Bi-level optimal planning model for energy storage systems in a virtual power plant. (March 2021)

• Record Type: Journal Article
• Authors: Li, Jinghua; Lu, Bo; Wang, Zhibang; Zhu, Mengshu
• Is Part Of: Renewable energy. Volume 165:Part 2(2021)
• Page Start: 77
• Page End: 95
• Publication Date: 2021-03
• Subjects: Energy storage system -- Virtual power plant -- Bi-level programming -- Decomposition-coordination algorithm -- Location and capacity; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2020.11.082
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Determining the optimal location and capacity of energy storage systems (ESS) is a crucial planning problem for the virtual power plant (VPP). However, the trading characteristics of VPP have not been considered in the existing ESS optimal planning research due to its complicated mathematical relationships. In this paper, considering the VPP trading environment, a bi-level optimization model is established to determine ESS's optimal location and capacity. The proposed model is a mixed-integer, nonlinear, multi-period, and large-scale optimal problem, which is difficult to solve directly by traditional methods. Usually, linearizing or decomposing methods are used to simplify the original problem for solving. However, the available simplification methods greatly change the original problem, resulting in a large deviation between the simplified and the original problems. This paper proposes a novel interactive solution framework for the bi-level model based on the decomposition-coordination algorithm for improving the solution accuracy. The simulation results show the effectiveness of the ESS planning scheme in VPP under different objectives, EVs types, and electricity prices. Further analyses based on the IEEE 17-bus VPP system demonstrate that the proposed method can save nearly 29.01%, 28.08%, 26.62% of the ESS cost on average compared with the loss sensitivity factor, MILP-Based, and Benders decomposition methods, respectively. Highlights: Designed bi-level optimization model for optimal siting and sizing of ESS in VPP. Formulated the characteristics of VPP and applied in the optimization model. Introduced decomposition-coordination algorithm for good computational efficiency. Analyzed the influence of multiple DERs aggregation on planning solutions in VPP.