Multistage robust optimization for the day-ahead scheduling of hybrid thermal-hydro-wind-solar systems

Zhiming Zhong (), Neng Fan () and Lei Wu ()
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Zhiming Zhong: University of Arizona
Neng Fan: University of Arizona
Lei Wu: Stevens Institute of Technology

Journal of Global Optimization, 2024, vol. 88, issue 4, No 8, 999-1034

Abstract: Abstract The integration of large-scale uncertain and uncontrollable wind and solar power generation has brought new challenges to the operations of modern power systems. In a power system with abundant water resources, hydroelectric generation with high operational flexibility is a powerful tool to promote a higher penetration of wind and solar power generation. In this paper, we study the day-ahead scheduling of a thermal-hydro-wind-solar power system. The uncertainties of renewable energy generation, including uncertain natural water inflow and wind/solar power output, are taken into consideration. We explore how the operational flexibility of hydroelectric generation and the coordination of thermal-hydro power can be utilized to hedge against uncertain wind/solar power under a multistage robust optimization (MRO) framework. To address the computational issue, mixed decision rules are employed to reformulate the original MRO model with a multi-level structure into a bi-level one. Column-and-constraint generation (C &CG) algorithm is extended into the MRO case to solve the bi-level model. The proposed optimization approach is tested in three real-world cases. The computational results demonstrate the capability of hydroelectric generation to promote the accommodation of uncertain wind and solar power.

Keywords: Hybrid power system; Day-ahead scheduling; Multistage robust optimization; Renewable energy (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10898-023-01328-2

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