Real-time Operation Optimization of Microgrids with Battery Energy Storage System: A Tube-based Model Predictive Control Approach

TL;DR

This paper introduces a tube-based model predictive control method for real-time microgrid management with battery energy storage, effectively handling uncertainties and battery degradation for improved operational efficiency.

Contribution

It proposes a novel tube-based MPC framework with cascaded controllers and a real-time battery model, addressing uncertainties and battery degradation in microgrid energy management.

Findings

Effective handling of uncertainties in renewable generation forecasts.

Demonstrated superior performance in case studies with high renewable penetration.

Monte Carlo simulations show a competitive ratio below 1.10.

Abstract

Battery energy storage systems (ESS) are widely used in microgrids to complement high renewables. However, the real-time energy management of microgrids with battery ESS is challenging in two aspects: 1) the evolution process of battery energy level is across-time coupled; 2) uncertainties unavoidably arise in the forecasting process for renewable generation. In this paper, a tube-based model predictive control (MPC) approach is innovatively proposed in accommodating the real-time energy management of microgrids with battery ESS. Firstly, a real-time operation model of battery, including the degradation cost and time-aware SoC range, is proposed for the battery ESS. In particular, the battery feature shallower-cheaper is depicted and the terminal SoC requirement is achieved. Secondly, two cascaded MPC controllers are designed in the proposed tube-based MPC, in which reference trajectories are generated by the nominal MPC without uncertainties, and then the ancillary MPC steers the actual trajectories to the nominal ones upon the realization of uncertainties. Specifically, in this paper, the battery SoC is viewed as the state variable of the system, while the generator power output and exchange power with the utility are seen as control variables. Lastly, numerous case studies demonstrate the effectiveness of the proposed approach, including both the low and high penetration level of renewables. Additional Monte Carlo simulations of consecutive 365 days show that the competitive ratio of the proposed approach is excellently below 1.10.