Practical considerations for customer-sited energy storage dispatch on multiple applications using model predictive control

Government subsidies for energy storage and renewable generation have led to the cost of energy storage come down during recent years. This has motivated people to deploy behind-the-meter energy storage units, to reduce their monthly electricity bill. For optimal control of the battery to incorporate maximum photovoltaic energy generation as well as demand charge reduction, data-driven and advanced Battery Energy Storage System (BESS) control strategies are required. This paper explores different use cases where customers could deploy energy storage systems for demand charge reduction as well as when customers could deploy energy storage systems for demand charge reduction while satisfying a utility set objective. From historical load and PV data, different use cases are simulated using a Model Predictive Control (MPC) based BESS control model. MPC requires machine-learning (ML) based forecasts of photovoltaic (PV) as well as load as inputs. A sensitivity analysis on the effect of different energy forecasts on the performance of MPC is presented in the paper. A degradation analysis with as a function of charge/discharge cycles is also presented in the paper to evaluate the trade-off between economic objectives and battery health.