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

• Published in: Energies (Basel) Vol. 15; no. 4; p. 1303
• Main Authors: Wang, Songkai, Jia, Rong, Shi, Xiaoyu, Luo, Chang, An, Yuan, Huang, Qiang, Guo, Pengcheng, Wang, Xueyan, Lei, Xuewen
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.02.2022
• Subjects: Algorithms; Alternative energy sources; base load; capacity allocation; commercial virtual power plants; Electricity; Electricity distribution; Energy resources; Energy sources; Energy utilization; hybrid dimension reduction algorithm; Hydroelectric power; Industrial plant emissions; Load distribution; Load fluctuation; Optimization; Peak load; peak shaving; Power plants; Renewable resources; Risk reduction; Virtual power plants; Weather; Wind power
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en15041303

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.