Model Predictive BESS Control for Demand Charge Management and PV-Utilization Improvement
For commercial/industrial building owners with behind-the-meter PV and batteries, this work shows how to stack services to improve economic returns.
The paper proposes a model predictive controller for battery energy storage systems that simultaneously reduces demand charges and improves PV utilization by 60-80% without significant loss in demand charge savings.
Adoption of battery energy storage systems for behind-the-meters application offers valuable benefits for demand charge management as well as increasing PV-utilization. The key point is that while the benefit/cost ratio for a single application may not be favorable for economic benefits of storage systems, stacked services can provide multiple revenue streams for the same investment. Under this framework, we propose a model predictive controller to reduce demand charge cost and enhance PV-utilization level simultaneously. Different load patterns have been considered in this study and results are compared to the conventional rule-based controller. The results verified that the proposed controller provides satisfactory performance by improving the PV-utilization rate between 60% to 80% without significant changes in demand charge (DC) saving. Furthermore, our results suggest that batteries can be used for stacking multiple services to improve their benefits. Quantitative analysis for PV-utilization as a function of battery size and prediction time window has also been carried out.