Robust Optimization for Microgrid Management With Compensator, EV, Storage, Demand Response, and Renewable Integration

Abstract

Navigating the complex terrain of microgrid energy management is challenging due to the uncertainties linked with abundant renewable resources, fluctuating demand, and a wide range of devices including batteries, distributed energy sources, electric vehicles, and compensatory devices. This paper presents an advanced two-stage robust day-ahead optimization model designed specifically for MG operations. The model primarily addresses challenges arising from the integration of power electronics-based generation units, the unpredictable nature of demand in microgrids, and the integration of small-scale renewable energy sources. The proposed model includes detailed formulations for MG energy management, covering optimal battery usage, efficient EV energy management, compensator usage, and strategic dispatching of DG resources. The multi-objective function aims to minimize various costs related to energy losses, power purchases, load curtailment, DG operation, and battery/EV expenses over a 24-hour period. To efficiently solve this optimization problem, the C&CG algorithm is utilized. Numerical simulations on a test system validate the effectiveness of the proposed model and solution algorithm, showing a significant reduction in the operating costs of the microgrid. This approach offers a robust framework to enhance the resilience and efficiency of microgrid energy management. The results conclusively demonstrate that the proposed approach surpasses comparable methods by at least 5%, highlighting its effectiveness in improving key indicators within the microgrid system.

Keywords

• Microgrid
• two-stage robust optimization
• demand response
• storage
• electric vehicle
• uncertainty