Collaborative optimization strategy for park-level integrated energy system clusters based on exergy-carbon spatiotemporal coupling pricing

In response to the demands of low-carbon economic operation and high-quality energy utilization for park-level integrated energy system clusters (PIES-C), this paper proposes a tri-stage collaborative optimization strategy for PIES-C based on an exergy-carbon spatiotemporal coupling pricing mechanism. In the first stage, a multi-objective collaborative optimization model for park-level PIES-C is established, categorizing energy interaction roles based on differences in energy purchase and sale. The second stage innovatively proposes an exergy-carbon spatiotemporal coupling pricing mechanism led by power-selling parks, which incorporates exergy value and carbon emission constraints to guide power-purchasing parks in optimizing their energy consumption behaviours. The third stage employs an improved Non-dominated Sorting Genetic Algorithm III (NSGA-III) to identify high-quality Pareto optimal solutions that balance the energy efficiency, low-carbon, and economic objectives of PIES-C. Within a typical daily operational cycle, this strategy achieves a 13.1% reduction in total system economic costs, a 10.2% decrease in carbon emission intensity, and an 8.7% improvement in energy conversion exergy efficiency. These results validate its engineering applicability in PIES-C and its significant contribution to the realization of energy-saving and low-carbon objectives.