Stackelberg Game-Based Low-Carbon Optimal Scheduling of IES With Dynamic Carbon Emission Factors Using Caterpillar Fungus Optimizer Algorithm

Abstract

To address multi-agent interest coordination in integrated energy parks, time-varying carbon responsibility of grid power purchases, and insufficient collaborative optimization of low-carbon coupling units, this paper proposes a Stackelberg game-based low-carbon optimal dispatch model for integrated energy systems (IESs) by incorporating dynamic carbon emission factors (DCEFs). A hybrid solution framework combining the caterpillar fungus optimizer (CFO) and CPLEX is employed to solve the resulting bilevel problem. The model establishes a bi-level Stackelberg game among an integrated energy system operator (IESO), an energy supplier (ES), and a load aggregator (LA). DCEFs and branch carbon emission flows trace carbon responsibility across power and heating networks, while carbon trading, carbon capture and storage (CCS), power-to-ammonia (P2A), methanation, and urea synthesis are integrated to improve carbon accounting and resource utilization. In the case study on an IEEE 69-bus power system and a 6-node heating network, compared with the best among the compared algorithms, the proposed CFO-CPLEX method improves the IESO profit by 18.0% and reduces carbon emissions by 13.2%, while its runtime is 15.2% longer than the fastest compared algorithm. Moreover, compared with scenario 3 (which includes the Stackelberg game and carbon trading but no DCEF), the proposed model increases the total park profit by 25.05%. Compared with scenario 1 (basic economic dispatch), the proposed model reduces carbon emissions by 11.76%.