Computational performance analysis for centralized coordinated charging methods of plug‐in electric vehicles: From the grid operator perspective

Abstract

With an ever‐increasing number of plug‐in electric vehicles (PEVs), there is a fast‐growing interest in PEVs' charging impact on the stability and the operating cost of power grid as well as the ecological environment. The centralized coordinated charging method is one of the promising solutions to mitigate such undesired impacts as elevating load peaks, increasing energy losses, and decreasing node voltage. However, the computational complexity is a critical issue to obtain the coordinated charging strategies especially for a large number of PEVs. In this context, it is very essential to analyze the computational performance of the centralized coordinated charging methods. In this paper, a paradigm for analyzing the computational performance is provided. Three centralized methods with different standpoint, viz., to minimize carbon emissions, to minimize load variance, and to minimize generation cost, are investigated to conduct a computational performance analysis from the grid operator perspective. First, the optimization theory is employed to transform the three engineering problems into the mathematical programming models. Then, whether the mathematical programming models are convex or nonconvex is analyzed. The results show that the first two mathematical programming models are convex, and the third mathematical programming model is nonconvex. And it demonstrates that the centralized scheduling model that is convex programming has a better computational performance theoretically. At last, simulations are carried out to verify the theoretical computational performance for different types of centralized coordinated charging methods.

A centralized vehicle‐to‐grid scheme with distributed computing capacity engaging internet of smart charging points: Case study

“…The decision making of the above process can be regarded as a mathematical optimization problem. 24 From the technological perspective, the models with the target of minimizing load variance, [25][26][27] minimizing power loss, [28][29][30] and minimizing load peak 31,32 are presented to improve the power quality of the grid and satisfy the energy requirements of PEV users. From the economic perspective, either the total charging cost of PEV users [33][34][35] or the total operating profit of EC 36,37 is considered for improving the financial benefit of each stakeholder.…”

“…Additionally, all PEVs involved in the centralized method are supervised by the EC and carry out strategies for a common goal. The decision making of the above process can be regarded as a mathematical optimization problem 24 . From the technological perspective, the models with the target of minimizing load variance, 25‐27 minimizing power loss, 28‐30 and minimizing load peak 31,32 are presented to improve the power quality of the grid and satisfy the energy requirements of PEV users.…”

Research on Economic Optimal Dispatching of Microgrid Cluster Based on Improved Butterfly Optimization Algorithm

“…For traditional algorithms, it is usually hard to find a feasible or optimal solution. In recent years, biomimetic-inspired intelligent optimization algorithms have become increasingly important in solving the optimal dispatch problem of the microgrid [9,10]. Popular algorithms such as PSO [11,12], genetic algorithm (GA) [13,14], and ant colony optimization (ACO) [15] have better global optimization ability and robustness.…”

Two‐layer optimal charging strategy for electric vehicles in old residential areas

“…With the increasing number of household EVs, their unlimited access to the power grid has influenced the stability of residential distribution networks. [6][7][8][9][10] First, the charging time of EVs usually coincides with the peak residential power consumption, which may overload the distribution network and disrupt residents' daily lives. [11][12][13][14] Second, as the number of EVs continues to increase, the distribution network capacity should likewise be expanded.…”