A review on microgrid optimization with meta-heuristic techniques: Scopes, trends and recommendation

Abstract

Microgrids (MGs) use renewable sources to meet the growing demand for energy with increasing consumer needs and technological advancement. They operate independently as small-scale energy networks using distributed energy resources. However, the intermittent nature of renewable energy sources and poor power quality are essential operational problems that must be mitigated to improve the MG's performance. To address these challenges, researchers have introduced heuristic optimization mechanisms for MGs. However, local minima and the inability to find a global minimum in heuristic methods create errors in non-linear and nonconvex optimization, posing challenges in dealing with several operational aspects of MG such as energy management optimization, cost-effective dispatch, dependability, storage sizing, cyber-attack minimization, and grid integration. These challenges affect MG's performance by adding complexity to the management of storage capacity, cost minimization, reliability assurance, and balance of renewable sources, which accelerates the need for meta-heuristic optimization algorithms (MHOAs). This paper presents a state-of-the-art review of MHOAs and their role in improving the operational performance of MGs. Firstly, the fundamentals of MG optimization are discussed to explore the scopes, requisites, and opportunities of MHOAs in MG networks. Secondly, several MHOAs in the MG domain are described, and their recent trends in MG's techno-economic analysis, load forecasting, resiliency improvement, control operation, fault diagnosis, and energy management are summarized. The summary reveals that nearly 25% of the research in these areas utilizes the particle swarm optimization method, while the genetic and grey wolf algorithms are utilized by nearly 10% and 5% of the works studied in this paper, respectively, for optimizing the MG's performance. This result summarizes that MHOA presents a system-agnostic optimization approach, offering a new avenue for enhancing the effectiveness of future MGs. Finally, we highlight some challenges that emerge during the integration of MHOAs into MGs, potentially motivating researchers to conduct further studies in this area.