Large Scale Multi-Period Optimal Power Flow With Energy Storage Systems Using Differential Dynamic Programming

With theincreased penetration of renewable sources, power gridsare becoming stressed due to fluctuating generation. To alleviate stress from inconsistent sources, utilities employ energy storage systems alongside renewable sources and rely on dispatching synchronous generators. However, optimal dispatch of such devices is limited by traditional AC optimal power flow methods that do not account for time-dependent constraints, such as the state of charge of energy storage systems and generator ramping constraints. Multi-period optimal power flow is proposed as a large non-convex non-linear problem to optimally dispatch and control generators and energy storage elements across multiple time periods. In this paper, we introduce a scalable, robust framework to solve multi-period optimal power flow using a differential dynamic programming scheme that makes it capable of scaling to large systems containing energy storage devices. We demonstrate the efficacy of this solution by optimizing the SyntheticUSA testcase over a set of time periods. A robust homotopy method is applied to achieve fast simulation times that can be parallelized for further improvements.

Automated summary

With the increasing popularity of renewable energy, power grids are facing greater challenges due to fluctuating generation. To address these issues, utilities are implementing energy storage systems and relying on synchronous generators for dispatching. This paper proposes a scalable and robust framework for solving the multi-period optimal power flow problem, demonstrating its efficacy through optimization of the Synthetic USA testcase.