Multi-Objective Optimal Dispatch of Integrated Energy Systems Guided by Dynamic Carbon Emission Factors

Regional integrated energy systems (RIES) represent a promising approach for the energy transition and sustainable development, leveraging the flexible, coordinated operation of a multienergy system. With the growing penetration of renewable energy sources and the increasing complexity of energy management, the optimization of RIES necessitates the integration of demand response (DR) mechanisms. The optimization problems are increasingly characterized by multiobjective optimization. This study proposes a novel bilevel multiobjective optimization model for RIES, designed to minimize operational costs, reduce carbon emissions, and enhance load stability simultaneously. The model utilizes dynamic carbon emission factors, derived from the carbon emission flow (CEF) calculation, and time-of-use (TOU) energy pricing as DR signals. The Pareto front for demand-side strategies is obtained using the NSGA-II algorithm, coordinated with an upper-level economic dispatch solved by GUROBI, thereby balancing system and user benefits and identifying the optimal trade-offs among these conflicting objectives. Validation through integrated case studies with a 30-bus power, 20-node gas, and 8-node heat system demonstrates that multiobjective optimization with DR significantly improves economic and environmental performance: achieving a 10.29% cost reduction and a 1.42% carbon decrease, load variation was effectively managed, with electric and thermal load reductions of 3.50% and 6.50%, respectively. However, maintaining system load stability comes at the expense of fully achieving economic and low-carbon objectives, highlighting the critical trade-offs inherent in multiobjective optimization.