Optimal Capacity Allocation of Energy Storage in Distribution Networks Considering Active/Reactive Coordination

Tao Xu, He Meng, Jie Zhu, Wei Wei, He Zhao, Han Yang, Zijin Li and Yuhan Wu
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Tao Xu: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
He Meng: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
Jie Zhu: State Grid Beijing Electric Power Research Institute, Beijing 100031, China
Wei Wei: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
He Zhao: State Grid Beijing Electric Power Research Institute, Beijing 100031, China
Han Yang: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
Zijin Li: State Grid Beijing Electric Power Research Institute, Beijing 100031, China
Yuhan Wu: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China

Energies, 2021, vol. 14, issue 6, 1-24

Abstract: Energy storage system (ESS) has been advocated as one of the key elements for the future energy system by the fast power regulation and energy transfer capabilities. In particular, for distribution networks with high penetration of renewables, ESS plays an important role in bridging the gap between the supply and demand, maximizing the benefits of renewables and providing various types of ancillary services to cope the intermittences and fluctuations, consequently improving the resilience, reliability and flexibility. To solve the voltage fluctuations caused by the high permeability of renewables in distribution networks, an optimal capacity allocation strategy of ESS is proposed in this paper. Taking the life cycle cost, arbitrage income and the benefit of reducing network losses into consideration, a bilevel optimization model of ESS capacity allocation is established, the coordination between active/reactive power of associate power conversion system is considered, and the large scale nonlinear programming problem is solved using genetic algorithm, simulated annealing and mixed integer second-order cone programming method. The feasibility and effectiveness of the proposed algorithm have been verified.

Keywords: energy storage; life cycle cost; genetic algorithm; mixed integer second-order cone programming (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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