Optimization Decomposition of Resistive Power Networks With Energy Storage

A fundamental challenge of a smart grid is: to what extent can moving energy through space and time be optimized to benefit the power network with large-scale energy storage integration? With energy storage, there is a possibility to generate more energy when the demand is low and store it for later use. In this paper, we study a dynamic optimal power flow (OPF) problem with energy storage dynamics in purely resistive power networks. By exploiting the recently discovered zero duality gap property in the OPF problem, we apply optimization decomposition techniques to decouple the coupling energy storage constraints and obtain the global optimal solution using distributed message passing algorithms. The decomposition methods offer new interesting insights on the equilibrium load profile smoothing feature over space and time through the relationship between the optimal dual solution in the OPF and the energy storage dynamics. We evaluate the performance of the distributed algorithms in several IEEE benchmark systems and show that they converge fast to the global optimal solution by numerical simulations.