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

Area of Science:

• Energy Systems Engineering
• Optimization Theory
• Sustainable Development

Background:

• Regional integrated energy systems (RIES) are crucial for energy transition and sustainable development.
• Increasing renewable energy integration and system complexity necessitate advanced energy management strategies.
• Demand response (DR) mechanisms are vital for optimizing complex, multi-objective RIES operations.

Purpose of the Study:

• To develop a novel bilevel multiobjective optimization model for RIES.
• To simultaneously minimize operational costs, reduce carbon emissions, and enhance load stability.
• To integrate dynamic carbon emission factors and time-of-use pricing as DR signals.

Main Methods:

• A bilevel multiobjective optimization model was formulated.
• The NSGA-II algorithm was used to obtain the Pareto front for demand-side strategies.
• An upper-level economic dispatch, solved by GUROBI, coordinated the system optimization.

Main Results:

• The model achieved a 10.29% reduction in operational costs and a 1.42% decrease in carbon emissions.
• Electric and thermal load reductions of 3.50% and 6.50% were realized, respectively, improving load stability.
• Significant trade-offs were observed between economic/environmental objectives and load stability.

Conclusions:

• Multiobjective optimization with DR effectively enhances the economic and environmental performance of RIES.
• The proposed model successfully balances system and user benefits while identifying optimal trade-offs.
• Maintaining load stability requires compromises in achieving maximum economic and low-carbon objectives.