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A probabilistic portfolio-based model for financial valuation of community solar

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  • Shakouri, Mahmoud
  • Lee, Hyun Woo
  • Kim, Yong-Woo

Abstract

Community solar has emerged in recent years as an alternative to overcome the limitations of individual rooftop photovoltaic (PV) systems. However, there is no existing model available to support probabilistic valuation and design of community solar based on the uncertain nature of system performance over time. In response, the present study applies the Mean-Variance Portfolio Theory to develop a probabilistic model that can be used to increase electricity generation or reduce volatility in community solar. The study objectives include identifying the sources of uncertainties in PV valuation, developing a probabilistic model that incorporates the identified uncertainties into portfolios, and providing potential investors in community solar with realistic financial indicators. This study focuses on physical, environmental, and financial uncertainties to construct a set of optimized portfolios. Monte Carlo simulation is then performed to calculate the return on investment (ROI) and the payback period of each portfolio. Lastly, inclusion vs. exclusion of generation and export tariffs are compared for each financial indicator. The results show that the portfolio with the maximum output offers the highest ROI and shortest payback period while the portfolio with the minimum risk indicates the lowest ROI and longest payback period. This study also reveals that inclusion of tariffs can significantly influence the financial indicators, even more than the other identified uncertainties.

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  • Shakouri, Mahmoud & Lee, Hyun Woo & Kim, Yong-Woo, 2017. "A probabilistic portfolio-based model for financial valuation of community solar," Applied Energy, Elsevier, vol. 191(C), pages 709-726.
    • Handle: RePEc:eee:appene:v:191:y:2017:i:c:p:709-726
    DOI: 10.1016/j.apenergy.2017.01.077
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    4. Wu, Yunna & Xu, Chuanbo & Ke, Yiming & Chen, Kaifeng & Sun, Xiaokun, 2018. "An intuitionistic fuzzy multi-criteria framework for large-scale rooftop PV project portfolio selection: Case study in Zhejiang, China," Energy, Elsevier, vol. 143(C), pages 295-309.
    5. Faria, Victor A.D. & Rodrigo de Queiroz, Anderson & DeCarolis, Joseph F., 2023. "Scenario generation and risk-averse stochastic portfolio optimization applied to offshore renewable energy technologies," Energy, Elsevier, vol. 270(C).
    6. Àlex Alonso-Travesset & Diederik Coppitters & Helena Martín & Jordi de la Hoz, 2023. "Economic and Regulatory Uncertainty in Renewable Energy System Design: A Review," Energies, MDPI, vol. 16(2), pages 1-30, January.
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    8. Lee, Minhyun & Hong, Taehoon & Jeong, Jaewook & Jeong, Kwangbok, 2018. "Development of a rooftop solar photovoltaic rating system considering the technical and economic suitability criteria at the building level," Energy, Elsevier, vol. 160(C), pages 213-224.
    9. Aghamolaei, Reihaneh & Shamsi, Mohammad Haris & O’Donnell, James, 2020. "Feasibility analysis of community-based PV systems for residential districts: A comparison of on-site centralized and distributed PV installations," Renewable Energy, Elsevier, vol. 157(C), pages 793-808.

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