Distributed online optimization for integrated energy systems: A multi-agent system consensus approach

Abstract

The integration of multi-energy within distribution networks has escalated the need for efficient operation and control of integrated energy systems (IES). Addressing the complexities of real-time scheduling and low-carbon optimization, we propose a novel artificial intelligence driven multi-agent system (MAS) approach for modeling the interactions and operations within the multi-agent integrated energy systems (MA-IES) framework. In this framework, distinct components such as electric, gas, and heat networks are conceptualized as autonomous agents, each responsible for managing its domain while interacting with other agents to achieve system-wide efficiency and economical goals. The agents communicate and coordinate through a distributed online optimization framework, utilizing the alternating direction multiplier method (ADMM) to ensure effective consensus despite the inherent nontransparency of information exchange. This MAS based approach allows for dynamic adaptation of strategies based on local data and global objectives, significantly enhancing the responsiveness and adaptability of MA-IES. We further integrate an objective function reliant on a tiered carbon pricing mechanism to assess and minimize the environmental impact of operations. Enhanced by adaptive penalty coefficients within the ADMM, our MA-IES framework demonstrates improved convergence rates and robustness in operational scenarios. Empirical validation through detailed case studies confirms the superior performance of our MAS-based model, demonstrating its potential to realize an efficient and economical low-carbon operation of MA-IES.