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.

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Virtual power plants (VPPs) integrate distributed energy effectively and overcome geographical restrictions. This could improve the broad benefits of distributed energy generation. Owing to the intermittency of distributed wind and photovoltaic power at different time scales, when multiple VPPs exist, the interactive energy management strategy should be addressed in terms of two dimensions, namely multi-operator and multi-timescale (day-ahead (24 hours), intraday (4 hours), and real-time (1 hour)). In this study, an interactive energy management framework for multiple VPPs was designed. Furthermore, a three-level energy-coordinated management model was employed for multi-VPP optimal operation including day-ahead cooperative scheduling, intraday noncooperative bidding, and real-time cooperative reserve. To solve this model, a chaotic search algorithm was applied to improve the ant-colony optimization algorithm. The resulting improved chaotic ant-colony optimization algorithm is proposed to increase the optimization velocity and solution efficiency. Finally, a demonstration project, namely the Sanliduo's VPP in Guangxi Province in China, was taken as an example. The following points can be stressed according to the obtained results: (a) the proposed model implements dispatching-balance-reserve interactive optimization such that the output of wind and photovoltaic power increased by 3.42% and 1.85%, respectively, and the revenue increased by 2.53%; (b) the proposed solution algorithm develops the optimal global strategy: compared with the ant-colony algorithm, the average convergence time increased by 44 seconds, and the supply cost decreased by 1.2%; (c) the robust coefficient provides effective risk decisionmaking tools for different types of decision-makers considering the operational characteristics of VPPs; (d) the openness of power markets is positively correlated to the balance revenue, and higher levels are beneficial for VPPs.

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Starting from the perspective of the uncertainty of supply and demand, using the Copula function and fuzzy numbers a scenario generation method, considering the uncertainty of scenery, and a random fuzzy model of energy demand uncertainty are proposed. Then, through the energy flow direction and the energy supply, production, conversion, storage, and demand, a multi-objective model considering the economic and environmental protection of a park is constructed. Here, the park refers to a microgrid that gathers distributed energy such as wind and photovoltaics and has requirements for cooling, heat, and electricity at the same time. Next, combining the constraints of each link, the particle swarm algorithm is used to solve the model. Finally, an example is analyzed in a certain park. The results of the example show that, on the one hand, the proposed scenario generation method and fuzzy number method can reduce the uncertainty of supply and demand, effectively fitting the wind and photovoltaic output and various energy demands. On the other hand, considering the economy and environmental protection of the park at the same time, the configuration of energy storage equipment can not only improve the economy of the park, but also promote the consumption of renewable energy.