Multi‐objective optimal configuration method for a standalone wind–solar–battery hybrid power system. Issue 1 (17th October 2016)

• Record Type: Journal Article
• Authors: Ma, Gang; Xu, Guchao; Chen, Yixi; Ju, Rong
• Is Part Of: IET renewable power generation. Volume 11:Issue 1(2017)
• Page Start: 194
• Page End: 202
• Publication Date: 2016-10-17
• Subjects: hybrid power systems -- wind power -- solar power -- secondary cells -- particle swarm optimisation -- reliability -- power supply quality; energy fluctuations -- energy wastes -- multiobjective optimisation -- weight coefficient transform -- particle swarm optimisation -- power supply reliability -- random components -- power load -- reliable power supply -- power sources -- power generation -- energy Internet -- multiobjective optimal configuration -- standalone wind‐solar‐battery hybrid power system; Renewable energy sources -- Periodicals; 333.79405
• DOI: 10.1049/iet-rpg.2016.0646
• Languages: English
• ISSNs: 1752-1416

Abstract

Abstract : With the development of the energy internet, the standalone wind–solar–battery hybrid power system has gradually become an effective means for the complete utilisation of clean energy for power generation. The allocation of the various types of power sources for ensuring a reliable power supply and for improving the economic efficiency of a system is a crucial issue in the system planning and design stages. The models of the power load and the power sources, containing random components, are first established in this study, based on which the power supply reliability and constraint conditions for the equipment quantities are considered for establishing various evaluation indexes for improving the system performance. Then, according to the combinations of the natural selection particle swarm optimisation algorithm and the weight coefficient transform method, a multi‐objective optimisation algorithm is proposed for optimising the configuration of a standalone wind–solar–battery hybrid power system. Finally, it is demonstrated by an example that the optimisation method not only meets the requirements of the multi‐objective optimisation system but is also significant in reducing energy wastes and fluctuations, cost saving, and in ensuring the reliability of the power supply.