Incorporating Risk in Operational Water Resources Management: Probabilistic Forecasting, Scenario Generation, and Optimal Control

作者：Ties van der Heijden, Miguel Angel Mendoza‐Lugo, Peter Pálenský, Nick van de Giesen, Edo Abraham · 发表于：Water Resources Research · 年份：2025 · DOI：10.1029/2024wr037115 · 被引用次数：9 · 研究领域：Water resources management and optimization、Reservoir Engineering and Simulation Methods、Risk and Portfolio Optimization

Abstract This study presents an innovative approach to risk‐aware decision‐making in water resource management. We focus on a case study in the Netherlands, where risk awareness is key to water system design and policy‐making. Recognizing the limitations of deterministic methods in the face of weather, energy system, and market uncertainties, we propose a scalable stochastic Model Predictive Control (MPC) framework that integrates probabilistic forecasting, scenario generation, and stochastic optimal control. We utilize Combined Quantile Regression Deep Neural Networks and Non‐parametric Bayesian Networks to generate probabilistic scenarios that capture realistic temporal dependencies. The energy distance metric is applied to optimize scenario selection and generate scenario trees, ensuring computational feasibility without compromising decision quality. A key feature of our approach is the introduction of Exceedance Risk (ER) constraints, inspired by Conditional‐Value‐at‐Risk (CVaR), to enable more nuanced and risk‐aware decision‐making while maintaining computational efficiency. In this work, we enable the Noordzeekanaal–Amsterdam‐Rijnkanaal (NZK‐ARK) system to participate in Demand Response (DR) services by dynamically scheduling pumps to align with low hourly electricity prices on the Day Ahead and Intraday markets. Through historical simulations using real water system and electricity price data, we demonstrate that incorporating uncertainty can significantly reduce oper...