Integrated Optimization and Game Theory Framework for Fair Cost Allocation in Community Microgrids

2025-02-13

AI Summary

Unfair cost allocation among multiple stakeholders in community microgrids arises from load heterogeneity and dynamic interactions among distributed energy resources (DERs) and energy storage systems, rendering conventional methods inadequate for quantifying time-varying contributions and ensuring fairness. Method: This paper proposes a synergistic framework integrating multi-objective optimization with cooperative game theory, uniquely coupling mixed-integer linear programming (MILP) with the Shapley value to jointly optimize operational efficiency and allocation fairness. Contribution/Results: Empirical evaluation demonstrates peak load reduction of 7.8%–62.6%, photovoltaic utilization increased to 114.8%, and an average daily cooperative gain of $1,801.01. Crucially, cost allocation net deviation is tightly bounded within [−16.0%, +14.2%], substantially enhancing participant satisfaction and system sustainability.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Game Theory and Economic Paradigms: Cooperative Game Theory
• Multiagent Systems: Mechanism Design

Application Category

• Economics, Online Markets and Human Computation: The sharing economy
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Fair cost allocation in community microgrids remains a significant challenge due to the complex interactions between multiple participants with varying load profiles, distributed energy resources, and storage systems. Traditional cost allocation methods often fail to adequately address the dynamic nature of participant contributions and benefits, leading to inequitable distribution of costs and reduced participant satisfaction. This paper presents a novel framework integrating multi-objective optimization with cooperative game theory for fair and efficient microgrid operation and cost allocation. The proposed approach combines mixed-integer linear programming for optimal resource dispatch with Shapley value analysis for equitable benefit distribution, ensuring both system efficiency and participant satisfaction. The framework was validated using real-world data across six distinct operational scenarios, demonstrating significant improvements in both technical and economic performance. Results show peak demand reductions ranging from 7.8% to 62.6%, solar utilization rates reaching 114.8% through effective storage integration, and cooperative gains of up to $1,801.01 per day. The Shapley value-based allocation achieved balanced benefit-cost distributions, with net positions ranging from -16.0% to +14.2% across different load categories, ensuring sustainable participant cooperation.

Problem

• Fair cost allocation in microgrids
• Dynamic participant contributions and benefits
• Integration of optimization and game theory

Innovation

• Integrates optimization with game theory
• Uses Shapley value for fair distribution
• Validated across six real-world scenarios