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PACPIM: New decision-support model of optimized portfolio analysis for community-based photovoltaic investment

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  • Shakouri, Mahmoud
  • Lee, Hyun Woo
  • Choi, Kunhee

Abstract

Inherent in large-scale photovoltaic (PV) investments is volatility that stems from a unique set of spatial factors, such as shading, building orientation, and roof slope, which can significantly affect both the level of risk and the return on investment. In order to systematically assess and manage the volatility, this study seeks to create a quantitative decision-support model: Portfolio Analysis for Community-based PV Investment Model (PACPIM). Focusing on residential PV systems, PACPIM determines optimized portfolios by applying the Mean–Variance Portfolio theory. The model is intended to play an instrumental role in: (1) maximizing the hourly electricity output of PV systems; (2) minimizing the hourly volatility in electricity output; and (3) optimizing the risk-adjusted performance of community-based PV investment. The application and framework of PACPIM were deployed with an actual residential community consisting of 24 houses and their simulated data utilizing PVWatts® for estimating hourly electricity production. Results reveal that the optimized portfolios developed by PACPIM (1) increased annual electricity output of PV systems by 4.6%; (2) reduced the volatility in electricity output by 4.3%; and (3) offered the highest risk-adjusted performance among all possible portfolios based on the Sharpe ratios. This study is expected to effectively assist project owners and investors in systematically assessing their community-based PV projects and in developing optimized investment strategies.

Suggested Citation

  • Shakouri, Mahmoud & Lee, Hyun Woo & Choi, Kunhee, 2015. "PACPIM: New decision-support model of optimized portfolio analysis for community-based photovoltaic investment," Applied Energy, Elsevier, vol. 156(C), pages 607-617.
    • Handle: RePEc:eee:appene:v:156:y:2015:i:c:p:607-617
    DOI: 10.1016/j.apenergy.2015.07.060
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    6. Laws, Nicholas D. & Epps, Brenden P. & Peterson, Steven O. & Laser, Mark S. & Wanjiru, G. Kamau, 2017. "On the utility death spiral and the impact of utility rate structures on the adoption of residential solar photovoltaics and energy storage," Applied Energy, Elsevier, vol. 185(P1), pages 627-641.
    7. Costa, Oswaldo L.V. & de Oliveira Ribeiro, Celma & Rego, Erik Eduardo & Stern, Julio Michael & Parente, Virginia & Kileber, Solange, 2017. "Robust portfolio optimization for electricity planning: An application based on the Brazilian electricity mix," Energy Economics, Elsevier, vol. 64(C), pages 158-169.

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