Multi-Time Scale Model Predictive Control-Based Demand Side Management for a Microgrid

Abstract

The microgrid (MG) integrating clean renewable energy has increasingly emerged as a critical solution for addressing energy challenges and promoting sustainable energy development. However, the inherent uncertainties in renewable energy output and load consumption present significant challenges to the economic and stable operation of the MG. This paper investigates a multi-time model predictive control (MSMPC) strategy for the optimal scheduling of grid-connected MG. The proposed method can dynamically update the optimal scheduling of the MG on two-time scales based on real-time measurement data. The dispatchable thermostatically controlled loads (TCLs) are incorporated into the demand side management (DSM) system to enhance flexibility and satisfy the future trend of a larger proportion of controllable TCLs. Furthermore, the TCL model considers the aging problem associated with excessive compressor cycling and the satisfaction of end users. Simulation results demonstrate that the proposed method significantly improves the economic performance and robustness of the MG system. Moreover, a real-time experiment conducted using RT-LAB further verifies the feasibility of the proposed approach.