Evolutionary Bi-Level Multi-Objective Optimization for Community Energy Management Systems: A Comprehensive Review

Energy Management Systems (EMS) play a pivotal role in the pursuit of energy efficiency in energy communities. Along with the emergence of the Internet of Things (IoT) and the smart grid, these tools have empowered residents as stakeholder entities and enabled their cooperation with higher levels of the energy hierarchy through Demand Response initiatives. This increasing impact of an entity’s decisions at one level of the hierarchy on the achievement of another entity’s objectives at a different level, complicates the optimization of EMS and paves the way for Stackelberg bi-level optimization frameworks. From the perspective of EMS, such a hierarchical decentralized approach is essential to address practical energy management issues, whereas from an optimization perspective, bi-level optimization pursues the favorable Stackelberg equilibrium. In addition, the existence of conflicting objectives has emboldened the utilization of Evolutionary Bi-level Multi-objective Optimization (EBLMO) approaches due to their generalization properties. This review focuses on EBLMO for EMS in energy communities. Specifically, we identified a total of 38 EBLMO studies, 30 of which through 8 search queries from 3 databases and the remaining 8 through manual identification. The studies have been analyzed and classified by stakeholder hierarchy, problem type, optimization formulation, algorithmic strategy, complexity-handling, decision-making approach and other EBLMO properties; current research trends and open challenges have been pinpointed, and concrete recommendations for future EBLMO research have been provided.