Implementation of repowering optimization for an existing photovoltaic‐pumped hydro storage hybrid system: A case study in Sichuan, China. (10th September 2019)

• Record Type: Journal Article
• Authors: Xu, Xiao; Hu, Weihao; Cao, Di; Liu, Wen; Chen, Zhe; Lund, Henrik
• Is Part Of: International journal of energy research. Volume 43:Number 14(2019)
• Page Start: 8463
• Page End: 8480
• Publication Date: 2019-09-10
• Subjects: multiobjective optimization -- particle swarm optimization -- power management strategy -- PV‐PHS hybrid system -- repowering optimization; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Power resources -- Research -- Periodicals; 621.042
• DOI: 10.1002/er.4846
• Languages: English
• ISSNs: 0363-907X

Abstract

Summary: For a remote area or an isolated island, where the grid has not extended, a standalone hybrid energy system can provide cheap and adequate power for local users. However, with the development of society, the load demand will increase and the original system cannot completely meet the load demand. This situation occurs in Xiaojin, Sichuan, China. The existing photovoltaic‐pumped hydro storage (PV‐PHS) hybrid system in this area as the original system cannot completely meet the load requirements at present. The term "repowering" aims to maximize the reliability of power supply and the utilization of the PV‐PHS hybrid energy system that differs from traditional planning optimization to build all components. The repowering strategy is to integrate wind turbines (WTs) and battery into the original system. For the repowering system, a power management strategy is proposed to determine the operating modes of the PHS and battery. Three objectives, which are minimizing percentage of the demand not supplied, levelized cost of energy, and curtailment rate of renewable energy, are considered in the optimization model. Simulation is conducted by single‐objective, biobjective, and triobjective particle swarm optimization (PSO) techniques. For the single‐objective optimization, the comparison of PSO and genetic algorithm (GA) is made. For the double‐objective optimization, multiobjective PSO (MOPSO) is compared with weighted sum approach (WSA), and fuzzy satisfying method is utilized to find the win‐win solution. The results reveal that the repowering strategy can help to achieve maximum reliability of power supply after load demand increases significantly, and the battery plays an important role in such a hybrid system. Abstract : (1) The comparative analysis of PSO and GA indicates that PSO performs better. (2) Significant reductions in LCOE can be reached if a small PDNS is tolerated. (3) The requirements for CR and PDNS are unfavorablefor the investors since the investment cost is greatly influenced by CR and PDNS.