Development of an integrated multi-objective optimization model for determining the optimal solar incentive design

To promote the deployment of the solar photovoltaic (PV) system from the long-term perspective, the solar PV industry in many countries still needs the financial support from the government despite its remarkable growth and price reductions in the last decade. Many countries with this financial burden on their government budget, however, are planning to reduce or to expire the financial support step by step. To bring the solar PV market to its full maturity, it is crucial to improve the solar policies and to sustain the financial support with acceptable and reasonable prices, which can maximize the benefits for the investors while minimizing the incentive budget for the government. Towards this end, this study aimed to develop an integrated multi-objective optimization (iMOO) model for determining the optimal solar incentive design from the perspectives of the investor and the government. A Microsoft Excel-based iMOO model was developed using life cycle cost analysis, genetic algorithm, and Pareto optimal solutions. The developed Microsoft Excel-based iMOO model was applied to six target regions to verify its effectiveness in determining the optimal solar incentive design. As a result, it was shown that depending on the various characteristics (e.g., solar radiation, electricity price, and installation cost) of a region, the optimal solar incentive design can be differently determined with a reasonable and acceptable level using the developed iMOO model. Among the six target regions, Newark required the lowest incentive budget of $US10,648.41 whereas Oklahoma City required the highest incentive budget of $US20,648.73 to offer their optimal solar incentives. The model developed in this study can help both the investor and the government in a decision-making process and provide some solutions and insights for planning solar policies and strategies.