Abstract
In this research, the joint virtual energy storage modeling with electric vehicle participation in energy local area Smart Grid is considered. This article first constructs a virtual energy storage model and a joint virtual energy storage model for air conditioning and electric vehicles. Therefore, for the optimization problem of virtual energy storage power, a continuous rolling optimization algorithm to determine the feasible solution of the high-dimensional complex constraint optimization problem is proposed to solve the optimization problem. Finally, the analysis, for example, illustrates the economics of joint virtual energy storage in the Smart Grid. The results prove that air conditioning and electric vehicles have the ability to jointly participate in virtual energy storage, and the comparison proves that joint virtual energy storage can effectively improve the economics of electricity consumption.
Highlights
Due to the access of large-scale distributed power equipment, especially the strong randomness of wind power and solar energy, energy storage capacity is of vital importance in the Smart Grid [1,2,3,4,5]
In literature [17], a hybrid constraint handling strategy (HCHS) based on nondominated sorting genetic algorithm II (NSGAII) is proposed to deal with the typical constraints, by which the constraint violations can be removed in several steps during the evolutionary process. e study in [1] proposed an artificial shark optimization (ASO) method to remove the limitation of existing algorithms for solving the economical operation problem of microgrid. e study in [18] presents a new metaheuristic optimization algorithm, the firefly algorithm, and an enhanced version of it, called chaos mutation firefly algorithm (CMFA), for solving power economic dispatch problems while considering various power constraints such as valve-point effects, ramp rate limits, prohibited operating zones, and multiple generator fuel options
In order to solve the above problems, this paper proposes constructing a standard function to measure the global constraint error that satisfies the local constraint decision variables during initialization and continuously revising the decision variables through continuous rolling optimization. Experiments show that this method can effectively improve the initialization efficiency of complex constrained optimization problems with high-dimensional decision variables and obtain the global optimized output power of electric vehicle virtual energy storage, which verifies the economics of electric vehicle with the combination of virtual energy storage
Summary
Due to the access of large-scale distributed power equipment, especially the strong randomness of wind power and solar energy, energy storage capacity is of vital importance in the Smart Grid [1,2,3,4,5]. E study in [8] used the building’s heat storage capacity to establish a building-based virtual energy storage system model, by managing the charging (or discharging) power of the virtual energy storage system according to the user’s indoor temperature limit and applying it to the microgrid; this literature successfully improved running economy. Based on the premise of satisfying the user’s comfort requirements, relying on its virtual energy storage to suppress the output of distributed wind turbines with strong randomness, in order to reduce the configuration capacity of energy storage equipment and reduce the cost of energy Internet management regulation, for the controllable load equipment air conditioning, with consideration of its load-adjustable space, the study in [11] established an air conditioning load double-layer optimal scheduling and control model and maximized the interests for both parties through the coordination and optimization between the macrolevel power company and the microlevel direct load control agents.
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