Optimal capacity allocation method of energy storage system for increasing wind power integration

Author(s):  
Dexin Li ◽  
Xiangyu Lv ◽  
Junhui Li ◽  
Pengcheng Yue ◽  
Chunguang Tian ◽  
...  
2021 ◽  
Vol 13 (5) ◽  
pp. 2526
Author(s):  
Fahad Alismail ◽  
Mohamed A. Abdulgalil ◽  
Muhammad Khalid

Since renewable power is intermittent and uncertain, modern grid systems need to be more elegant to provide a reliable, affordable, and sustainable power supply. This paper introduces a robust optimal planning strategy to find the location and the size of an energy storage system (ESS) and feeders. It aims to accommodate the wind power energy integration to serve the future demand growth under uncertainties. The methodology was tested in the IEEE RTS-96 system and the simulation results demonstrate the effectiveness of the proposed optimal sizing strategy. The findings validate the improvements in the power system reliability and flexibility.


IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 57400-57413 ◽  
Author(s):  
Yumin Zhang ◽  
Xueshan Han ◽  
Bo Xu ◽  
Mingqiang Wang ◽  
Pingfeng Ye ◽  
...  

2018 ◽  
Vol 8 (10) ◽  
pp. 1957 ◽  
Author(s):  
Xin Jiang ◽  
Guoliang Nan ◽  
Hao Liu ◽  
Zhimin Guo ◽  
Qingshan Zeng ◽  
...  

An optimal sizing model of the battery energy storage system (BESS) for large-scale wind farm adapting to the scheduling plan is proposed in this paper. Based on the analysis of the variability and uncertainty of wind output, the cost of auxiliary services of systems that are eased by BESS is quantized and the constraints of BESS accounting for the effect of wind power on system dispatching are proposed. Aiming to maximum the benefits of wind-storage union system, an optimal capacity model considering BESS investment costs, wind curtailment saving, and auxiliary services compensation is established. What’s more, the effect of irregular charge/discharge process on the life cycle of BESS is considered into the optimal model by introducing an equivalent loss of the cycle life. Finally, based on the typical data of a systems, results show that auxiliary services compensation can encourage wind farm configuration BESS effectively. Various sensitivity analyses are performed to assess the effect of the auxiliary services compensation, on-grid price of wind power, investment cost of BESS, cycle life of BESS, and wind uncertainty reserve level of BESS on this optimal capacity.


2015 ◽  
Vol 137 ◽  
pp. 545-553 ◽  
Author(s):  
Haoran Zhao ◽  
Qiuwei Wu ◽  
Shuju Hu ◽  
Honghua Xu ◽  
Claus Nygaard Rasmussen

Energies ◽  
2013 ◽  
Vol 6 (7) ◽  
pp. 3392-3404 ◽  
Author(s):  
Wei Wang ◽  
Chengxiong Mao ◽  
Jiming Lu ◽  
Dan Wang

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Leijiao Ge ◽  
Shuai Zhang ◽  
Xingzhen Bai ◽  
Jun Yan ◽  
Changli Shi ◽  
...  

Energy storage systems (ESSs) are promising solutions for the mitigation of power fluctuations and the management of load demands in distribution networks (DNs). However, the uncertainty of load demands and wind generations (WGs) may have a significant impact on the capacity allocation of ESSs. To solve the problem, a novel optimal ESS capacity allocation scheme for ESSs is proposed to reduce the influence of uncertainty of both WG and load demands. First, an optimal capacity allocation model is established to minimize the ESS investment costs and the network power loss under constraints of DN and ESS operating points and power balance. Then, the proposed method reduces the uncertainty of load through a comprehensive demand response system based on time-of-use (TOU) and incentives. To predict the output of WGs, we combined particle swarm optimization (PSO) and backpropagation neural network to create a prediction model of the wind power. An improved simulated annealing PSO algorithm (ISAPSO) is used to solve the optimization problem. Numerical studies are carried out in a modified IEEE 33-node distribution system. Simulation results demonstrate that the proposed model can provide the optimal capacity allocation and investment cost of ESSs with minimal power losses.


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