Abstract
To push forward the development of electric vehicles while improving the economy and environment of virtual power plants (VPPs), research on the optimization of VPP capacity considering electric vehicles is carried out. In this paper, based on this, this paper first analyzes the framework of the VPP with electric vehicles and models each unit of the VPP. Secondly, the typical scenarios of wind power, photovoltaic, electric vehicle charging and discharging, and load are formed by the Monte Carlo method to reduce the output deviation of each unit. Then, taking the maximization of the net income and clean energy consumption of the VPP as the objective function, the capacity optimal allocation model of the VPP considering multiobjective is constructed, and the conditional value-at-risk (CVaR) is introduced to represent the investment uncertainty faced by the VPP. Finally, a VPP in a certain area of Shanxi Province is used to analyze a calculation example and solve it with CPLEX. The results of the calculation example show that, on the one hand, reasonable selection of the optimal scale of EV connected to the VPP is able to improve the economy and environment of the VPP. On the other hand, the introduction of CVaR is available for the improvement of the scientific nature of VPP capacity allocation decisions.
Highlights
Introduction ough electric vehicles enjoy a rapid development with the support of national policies, they are confronted with the challenges, such as the increase of the peak-valley difference of the system and the decrease of power quality due to their characteristics of centralized charging [1]. e virtual power plant integrates distributed energy, energy storage systems, and controllable loads with refined control and demand response methods, which can effectively solve the problems brought by the centralized charging of electric vehicles
Virtual power plants composed of wind power and electric vehicles participate in the multiagent game of the market: this paper studies the multiagent bidding optimization of virtual power plants composed of wind power companies and electric vehicles in the mode of cooperation and joint venture to participate in the power market
Rough the study of this paper and other paper, we can find that (1) other papers mainly focus on the optimal scheduling of electric vehicles connected to the virtual power plant and participating in market bidding, while this paper focuses on the optimal configuration of electric vehicles connected to the virtual power plant. (2) most papers only consider the economy of virtual power plant, but in the case of increasingly serious environmental pollution, the environmental protection of virtual power plant needs to be considered
Summary
Where Rnet refers to the net income of the VPP; Rwsapleetr represents the total revenue of VPP sales; CwInpvetr, Cwoppetr, Cwgepetr, Cwrlpetr, CwTlpetr, Cwenpvetr, CwBpetr, and CwESpSetrare VPP investment and construction cost, operation and maintenance cost, thermal power generation cost, interruptible load compensation cost, transferable load cost, environmental cost, power purchase cost from the grid, and energy storage system cost, respectively:. Where puse refers to the user energy unit price; pgrid,s represents the unit price of energy sold to the grid; and pEV denotes the charging price of EVs. To effectively measure the uncertainty risk of wind turbines, photovoltaic panels, EV charging loads, and controllable loads, CVaR is introduced and multiplied by the investor’s risk preference B to represent the risk rrisk. (7) Scene probability constraints: the scene probability constraint means that the sum of the probabilities of each scene is 1, as shown in
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