Optimal Placement of Energy Storage with Synthetic Inertia Control on a Grid with High Penetration of Renewables using Mean-Variance Mapping Optimization

Utilizing energy storage systems equipped with virtual/synthetic inertia control has emerged as one of the solutions to solve low inertia issues in power systems with massive penetration of renewables. However, while the aspect regarding the control of the synthetic inertia control has been widely investigated, the optimal placement of synthetic inertia control is rarely investigated. Therefore, the optimal placement of energy storage system with synthetic inertia control based on swarm-based mean-variance mapping optimization is proposed. The performance of the proposed method is tested by considering several cases, both single and multiple contingencies. By utilizing the proposed method, the optimal placement of energy storage system equipped with synthetic inertia control could be obtained, in which the resulting frequency response is better compared to randomly placed ones. The proposed method could also be applied by considering multiple contingencies in different locations. Furthermore, the resulting fitness values for different independent optimization repetitions for various study cases are consistent, leading to the efficacy to be applied in the actual power system planning.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…The flowchart of the MVMO algorithm is presented in Figure 4, with the basic steps are as described below [28,29,[32][33][34]:…”

Section: Original Mean-variance Mapping Optimizationmentioning

confidence: 99%