The Multi-Objective Distributed Robust Optimization Scheduling of Integrated Energy Systems Considering Green Hydrogen Certificates and Low-Carbon Demand Response

To address the issues of energy wastage and uncertainty impacts associated with high levels of renewable energy integration, a multi-objective distributed robust low-carbon optimization scheduling strategy for hydrogen-integrated Integrated Energy Systems (IES) is proposed. This strategy incorporates a green hydrogen trading mechanism and low-carbon demand response. Firstly, to leverage the low-carbon and clean characteristics of hydrogen energy, an efficient hydrogen utilization model was constructed, consisting of electricity-based hydrogen production, waste heat recovery, multi-stage hydrogen use, hydrogen blending in gas, and hydrogen storage. This significantly enhanced the system’s renewable energy consumption and carbon reduction. Secondly, to improve the consumption of green hydrogen, a novel reward–punishment green hydrogen certificate trading mechanism was proposed. The impact of green hydrogen trading prices on system operation was discussed, promoting the synergistic operation of green hydrogen and green electricity. Based on the traditional demand-response model, a novel low-carbon demand-response strategy is proposed, with carbon emission factors serving as guiding signals. Finally, considering the uncertainty of renewable energy, an innovative optimal trade-off multi-objective distributed robust model was proposed, which simultaneously considered low-carbon, economic, and robustness aspects. The model was solved using an improved adaptive particle swarm optimization algorithm. Case study results show that, after introducing the reward–punishment green hydrogen trading mechanism and low-carbon demand response, the system’s total cost was reduced by approximately 5.16% and 4.37%, and carbon emissions were reduced by approximately 7.84% and 6.72%, respectively. Moreover, the proposed multi-objective distributed robust model not only considers the system’s economy, low-carbon, and robustness but also offers higher solving efficiency and optimization performance compared to multi-objective optimization methods.