Demand Response Optimization MILP Framework for Microgrids with DERs
K. Victor Sam Moses Babu, Pratyush Chakraborty, Mayukha Pal
TL;DR
The paper tackles DR in microgrids with high solar penetration by formulating a comprehensive MILP framework that jointly optimizes demand response, solar allocation, and battery storage. It introduces load classification, dynamic price thresholds, and multi-period coordination within a 24-hour horizon, solved via branch-and-cut with the CBC solver under strict time and optimality constraints. Across seven diverse scenarios, the approach consistently achieves at least 10% peak load reduction and substantial energy cost savings, with the largest gains (up to 38.0%) occurring when solar generation is plentiful or prices are favorable. The work demonstrates practical potential for real-world microgrid operation, balancing technical stability with economic efficiency, and points to future enhancements in forecasting and distributed optimization for scalability.
Abstract
The integration of renewable energy sources in microgrids introduces significant operational challenges due to their intermittent nature and the mismatch between generation and demand patterns. Effective demand response (DR) strategies are crucial for maintaining system stability and economic efficiency, particularly in microgrids with high renewable penetration. This paper presents a comprehensive mixed-integer linear programming (MILP) framework for optimizing DR operations in a microgrid with solar generation and battery storage systems. The framework incorporates load classification, dynamic price thresholding, and multi-period coordination for optimal DR event scheduling. Analysis across seven distinct operational scenarios demonstrates consistent peak load reduction of 10\% while achieving energy cost savings ranging from 13.1\% to 38.0\%. The highest performance was observed in scenarios with high solar generation, where the framework achieved 38.0\% energy cost reduction through optimal coordination of renewable resources and DR actions. The results validate the framework's effectiveness in managing diverse operational challenges while maintaining system stability and economic efficiency.