Peak Demand Optimization of Commercial Buildings Based on Energy Storage Systems

Large and stochastic peak energy demands of buildings contribute to grid inefficiency. Here, we present an optimization algorithm for behind-the-meter peak shaving with energy storage systems. We employ a Markov decision process to execute charging and discharging decisions on sub-hourly time scales according to the on-site energy profile and the size of the energy storage system. The algorithm minimizes the peak load drawn from the grid to maximize savings for the asset owner and efficiency gains for the grid network. Our technology-agnostic solution can serve battery energy storage systems and lesser-studied use cases including pumped hydro storage and municipal water systems. We demonstrate how the algorithm can inform on sizing energy storage systems to best serve a given building. With these contributions, we provide an AI platform to optimize energy storage assets and smart grid systems.