Alternative risk models for optimal investment in portfolio-based community solar

One of the factors that determines the viability of investment in portfolio-based community solar is the volatility in power generation as a result of temporal, spatial, and environmental risk factors. In the context of community solar, volatility in power generation is undesired and considered as a risk factor which is often quantified by the variance or standard deviation of the generated power. One drawback to such an approach may be that any deviation above the average generated power is considered as risk. However, in reality, only the portion of the volatility that falls below the average generation is suboptimal, and in fact, investors welcome higher power generation. This study investigates the application of alternative risk models to variance, namely semivariance, mean absolute deviation, and conditional value-at-risk in reducing the volatility and improving the power generation performance of portfolio-based community solar. To this end, a community consisting of seven houses was used as a case study, and the effects of implementing alternative risk quantification techniques on power generation were studied using a modified modern portfolio theory model. The results of this study indicate that alternative risk models perform similarly; however, they can provide the community with more diverse options for generating solar power with less unfavorable volatility. The findings of this study can be of great help in removing the barriers to renewable energy penetration by providing more economically efficient energy portfolios.