Structuring Financial Incentives for Residential Solar Electric Systems

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Renewable Energy

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This paper introduces a simple methodology to aid in the decision of how to distribute financial incentive funding for residential solar electric systems in order to maximize demand. Incentive funding can be used more effectively if adjusted according to the decrease in price and the increase in demand of solar electric systems. The decision of how to reduce these incentives due to changing annual funds, while increasing or even maintaining the growth of solar electric system demand, is of great interest to policymakers and public benefit funded program administrators. In order to aid in this process, a three step methodology is described in this paper. The first step uses the concept of learning-by-doing to determine the relationship between historic pricing and demand. The second step employs discrete choice modeling with spatial analysis to determine the relationship between market, financial, and social factors with historic demand. The final step uses nonlinear programming to forecast incentive structuring for maximizing demand. Finally in order to validate the model the study uses solar market data from Portland, Oregon. The results show a decrease in incentives over the period of study from 2014 until 2020, except for 2017 and 2018 when federal and state tax credits expire respectively.