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.
Highlights
The other is commercial virtual power plants (CVPP), which refers to the virtual power plant from commercial revenue and connects renewable energy resources as a portfolio to the power market [6]
The main contributions are as follows: (1) A paratactic model combining the base load type (BLT), which is responsible for the stable part of the power system load, and the peak shaving type (PST), which is responsible for the peak part of the power system load, is proposed for determining the operation model of CVPP. (2) A hybrid dimension reduction algorithm on ReliefF and Adaptive Particle Swarm Optimization (APSO) is a new avenue for resolving the proposed paratactic model
By uniting the CVPP generation power curve and the load curve, this paper divides the CVPP capacity allocation model into the base load type (BLT), which is responsible for the stable part of power system load, and the peak shaving type (PST), which is responsible for the peak part of the power system load
Summary
With the continuous increasing proportion of the renewable energy (RER) in the power grid, scholars around the world have proposed virtual power plant (VPP) technology in recent years to realize the integration and control of this RER [1]. Through advanced communication technology and software management systems, VPPs can be considered as the aggregation and optimization of RER, energy storage facilities, controllable loads, and other types of power resources in the power grid [2,3]. Arranging the capacity of various renewable energies in the most appropriate proportion to meet the different needs of the market has become a new problem. Allocating the capacity of renewable energy in CVPP to meet different load requirements and giving full play to the benefits of renewable energy will be the main problem of VPP dispatching operation
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