Optimal subsidy estimation method using system dynamics and the real option model: Photovoltaic technology case

In this paper, we propose a method of optimizing financial subsidies and public research and development investments for renewable energy technologies, rather than optimizing financial subsidy alone. By combining system dynamics with real option models, we capture dynamic complex interactions among investors, consumers, and policymakers, as well as future uncertainties of key energy, economic, and environmental factors. Our method thereby makes subsidy optimization more accurate and flexible. To evaluate our model, we apply it to the Korean photovoltaic subsidy and determine that the government can achieve the photovoltaic diffusion target, even if it reduces the total subsidy by $US 359.5million. The optimal approach is to increase research and development funding by $US 310.4million while reducing the financial subsidy by $US 669.9million. Our method helps policymakers optimize their subsidy allocation and therefore reduces subsidy inefficiencies.