• Graph-theoretic sorting enables real-time carbon tracing in large grids • A new consistency index aligns market contracts with physical constraints. • Energy storage acts as a tool for spatiotemporal carbon responsibility shifting. • Multi-agent dispatch breaks the economy-environment trade-off dilemma. In traditional power system analysis, the decoupling of physical power flow and environmental attributes leads to the ambiguous definition of carbon responsibility and the misalignment between market trading and physical constraints. To address these challenges, this paper proposes a multi-agent collaborative dispatch framework based on electro-carbon coupling (ECC) theory. Firstly, the measurement model of generalized carbon emission flow (CEF) is established, utilizing a graph-theoretic topological sorting algorithm to overcome the computational bottleneck in large-scale networks and achieve accurate real-time carbon traceability. Secondly, to solve the deviation between commercial trading and physical transmission capacity, a green certificate validity verification mechanism and stepped carbon price model based on the carbon flow consistency index (CFCI) are introduced. To further bridge the timescale mismatch between market clearing and real-time operation, and aiming at the non-convexity of physics-market coupling constraints, a multi-time scale low-carbon dispatch strategy based on multi-agent deep reinforcement learning (MADRL) is proposed. The strategy innovatively quantifies the carbon responsibility time-shifting value of energy storage. The case study based on an integrated IEEE 14-bus transmission and 33-bus distribution system shows that the proposed framework effectively suppresses nominal-physical carbon mismatch, improves the overall low-carbon cost-effectiveness of the system, and verifies the effectiveness of energy storage in spatiotemporal carbon shifting.
Tai et al. (Fri,) studied this question.