Stochastic Economic Dispatch for Combined Water-energy Systems Considering Data-driven Scenario Generation Approach

Abstract Submission: Numerous bilateral opportunities exist within the water-energy nexus, leading to an increased focus on the efficient operation of combined water-energy systems. Emphasis is being placed on developing methods that prescribe the most economic and efficient use of distributed energy resources and the design energy-efficient water systems. However, achieving this objective is often complicated by significant uncertainties in the availability and variability of renewable energy sources, along with fluctuations in water demand, which introduce complexities into the optimization process and impact the reliability of traditional deterministic models. This work combines a k-nearest neighbor algorithm with a scenario reduction method for scenario generation to address a two-stage stochastic economic dispatch problem applied to Telford Borough in Pennsylvania. The proposed framework provides reliable economic power dispatch for water supply systems while accounting for uncertainties in future energy demands, which are directly linked to fluctuating water demand. The study demonstrates the advantages of employing a stochastic scheduling approach while comparing it to the deterministic power dispatch results. The findings highlight the method’s effectiveness in improving operational stability and peak load management, thereby offering a robust solution for large-scale integrated water-energy systems operation.

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