Probabilistic multi-objective microgrid planning methodology for optimizing the ancillary services provision

Microgrids have attracted the attention of researchers and stakeholders because of their well-known impacts and related benefits. Especially the provision of ancillary services has drawn attention. However, traditional planning should be re-defined to optimally achieve such promising advantages, which leads to a complex decision-making problem that should be progressively addressed with a more modern and comprehensive holistic approach. Hence, this paper proposes an enhanced version of the probabilistic multi-objective microgrid planning methodology (POMMP2) that includes a novel strategy to apply graph partitioning and metaheuristic optimization to consider simultaneously the planning of a cluster-based topology with the size and location of distributed energy resources under the paradigm of a microgrid with capacity for providing ancillary services. POMMP2 is tested in an adapted IEEE-37 node system, and NSGAII and AHP are used as optimization and decision-making algorithms, respectively. Results show the benefits of a planning methodology viewed as a comprehensive problem and not a set of independent tasks.