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A Review of Optimization Algorithms in Solving Hydro Generation Scheduling Problems

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  • Ali Thaeer Hammid

    (Computer Engineering Techniques Department, Faculty of Information Technology, Imam Ja’afar Al-Sadiq University, Baghdad 10012, Iraq
    Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang, Pahang, Pekan 26600, Malaysia)

  • Omar I. Awad

    (State Key Laboratory of Automotive Safety and Energy, Tsinghua University, Beijing 100084, China)

  • Mohd Herwan Sulaiman

    (Faculty of Electrical and Electronics Engineering, Universiti Malaysia Pahang, Pahang, Pekan 26600, Malaysia)

  • Saraswathy Shamini Gunasekaran

    (College of Computing and Informatics, Universiti Tenaga Nasional, Selangor, Kajang 43000, Malaysia)

  • Salama A. Mostafa

    (Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, Johor, Batu Pahat 86400, Malaysia)

  • Nallapaneni Manoj Kumar

    (School of Energy and Environment, City University of Hong Kong, Kowloon, Hong Kong, China)

  • Bashar Ahmad Khalaf

    (College of Basic Education, University of Diyala, Diyala 32001, Iraq)

  • Yasir Amer Al-Jawhar

    (Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Batu Pahat 86400, Malaysia
    Iraqi Ministry of Communications (M.O.C.), Mamoon, Baghdad 10012, Iraq)

  • Raed Abdulkareem Abdulhasan

    (Faculty of Electrical and Electronic Engineering, Universiti Tun Hussein Onn Malaysia, Johor, Batu Pahat 86400, Malaysia)

Abstract

The optimal generation scheduling (OGS) of hydropower units holds an important position in electric power systems, which is significantly investigated as a research issue. Hydropower has a slight social and ecological effect when compared with other types of sustainable power source. The target of long-, mid-, and short-term hydro scheduling (LMSTHS) problems is to optimize the power generation schedule of the accessible hydropower units, which generate maximum energy by utilizing the available potential during a specific period. Numerous traditional optimization procedures are first presented for making a solution to the LMSTHS problem. Lately, various optimization approaches, which have been assigned as a procedure based on experiences, have been executed to get the optimal solution of the generation scheduling of hydro systems. This article offers a complete survey of the implementation of various methods to get the OGS of hydro systems by examining the executed methods from various perspectives. Optimal solutions obtained by a collection of meta-heuristic optimization methods for various experience cases are established, and the presented methods are compared according to the case study, limitation of parameters, optimization techniques, and consideration of the main goal. Previous studies are mostly focused on hydro scheduling that is based on a reservoir of hydropower plants. Future study aspects are also considered, which are presented as the key issue surrounding the LMSTHS problem.

Suggested Citation

  • Ali Thaeer Hammid & Omar I. Awad & Mohd Herwan Sulaiman & Saraswathy Shamini Gunasekaran & Salama A. Mostafa & Nallapaneni Manoj Kumar & Bashar Ahmad Khalaf & Yasir Amer Al-Jawhar & Raed Abdulkareem A, 2020. "A Review of Optimization Algorithms in Solving Hydro Generation Scheduling Problems," Energies, MDPI, vol. 13(11), pages 1-21, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2787-:d:365787
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    Cited by:

    1. Zhong, Zhiming & Fan, Neng & Wu, Lei, 2024. "Multistage Stochastic optimization for mid-term integrated generation and maintenance scheduling of cascaded hydroelectric system with renewable energy uncertainty," European Journal of Operational Research, Elsevier, vol. 318(1), pages 179-199.
    2. Yoan Villeneuve & Sara Séguin & Abdellah Chehri, 2023. "AI-Based Scheduling Models, Optimization, and Prediction for Hydropower Generation: Opportunities, Issues, and Future Directions," Energies, MDPI, vol. 16(8), pages 1-27, April.
    3. Nallapaneni Manoj Kumar & Aneesh A. Chand & Maria Malvoni & Kushal A. Prasad & Kabir A. Mamun & F.R. Islam & Shauhrat S. Chopra, 2020. "Distributed Energy Resources and the Application of AI, IoT, and Blockchain in Smart Grids," Energies, MDPI, vol. 13(21), pages 1-42, November.
    4. Panagiotis I. Bakanos & Konstantinos L. Katsifarakis, 2020. "Optimizing Current and Future Hydroelectric Energy Production and Water Uses of the Complex Multi-Reservoir System in the Aliakmon River, Greece," Energies, MDPI, vol. 13(24), pages 1-23, December.
    5. Sunil Kumar Mishra & Amitkumar V. Jha & Bhargav Appasani & Nicu Bizon & Phatiphat Thounthong & Pongsiri Mungporn, 2023. "Ocean Wave Energy Control Using Aquila Optimization Technique," Energies, MDPI, vol. 16(11), pages 1-21, June.
    6. David Lucas dos Santos Abreu & Erlon Cristian Finardi, 2022. "Continuous Piecewise Linear Approximation of Plant-Based Hydro Production Function for Generation Scheduling Problems," Energies, MDPI, vol. 15(5), pages 1-23, February.
    7. Cui Zheyuan & Ali Thaeer Hammid & Ali Noori Kareem & Mingxin Jiang & Muamer N. Mohammed & Nallapaneni Manoj Kumar, 2021. "A Rigid Cuckoo Search Algorithm for Solving Short-Term Hydrothermal Scheduling Problem," Sustainability, MDPI, vol. 13(8), pages 1-14, April.
    8. Avesani, Diego & Zanfei, Ariele & Di Marco, Nicola & Galletti, Andrea & Ravazzolo, Francesco & Righetti, Maurizio & Majone, Bruno, 2022. "Short-term hydropower optimization driven by innovative time-adapting econometric model," Applied Energy, Elsevier, vol. 310(C).

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