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This paper explores the micro Energy-Water-Hydrogen (m-EWH) nexus, an engineering system designed to reduce carbon emissions in the power sector. The m-EWH nexus leverages renewable energy sources (RES) to produce hydrogen via electrolysis, which is then combined with carbon captured from fossil fuel power plants to mitigate emissions. To address the uncertainty challenges posed by RES, this paper proposes a real-time decision-making framework for the m-EWH nexus, which requires the rapid solution of large-scale mixed-integer convex programming (MICP) problems. To this end, we develop a machine learning-accelerated solution method for real-time optimization (MARO), comprising three key modules: (1) an active constraint and integer variable prediction module that rapidly solves MICP problems using historical optimization data; (2) an optimal strategy selection module based on feasibility ranking to ensure solution feasibility; and (3) a feature space extension and refinement module to improve solution accuracy by generating new features and refining existing ones. The effectiveness of the MARO method is validated through two case studies of the m-EWH nexus, demonstrating its capability to swiftly and accurately solve MICP problems for this complex system. 

This paper investigates a novel engineering problem, i.e., security-constrained multi-period operation of micro energy-water nexuses. This problem is computationally challenging because of its high nonlinearity, nonconvexity, and large dimension. We propose a two-stage iterative algorithm employing a hybrid physics and data-driven contingency filtering (CF) method and convexification to solve it. The convexified master problem is solved in the first stage by considering the base case operation and binding contingencies set (BCS). The second stage updates BCS using physics-based data-driven methods, which include dynamic and filtered data sets. This method is faster than existing CF methods because it relies on offline optimization problems and contains a limited number of online optimization problems. We validate effectiveness of the proposed method using two different case studies: the IEEE 13-bus power system with the EPANET 8-node water system and the IEEE 33-bus power system with the Otsfeld 13-node water system.