Optimal decision-making for cooperative multi-microgrid system under varying levels of cooperation and uncertainty

Abstract

Microgrid is a localized energy supply network based on distributed energy resources, and can operate independently from a central energy network. It can distribute the load of central grid and reduce the carbon emission by using renewable energy sources. However, renewable energy generation has intermittent and uncertain natures, which requires uncertainty modeling to be developed. Besides, high dependence on renewable energy can lead to a supply-demand mismatch. To relieve the mismatch, constituent microgrids in a cooperative multi-microgrid (MMG) can trade energy with each other having a common objective of the entire system. To investigate the effects of energy trading in MMG as the number of microgrids increases, this thesis developed a generalized platform that can formulate and solve the decision-making problem for a general MMG with an arbitrary number of microgrids and various cooperation levels. This thesis divides the cooperation levels into three based on the degree of cooperation. According to the results of the decision-making through the developed platform, the medium level gets more promising as the number of microgrids in MMG increases despite limitations on the direction and total amount of energy trading. However, a numerical issue was found about the computational cost for the medium level. Incorporation of distributed optimization and game theory is required as a future work to relieve the issue.