An improved optimal allocation scheme of energy storage system in a distribution system based on transient stability. (February 2021)

• Record Type: Journal Article
• Authors: Yin, He; Lan, Hai; Yu, David C.; Hong, Ying-Yi; Li, Rui-Ye
• Is Part Of: Journal of energy storage. Volume 34(2021)
• Publication Date: 2021-02
• Subjects: Multi-objective particle swarm optimization (MOPSO) -- Non-dominated sorting genetic algorithm III (NSGA III) -- Energy storage system (ESS) -- Transient stability analysis (TSA) -- Droop control; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2020.101977
• Languages: English
• ISSNs: 2352-152X

Abstract

Highlights: Multi-objective particle swarm Optimization-Non-dominated sorting genetic algorithm Ⅲ (MOPSONSGA Ⅲ) is firstly applied in optimal allocation of ESSs for handling many-objective optimization problems. A two-step energy storage planning scheme considering transient responses during operation is first proposed in this work. All the feasible solutions chosen by PSA and PSO are evaluated by the millisecond transient simulation in the MOPSO optimization process. A new MOPSONSGA Ⅲ based on TSA is first proposed to introduce transient performance assessment of power system into optimizing process. The ESS configuration scheme introduced in this paper provides the most detailed and reasonable energy storage planning scheme. Five energy storage planning indicators (rated power, capacity, installation position, seven different alternative ESS, response time) and four energy storage controller parameters (droop control strategy) are considered. Abstract: Along with the development of renewable energy generation technology, energy storage system (ESS), highly effective equipment for suppressing renewable energy fluctuations, plays an indispensable part in power system. A two-step method is proposed to optimally allocate ESS and droop controller considering ESS performances in time domain. In the first step, the top ranked solutions of EES size and locations are determined by economic analysis and Power Sensitivity Analysis (PSA). In the second step, using the solutions obtained in step one as the constraint of solution space, a new Multi-objective Particle Swarm Optimization-Non-dominated Sorting Genetic Algorithm Ⅲ (MOPSONSGA Ⅲ) based on Transient Stability Analysis (TSA) is proposed. Optimization objectives are ESS capital cost and the time-domain voltage and frequency performances subject to a sudden change of wind power. Compared with traditional optimal allocation schemes of ESS, the proposed method produces a economical ESS allocation scheme, while the fluctuation of frequency is cut by 57% and the times of the frequency beyond the limit is reduced by 30% in the IEEE-34 bus system. Graphical abstract: Image, graphical abstract