Two-Timescale Online Optimization of Behind-the-Meter Battery Storage for Stacked Revenue by Providing Multi-Services

Abstract

Behind-the-meter (BTM) battery energy storage systems (BESS) are becoming increasingly important in the power system with the proliferation of intermittent distributed renewable energy sources. Stacked revenue can be achieved by providing multi-services to the power grid, justifying the substantial upfront cost of BTM BESS and promoting their future adoption. This paper focuses on optimizing the operation strategy of BTM BESS to maximize the time average stacked revenue obtained from multiple service markets, including energy arbitrage, frequency regulation, photovoltaic (PV) power smoothing and reactive power compensation. Challenges arise from the coupling of operation decisions both among multiple services and over the temporal dimension, considering the different decision timescales of service markets as well as the battery dynamics. The uncertainty of stochastic parameters further complicates the optimization process. To address these challenges, this paper proposes a novel two timescale online optimization scheme based on the Lyapunov optimization framework. The uncertainties are tackled by making decisions online, and the computation complexity is highly relieved by relaxing the temporal coupling with a drift-plus-penalty technique. Theoretic analyses are conducted to prove that the solution of this relaxed online decision problem is always feasible for the original one, and it can achieve near-optimum with a constant optimality gap. Extensive simulations utilizing the energy and frequency regulation data from the real market validate the effectiveness of our proposed scheme.