Core Function and Synergistic Optimization of Energy Storage Systems in Community Microgrids

Abstract

To address the volatility issues that the introduction of wind, photovoltaic power and other renewable energy sources, community microgrids have emerged as a promising platform to support the absorption of clean energy as well as augment the grid resilience. Energy Storage Systems (ESS) is a significant provider of their operation. This paper innovatively proposes a hierarchical technical architecture based on "Physical-Interface-Control-System". Specifically, it entails turning the equipment selection of the physical layer relating to energy storage, AC/DC conversion and mode switching at the power conversion layer, droop control and model predictive control algorithms at the control layer and optimization of multi-timescale scheduling at the system layer. The paper also cohesively expounds on the twofold applications of ESS in dynamic peak shaving and valley filling in addition to enhancing the grid resilience. By analyzing a commercial microgrid case study in South Carolina, USA, this paper proves the usefulness of intelligent optimization strategies at the system layer. In a strong compliance with equipment constraints, the collaborative optimization of ESS configuration and dynamic scheduling with a 23% decrease in the annual demand charges, a 90.5% rate of renewable energy penetration and 48 minutes of full-load backup (scalable to 8 hours on the critical loads). Nevertheless, large capital requirements, sensitivity to forecasting and battery life are still some of the issues that restrain the widespread application of ESS. To optimize the economic viability, future research should focus on improving the intelligent algorithms and developing some supportive business and policy mechanisms.