Optimal sizing of energy storage considering the spatial‐temporal correlation of wind power forecast errors. Issue 4 (11th January 2019)

• Record Type: Journal Article
• Authors: Wang, Chengfu; Teng, Qijun; Liu, Xiaoyi; Zhang, Feng; He, Suoying; Liang, Zhengtang; Dong, Xiaoming
• Is Part Of: IET renewable power generation. Volume 13:Issue 4(2019)
• Page Start: 530
• Page End: 538
• Publication Date: 2019-01-11
• Subjects: energy storage -- power grids -- load forecasting -- wind power plants -- optimisation -- power generation economics -- wind power; multiple nearby wind farms -- correlation model -- high‐dimensional forecast errors -- spatial‐temporal characteristics -- optimal ESS sizing model -- optimal sizing -- spatial‐temporal correlation -- power deviation -- stochastic wind power forecast error -- regional power grid -- energy storage system; Renewable energy sources -- Periodicals; 333.79405
• DOI: 10.1049/iet-rpg.2018.5438
• Languages: English
• ISSNs: 1752-1416

Abstract

Abstract : Energy storage is considered as an effective approach to deal with the power deviation that caused by the stochastic wind power forecast error. Because of the spatial‐temporal correlation of forecast errors for wind farms, which locate close to each other and integrate into the same regional power grid, energy storage could be deployed effectively and economically. Therefore, this study proposes an optimisation method to size the capacity of energy storage system (ESS) considering the spatial‐temporal correlation of forecast errors for multiple nearby wind farms. The copula theory based correlation model of high‐dimensional forecast errors is built to capture the spatial‐temporal characteristics of forecast error. Then, according to multiple scenarios technique, an optimal ESS sizing model is established to minimise the investment and operation costs of the ESS. Meanwhile, an operation strategy considering the trend of prediction and correlation of errors is used to implement rolling operation strategy of ESS. The case studies demonstrate the effectiveness of the new method and illustrate the significant impact of the correlation of forecast error on the capacity of ESS.