Distributed Optimization-Based Hourly Coordination for V2G and G2V

Network-constrained economic dispatch (NCED) problem, which takes into account the random mobility of electric vehicles (EV) and additional variables corresponding to each EV’s charge/discharge cycles, is large scale, complex and computationally expensive. To reduce the computational burden associated with this optimization problem, distributed optimization is introduced. Since EVs randomly move from one bus to another bus, this paper proposes a temporal, rather than a geographical, decomposition approach to divide a ramp-constrained NCED. Thousands of EVs are considered over the scheduling horizon in order to take advantage of parallel computing and achieve reduced solution time. A ramp-constrained NCED is formulated for each sub-horizon while the connections between subproblems are modeled as shared variables/constraints. In order to coordinate the subproblems and find the optimal solution for the entire operation horizon, distributed auxiliary problem principle (APP) is proposed. Further, an efficient initialization strategy is presented to enhance the convergence time of the solution algorithm. The proposed method is employed to solve a week-ahead NCED on a 6-bus and IEEE 118-bus test systems. The results are compared with those of a centralized approach and effectiveness of the proposed method in reducing the solution time is verified.