Optimisation for operational decision-making in a watershed system with interconnected dams

Tiago Gonçalves Vaz, Beatriz Brito Oliveira and Luís Brandão

Applied Energy, 2024, vol. 367, issue C, No S0306261924007682

Abstract: In the energy production sector, increasing the quantity and efficiency of renewable energies, such as hydropower plants, is crucial to mitigate climate change. This paper proposes a new and flexible model for optimising operational decisions in watershed systems with interconnected dams. We propose a systematic representation of watersheds by a network of different connection points, which is the basis for an efficient Mixed-Integer Linear Programming model. The model is designed to be adaptable to different connections between dams in both main and tributary rivers. It supports decisions on power generation, pumping and water discharge, maximising profit, and considering realistic constraints on water use and factors such as future energy prices and weather conditions. A relax-and-fix heuristic is proposed to solve the model, along with two heuristic variants to accommodate different watershed structures and sizes. Methodological tests with simulated instances validate their performance, with both variants achieving results within 1% of the optimal solution faster than the model for the tested instances. To evaluate the performance of the approaches in a real-world scenario, we analyse the case study of the Cávado watershed (Portugal), providing relevant insights for managing dam operations. The model generally follows the actual decisions made in typical situations and flood scenarios. However, in the case of droughts, it tends to be more conservative, saving water unless necessary or profitable. The model can be used in a decision-support system to provide decision-makers with an integrated view of the entire watershed and optimised solutions to the operational problem at hand.

Keywords: Optimisation; MILP; Relax-and-fix; Interconnected dams; Watershed system (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.apenergy.2024.123385

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