Multistage Stochastic optimization for mid-term integrated generation and maintenance scheduling of cascaded hydroelectric system with renewable energy uncertainty

Zhong, Zhiming; Fan, Neng; Wu, Lei

doi:10.1016/j.ejor.2024.05.011

Title: Multistage Stochastic optimization for mid-term integrated generation and maintenance scheduling of cascaded hydroelectric system with renewable energy uncertainty

Journal Article · 09 May 2024 · European Journal of Operational Research

DOI: https://doi.org/10.1016/j.ejor.2024.05.011 · OSTI ID:2438051

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University of Arizona, Tucson, AZ (United States); Stevens Institute of Technology
University of Arizona, Tucson, AZ (United States)
Stevens Institute of Technology, Hoboken, NJ (United States)

The uncertainties resulting from the escalating penetration of renewable energy resources pose severe challenges to the efficient operation of modern power systems. Hydroelectricity is characterized by its flexibility, controllability, and reliability, and thus becomes one of the most ideal energy resources to hedge against such uncertainties. This paper studies the mid-term integrated generation and maintenance scheduling of a cascaded hydroelectric system (CHS) consisting of multiple cascaded reservoirs and hydroelectric units. To precisely describe the mid-term water regulation policies, the hydraulic coupling relationship and water-energy nexus of CHS are incorporated into the proposed optimization model. The uncertainties of natural water inflow and the power outputs of wind/solar energy generation are taken into consideration and captured via a stochastic process modeled by a scenario tree. A multistage stochastic optimization (MSO) approach is developed to coordinate the complementary operations of multiple energy resources, by optimizing the mid-term water resource management, generation scheduling, and maintenance scheduling of CHS. The proposed MSO model is formulated as a large-scale mixed-integer linear program that presents significant computational intractability. To address this issue, a tailored Benders decomposition algorithm is developed. Two real-world case studies are conducted to demonstrate the capability and characteristics of the proposed model and algorithm. The computational results show that the proposed MSO model can exploit the flexibility of hydroelectricity to efficiently respond to variable wind and solar power, and reserve water resources for the generation in peak months to reduce the consumption of fossil fuel. Furthermore, the proposed solution approach also exhibits promising computational efficiency when handling large-scale models.

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Research Organization:: Stevens Institute of Technology, Hoboken, NJ (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Water Power Technologies Office

Grant/Contract Number:: EE0008944

OSTI ID:: 2438051

Journal Information:: European Journal of Operational Research, Journal Name: European Journal of Operational Research Journal Issue: 1 Vol. 318; ISSN 0377-2217

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Title: Multistage Stochastic optimization for mid-term integrated generation and maintenance scheduling of cascaded hydroelectric system with renewable energy uncertainty

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