This paper proposes a Stackelberg game-based collaborative optimization strategy for Park-Level Integrated Energy Systems (PIESs) operating in carbon and green certificate markets. The strategy addresses interest conflicts and low-carbon transition challenges in multi-agent optimization by integrating a carbon capture, power-to-gas, and liquid carbon dioxide energy storage technology chain. Innovatively integrates LCES into the CCS-P2G-LCES chain, achieving internal carbon cycling and energy storage. First, a market environment for PIESs integrating carbon trading and green certificate trading is constructed, and a deeply coupled low-carbon technology chain model of CCS-P2G-LCES is established to realize internal carbon resource cycling and energy time shifting. Second, a one-leader, multiple-follower Stackelberg game framework is developed with the Integrated Energy Service Provider (IESP) as the leader and the User Load Aggregator (ULA) and Electric Vehicle Aggregator (EVA) as followers. The IESP guides demand response on the user and electric vehicle sides by formulating differentiated energy prices. On this basis, a collaborative optimization dispatch model is constructed with the objective of maximizing the comprehensive revenue of the IESP. Finally, case study analysis verifies that the proposed method not only enhances operational revenue and reduces user energy costs, but also significantly reduces system carbon emissions and improves renewable energy consumption rates. The results demonstrate the feasibility and superiority of integrating market mechanisms, low-carbon technologies, and multi-agent game-based collaborative optimization.

In view of the carbon emission reduction and new energy consumption problems in the integrated energy system (IES), this paper, for the first time, combines power to gas (P2G) with liquid carbon dioxide energy storage (LCES) and takes demand response (DR) into account simultaneously to construct a new type of IES capacity configuration optimization model. Firstly, based on the operation characteristics and coupling features of various devices within the system, the IES model was constructed. Meanwhile, the electricity, heat, and cold DR models were, respectively, established according to price-based and incentive-based methods. Finally, a two-layer collaborative optimization configuration model was built, with the upper layer aiming to minimize the annual total cost of the system and the lower layer aiming to minimize the annual operation cost of the system. Through case studies, the effectiveness of the established model was verified, and the impacts of DR, P2G, and LCES on the system capacity configuration results, economic efficiency, and environmental friendliness were investigated. Additionally, the impact of natural gas prices on the system optimization results was studied. The results showed that considering LCES and P2G could reduce the cost of the IES by 7.26% and the carbon emissions of the system by 31.03%, verifying the effectiveness of the proposed method and providing a feasible solution for IES capacity configuration.