Demand Response Optimization MILP Framework for Microgrids with DERs

2025-02-12

AI Summary

To address operational instability and deteriorating economic performance caused by source-load mismatch in high-renewable-penetration microgrids, this paper proposes a demand response (DR) coordinated optimization framework based on mixed-integer linear programming (MILP). The method innovatively integrates load classification, dynamic electricity price thresholding, and a multi-period rolling scheduling mechanism to enable global coordination among photovoltaic generation, energy storage, and flexible loads. The formulated MILP model accurately captures load elasticity and price-responsive behavior. Evaluated across seven representative scenarios, the approach consistently reduces peak load by 10% and achieves energy cost savings of 13.1%–38.0%, with the maximum reduction attained under high PV generation conditions. The results demonstrate significant improvements in both system stability and economic efficiency.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Planning, Routing, and Scheduling: Mixed Discrete/Continuous Planning
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

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.

Problem

• Optimizes demand response in microgrids
• Manages renewable energy integration challenges
• Reduces peak load and energy costs

Innovation

• MILP framework optimizes DR operations
• Dynamic price thresholding enhances efficiency
• Multi-period coordination ensures system stability