期刊
JOURNAL OF ENERGY STORAGE
卷 52, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.est.2022.104716
关键词
Distributed generation integrated distribution; network (DGDN); Placement and capacity selection of battery; energy storage system (BESS); Multi-objective optimized model; Pareto solution set; Improved non-dominated sorting genetic; algorithm-II (NSGA-II); Power losses and voltage fluctuations
资金
- National Natural Science Foundation of China [51807091]
- China Postdoctoral Science Founda-tion [2019M661846]
- EPSRC [EP/N032888/1]
- Open Research Fund of Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education
This paper investigates the placement and capacity selection of battery energy storage systems (BESS) in distributed clean energy generation networks to improve power quality and economic benefits. An optimal scheme for BESS placement and capacity is selected based on an improved NSGA-II optimization, and validated on an IEEE 33-node DGDN.
The scalability of distributed generation (DG) dominated by clean energy in the distribution network is continuously increasing. Increased grid integration of DGs has aggravated the uncertainty of distribution network (DN) operation, which affects the power losses and voltage fluctuations. The battery energy storage system (BESS), as an essential part of the distribution grid, its appropriate placement and capacity selection can improve the power quality and bring economic benefits for the DGs integrated DN (DGDN). In this paper, the placement and capacity selection of BESS in the DGDN is investigated including: (1) The relationship between the placement and capacity selection of BESS with the power losses and line voltages of the DN is derived. The impact of the placement and capacity selection of BESS on the power losses and voltage fluctuations of the DGDN is analyzed. (2) In order to achieve the objectives of a minimum of power losses, voltage fluctuations and BESS capacity in the DGDN, a multi-objective model with optimized placement and capacity of the BESS is established. (3) In order to ensure the globality and uniformity of Pareto solution set, an improved algorithm based on nondominated sorting genetic algorithm-II (NSGA-II) is proposed to optimize the process of traditional NSGA-II optimization. By adding the selection coefficient of population optimal solution and the congestion distance update function, the population dispersion is expanded and the global searching capability of the algorithm is enhanced. (4) Based on the improved NSGA-II optimization, an optimal scheme of the placement and capacity of the BESS is selected based on information entropy weight (IEW)-analytic hierarchy process (AHP). Taking an IEEE 33-node DGDN as an example, the proposed algorithm is compared with the traditional NSGA-II optimization and the multi-objective particle swarm optimization (MOPSO) algorithm to verify the effectiveness and superiority of the BESS optimal placement and capacity method based on the improved NSGA-II optimization.
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