Stochastic distributed optimization of reactive power operations using conditional ensembles of V2G capacity

Energy storage and reactive power supplied by electric vehicles (EV) through vehicle-to-grid (V2G) operation can be coordinated to provide voltage support, thus reducing the need of grid reinforcement and active power curtailment. Optimization and control approaches for V2G-enabled reactive power control should be robust to variations and offer a certain level of optimality by combining real-time control with several-hours-ahead network management schemes. This paper introduces an optimization and control framework that can be used to manage energy storage availability in near future while using the remaining capacity of V2G to generate reactive power and cooperatively perform voltage control. Stochastic distributed optimization of reactive power is realized by integrating a Markov chain-based distributed sub-gradient method with conditional ensemble predictions of V2G capacity. Hence, the obtained solutions can reflect on the system requirements for the upcoming hours along with the instantaneous cooperation between distributed EVs.