Role of optimization techniques in microgrid energy management systems—A review

Expeditious urbanization, population growth, and technological advancements in the past decade have significantly impacted the rise of energy demand across the world. Mitigation of environmental impacts and socio-economic benefits associated with the renewable energy systems advocate the higher integration of the distributed energy systems into the conventional electricity grids. However, the rise of renewable energy generation increases the intermittent and stochastic nature of the energy management problem significantly. Therefore, an optimal energy management technique is required to achieve a high level of system reliability and operational efficiency. A state-of-the-art systematic review of the different optimization techniques used to address the energy management problems in microgrids is presented in this article. The different optimization techniques used in energy management problems, particularly focusing on forecasting, demand management, economic dispatch, and unit commitment, are identified and critically analyzed in this review. The inferences from the review indicated that the mixed integer programming techniques were widely used, considering their simplicity and performance in solving the energy management problem in microgrids. The multi-agent-based techniques and meta-heuristics algorithms outperformed the other conventional techniques in terms of the efficiency of the system due to the decentralized nature of the EMS problem in microgrids and the capability of these techniques to act effectively in such scenarios. In addition, it was also evident that the use of advanced optimization techniques was limited in the scope of forecasting and demand management. Advocating the need for more accurate scheduling and forecasting algorithms to address the energy management problem in microgrids. Finally, the need for an end-to-end energy management solution for a microgrid system and a transactive/collaborative energy sharing functionality in a community microgrid is presented.