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


The emergence of microsources holds promise to scale back the carbon emissions and exploit more renewables so as to meet the worldwide growing electrical energy demands. But, there exist many challenges, like optimizing the tradeoff between the utilization of renewable and nonrenewable energy sources, to leverage cheap electric power while minimizing carbon emissions. Game theoretic approaches have been widely used in varied scientific domains and have recently also increasingly been employed in good grids, whereby evolutionary paradigms are widely deployed as a standard heuristic search technique to unravel 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. During 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 seek out optimal strategies that maximize the profit of utilities, and minimize carbon emissions in an open energy market among interconnected microsources.

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