Frequency-Domain Modeling and Multi-Agent Game-Theory-Based Low-Carbon Optimal Scheduling Strategy for Integrated Energy Systems

Driven by the dual-carbon strategy, achieving low-carbon economic operations through coordinated optimization of multi-energy flows in integrated energy systems (IES) has emerged as a critical research focus. This paper proposes a low-carbon optimized scheduling strategy for IES based on frequency-domain modeling and multi-agent collaborative game theory, presenting a dual-dimensional innovative methodology for electricity–heat–gas integrated energy systems. At the physical modeling level, the study overcomes the limitations of conventional steady-state models and finite difference methods by pioneering a frequency-domain analytical approach for day-ahead scheduling. Through Fourier transform, the partial differential equations (PDEs) governing thermal and gas network dynamics are converted into linear complex algebraic equations, significantly reducing solution complexity while preserving modeling accuracy and enhancing computational efficiency. In operational optimization, a multi-agent cooperative mechanism is established by partitioning system operators into a tripartite alliance comprising power-to-gas (P2G) facilities, carbon capture units, and energy storage systems. A collaborative optimization model incorporating dynamic energy transmission characteristics is developed, with innovative application of Shapley value method to quantify agent contributions and allocate collaborative surplus. Simulation results demonstrate that the proposed strategy maintains dynamic constraint accuracy in gas–thermal networks while achieving notable improvements: significant reduction in total operational costs, enhanced wind power accommodation rates, and decreased carbon emission intensity. This research provides novel insights that help to resolve the modeling accuracy–computational efficiency dilemma in multi-energy coupled systems, concurrently establishing an equitable and economically viable benefit distribution mechanism for multi-agent collaboration. The findings offer substantial theoretical significance for advancing the low-carbon transition of modern power systems.