Metaheuristic search in smart grid: A review with emphasis on planning, scheduling and power flow optimization applications

A multitude of optimization tasks ensue in the context of the smart grid, often exhibiting undesirable characteristics like non-convexity, mixed types of design variables and multiple - and often conflicting - objectives. These tasks can be broadly categorized into three classes of problems, namely optimal power flow (OPF), scheduling and planning. Metaheuristic search methods form a generic class of optimization techniques, that have been shown to work successfully for complex problems. Not surprisingly, they have been widely applied in the smart grid, their use spanning almost every smart grid-related optimization task. In this work, we review the use of metaheuristic search for OPF, scheduling and planning through a unified approach, keeping in mind that these problems share many common challenges and objectives. The use of different metaheuristic methods is discussed extensively with regard to problem handling, multi-objective optimization performance and method accuracy in relation to computational complexity. An attempt to arrive at quantitative conclusions is also being made, by compiling tables which present collective results on common test grids. Lastly, the paper identifies promising directions for future research, concerning metaheuristic search application practices, method development and new challenges that we believe will shape the future of smart grid optimization.

Automated summary

Metaheuristic search methods are widely used in optimization tasks in the context of the smart grid, particularly in optimal power flow, scheduling, and planning. Different methods are discussed in terms of problem handling, multi-objective optimization performance, and method accuracy in relation to computational complexity. The paper also identifies promising directions for future research in metaheuristic search application practices and method development.