Two-tier optimal scheduling of integrated energy systems in parks considering P2G-CCS-CHP coupling and electricity-gas-heat-cooling price-demand response

This study proposes a two-level optimization framework for scheduling park integrated energy systems (PIES). The approach enhances coordination between energy supply and demand while addressing major issues such as weak source–load interactions, excessive carbon emissions, underutilization of wind and solar resources, severe grid peak–valley fluctuations caused by large-scale renewable integration, and inefficient system operation. The model incorporates wind and solar output uncertainty, couples power-to-gas (P2G), carbon capture and storage (CCS), and combined heat and power (CHP) units, and embeds price-based demand response to strengthen flexibility and economic efficiency. Key steps include generating/reducing typical daily wind-sun output situations via kernel density determination and Copula theory, analyzing impacts of wind-solar grid-connected power and integrated demand response on loads to propose source-load coordinated peak-shaving, establishing an electricity-gas-heat-cooling price-based demand response model to enhance price signal incentives, constructing a CHP model with P2G and CCS (weakening electro-thermal coupling, expanding electricity adjustment range, and reducing emissions), and improving the conventional stepped carbon quota trading strategy by introducing reward-penalty coefficients to form an enhanced reward-penalty stepped carbon trading model. The two-level model optimizes grid load curves (upper level) and PIES low-carbon economy (lower level), solved jointly via Gurobi and Improved Dung Beetle Optimization. A case study on a northern China comprehensive demonstration park verifies effectiveness: the model suppresses grid load fluctuation, achieves peak removing/valley filling, improves renewable energy absorption, and reduces PIES carbon emissions and total cost.