is called PV+CCHP model.
  • Also a solar thermal system can be added to use solar produced heat to provide additional heating for the home for domestic hot-water or space-heating in the winter and cooling via the absorption chiller in the summer.
  • Wind energy and solar energy power generation have been developed as effective forms of renewable energy application. However, a common drawback with

a stand-alone wind energy and a solar energy generating power system is the unpredictable electric power output, since the output power depends on unpredictable weather changes.

  • In a house or an apartment, the efficient power should be DC power as it has no AC losses and can be obtained directly from solar PVs, fuel cells or battery. Mini distributed DC power could be set up in a house to power electrical equipment directly.
  • Energy storage plays an important role in enhancing the energy consumption efficiency in a wide number of residential and industrial energy applications.

The effect of installation of next-generation home energy systems in Japan[edit | edit source]

Takahiro Tsurusaki, Jyukankyo Research Institute, Inc., Japan Chiharu Murakoshi, Jyukankyo Research Institute, Japan Haruki Tsuchiya, Research Institute for Systems Technology, Japan Toshihide Tanaka, Osaka Gas Company, Japan Kanya Ishii, Osaka Gas Company Takehiko Nishio, Osaka Gas Company, Japan Hidetoshi Nakagami, Jyukankyo Research Institute, Japan

This paper mainly focus on a simulation model of a home energy system with a SOFC CHP system, a PV system, and a battery. The effects of each system and combined systems are evaluated in terms of energy bill, primary energy use and reduction of CO2 emissions. This combination is superior to that of a typical heat pump water heater with the same PV system. In this paper we also discuss conditions to achieve net zero energy consumption.

BACKGROUND[edit | edit source]

  • Today the concept of zero energy houses is popular among leading home builders as the Japanese government aims that net zero energy houses be common in the new housing market by 2020. Net zero energy houses require not only high levels of insulation and high efficiency appliances, but also some kind of distributed home energy system, such as a combined heat and power (CHP) system and a photovoltaic generation (PV) system.
  • The gas engine CHP system has a power generation efficiency of about 32-35 % (upper heating value) and a heat generation efficiency of about 56 %, according to specifications.
  • The SOFC CHP unit is in great demand in Japan as it has been installed at a greater rate since 2010 and it has good electrical efficiency of 35.3%.
  • Since few years there is trend of installing PV+CHP unit in order to increase the power generation efficiency. Moreover, the combination of a CHP system and a battery is also studied. As fuel cell power generation efficiency declines in partial load, it can be energy efficient to operate with as much power output as possible by charging a battery when the demand is low, despite charge and discharge losses.

METHOD[edit | edit source]

  • The scope of the simulation is energy for residential usage, including heating, cooling, domestic hot water, cooking, and lighting and appliances.
  • The SOFC CHP has a heat storage tank which is used to store excessive heat output when demand is less then the generated thermal output by the CHP unit, when the storage tank becomes full the heat is released in atmosphere.
  • A grid connected PV system for residential use is included in the system, and surplus electricity is fed into grid.
  • A energy storage unit namely lithium-ion battery is used which has charge and discharge are efficiency both 95 %.
  • Monthly and hourly energy demand for various appliances demanding thermal and electrical power is included.
  • Monthly and Hourly PV output is even recorded.

RESULTS[edit | edit source]

  • Annual energy cost has reduced.
  • Primary annual energy consumption reduces.
  • Power generation efficiency increases.
  • CO2 emmission has reduced to a considerable amount.
  • Increase PV penetration.

Control strategies and configurations of hybrid distributed generation systems[edit | edit source]

Maria Stefania Carmelia, , , Francesco Castelli-Dezzab, , Marco Maurib, , Gabriele Marchegianic, , Daniele Rosatid,

This paper mainly focus on the main topologies which can be adopted for a general hybrid generation system and it focuses on a particular hybrid system which combines two different energy sources, evidencing high level and local level power flow control strategies in both stand-alone and grid connected operation. A full experience in the realization of a hybrid plant which uses an internal combustion engine with co-generation functionalities and solar source, installed in Delebio, Italy is then presented. System design aspects, with particular attention to the possible topologies and power flow control strategies, are analyzed.

NOTES[edit | edit source]

  • Grid connect hybrid generation system described in this paper include following units:

CHP unit PV array Battery bank unit

1)CHP unit: This unit is used to generate heat and electrical power at the output. It consists of mainly units- Internal combustion engine Induction machine Heat exchanger Gas heat generator

2)PV array:For the work in the paper they have included 3 PV arrays. Where each array is connected to a DC-DC converter followed by an inverter unit.

3)Battery bank unit: This unit is used to store excessive electrical power generated by the PV unit. The a Bidirectional DC converter is used to connect the battery bank in the system. This converter is mainly used to transfer power in both directions.

  • Different Modes in which the Hybrid system can be used

1)Mode I: The power generated by the PV array is delivered into the grid whereas CHP unit priority is to satisfy the thermal load demand. 2)Mode II: The hybrid system priority is to satisfy the electric load power requirements and it is full load operation of CHP is desirable(else the efficiency of CHP is affected).


HAJIAN GELAREHa1 AND AHADI MOHAMMAD SAEEDb aMaster of Science, Electronic Engineer, Razi University, Kermanshah, Iran bMaster of Science, Hydraulics Water and Wastewater, Research Center, Kermanshah, Iran

This paper mainly focus to presents a novel architecture to produce the electricity demand of a building and use the heat byproduct of this process for internal usage, simultaneously. In this paper, a novel architecture is proposed for connection of photovoltaic (PV) system and combined heat and power (CHP) to supply demand. The main advantage of proposed scheme is reducing of fossil fuel consumption and greenhouse gases. Also, the economic impact of our design on total cost of production is analyzed and discussed.

PV structure[edit | edit source]

1)The PV in the paper is installed at the top of the roof at certain tilt angle. 2)The DC output of the PV is then fed into DC isolator. 3)The output of DC isolator is then given to inverter unit to converter the DC into AC power. 4)The AC power is given to AC isolator from there it is used to satisfy the load demands. 5)Any excessive power bein generated is fed into grid as this paper present grid connected PV system.

CHP structure[edit | edit source]

1)The CHP consists of mainly-

       FUEL storage tank
       Heat Exchanger
       Heat storage unit

2)The CHP unit generates two output Electrical as well as Thermal. 3)The CHP thermal output is mainly used for space heating and water boiling.

Hybrid System[edit | edit source]

1)The two systems are combined together i.e. PV and CHP. 2)This hybrid system is able to fulfill thermal as well as electrical demands. 3)Fossil fuel required is reduced. 4)Efficiency is increased. 5)CO2 emission is reduced.

Analysis of hybrid energy systems for application in southern Ghana[edit | edit source]

Muyiwa S. Adaramolaa, , , Martin Agelin-Chaabb, Samuel S. Paulc

This paper focus on an economic analysis of the feasibility of utilizing a hybrid energy system consisting of solar, wind and diesel generators for application in remote areas of southern Ghana using levelized cost of electricity (LCOE) and net present cost of the system. Sensitivity analysis on the effect of changes in wind speed, solar global radiation and diesel price on the optimal energy was investigated and the impact of solar PV price on the LCOE for a selected hybrid energy system was also presented

INTRODUCTION[edit | edit source]

A convergence of factors such as global decline in fossil fuel reserves, damaging effects of global warming, and rising energy demand due to increasing population are forcing a shift to low-carbon sources of energy. As a tropical country Ghana has abundant solar energy resources. The problem with solar and wind energy sources is that they are unpredictable and can be unreliable. A stand-alone solar energy system cannot provide electricity around the clock throughout the year if there are cloudy days when there is no sunlight. Similarly a stand-alone wind energy system may not produce usable energy for considerable portion of time during the year due to relatively high cut-in wind speed. Thus, hybrid system which included both of this technologies will be a good option for satisfying energy needs. Feasibility, reliability and economic analyses conducted in a number of studies showed that hybrid power systems are more reliable and cheaper than single source energy systems. The objective of this article is to study an economic analysis of a hybrid energy system consisting of solar, wind and conventional diesel generators for application in rural areas of southern Ghana. It is believed that this information will broaden the scope of options available to policy makers and all stakeholders in the energy sector as the country seeks to make critical investments in energy.

LOAD AND ENERGY RESOURCES[edit | edit source]

1)The solar energy(monthly) and wind energy(hourly and monthly) resources at the selected site as well as the cost of diesel (to fuel the generator) and electrical loads(data for 10 years).

POWER PLANT COMPONENTS[edit | edit source]

1)The hybrid solar PV–wind–diesel generator energy system (also called PV–wind–Gen hybrid) consists of two parts: (1) power plant which is made up of a PV module, a wind turbine, a diesel generator, a battery and a power converter; and (2) a mini-grid transmission and distribution system. 2)The hybrid energy system is designed and analyzed using National Renewable Energy Laboratory software, Hybrid Optimization Model for Electric Renewable (HOMER). 3)For each component of the energy system, the HOMER software requires information about the cost (capital, replacement, operation and maintenance), number (or size) of units to be used, operating hours and lifetime, and other specific component properties. In addition, the economic information (such as applicable real interest rate at a desire location and the overall system fixed, operating and maintenance costs) is required.

OUTPUT POWER[edit | edit source]

1)The power at the output of PV module can be calculated. 2)The power at the output of Wind resource can be calculated. 3)The generator system is used to supplement the power production by the renewable energy conversion systems especially when the required electrical load is not fully provided by these systems. 4)The battery is used to meet the electrical load during the nonavailability of power from the energy generating systems. 5)A power converter maintains the flow of energy between the AC electrical load and DC components of the hybrid energy system.

SYSTEM SIMULATION[edit | edit source]

1)During the simulation process, HOMER ensures that the system’s operating capacity is sufficient to provide for both the primary load and operating reserve. 2)In HOMER, three operating reserves are required: one relates to the variability of electrical load and the other two relate to variability of wind speed and solar radiation.

ECONOMICS[edit | edit source]

1)Based on all the cost data for each system component, the discount rate and the project economic lifetime, the optimized system configurations are ranked based on the minimum value of total net present cost. 2) The revenues from the system include income from selling power to the grid and any salvage value that occurs at the end of the project lifetime.

RESULTS[edit | edit source]

1)From the simulation results it can seen that the Electricity generated by the PV system and the Wind resource is around 50%of the total power demand. 2)On the other hand, the relative large contribution of renewable resource can reduce the dependence of the hybrid power system on diesel price, lower the operating and maintenance as well as fuel costs.

Large-scale integration of wind power into different energy systems[edit | edit source]

Henrik Lund,

The paper presents the ability of different energy systems and regulation strategies to integrate wind power. The ability is expressed by the following three factors: the degree of electricity excess production caused by fluctuations in wind and Combined Heat and Power (CHP) heat demands, the ability to utilize wind power to reduce CO2 emission in the system, and the ability to benefit from exchange of electricity on the market. Energy systems and regulation strategies are analysed in the range of a wind power input from 0 to 100% of the electricity demand. Based on the Danish energy system, in which 50% of the electricity demand is produced in CHP, a number of future energy systems with CO2 reduction potentials are analysed, i.e. systems with more CHP, systems using electricity for transportation (battery or hydrogen vehicles) and systems with fuel-cell technologies. For the present and such potential future energy systems different regulation strategies have been analysed, i.e. the inclusion of small CHP plants into the regulation task of electricity balancing and ancillary grid stability services and investments in electric heating, heat pumps and heat storage capacity. The results of the analyses make it possible to compare short-term and long-term potentials of different strategies of large-scale integration of wind power.Monthly data of electricity being generated by each unit in hybrid system is also presented.

ENERGY PLAN MODEL[edit | edit source]


  • Inputs required for technical analysis:-

1)Annual consumption of electricity, even required for transport. 2)Solar thermal and industrial CHP production input for district heating. 3)Capacity and operating efficiency of CHP, Heat pumps, Boilers, power stations.

  • The model emphasizes the analysis of different regulation strategies:-

1)Regulation strategy I: meeting heat demand. 2)Regulation strategy II: meeting both heat and electricity demands.

Dynamic programming to a CHP-HES system[edit | edit source]

X.P. Chen Member, IEEE, Z.T. Li, W.Xiong,

M.H.Wang, X.F. Yuan Electrical Engineering School, Guizhou University

Guiyang City, P.R.China

e-mail: This paper focus on to develop a optimal energy management algorithm for operating a targeted system that consists of a CHP with hybrid energy storage (CHP-HES). By applying a dynamic programming to the system, its energy efficiencies were improved dramatically with excellent dynamic performance during the test.

NOTES[edit | edit source]

1)CHP (combined heating and power) is widely considered as the key to solve the problems of enhancing energy efficiency and reducing carbon dioxide emissions. 2)stand-alone CHP application as decentralized energy technologies can reduce the energy loss mainly caused by the heat wasted during the production understood to reach up to 65% loss of the primary energy input. 3)It is believed that integration of an energy storage system into a CHP system will improve energy efficiency of the system 4)Household electricity consumption of the house is analysed. 5)In order to keep high efficiencies of the energy system, domestic electrical energy supply requires both high power and energy capability. 6)Hybrid energy storage system is the preferable option for enhancement of system dynamics, especially when batteries are coupled with super-capacitors.

RESULTS[edit | edit source]

                       CHP standalone                                                                                  CHP with battery integrated
  • Electrical energy 33% 35%
  • Efficiency 65% 50%

Energetic hybrid systems for residential use [edit | edit source]

Mustapha Hatti, Nachida Kasbadji Merzouk and Achour Mahrane Unité de Développement des Equipements Solaires, UDES / EPST-Centre de Développement des Energies Renouvelables 11 Route Nationale, B.P 386-42415, Bou Ismail, Tipaza, Algeria.

The main goal of this paper is to present combined technologies (wind, fuel cells and solar power) to achieve synergies in terms of cost and energetic efficiency compared to systems based on a single energy source and energy conversion technology. The consumptions can be mobility, space and water heating, cooling, cooking, illuminations, electronics. Finally the paper describes the models required to simulate the components and sub-systems of a Wind-Photovoltaic’s-Fuel cells-Micro-turbine and Diesel power system for residential applications in highlands regions of Algeria.

INTRODUCTION[edit | edit source]

  • Electricity demand is increasing year by year
  • Hybrid renewable energy resource can provide higher quality and more reliable Power to the consumers then single energy resource.
  • The CO2 emission by habitats is increasing year by year to satisfy the increasing energy demand.
  • Expectation to reduce the CO2 emission and reduce the dependence of supply on fossil fuel.


  • Photovoltaic
  • Batteries and superconductors
  • Diesel Generators
  • Wind generator

ADVANTAGES[edit | edit source]

  • As the fossil fuel consumption is reduced, the use of Hybrid system with renewable energy technologies, can help reduce cost of production of electricity.
  • Diesel generators are mainly included to meet the peak load requirements when the power generated by the renewable sources couldn't meet the energy demands. A battery bank can be a good replacement for Generator( to make a complete renewable based hybrid system).
  • Adding CHP unit can also be used to satisfy Thermal needs(hot water, space heating).
  • Increases the economy of Wind and solar power and help increasing their penetration level.
  • The CO2 emission reduces considerably.

Renewable energy strategies for sustainable development [edit | edit source]

Henrik Lund Department of Development and Planning, Aalborg University, Fibigerstraede 13, 9220 Aalborg, Denmark

This paper discusses the perspective of renewable energy (wind, solar, wave and biomass) in the making of strategies for a sustainable development. Such strategies typically involve three major technological changes: energy savings on the demand side, efficiency improvements in the energy production, and replacement of fossil fuels by various sources of renewable energy. Consequently, large-scale renewable energy implementation plans must include strategies for integrating renewable sources in coherent energy systems influenced by energy savings and efficiency measures.

  • Potential of renewable energy sources is very high in Denmark. The estimated potential of all resources has been listed in paper.
  • Energy-PLAN model is used for analysis of large scale integration of renewable energy.


Michael Woodhouse, Ted James, Robert Margolis, David Feldman, Tony Merkel, and Alan Goodrich National Renewable Energy Laboratory, Golden, Colorado, USA

This paper mainly examine whether solar will reach grid parity in the United States. In close consultation with U.S.-based residential PV installation firms, NREL has constructed a detailed analysis of PV system prices.

  • Estimated residential PV system cost including cost of everything goes around 5.73$/W and best case it goes to 3.55$/W including grid interconnection.
  • This estimated system cost can be used to determine LCOE.
  • The operation and maintenance is required for just two main things-

1)10-year inverter replacement with labor price. 2)end of the year one check by installer.

  • The effect of including federal investment tax credit for the PV system is shown in the paper.
  • The PV LCOE is then compared with electricity prices.

The value of module efficiency in lowering the levelized cost of energy of photovoltaic systems [edit | edit source]

Xiaoting Wanga,∗, Lado Kurdgelashvili b, John Byrne b, Allen Barnett a a Department of Electrical and Computer Engineering, University of Delaware, Newark, DE, United St

One standard that is used to compare different energy generation technologies or systems is the levelized cost of energy (LCOE). The relatively high LCOE of photovoltaics (PV) is an obstacle to adopting it as a major electricity source for terrestrial applications. In a conventional PV system, the cost of the module contributes approximately half of the expense and the other costs are together summarized as balance of system (BOS). Across different PV systems with the same installation area, this part of BOS ($/W) is directly dependent on the module efficiency. Therefore, the LCOE is affected by the module efficiency even if the module price ($/W) remains the same.

INTRODUCTION[edit | edit source]

  • Cost reduction for PV can be achieved through combination of market, tax and regulatory incentives (e.g., tax credits, rebates, solar energy mandates)

and research and development (R&D) support.

  • This paper, increased module efficiency can reduce levelized (i.e., lifetime) energy production costs of PV systems.
  • One measure to compare different PV technologies is levelized cost of energy (LCOE), a concept that was introduced at the beginning. The LCOE is calculated using the solar advisor model (SAM).

LCOE[edit | edit source]

  • The levelized cost of energy (LCOE) is “the cost that, if assigned to every unit of energy produced (or saved) by the system over the analysis period, will equal the total life-cycle cost (TLCC) when discounted back to the base year”.
  • LOCE calculations requires two sets of information: (1) system cost items, payment method, financing and incentives; and (2) performance parameters and case study location.
  • LCOE is calculated by running solar advisor model (SAM), a performance and economic model.

NOTES[edit | edit source]

  • Currently, the efficiency of good conventional silicon modules lies in the range of 13–15%.
  • The efficiency of the new module is incorporated in the LCOE equations by changing the denominator.
  • Moreover the Numerator maximum cost is installation cost so, it is changed.
  • A new LCOE equation for calculation is considered to include effect of efficiency change.
  • The BOS is divided into three categories: electrical system, structural system, and business processes.

1)The electrical installation cost is also considered linearly proportional to the system power capacity. Therefore, all of the electrical system cost is power-related cost, or PRC. 2)The second part of the BOS, the structural system, includes site preparation, racking and relevant installations. Since these costs are linearly proportional to the total area of the modules, the structural system cost is area-related cost, or ARC. 3)The third part of the BOS, the business processes, including financing and contractual costs, permitting, interconnection etc., is usually constant so this cost is fixed cost, or FC.

  • When module efficiency varies, the total PRC ($) changes linearly in proportion to the power capacity (W).
  • The new equation of LCOE considering the PRC price shows that as module efficiency increases the LCOE cost of the system decreases.

Dispatch strategy and model for hybrid photovoltaic and trigeneration power systems[edit | edit source]

Amir Nosrat, Joshua M. Pearce,

This paper introduces a dispatch strategy for PV+CCHP hybrid system that accounts for electric, domestic hot water, space heating, and space cooling load categories. The dispatch strategy was simulated for a typical home in Vancouver and the results indicate an improvement in performance of over 50% available when a PV-CHP system also accounts for cooling. The dispatch strategy and simulation are to be used as a foundation for an optimization algorithm of such systems.

INTRODUCTION[edit | edit source]

The advent of small scale combined heat and power (CHP) systems has provided the opportunity for inhouse power backup of residential-scale photovoltaic (PV) arrays. These hybrid systems enjoy a symbiotic relationship between components, but have large thermal energy wastes when operated to provide 100% of the electric load. In a novel hybrid system is proposed here of PV-trigeneration. In order to reduce waste from excess heat, an absorption chiller has been proposed to utilize the CHP-produced thermal energy for cooling of PV-CHP system.

SYSTEM OVERVIEW[edit | edit source]

  • The main units are

PV CCHP Battery

The output from battery is given to a inverter to convert DC-AC and then finally the output is given to electrical load. The output of CCHP is given electrical and thermal load. The output of absorption chiller is used for space cooling and output of heat exchanger is directly given for space heating and domestic water heating.

DISPATCH STRATERGY[edit | edit source]

  • The dispatch strategy is intended to control the system such that the load requirements (electric and thermal [domestic hot water usage, space heating, and space cooling]) are met. The thermal load is further split into.
  • In the proposed system the thermal output of a CHP unit tends to be larger than the electrical output, the dispatch strategy first prioritizes matching the electrical load and in the event that the thermal load is not met afterwards, is altered to match the thermal load.
  • Excess electric power is first placed into the batteries, and in the case the batteries are at their maximum state of charge, the electricity is dumped either onto the grid or into the ground based on whether the system is a grid-connected or stand-alone.
  • In the paper the dispatch strategy is simulated using MATLAB.
  • Model validation was done using HOMER software.
  • The results shows there is significant improvement in PV+CCHP as compared PV+CHP module.

Optimal sizing of renewable energy and CHP hybrid energy microgrid system[edit | edit source]

Yanhong Yang ; Grad. Univ. of Chinese Acad. of Sci., Beijing, China ; Wei Pei ; Zhiping Qi

The paper mainly focus on a new method for optimal sizing renewable energy generations and combined heat and power (CHP) units in a hybrid energy microgrid, considering system operation because of the stochastic varieties of renewable source and the heat and power requirements. A new microgrid system planning model was developed, which was based on hourly energy balance and can meet customer requirements with minimum system annual cost. To solve the planning model, the simulated annealing algorithm was used. The paper presents the development and implementation of the method, and demonstrates its application on the hybrid energy microgrid system of an office park in Beijing.

INTRODUCTION[edit | edit source]

  • When designing a hybrid system the sizing of the elements and determining the most adequate operation strategy should be considered at the same time.
  • HOMER uses relatively simple strategies based on the ones studied by Barley and it is able to obtain an optimal design of a hybrid system by selecting the most appropriate strategy.
  • In this paper we have developed a planning model considering unit deployment and system operation to size the microgrid system components. Simulated Annealing (SA) optimization method has been used to achieve global optimum.

SYSTEM DESCRIPTION[edit | edit source]

  • A typical hybrid energy microgrid system is shown in the paper, it is composed of PV array generation, wind turbine generation, microturbine/engine combined heat and power system, energy storage and boiler, it provides both power and heat to the customer.
  • If one has the location information where the photovoltaic array will be installed, then the solar radiation and power output of photovoltaic can be simulated.
  • The wind power computation model has two parts, simulate wind speed and compute wind turbine power output.
  • Microturbine CHP can have a stable power output, but the heat to power ratio and fuel consumption of it are various at partial loading. One can get the heat to power ratio and fuel consumption of partial loading through curve fitting method.

CONCLUSIONS[edit | edit source]

  • This paper presented an efficient method to optimal sizing hybrid energy microgrid system including renewable energy generation and CHP unit.
  • The study case shows that the renewable energy generation and CHP unit can complement each other, and heat recovery plays a significant role in the optimization performed.

Modeling and Simulation of Photovoltaic module using MATLAB/Simulink[edit | edit source]

S. Sheik Mohammeda, aFaculty of Engineering, Dhofar University, PB. No. 2509, Salalah, Sultanate of Oman, PC-211. a Corresponding Author E-mail:

This paper presents modeling of Photovoltaic (PV) module using MATLAB/Simulink. The model is developed based on the mathematical model of the PV module. Two particular PV modules are selected for the analysis of developed model. The essential parameters required for modeling the system are taken from datasheets. I-V and P-V characteristics curves are obtained for the selected modules with the output power of 60W and 64W from simulation and compared with the curves provided by the datasheet. The results obtained from the simulation model are well matched with the datasheet information.

INTRODUCTION[edit | edit source]

  • The main aim of this paper is to provide a reader with the fundamental knowledge on design and building the blocks of PV module based on the mathematical equations using MATLAB/Simulink.


  • Usually, the cells operate in reverse direction so that the current drift is desirable.
  • Solar-cell V-I and P-V characteristics varies with temperature and solar irradiation.
  • Maximum Power Point is the operating point at which the power is maximum across the load.
  • Efficiency of solar cell is the ratio between the maximum power and the incident light power.
  • Fill Factor (FF) is essentially a measure of quality of the solar cell. It is calculated by comparing the maximum power to the theoretical power (Pt) that would be output at both the open circuit voltage and short circuit current together.
  • Typical fill factors range from 0.5 to 0.82. The fill factor diminishes as the cell temperature is increased.


  • The Solarex MSX60/MSX64 PV modules are chosen for modeling.
  • These modules consist of 36 polycrystalline silicon solar cells electrically configured as two series strings of 18 cells each.

CONCLUSION[edit | edit source]

  • The I-V and P-V characteristics outputs are generated using the developed model for the selected modules and the obtained results are well matched with the datasheet information.

Energy, economic and environmental analysis on RET-hydrogen systems in residential buildings[edit | edit source]

M. Beccali, , S. Brunone, M. Cellura, V. Franzitta

The paper focus mainly on analyze energy, economic and environmental performances of a set of scenarios dealing with the production and the use of hydrogen as energy carriers in residential applications in combination with renewable energy (RE). Many energy systems have been considered according to several fuel-device combinations (electric grid, fuel cell, PV panels, wind turbines, boiler etc.). HOMER software was used to calculate energy balance of the system and its components.The net present cost and the cost of energy are the two main parameters used to compare economic performances of the systems with both actual and expected costs in the medium term. Sensitivity analysis for the same has been carried out.

INTRODUCTION[edit | edit source]

  • This paper analyses the use of hydrogen technologies in residential buildings connected to an existing electric grid. The aim of the study is to investigate the economic and environmental impacts of the use of hydrogen fuel cells as a substitute for electricity provided by grid and heat from a gas boiler. The building is thought of as a self-sufficient system, and the electric grid represents only an emergency device that operates when the fuel cell is not running.

SYSTEM AND SIMULATION[edit | edit source]

  • The house which is being used for the case is being analysed(Area of each room including the number of person staying in an house).
  • The consumption of electricity by the house during and cold and hot day is noted in graphical form.
  • Yearly, electricity demand is also noted in form of hours 1-(365*24).
  • The thermal load profile of the house is also noted hourly in graphical form.
  • We are interested in case 6 where we have fuel cell CHP, PV electrolyzer and grid connection when needed and simulation is done using HOMER software.
  • In system no. 6, heating and cooling are provided by heat pumps. Electric energy demand includes energy for domestic devices and for heat pumps. It is entirely met by a PV installation and by a hydrogen fuel cell. The fuel cell runs when solar radiation is low or null.
  • Surplus production of electricity from the PV can occur in correspondence with high solar radiation, and it is used for the operation of an electrolyzer to produce hydrogen.
  • Electrolytic hydrogen is then used to supply the fuel cell.

ECONOMICS ANALYSIS[edit | edit source]

  • The costs of the main components of the systems. Equipment performances and costs data have been assumed by market surveys and by literature.
  • NPC is the present value of installation and operation costs of the system over its lifetime.
  • COE is the average cost per kWh of useful electricity produced by the system.
  • Equipment cost and technical parameters, cost of fuels (hydrogen and natural gas), electrical and thermal energy demand, pollutant emission factors, global solar radiation and wind speed annual time-profiles have been used as input for simulations.

SENSITIVITY ANALYSIS[edit | edit source]

  • A sensitive analysis has been carried out in order to assess the influence of the variation of the COE carriers and devices.
  • Factors that were considered for analysis were:-

!)Change in capital and fuel cost. !)Change the cost of hydrogen distributed by pipeline. !)Change the cost of natural cost distributed by pipeline.

Hybrid solar fuel cell combined heat and power systems for residential applications: Energy and exergy analyses[edit | edit source]

Mehdi Hosseini, , , Ibrahim Dincer, Marc A. Rosen

In this paper A residential solar PV–electrolyzer system is developed and coupled with a high temperature solid oxide fuel cell (SOFC) system (PV–FC) for supplying the electricity demand. It is possible for the PV system to generate electricity in excess of the demand during off-peak hours. The surplus electricity is used by the water electrolyzer for hydrogen production. The hydrogen produced is stored in a storage tank. The fuel cell is fed with the hydrogen generated by the electrolyzer. The PV–FC system is coupled with a heat recovery unit, which provides the residential area with thermal energy, to improve energy utilization. The heat recovery unit consists of a heat recovery steam generator and an absorption chiller utilizing the thermal energy of the SOFC flue gas for heating and cooling purposes. Determining system operational parameters is important for the design and implementation of the CHP system in a residential area. Therefore, the residential CHP system is assessed here based on energy and exergy. The hourly demand of the residential area is taken into consideration for component selection and sizing, and energy and exergy efficiencies of the developed system are presented.

SYSTEM DESCRIPTION[edit | edit source]

  • The solar PV system is the main part of the electricity generation module. A PEM water electrolyzer is utilized for hydrogen production from surplus PV electricity. Hydrogen is stored during day when loads are below the peak and input to the SOFC to provide residential electricity at night.
  • An atmospheric SOFC is used in the modeling, so the hydrogen temperature rises when its pressure decreases from the storage tank pressure (5 bar) to atmospheric pressure (1 bar). If preheating is still required, an external heat source is considered for this purpose, however, only the amount of the required heat is calculated in the analysis
  • High temperature gases leaving the stack are directed to a heat recovery steam generator (HRSG), for steam generation. The HRSG provides steam for an absorption chiller.
  • The PV and SOFC power output are converted in a DC/AC converter to meet the power requirements of the house.
  • Solar radiation data hourly is noted.

RESULTS[edit | edit source]

  • Efficiencies of the PV system has been noted.
  • The power generated by the photovoltaic and the energy demand has been noted.
  • Excessive power not fulfilled by PV is given by CHP unit.
  • If the power generated by the PV module generally is summer is higher then the demand then it is used for electrolyzer operates to generate hydrogen.

Energy dispatching based on predictive controller of an off-grid wind turbine/photovoltaic/hydrogen/battery hybrid system[edit | edit source]

Juan P. Torreglosaa, c, , Pablo Garcíab, , Luis M. Fernándezb, , Francisco Juradoa, , This paper mainly focus on energy dispatching based on Model Predictive Control (MPC) for off-grid photovoltaic (PV)/wind turbine/hydrogen/battery hybrid systems. The renewable energy sources supply energy to the hybrid system and the battery and hydrogen system are used as energy storage devices. The modeling of the hybrid system was developed in MATLAB-Simulink, taking into account datasheets of commercially available components. To show the proper operation of the proposed energy dispatching, a simpler strategy based on state control was presented in order to compare and validate the results for long-term simulations of 25 years (expected lifetime of the system) with a sample time of one hour.

INTRODUCTION[edit | edit source]

  • Depending on the objectives to meet by the energy dispatching there are two kinds of simulations that can be carried out: short term and long-term simulations.

1)Short-term simulations are focus on the dynamics of the sources which compose the system and take them into account to face the net power variations due to the changes in load power or disturbances in the renewable energy sources. 2)Long-term simulations are used when the main objective is to show the proper operation of the system during a considerable period of time (from months to the whole life of the system). they pay attention to other parameters such as operation costs, degradation of the sources, level of charge of the storage devices, etc.

SYSTEM UNDER STUDY[edit | edit source]

  • In this HS, the main energy sources are the wind turbine and PV panels (renewable sources), whose operation is assisted by the battery and hydrogen system (composed by fuel cell, hydrogen tank and electrolyzer) working as backup and storage systems.
  • In the hydrogen system, the fuel cell is supplied by the hydrogen provided by the tank, which is filled by the electrolyzer.
  • The energy that flows among the energy sources is controlled by DC/DC converters which connect them to a common DC bus.
  • In this HS, when the renewable energy is higher than the energy demanded by the load, this energy excess can be stored as electricity in the battery or as hydrogen in the tank (produced by the electrolyzer).
  • On the other hand, when the renewable energy is lower than the demanded energy, this energy deficit can be supplied by the battery and/or fuel cell.
  • The sizing of the HS was carried out using Simulink Design Optimization of MATLAB

SIMULATION RESULTS[edit | edit source]

  • The control strategies presented in this work were simulated for 25 years (the estimated lifetime of the HS) with a sample time of one hour to evaluate and validate the performance of the proposed energy dispatching based on MPC.
  • The sun irradiance, wind speed (hourly) and load power consumption profile (hourly for 4 different days of different seasons) used in the simulations.
  • The efficiency for the hybrid system, the battery and the hydrogen system are calculated.
  • Fuel cell efficiency is higher at low power demand.

Modelling an off-grid integrated renewable energy system for rural electrification in India using photovoltaics and anaerobic digestion[edit | edit source]

J.G. Castellanos, M. Walker, D. Poggio, M. Pourkashanian, W. Nimmo,

This paper focus on the design optimisation and techno-economic analysis of an off-grid Integrated Renewable Energy System (IRES) designed to meet the electrical demand of a rural village location in West Bengal e India with an overall electrical requirement equivalent to 22 MWh year�1. Micro-grid modelling software used was HOMER.

INTRODUCTION[edit | edit source]

  • There is an increased interest in installing small scale renewable generation systems to electrify these communities.
  • However, due to the intermittence in energy generation of many renewable systems depending on one single source, this option may be unreliable. To increase the reliability of the renewable energy system, the most suitable method is to develop Integrated Renewable Energy Systems (IRES) which rely on multiple generation technologies.
  • Well managed integrated renewable energy systems, which combine a higher number of technologies, potentially produce cheaper energy than simple energy systems.

MATERIALS AND METHODS[edit | edit source]

  • Due to abundance of bio-gas and Solar light the chosen energy conversion technologies were PV and anaerobic digestion (AD), with a Combine Heat and Power (CHP) generator fueled by biogas. Even storage unit was added to store excessive DC generated by the solar input during sunny days.
  • The load demand of around 1000 residents in the village were noted hourly in graphical form.
  • The demand is split into various categories and includes economic activity i.e. grinding spices, water pumping, the operation of a medical centre, adult and child education facilities, lighting and entertainment.
  • Micro-grid modelling was performed using HOMER. This software allows simulation of the performance of an energy system with uncertain operational conditions, allowing robust design with reduced project capital risk.
  • Here we will talk about the modelling and results just of the Gth scenario which include PV+STORAGE+CHP+DC=AC+AD.
PV[edit | edit source]
  • Average radiation data of solar is noted whereas the graphical data representing monthly daily radiation data.A derating factor equivalent to 80% and

ground reflectance of 20% were assumed. The 20 years lifetime PV panels were considered not to have a tracking device, thus the angle at which the panels are mounted relative to the horizontal was set at 23degrees�.

STORAGE[edit | edit source]
  • Vanadium redox battery were considered for storage for the DC generated from the PV.
CHP[edit | edit source]
  • The fuel cell system consisted of three elements: fuel cell, electrolyser and hydrogen tank. The fuel cell operating lifetime was considered to be 40,000H.
  • The expected operating lifetime of the CHP generators was 60,000 h and their operation schedule was assumed to be fixed and manually programmed.
  • Capital and O&M costs of the main components of the IRES are noted.

RESULTS[edit | edit source]

  • The Net present cost of different scenarios were compared which depends on the capital as well as O&M cost and it was found the scenario G was the best fit.
  • Similarly the LCOE of the different scenarios were compared and the one for the scenario G was minimum.

Economic and environmental based operation strategies of a hybrid photovoltaic–microgas turbine trigeneration system[edit | edit source]

Firdaus Basrawia, , , Takanobu Yamadab, , Shin’ya Obarac,

This paper mainly focus on the economic and environmental performance of a photovoltaic (PV) and microgas turbine trigeneration system (MGT-TGS) based hybrid energy system with various operation strategies.The hybrid system covers power, heating and cooling load of a selected building under a tropical region. A case of MGT-TGS without PV was also studied for comparison. Each system had an MGT with electrical output capacity of 30 kW or 65 kW as the core prime mover. Economic performance was analyzed using life cycle cost analysis and environmental performance was analyzed based on the actual emissions of MGT reported in literatures.

MATERIALS AND METHOD[edit | edit source]

  • Ambient temperature conditions were noted.
  • The area and dimensions of the rooms were noted. Moreover, the cooling, heating an power demand were also noted.Power demand increases during day hours whereas cooling demand increases during night hours.

HYBRID SYSTEM[edit | edit source]

  • Power-match operation can be used in which PV can cover the power demand up to the maximum level during day time, and Micro-gas turbine in CHP will follow the rest of the power demand.
  • Imbalance between the heat supply and demand can be controlled by the heat storage, and insufficient heat can be covered by the boiler.
  • Heat-match is expected to have higher cost because of the use of battery.
  • The problem with above mentioned stratergy is the MGT are designed to operate at full-load conditions.
  • Under partial load operation, their power generation efficiency will decrease and their emissions level will also increase rapidly.
  • Thus, another good operation strategy is by using smaller MGT to run at base load, and the PV and battery will cover the power demand at peak load. Insufficient and imbalance cooling and heating load can be covered by the heat storage and the boiler. Although this operation strategy is expected to have the lowest emissions level, it may also have high cost due to the use of battery.

ECONOMIC ANALYSIS[edit | edit source]

  • All inflow or outflow of money throughout the life cycle will be calculated based on the present worth. Net Profit NP gained for 25 years of life cycle of the investment on the energy system can be calculated.
  • This profit depends on PrPe is profit by not buying electricity from the grid [US$], Ceq is the equipment cost [US$], Cins is the installation cost of the equipment [US$], CO&M is the operation and maintenance cost of equipment [US$], Crep is the replacement cost for equipment that has life time less than 25 years [US$], Csal is the salvage and market value of equipment in the end of their life time and in the end of 25 years of life cycle [US$], and Cfuel is the fuel cost [US$].
  • Present worth PWx of a uniform series of payment can be calculated and it depends on PWFAUP is Present Worth Factor for annual uniform payment [–] and AUPx is amount of annual uniform payment. In the case of operation and maintenance cost, AUP is the amount of operation and maintenance cost for a year.
  • The present worth factor depends on the interest rate and the life time of project.
  • Equipment including battery and MGT-CGS that have lifetime less than 25 years and therefore cost for their replacement are also needed.
  • Fuel cost should also be considered.

RESULTS[edit | edit source]

  • When the electricity price was highly subsidized, none of the hybrid system can give Net Profit throughout the 25-year life cycle time. Even the simplest MGT-TGS without PV cannot generate Net Profit. However, when the unsubsidized price of electricity was considered, all hybrid systems show positive Net Profit.
  • Sensitivity analysis was carried out considering the changes of Net Profit when natural gas price increases.

Optimal Power Scheduling in a Virtual Power Plant[edit | edit source]

Davide Aloini, Emanuele Crisostomi, Marco Raugi and Rocco Rizzo

This paper focus on a novel approach where the Energy Management System of a Virtual Power Plant decides the optimal power scheduling not on the basis of some predefined policies, but upon the solution of an optimization problem. The scheduling decision is dynamic as it depends on variable factors, not fully predictable, such as renewable sources availability, electrical energy price, controllable and uncontrollable loads demand and possibility of storing or releasing stored energy. The optimal solution is computed according to a novel cost function that explicitly takes into account only direct costs.


  • Optimal management of a VPP requires both short-run and long-run decisions.
  • The short-run is a time period in which some production factors are fixed. Fixed costs, such as those due to the existing plant instalLment and annual Operation and Maintenance (O&M) costs, do not have a significant impact on firm’s short-run decisions, as only variable costs and revenues affect profits.
  • On the contrary, in the long-run period all the production factors are considered as variable. Typical long-run decisions include investment decisions (e.g., plant upgrade), entering or leaving a specific industry, etc.
  • a popular way to express cost functions is the so-called Levelised Costs Of Electricity (LCOE) form.
  • LCOE can be calculated using Electrt is the amount of electricity produced in the year t, (1 + r)−t is the discount factor for year t (r is also called discount rate), and the terms at the numerator represent investment costs, O&M costs, fuel costs, carbon costs and decommissioning costs in the year t.

METHODOLOGY[edit | edit source]

  • The optimization algorithm is explained through a case study, to improve the clarity of the presentation; the investigated scenario consists of (a) three distributed energy resources, i.e., a photovoltaic (PV) plant, a wind (W) farm and a Combined Heat and Power (CHP) unit; (b) two storage systems, i.e., a small pumped hydro storage and 10 low-speed flywheels; (c) controllable and uncontrollable loads; and (d) the grid, from which the EMS can buy/sell energy.
  • PV: We consider the data of an Italian Solar PV characterized by a Net Capacity of 6MW, a capacity factor of 16%, variable O&M costs equal to 36.68e/MWh
  • Wind plant: We consider the data of a French Onshore wind characterized by a Net Capacity of 45MW, a capacity factor of 27%, variable O&M costs equal to 14.00e/MWh.
  • CHP: We consider the data of an American simple gas turbine CHP characterized by a Net Electrical Capacity of 40MW, a capacity factor of 85%, variable O&M costs equal to 0.73e/MWh and 54.41e/MWh for fuel and carbon costs.
  • Small pumped hydro:In this case-study we assume a Net Capacity of 10MW, a capacity factor of 70% and variable O&M costs equal to 2.58e/MWh
  • Low-speed flywheel:Here we consider variable costs equal to 2.78e/MWh, a 90% efficiency and a Net Capacity of 1650kW with a discharge cycle of 120 s.
  • Renewable resources are always 100% exploited, because they are convenient as they do not require carbon/fuel costs.
  • At some particular moments (i.e., night time), the EMS buys (or sells less) energy from (to) the grid as it is cheaper than using the CHP; CHP production is thus reduced during off-peak hours and is fully restored during on-peak hours, always according to its modulating capabilities.

DISCUSSIONS AND RESULTS[edit | edit source]

After solving the optimiZation problem, the EMS finds the optimal power flow values, and decides

  • How much energy should be produced and by whom (e.g., by the CHP rather than by the PV plant);
  • Whether surplus energy should be stored, or stored energy should be supplied to the grid/loads and by which storage system (i.e., either use the small pumped hydro or the flywheels);
  • Whether energy should be bought or sold from the grid.


Maciej Z. Lukawski1,2,* , Konstantinos Vilaetis1,2, Lizeta Gkogka1,2 , Koenraad F. Beckers1,2 , Brian J. Anderson3 , Jefferson W. Tester1,2 1Cornell Energy Institute, Cornell University, Ithaca, NY 14853, USA 2 School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY 14853, USA 3Department of Chemical Engineering, West Virginia University, Morgantown, WV 26506, USA

  • Corresponding author:

This paper focus on an in-depth technical and economic analysis of supplementing the existing natural gas-fired combined cycle heat and power (CHP) plant with an Enhanced Geothermal System (EGS) and a torrefied biomass boiler.Design of the district heating system and its operating parameters were optimized to obtain a minimum levelized costs of energy. An Organic Rankine Cycle (ORC) waste heat recovery unit was considered to utilize the excess thermal energy available in the summer from the EGS reservoir for generating electricity. A torrefied biomass boiler was used to supplement the heat output of EGS reservoir to meet peak winter heat demand. Proposed solutions were evaluated in terms of levelized cost of electricity (LCOE), fossil fuel consumption, and CO2 emissions.

ECONOMIC EVALUATION[edit | edit source]

  • In this paper the discount rate is considered to be 6% and payback time is 20years.
  • Capital investment costs:
  • Operation and maintenance (O&M) costs:
  • Natural gas price:
  • Heat credits:
  • Electric power generated:
  • All the above mentioned factors are required to calculate the LCOE of the system.