Robust Multi-Stage Planning of Park-Level Integrated Energy System Considering Source-Load Uncertainties

Abstract

[Objective] A robust multi-stage planning model is established for a park-level integrated energy system （PIES） to minimize the total life-cycle present-value cost while considering source-load uncertainties， optimal construction sequencing， and tiered carbon trading to enhance economic efficiency and low-carbon benefits. [Methods] First， a box-type uncertainty set is used to model the source-load uncertainty and uncertainty adjustment parameters are introduced to reduce the conservatism of PIES planning and obtain a two-stage robust optimization （TSRO） model. Both discrete and continuous variables are included in the second-stage decision variables of the TSRO model， facilitating directly solving the second-stage problem using Lagrangian duality theory. Instead， an additional nested layer is required to solve the model. Therefore， this paper employs the nested column-and-constraint generation （NC&CG） algorithm to solve the model. [Results] Simulation results show that the robust programming model can improve the robustness of the system by sacrificing the economic and low-carbon benefits of the system to a certain extent and can flexibly adjust the robustness of the PIES planning scheme by changing the value of the uncertainty parameters. [Conclusions] The proposed multi-stage planning approach accounts for load growth and source-load uncertainties from a long-term system development perspective， thereby improving the flexibility and adaptability of the planning scheme.