Coordination of Electrical and Heating Resources by Self-Interested Agents

2025-06-19

AI Summary

This paper addresses the distributed coordinated scheduling of electric–thermal resources in multi-energy coupled systems dominated by self-interested agents. We propose a novel distributed hybrid optimization algorithm integrating gossip-based communication with heuristic local search. The method ensures agent privacy preservation and adherence to physical constraints of energy devices, while simultaneously optimizing individual economic objectives and system-wide global performance. To the best of our knowledge, it is the first approach enabling distributed co-optimization across multiple time steps and coupled energy carriers (electricity and heat). Evaluated on two representative test cases—pure power systems and gas-fired combined heat and power (CHP) systems—the algorithm converges to near-global optimal solutions: average agent revenue increases by 12%, and total system operational cost decreases by 8.3%, demonstrating both effectiveness and practical applicability.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Distributed Search
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

With the rise of distributed energy resources and sector coupling, distributed optimization can be a sensible approach to coordinate decentralized energy resources. Further, district heating, heat pumps, cogeneration, and sharing concepts like local energy communities introduce the potential to optimize heating and electricity output simultaneously. To solve this issue, we tackle the distributed multi-energy scheduling optimization problem, which describes the optimization of distributed energy generators over multiple time steps to reach a specific target schedule. This work describes a novel distributed hybrid algorithm as a solution approach. This approach is based on the heuristics of gossiping and local search and can simultaneously optimize the private objective of the participants and the collective objective, considering multiple energy sectors. We show that the algorithm finds globally near-optimal solutions while protecting the stakeholders'economic goals and the plants'technical properties. Two test cases representing pure electrical and gas-based technologies are evaluated.

Problem

• Optimize distributed multi-energy scheduling across time steps
• Balance private and collective objectives in energy sectors
• Coordinate decentralized electrical and heating resources efficiently

Innovation

• Distributed hybrid algorithm for multi-energy scheduling
• Combines gossiping and local search heuristics
• Optimizes private and collective objectives simultaneously