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Community Solar Lit Review
- 1 Note to Readers
- 2 Back ground
- 2.1 Community Solar
- 2.1.1 Restrictions on Community solar
- 2.1.2 What is security?
- 2.1.3 States having shared solar policies
- 2.1.4 Potential of Shared solar
- 2.1.5 PV site suitability analysis using GIS-based spatial fuzzy multi-criteria evaluation (2011)
- 2.1.6 Geographical Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) methods for the evaluation of solar farms locations: Case study in south-eastern Spain (2013)
- 2.1.7 Solar Power Prediction for Smart Community Microgrid (2016)
- 2.1.8 User-centered design for smart solar-powered micro-grid communities(2014)
- 2.2 References
- 2.1 Community Solar
Note to Readers
Please leave any comments on the Discussion page (see tab above) including additional resources/papers/links etc. Papers can be added to relevant sections if done in chronological order with all citation information and short synopsis or abstract. Thank You.
- Google for Community Solar
- Google for shared solar
- Google for solar gardens
“community shared solar” is defined as a solar-electric system that provides power and/or financial benefit to multiple community members.
David Feldman, Anna M. Brockway, Elaine Ulrich, and Robert Margolis,"Shared Solar: Current Landscape, Market Potential, and the Impact of Federal Securities Regulation" NREL,April 2015..
Restrictions on Community solar
- Uncertainty about the applicability of Securities and Exchange Commission (SEC) requirements i.e when and how shared solar program is considered as "security".
- subject to regulation State and local laws.
- uncertain tax credit applicability such as 25D for residential purposes only upto 30% which restricts off site solar plant users. Section 48 for business have ventures have similar difficulties.
What is security?
A security is an investment instrument issued by a corporation, government, or other organization that offers evidence of debt or equity. Any transaction that involves an investment of money in an enterprise, with an expectation of profits to be earned through the efforts of someone other than the investor, is a transaction involving a security. Community shared solar organizers must be sure to comply with both state and federal securities regulations, and avoid inadvertently offering a security.
There is a need of no action letter from SEC for shared solar project
To pass as Federal security there is a Howey Test criteria:
- An investment of money
- In a common enterprise
- Based solely on the efforts of a promoter or a third party
- For which there is an expectation of profits
To pass as state security there is Risk capital test criteria:
- Funds are being raised for a business venture or enterprise
- The transaction is offered indiscriminately to the public at large
- The investors are substantially powerless to affect the success of the enterprise
- The investors’ money is substantially at risk because it is inadequately secured.
Even if a project is classified as security there are exemptions
- Group net metering or VNM: California, Connecticut, Massachusetts, Maine, New Hampshire, Vermont
- Statewide shared energy program: California, Colorado, Delaware, District of Columbia, Massachusetts, Minnesota
- Incentives: Washington
It is estimated that 49% of households and 48% of businesses cannot host a PV system of adequate size on their property or virtually net meter an entire system themselves. By opening the market to these customers, shared solar could represent 32%–49% of the distributed PV market in 2020, growing cumulative PV deployment in 2015–2020 by 5.5–11.0 GW and representing $8.2–$16.3 billion of cumulative investment.
Jason Coughlin, Jennifer Grove, et al., "Guide to community solar: Utility, private, and non-profit project development"U.S. Department of Energy, 2011.
- Great Overview of community shared solar
- Three different models – group members to be located within the same utility service territory, require solar array. Also talks about some main methods of implementation - Comparison between three main models (Utility,Special purpose entity & Non profit)
- Includes Various state policies to support community shared solar
Billing methods – Group billing, virtual net metering and joint ownership 1.Group billing – master metering, net metering ((total from utility – PV generation)/total participants) based on agreement 2.Virtual net metering – Credits appear on each individual customer’s bill, Colorado best example, Few examples of California, Colorado, Massachusetts, Delaware’s community solar program 3.Joint ownership – joint ownership between customer/owner and utility, businesses etc, Example of Maine and Washington’ s program
- Tax policy & incentives
- Security compliance – security laws are intended to protect individuals who provide financial support for a project with an expectation to receive profits from the efforts of others or with the expectation to receive a valuable benefit when the investor does not have control over managerial decisions of the venture.
- Basic step by step guidelines for community solar program
Baylin, F. et al. "Economic Analysis Of Community Solar Heating Systems That Use Annual Cycle Thermal Energy Storage". (1981): n. pag. Web. 19 Jan. 2017.
- Very technical detail about how to store heat in underground tanks
- Multiple geographic locations and community sizes looked at
Asmus, Peter. "Exploring New Models Of Solar Energy Development". The Electricity Journal 21.3 (2008): 61-70. Web. 19 Jan. 2017.
- Brings up "community choice aggregation", California legislation.
- Well defined community solar definition
- Solar shares
- List and overview of current (up until 2008) community solar projects
The following were found using the search term "Community solar pv" on google scholar
Ashok, S. "Optimised Model For Community-Based Hybrid Energy System". Renewable Energy 32.7 (2007): 1155-1164. Web. 18 Jan. 2017.
- Community 'microgrid' idea for rural area, case study in India
- Uses multiple generation sources; wind, hydro, solar, and tries to minimize cost of each
- 'dated' -> optimization plan results in not choosing PV due to cost, although it was included originally.
Noll, Daniel, Colleen Dawes, and Varun Rai. "Community Organizations And Active Peer Effects In The Adoption Of Residential Solar PV". SSRN Electronic Journal 67 330-343. Web. 18 Jan. 2017.
- Analyses of residential PV increase
- Solar Community Organizations (SCOs)
- Review of residential PV across U.S.
St. Denis, Genevieve and Paul Parker. "Community Energy Planning In Canada: The Role Of Renewable Energy". Renewable and Sustainable Energy Reviews 13.8 (2009): 2088-2095. Web. 19 Jan. 2017.
- Large vs small communities and interest in renewable s
- energy usage management
- conservation and efficiency vs renewables
The following were found using the search term "Community solar projects" on google scholar
Walker, Gordon. "What Are The Barriers And Incentives For Community-Owned Means Of Energy Production And Use?". Energy Policy 36.12 (2008): 4401-4405. Web. 19 Jan. 2017.
- cooperatives, community charities, etc -> diffenent community solar models
- high level overview
Huijben, J.C.C.M. and G.P.J. Verbong. "Breakthrough Without Subsidies? PV Business Model Experiments In The Netherlands". Energy Policy 56 (2013): 362-370. Web. 19 Jan. 2017.
- micro generation deployment models, company control, plug and play, community microgrid
- Main issues being legislation
- Use of net metering
Palit, Debajit. "Solar Energy Programs For Rural Electrification: Experiences And Lessons From South Asia". Energy for Sustainable Development 17.3 (2013): 270-279. Web. 19 Jan. 2017.
- Massive benefits to heavily populated rural areas
- Some technical detail and policy
Seyfang, Gill, Jung Jin Park, and Adrian Smith. "A Thousand Flowers Blooming? An Examination Of Community Energy In The UK". Energy Policy 61 (2013): 977-989. Web. 19 Jan. 2017.
- surveys among community energy groups in UK
- development factors
Chaurey, A. and Kandpal, T.C., 2010. A techno-economic comparison of rural electrification based on solar home systems and PV microgrids. Energy policy, 38(6), pp.3118-3129. 22 Jan. 2017.
- Off-grid PV power plants
- Micro grids or mini grids distribution networks
- Micro grid: more economic option for flat geographical terrains
- Techno economic comparison of SHS and micro grids
Denis, G.S. and Parker, P., 2009. Community energy planning in Canada: The role of renewable energy. Renewable and Sustainable Energy Reviews, 13(8), pp.2088-2095. 23rd Jan 2017.
- Renewable energy policy
- Community energy plan
- Community energy management
- Energy conservation
- Climate change policy
Shakouri, M., Lee, H.W. and Choi, K., 2015. PACPIM: new decision-support model of optimized portfolio analysis for community-based photovoltaic investment. Applied Energy, 156, pp.607-617. 23rd Jan 2017.
- Solar energy
- Community-based investments
- Mean–Variance Portfolio theory
- Decision-support model
- Residential photovoltaic systems
Abstract -Application of a GIS-based spatial multi-criteria evaluation approach, in terms of the FLOWA module to assess the land suitability for large PV farms implementation in Oman
- According to IEA, by 2050, PV and Concentrated solar power will be able to generate 9000TWh of electricity
- GIS and Multi-Criteria Analysis (MCA) together provide a fine lens for the optimal site selection for plants.
- Overview of GIS base MCA and it's methodology to PV farms siting
- Classification of various technical (solar radiation, land accessibility, land use), economical(Grid proximity, land slope, load poles) and environmental(sensitive areas, Hydrographi line, sand/dust risk) factors affecting optimum locations for large PV farms
- Case study of Oman with different PV technology
Geographical Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) methods for the evaluation of solar farms locations: Case study in south-eastern Spain (2013)
Abstract - Combination of a Geographic Information System(GIS) and tools or multi-criteria decision making(MCDM) methods in order to obtain the evaluation of the optimal placement of photovoltaic solar power plants in the area of Cartagena(Region of Murcia),in south east Spain. The use of MCDM the criteria or factors are weighted in order to evaluate potential sites to locate a solar plant. Analysis and calculation of the weights of the factors are conducted using Analytic Hierarchy Process(AHP). The assessment of the alternatives according to their degree of adequacy was carried out through the TOPSIS method (Technique for Order Preference by Similarity to Ideal Solution). .
- Detailed method for the evaluation of solar farm location.
- Analysis and calculation of the weight of all relevant factors are conducted using Analytic Hierarchy process (AHP). AHP method was used to determine the importance of the different criteria used in the process.
- Assessment of the alternatives is carried out through the TOPSIS method (This method ends with the calculation of the relative proximity to the ideal solution of each alternative that will be called Ranking.
- Main criteria for selection of PV Farm with weight: Location(48.625%), climate(28.562%), Orographical(17.259%),Environmental(5.553%).
- Complete case study for south-eastern Spain using Geographic Information System(GIS) system with Multi criteria decision making methods (GIS-MCDM).
Abstract - Microgrids will enable the integration of distributed renewable energy such as roof top solar panels within smart city communities. For microgrids to operate reliably and efficiently, prediction algorithms are important because of the fluctuation of solar energy and its dependence on weather. This paper presents a machine learning based algorithm, which learns a regression tree model with time of the day and humidity as main parameters.This work shows that solar panel prediction in Houston is heavily dependent on humidity of the region.
- Application of regression tree algorithm for solar power prediction.
- Various solar energy prediction techniques were discussed like Numeric weather prediction method (NWP), persistence method, Auto regressive Model, Support vector machines, Neural network technique etc.
- Most Important variable to predict power are humidity, time of the day and sky condition etc.
- Historical data for various variables for model learning through NOAA(The National Oceanic and Atmospheric Administration) are temperature, humidity, pressure, wind speed and wind direction
- solar power production is inversely proportional to the humidity.
Abstract - CoSSMic (Collaborating Smart Solar powered Micro-grids) is an EU funded project aimed at developing a system for smart management and control of solar energy. The system must be relevant to a community of end-users and other stakeholders. Two processes are described to contribute in meeting this aim, user-centered design and lean startup product design. Iterative approach to the design of the system which incorporates these two processes are described.
- Two feasibility studies are conducted in Konstanz, Germany and the Province of Caserta, Italy. Both trial locations are quite different in terms of population, solar irradiation, available equipment and electrical consumption.
- Iterative approach with an emphasis on user-centered design and lean startup product design.
- Establishment of workshops to design the model and strucure.
- quite successful in building up relations between potential users and the project teams
- Yassine Charabi, Adel Gastli, PV site suitability analysis using GIS-based spatial fuzzy multi-criteria evaluation, Renewable Energy, Volume 36, Issue 9, September 2011, Pages 2554-2561, ISSN 0960-1481, http://dx.doi.org/10.1016/j.renene.2010.10.037](2011)
- Juan M. Sánchez-Lozano, Jerónimo Teruel-Solano, Pedro L. Soto-Elvira, M. Socorro García-Cascales, Geographical Information Systems (GIS) and Multi-Criteria Decision Making (MCDM) methods for the evaluation of solar farms locations: Case study in south-eastern Spain, Renewable and Sustainable Energy Reviews, Volume 24, August 2013, Pages 544-556, ISSN 1364-0321, http://dx.doi.org/10.1016/j.rser.2013.03.019
- W. Cabrera, D. Benhaddou and C. Ordonez, "Solar Power Prediction for Smart Community Microgrid," 2016 IEEE International Conference on Smart Computing (SMARTCOMP), St. Louis, MO, 2016, pp. 1-6. doi: 10.1109/SMARTCOMP.2016.7501718
- L. W. M. Wienhofen, C. Lindkvist and M. Noebels, "User-centered design for smart solar-powered micro-grid communities," 2014 14th International Conference on Innovations for Community Services (I4CS), Reims, 2014, pp. 39-46. doi: 10.1109/I4CS.2014.6860551