Open Access

ARTICLE

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

Zhibiao Luo¹, Bo Yang^1,*, Jingbo Wang², Shuai Zhou³
1 Faculty of Electric Power Engineering, Kunming University of Science and Technology, Kunming, China
2 Department of Electrical Engineering and Electronics, University of Liverpool, Liverpool, UK
3 Department of Electrical and Electronic Engineering, Auckland University of Technology, Auckland, New Zealand
* Corresponding Author: Bo Yang. Email:
(This article belongs to the Special Issue: Artificial Intelligence in Energy Systems: Challenges, Opportunities, and Emerging Applications)

Energy Engineering https://doi.org/10.32604/ee.2026.084853

Received 30 April 2026; Accepted 15 June 2026; Published online 20 July 2026

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%.

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Keywords

Stackelberg game; integrated energy system; dynamic carbon emission factor; carbon emission flow; caterpillar fungus optimizer

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