Plug-in electric vehicles (PEVs) are considered as flexible loads since their charging schedules can be shifted over the course of a day without impacting drivers’ mobility. This property can be exploited to reduce charging costs and adverse network impacts. The increasing number of PEVs makes the use of distributed charging coordinating strategies preferable to centralized ones. In this paper, we propose an agent-based method which enables a fully distributed solution of the PEVs’ Coordinated Charging (PEV-CC) problem. This problem aims at coordinating the charging schedules of a fleet of PEVs to minimize costs of serving demand subject to individual PEV constraints originating from battery limitations and charging infrastructure characteristics. In our proposed approach, each PEV’s charging station is considered as an agent that is equipped with communication and computation capabilities. Our multiagent approach is an iterative procedure which finds a distributed solution for the first order optimality conditions of the underlying optimization problem through local computations and limited information exchange with neighboring agents. In particular, the updates for each agent incorporate local information such as the Lagrange multipliers, as well as enforcing the local PEV’s constraints as local innovation terms. Finally, the performance of our proposed algorithm is evaluated on a fleet of 100 PEVs as a test case, and the results are compared with the centralized solution of the PEV-CC problem.
Recently, consensus-based approaches @cite_15 have been used to provide distributed control schemes for applications in electric power systems such as solving optimal power management problems @cite_27 @cite_13 @cite_19 , the Economic Dispatch problem @cite_28 @cite_20 @cite_12 @cite_21 and Optimal Power Flow problems @cite_22 . A neighborhood consensus potential @cite_8 @cite_15 in the iterative update procedure ensures that entities reach an agreement on a common variable, usually corresponding to electricity price in the aforementioned problems.
@cite_22 @cite_8 @cite_28 @cite_21 @cite_19 @cite_27 @cite_15 @cite_13 @cite_12 @cite_20
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The paper presents a fully distributed approach for economic dispatch in power systems. The approach is based on the consensus + innovations framework, in which each network agent participates in a collaborative process of neighborhood message exchange and local computation. The distributed approach is shown to converge to the optimal dispatch under rather weak assumptions on the agent communication network connectivity. Intuitively, the proposed approach includes a consensus term which achieves convergence to a common incremental cost value and an innovation term which ensures that the total generation is equal to the load to be supplied. Further, in the proposed approach each network bus only needs to be aware of its local cost parameters and the local predicted load and the neighborhood communication involves exchanging only the locally determined marginal energy price with a few neighbors. Robustness of the proposed approach and techniques for convergence rate improvement are discussed. Finally, simulation studies on the benchmark IEEE 14 bus system demonstrate the effectiveness of the approach. In a smart grid, effective distributed control algorithms could be embedded in distributed controllers to properly allocate electrical power among connected buses autonomously. By selecting the incremental cost of each generation unit as the consensus variable, the incremental cost consensus (ICC) algorithm is able to solve the conventional centralized economic dispatch problem in a distributed manner. The mathematical formulation of the algorithm has been presented in this paper. The results of several case studies have also been presented to show that the difference between network topologies will influence the convergence rate of the ICC algorithm. Efficient and reliable demand side management techniques for community charging of plug-in hybrid electrical vehicles (PHEVs) and plug-in electrical vehicles (PEVs) are needed, as large numbers of these vehicles are being introduced to the power grid. To avoid overloads and maximize customer preferences in terms of time and cost of charging, a constrained nonlinear optimization problem can be formulated. In this paper, we have developed a novel cooperative distributed algorithm for charging control of PHEVs PEVs that solves the constrained nonlinear optimization problem using Karush-Kuhn-Tucker (KKT) conditions and consensus networks in a distributed fashion. In our design, the global optimal power allocation under all local and global constraints is reached through peer-to-peer coordination of charging stations. Therefore, the need for a central control unit is eliminated. In this way, single-node congestion is avoided when the size of the problem is increased and the system gains robustness against single-link node failures. Furthermore, via Monte Carlo simulations, we have demonstrated that the proposed distributed method is scalable with the number of charging points and returns solutions, which are comparable to centralized optimization algorithms with a maximum of 2 sub-optimality. Thus, the main advantages of our approach are eliminating the need for a central energy management coordination unit, gaining robustness against single-link node failures, and being scalable in terms of single-node computations. This paper presents a distributed algorithm based on auction techniques and consensus protocols to solve the nonconvex economic dispatch problem. The optimization problem of the nonconvex economic dispatch includes several constraints such as valve-point loading effect, multiple fuel option, and prohibited operating zones. Each generating unit locally evaluates quantities used as bids in the auction mechanism. These units send their bids to their neighbors in a communication graph that supports the power system and which provides the required information flow. A consensus procedure is used to share the bids among the network agents and resolves the auction. As a result, the power distribution of generating units is updated and the generation cost is minimized. The effectiveness of this approach is demonstrated by simulations on standard test systems.