Two-stage robust optimization of distributed energy systems considering nonlinear multi-energy transmission and conversion

Nonlinear and interdependent nature of multi-energy transmission and conversion processes presents substantial challenges in the optimal operation of distributed energy systems, especially compounded by the uncertainties of renewable energy sources and loads. To address this challenge, this research constructs the holistic model of a distributed energy system that captures the nonlinear characteristics based on the heat current model of thermal systems, and formulates a nonlinear two-stage robust optimization (TRO) framework to effectively manage the uncertainties as sociated with renewable energy and loads. Subsequently, to effectively solve the nonlinear TRO problem, we introduce a two-tiered iterative approach by integrating a hierarchical divide-and-conquer strategy with the Column-and-Constraint Generation (C&CG) algorithm. Optimization results demonstrate that the nonlinear TRO approach can reduce operational costs by 4.33 % compared to traditional deterministic optimization methods. Moreover, some potential operational risks may stem from overlooking the nonlinearities of multi-energy transmission and conversion in simplified TRO models. That is, ignoring these nonlinearities will result in increased costs and may even render robust decision-making strategies infeasible, highlighting the critical importance of comprehensively considering nonlinear multi-energy transmission and conversion processes in the robust optimization.