Robust capacity optimization methods for integrated energy systems considering demand response and thermal comfort

Integrated Energy System can realize the cascade utilization of energy, which improves the utilization of energy efficiently and reduce the carbon emission. Taking into account the uncertainty of multi-energy load and renewable energy forecasting, this paper presents a bi-level robust optimization model with demand response and thermal comfort, for the capacity planning and operation problem of Integrated Energy System. The outer level optimization is planning to find the optimal integrated energy system configuration to minimize economic investment, while the inner level is to robustly optimize the system scheduling to simultaneously reduce carbon emissions and dissatisfaction of residents' participation in demand response. In the simulation, the Pareto front of the multi-objective problem is obtained via NSGA-II algorithm and Gurobi solver, and the best design plans on the Pareto front selected by Topsis method are analyzed and discussed. The results from three modes are compared in simulation, which illustrates the economic and environmental benefits from demand response and thermal comfort. In addition, the impact of different thermal time scales and forecast uncertainties on integrated energy system planning are also discussed. Finally the sensitivity of the influence of the low carbon grid constrains on the optimization is analyzed.

Automated summary

This paper proposes a bi-level robust optimization model for capacity planning and operation problem of Integrated Energy System, comparing economic and environmental benefits from different simulation modes, and analyzing the sensitivity of forecast uncertainties and low carbon grid constraints on optimization.