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The development of electric vehicle (EV) industry is facing a series of issues, among which the efficient charging of multiple EVs needs solving desperately. This paper investigates the coordinated charging of multiple EVs with the aim of reducing the charging cost, ensuring a high battery state of charge (SoC), and avoiding the transformer overload. To this end, we first formulate the EV coordinated charging problem with the above multiple objectives as a Markov Decision Process (MDP) and then propose a multi-agent deep reinforcement learning (DRL)-based algorithm. In the proposed algorithm, a novel interaction model, i.e., communication neural network (CommNet) model, is adopted to realize the distributed computation of global information (namely the electricity price, the transformer load, and the total charging cost of multiple EVs). Moreover, different from the most existing works which make specific constraints on the size, the location, or the topology of the distribution network, what we need in the proposed method is only the transformer load. Besides, due to the use of long and short-term memory (LSTM) for price prediction, the proposed algorithm can flexibly deal with various uncertain price mechanisms. Finally, simulations are presented to verify the effectiveness and practicability of the proposed algorithm in a residential charging area. [ABSTRACT FROM AUTHOR]

The microgrid is widely recognized as a promising concept for integrating distributed energy resources (DERs). Considering the enormous number of DERs, the future smart grid will likely be a grid containing a number of interconnected microgrids. Thus, the optimal power flow (OPF) problem should be studied by properly taking account of the coupling between the microgrids. In this paper, the models of standalone microgrid and coupled microgrids are formulated first. Then, a decentralized approach is shown in detail to cooperatively solve the coupled OPF problem. Specifically, each microgrid solves the local OPF problem, leading to the optimal solution of the coupled OPF problem via negotiation between microgrids. The privacy of each microgrid is also preserved since no sensitive information is shared between microgrids. Finally, simulation results show that the optimality gaps between the proposed approach and an existing solver are small for both the AC OPF and the DC OPF. Furthermore, the total cost of the microgrids is reduced by the cooperative OPF. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel Newton method-based distributed algorithm (NMDA), which is also effective in solving the general single-area EDP (SAEDP), to deal with the multi-area economic dispatch problem (MAEDP), of which the focus is to minimize the total generation cost in the presence of system and generator constraints. To develop the NMDA, we first introduce a virtual SAEDP formulation to fit the framework of Newton method (NM), and then employ the average consensus protocol to obtain the global information needed to execute the NM and backtracking line search algorithm in a distributed manner. Compared with the centralized methods that can yield the optimal solution, the proposed NMDA provides a suboptimal solution with a very small relative error. The NMDA ensures the instantaneous system power balance throughout the iteration process while the centralized methods compared in this paper cannot do so. We also provide a rigorous theoretical analysis for the convergence of NMDA. Moreover, the advantage of NMDA in terms of the convergence speed is validated by comparing with other distributed methods such as the gradient-based ADMM (G-ADMM) and quasi Newton-based primal dual interior point (QN-PDIP) method. Finally, case studies demonstrate the effectiveness and scalability of the proposed distributed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a novel dynamic coordination problem between economic dispatch and demand response is formulated by taking the battery energy storage systems into consideration, which aims at making effective use of the power energy and mitigating the impact of the intermittency of the renewable energy resources. The formulated problem is equivalently modeled as a social welfare maximization problem, then a distributed coordination algorithm is developed by integrating the average consensus protocol and alternating direction method of multipliers. Sufficient conditions under which the developed algorithm converges are derived. It is also shown that the commonly used quadratic cost and utility functions can guarantee the convergence. The developed algorithm efficiently guarantees the electricity market equilibrium and maximizes the social welfare over each time slot. Moreover, the algorithm maintains instantaneous power balance, which eliminates the need of extra algorithms to track the power mismatch between the supply and demand sides. Finally, the performance and scalability of the proposed algorithm are verified through a set of case studies on a simplified model of the Australian power grid, in which the effects of the renewable generation and power loss are also discussed. [ABSTRACT FROM AUTHOR]