Cooperative Flexibility Exchange: Fair and Comfort-Aware Decentralized Resource Allocation
Rabiya Khalid, Evangelos Pournaras
TL;DR
The paper tackles the challenge of balancing grid efficiency with user comfort in demand-side management by proposing a decentralized multi-agent approach that combines Iterative Economic Planning and Optimized Selections ($\text{I-EPOS}$) with a cooperative slot-exchange mechanism. Agents first select plan schedules via I-EPOS, then negotiate time-slot exchanges through a blackboard facilitator to improve individual comfort without increasing the global inefficiency cost $I$, thus preserving system stability. The framework introduces an altruism parameter $\beta$ to model agent behavior and a fairness metric $U$ to monitor equity across users, showing that slot exchanges improve comfort and fairness especially at lower $\beta$ values while keeping $I$ unchanged. Experimental results on a large-scale, realistic residential dataset demonstrate scalability to $n=1000$ agents, high exchange success rates, and meaningful comfort gains, supporting practical adoption in future smart grids. The work contributes a privacy-preserving, scalable DSM solution with open-source code and datasets for reproducibility and further research.
Abstract
The growing electricity demand and increased use of smart appliances are placing new pressures on power grids, making efficient energy management more important than ever. The existing energy management systems often prioritize system efficiency (balanced energy demand and supply) at the expense of user comfort. This paper addresses this gap by proposing a novel decentralized multi-agent coordination-based demand-side management system. The proposed system enables individual agents to coordinate for demand-side energy optimization while improving the user comfort and maintaining the system efficiency. A key innovation of this work is the introduction of a slot exchange mechanism, where agents first receive optimized appliance-level energy consumption schedules and then coordinate with each other to adjust these schedules through slot exchanges. This approach improves user comfort even when agents show non-altruistic behaviour, and it scales well with large populations. The system also promotes fairness by balancing satisfaction levels across users. For performance evaluation, a real-world dataset is used, and the results demonstrate that the proposed slot exchange mechanism increases user comfort and fairness without raising system inefficiency cost, making it a practical and scalable solution for future smart grids.