Evaluating renewable energy policies using hybrid clustering and analytic hierarchy process modeling

Abstract

When the majority of energy (>90%) is generated by fossil fuels, carbon dioxide emissions increase the greenhouse gas effect of the region. Therefore, renewable, sustainable, and economically viable energy sources are needed as alternatives to fossil fuels. The facilities and installation costs for generating renewable energy is much higher than the cost of fossil fuel facilities. Thus, governments need effective policies, regulations, and incentive programs to promote the usage of renewable energy. Renewable energy can be classified into different categories, such as offshore and onshore wind power, photovoltaic solar power, and geothermal generated power. The policies used for promoting specific categories vary significantly. These policies depend on the policy goals, regulations, taxation, incentives, and promotional schemes. The purpose of this study is to apply clustering techniques and the analytic hierarchy process (AHP) to analyze types of renewable energies and their attributes with respect to economic factors, energy resources and supplies, and their environmental effects. The AHP approach is used to evaluate actions that can resolve challenges found in the development of renewable energy. The study provides scientific results to help government managers plan renewable energy policies. The data for the case study are collected from Taiwan's renewable energy statistics related to photovoltaic cells, wind farms, ocean thermal energy, geothermal energy, hydro power, and solid waste fuels. The research has two major results and findings. First, analytic models are developed for the decision support of renewable energy policies using intelligent clustering techniques. Second, the most suitable policies for promoting four renewable energy clusters are identified using the AHP approach.