Model Predictive BESS Control for Demand Charge Management and PV-Utilization Improvement

TL;DR

This paper presents a model predictive control approach for battery energy storage systems that simultaneously reduces demand charges and improves PV-utilization, demonstrating significant benefits through simulation results.

Contribution

It introduces a novel model predictive controller that effectively manages multiple services for BESS, enhancing PV-utilization and demand charge savings.

Findings

PV-utilization increased by 60-80% with the proposed controller.

Demand charge savings remained stable despite PV-utilization improvements.

Battery stacking of services enhances economic benefits.

Abstract

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.