Open Energy Market Strategies in Microgrids: A Stackelberg Game Approach Based on a Hybrid Multiobjective Evolutionary Algorithm

The emergence of microsources holds promise to reduce the carbon emissions and exploit more renewables in order to meet the worldwide growing electrical energy demands. However, there exist several challenges, such as optimizing the tradeoff between the use of renewable and nonrenewable energy sources, to leverage affordable electric power while minimizing carbon emissions. Game theoretic approaches have been widely used in various scientific domains and have recently also increasingly been used in smart grids, whereby evolutionary paradigms have been widely deployed as a popular heuristic search method to solve and optimize complex real-life scientific problems. A promising approach is the development of such evolutionary algorithms and game theoretic approaches in the context of open energy markets. In this paper, we develop an analytic model of a multileader and multifollower Stackelberg game approach and propose a bi-level hybrid multiobjective evolutionary algorithm to find optimal strategies that maximize the profit of utilities, and minimize carbon emissions in an open energy market among interconnected microsources.