A multi-stage two-layer stochastic design model for integrated energy systems considering multiple uncertainties

Building multi-energy complementary integrated energy systems (IES) is a crucial way to achieve the goal of carbon peaking and carbon neutrality. However, uncertainties at both the source and load ends pose particular challenges to the optimal design and operation of the system. This paper proposes a multi-stage stochastic planning model considering CO2, NOx, and SO2 emissions and multiple uncertainties. The k-means algorithm was used to obtain typical scenarios and typical parameters of the reference building. The uncertain scenarios with probability distributions are generated by Latin hypercubic sampling and backward curtailment method. Then, a novel multi-objective sand cat swarm optimization (MOSCSO) algorithm is proposed for solving the upper-layer design model. The lower layer scheduling model is constructed as a mixed-integer linear programming model, and the solver is invoked for optimization. A case study validates the proposed model. Compared to different operating strategies, the two-layer optimization model can achieve a maximum of 13.54%, 12.38%, and 21.73% improvement in economic, energy, and environmental. Compared to electricity price uncertainty, the sensitivity analysis suggests that gas price uncertainty negatively impacts the system. In addition, the two-layer optimization model under a single uncertainty provides the best economic, energy, and environmental compared to the operational strategies.