Multi-Stage Adaptive Stochastic-Robust Scheduling Method with Affine Decision Policies for Hydrogen-Based Multi-Energy Microgrid

Zero-carbon clean energy such as hydrogen has been developed rapidly to reduce carbon emissions, gradually promoting them as the main energy supply for multi-energy microgrids (MEMGs), which motivates the deployment of hydrogen-based MEMG (H-MEMG). The main difficulty of the H-MEMG scheduling problem is how to handle source/load uncertainties for ensuring the solution feasibility and economics in the actual operation. To this end, this paper proposes a novel multi-stage adaptive stochastic-robust optimization (MASRO) approach, which combines the ideas of stochastic programming and multi-stage robust optimization. The established model has the objective of the expected operation cost and ensures the solution feasibility by designed constraints rather than the 'min-max' structure. Specifically, first, affine policies are adapted to describe the complex relationship between decision variables and uncertainty realizations; Second, an affine policy-based solution approach is proposed for the MASRO H-MEMG scheduling model. Then, the complex conversion relationship and coupling constraints are reformulated, and a tractable mixed-integer linear programming (MILP) model is established; Third, based on the solved affine functions, the real-Time rolling and non-rolling economic dispatch models are proposed to respectively pursue the economic and computational requirements, and both can guarantee solution robustness and nonanticipativity. Numerical tests are implemented on a real H-MEMG, verifying that the proposed method could guarantee the feasibility and economic efficiency of actual H-MEMG operations.