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ZHANG Hong,CHEN Zhao,HUANG Rong,DING Kun,DONG Haiying.Fuzzy Multi-objective Optimization Model of Wind-PV-CSP Hybrid Power Generation System[J].JOURNAL OF POWER SUPPLY,2021,19(2):112-120

Fuzzy Multi-objective Optimization Model of Wind-PV-CSP Hybrid Power Generation System

Received:February 19, 2019 Revised:February 14, 2021

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DOI：10.13234/j.issn.2095-2805.2021.2.112

Keywords:concentrating solar power (CSP) plant with thermal storage fuzzy multi-objective optimization peakshav-ing maximum satisfaction index particle swarm optimization based on differential evolution (DE-PSO) algorithm

Fund Project:Science and Technology Project of State Grid Corporation of China

Author	Institution	Email
ZHANG Hong	School of Automation and Electrical Engineering, Lanzhou Jiao Tong University, Lanzhou , China
CHEN Zhao	Electric Power Research Institute, State Grid Gansu Electric Power Company, Lanzhou , China
HUANG Rong	Electric Power Research Institute, State Grid Gansu Electric Power Company, Lanzhou , China
DING Kun	Electric Power Research Institute, State Grid Gansu Electric Power Company, Lanzhou , China
DONG Haiying	School of Automation and Electrical Engineering, Lanzhou Jiao Tong University, Lanzhou , China;School of New Energy and Power Engineering, Lanzhou Jiao Tong University, Lanzhou , China	hydong@mail.lzjtu.cn

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Abstract:

To realize the friendly integration of high proportion of new energy represented by wind and photovoltaic (PV) into the power system, wind farm, PV power station and concentrating solar power (CSP) plant are combined as a multi-power supply system, and a fuzzy multi-objective optimization model of wind-PV-CSP hybrid power generation system is proposed. The CSP plant with thermal storage has satisfying schedulability and controllability, and it can provide rotating standby and climbing support, and reduce the randomness and uncertainty in wind and PV output, thus realizing its peak-shaving function. An optimization model is built, which considers the maximum grid-connection benefit and minimum variance of output power. The deterministic model is fuzzified by defining a objective membership function, and the multi-objective optimization model is transformed into a single-objective one using the maximum satisfaction index method, which is further solved by the particle swarm optimization based on differential evolution (DE-PSO) algorithm. The simulation results of an example system show that the fuzzy multi-objective optimization can make full use of the advantages of the CSP plant to achieve the overall optimal operation effect, thereby verifying the feasibility and effectiveness of the proposed optimal operation model.