A Group Recommender for Investment in Microgrid Renewable Energy Sources

Abstract

Integration of renewable energy sources such as photovoltaic arrays and wind turbines into electric power microgrids can significantly reduce greenhouse gas (GHG) emissions. However, deciding on investment in microgrid renewable energy sources is a complex problem due to (1) the space of alternatives which is exponential in a number of components; (2) the complex interactions between old and new equipment in every time interval over an investment time horizon; (3) the multiple criteria that should be considered such as net present value, GHG emissions, and system reliability; and (4) dealing with a group of decision makers with diverse priorities. In this paper, we propose and report on the development of a Power Microgrid Operation and Investment Recommender (PMOIR) to guide a group of decision makers toward investment decisions on microgrid renewable energy sources. This is done under the assumption of optimal operational control over the investment time horizon. PMOIR uses a framework of extracting user preferences, estimating the group utility, optimizing and diversifying a small number of recommended alternatives, and voting. To support optimization, we mathematically model different power components and formalize the overall optimization problem, which is implemented using a mixed integer linear programming model. We also conduct an experimental study to demonstrating PMOIR feasibility, in terms of computational time, to be applied on microgrids involving 200 power components, over a five-year time horizon, with around 8 million binary variables.