PV and CHP Hybrid System: Difference between revisions

This page was created as part of the 2015 MTU graduate course MY5490/EE5490: Solar Photovoltaic Science and EngineeringIt's open edit now, so feel free to improve it.Read more...

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1. Expanding Photovoltaic Penetration with Residential Distributed Generation from Hybrid Solar Photovoltaic Combined Heat and Power Systems

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.

1.1 Technical Limitation to PV penetration in the current grid

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.

1.2 Electrical and heat requirements of representative U.S. single family

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.

1.3 Design of Solar PV and CHP hybrid system

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.

1.4 Sizing of Solar PV and CHP hybrid system

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.

1.4 PV penetration level-Percentage of PV generated electricity

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.

2. Institutional scale operational symbiosis of photovoltaic and cogeneration energy systems

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.

2.1 Materials and Methods

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).

2.2 Proposed CHP system

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.

2.3 Solar PV system

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.

2.4 Design scenario PV+CHP

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.

2.5 Results and Discussion

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%.

3. Optimizing design of household scale hybrid solar photovoltaic + combined heat and power systems for Ontario

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.

3.1 Introduction

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.

3.2 Background

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.

3.3 Data Collection and Analysis

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.

3.4 Hybrid PV+CHP+Battery design for a residential system

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.

3.5 Results and Discussion

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.

4.Optimal sizing of hybrid solar micro-CHP systems for the household sector

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.

4.1 Overview

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.

4.2 Energy system modeling

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.

4.3 Micro-CHP modeling

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.

4.4 Sizing of system

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.

4.5 Objective function

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.

4.6 Case study for residential in Rome

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.

4.7 Sensitivity Analysis

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.

5.Simulations of greenhouse gas emission reductions from low-cost hybrid solar photovoltaic and cogeneration systems for new communities

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.

5.1 Overview

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.

5.2 Methodology

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.

5.3 Data selection

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.

5.4 Results and Discussion

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.