Load-Pressure-Based Source-Load-Matching Dynamic Pricing and Coordinated Optimization for Multi-Park Integrated Energy Systems

Given that fixed energy tariffs and demand-response compensation rates cannot adequately capture temporal variations in source-load conditions across multi-park integrated energy systems, this study develops a coordinated optimization approach driven by load-pressure-based dynamic pricing. First, the original electric, thermal, and cooling load profiles are evaluated together with the day-ahead forecasts of wind and photovoltaic generation. These data are then converted into five pricing indicators: net electric load pressure, thermal load pressure, cooling load pressure, renewable energy output level, and the peak-valley period factor. These multidimensional source-load indicators are further translated into time-varying electricity and heat tariffs, together with compensation rates for shifting and curtailing electric, thermal, and cooling demands. Second, a coordinated scheduling model is formulated for interconnected energy parks that integrates renewable generation, shared energy storage, multi-energy conversion devices, electricity transfers among parks, and power exchanges between each park and the distribution grid. The objective function accounts for the emission-related costs arising from grid electricity procurement and natural gas use. Once obtained, the dynamic price signals are introduced into the scheduling stage as fixed inputs, enabling the simultaneous coordination of equipment operation, shared storage charging and discharging, electricity transfers among parks, and multi-energy demand response. Following linearization, the scheduling problem is converted into a mixed-integer linear model, which is implemented in YALMIP and solved by CPLEX without iterative price updates. The numerical results indicate that the resulting energy tariffs and demand response incentives vary with the predicted day-ahead source-load state and encourage flexible electric, thermal, and cooling demands to be redistributed across the scheduling horizon. Shared energy storage and inter-park electricity exchange redistribute the equivalent net-load profile and contribute to peak-load regulation, while wind power is coordinately allocated among direct park supply, storage charging, and export to the distribution grid. The electricity, heat, and cooling power balances of all parks are maintained, verifying the feasibility and coordination capability of the proposed method.