Research on Capacity Allocation Optimization of Commercial Virtual Power Plant (CVPP)

Songkai Wang, Rong Jia, Xiaoyu Shi, Chang Luo, Yuan An, Qiang Huang, Pengcheng Guo, Xueyan Wang and Xuewen Lei
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Songkai Wang: School of Water Resources and Hydropower, Xi’an University of Technology, Xi’an 710048, China
Rong Jia: Key Laboratory of Smart Energy in Xi’an, Xi’an University of Technology, Xi’an 710048, China
Xiaoyu Shi: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Chang Luo: Hanjiang-to-Weihe River Valley Water Diversion Project Construction Co., Ltd., Xi’an 710048, China
Yuan An: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Qiang Huang: School of Water Resources and Hydropower, Xi’an University of Technology, Xi’an 710048, China
Pengcheng Guo: School of Water Resources and Hydropower, Xi’an University of Technology, Xi’an 710048, China
Xueyan Wang: School of Electrical Engineering, Xi’an University of Technology, Xi’an 710048, China
Xuewen Lei: School of Water Resources and Hydropower, Xi’an University of Technology, Xi’an 710048, China

Energies, 2022, vol. 15, issue 4, 1-18

Abstract: Commercial virtual power plants (CVPP) connect the form of renewable energy resource portfolio to the power market and reduce the risk of the unstable operation of a single renewable energy. Combining different kinds of large-scale renewable energy in CVPP to provide capacity services like base load, peak shaving, and valley-filling, etc., for the system loads is an urgent problem to be solved. Therefore, it is valuable to analyze the capacity allocation ratio of the CVPP to maximize the utilization of all kinds of energy, especially for the large-scale multi-energy base. This paper proposed a multi-energy coordinated operation framework by considering various load demands, including base load and peak shaving for the capacity allocation of CVPP based on the world’s largest renewable energy resource base on the upstream area of the Yellow River. The main procedures of this framework are as follows: (1) A paratactic model satisfying base load and peak shaving is proposed to determine the ability of the CVPP operation model’s capacity services to meet the different demands of the power system load. (2) A hybrid dimension reduction algorithm with a better convergence rate and optimization effect solves the proposed paratactic model based on the ReliefF and the Adaptive Particle Swarm Optimization (APSO). The results show that the large-scale CVPP with different compositions can achieve both of the goals of a stable base load output and stable residual load under different weather conditions. Compared with the operation on sunny days, the base load fluctuation and residual load fluctuation of CVPP on rainy days are reduced by 14.5% and 21.9%, respectively, proving that CVPP can alleviate renewable energy’s dependence on weather and improve energy utilization.

Keywords: commercial virtual power plants; capacity allocation; base load; peak shaving; hybrid dimension reduction algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
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