Decentralized Electric Vehicle Charging Control via a Novel Shrunken Primal-Multi-Dual Subgradient (SPMDS) Algorithm

The charging processes of a large number of electric vehicles (EVs) require coordination and control for the alleviation of their impacts on the distribution network and for the provision of various grid services. However, the scalability of existing EV charging control paradigms are limited by either the EV population size or the distribution network dimension, largely impairing EVs’ aggregate service capability and applicability. To overcome the scalability barrier, this paper, motivated by the optimal EV charging scheduling problem for the valley-filling service, (1) proposes a novel dimension reduction methodology by grouping EVs (primal decision variables) and establishing voltage (global coupled constraints) updating subsets for each EV group in the distribution network and (2) develops a novel decentralized shrunken primal-multidual subgradient (SPMDS) optimization algorithm to solve this reduced-dimension problem. The efficiency and efficacy of the proposed algorithm are demonstrated through simulations over a modified IEEE 123-bus test feeder.