This literature review supported the following paper:

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.

Page data
Type Literature review
Authors Aishwarya Shrikant Mundada
Published 2015
License CC-BY-SA-4.0
Impact Number of views to this page and its redirects. Updated once a month. Views by admins and bots are not counted. Multiple views during the same session are counted as one. 2,362
Issues Automatically detected page issues. Click on them to find out more. They may take some minutes to disappear after you fix them. No main image, Sections nested too deep
Cookies help us deliver our services. By using our services, you agree to our use of cookies.