IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i11p5900-d561020.html
   My bibliography  Save this article

Optimizing the Operation Release Policy Using Charged System Search Algorithm: A Case Study of Klang Gates Dam, Malaysia

Author

Listed:
  • Sarmad Dashti Latif

    (Civil Engineering Department, College of Engineering, Komar University of Science and Technology, Sulaimany 46001, Kurdistan Region, Iraq)

  • Suzlyana Marhain

    (Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN), Kajang 43000, Malaysia)

  • Md Shabbir Hossain

    (School of Energy, Geoscience, Infrastructure, and Society, Heriot-Watt University, Putrajaya 62200, Malaysia)

  • Ali Najah Ahmed

    (Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional (UNITEN), Kajang 43000, Malaysia)

  • Mohsen Sherif

    (Civil and Environmental Engineering Department, College of Engineering, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates
    National Water and Energy Center, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates)

  • Ahmed Sefelnasr

    (National Water and Energy Center, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates)

  • Ahmed El-Shafie

    (Department of Civil Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

Abstract

In planning and managing water resources, the implementation of optimization techniques in the operation of reservoirs has become an important focus. An optimal reservoir operating policy should take into consideration the uncertainty associated with uncontrolled reservoir inflows. The charged system search (CSS) algorithm model is developed in the present study to achieve optimum operating policy for the current reservoir. The aim of the model is to minimize the cost of system performance, which is the sum of square deviations from the distinction between the release of the target and the actual demand. The decision variable is the release of a reservoir with an initial volume of storage, reservoir inflow, and final volume of storage for a given period. Historical rainfall data is used to approximate the inflow volume. The charged system search (CSS) is developed by utilizing a spreadsheet model to simulate and perform optimization. The model gives the steady-state probabilities of reservoir storage as output. The model is applied to the reservoir of Klang Gates for the development of an optimal reservoir operating policy. The steady-state optimal operating system is used in this model.

Suggested Citation

  • Sarmad Dashti Latif & Suzlyana Marhain & Md Shabbir Hossain & Ali Najah Ahmed & Mohsen Sherif & Ahmed Sefelnasr & Ahmed El-Shafie, 2021. "Optimizing the Operation Release Policy Using Charged System Search Algorithm: A Case Study of Klang Gates Dam, Malaysia," Sustainability, MDPI, vol. 13(11), pages 1-19, May.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:11:p:5900-:d:561020
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/11/5900/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/13/11/5900/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sabah Saadi Fayaed & Seef Saadi Fiyadh & Wong Jee Khai & Ali Najah Ahmed & Haitham Abdulmohsin Afan & Rusul Khaleel Ibrahim & Chow Ming Fai & Suhana Koting & Nuruol Syuhadaa Mohd & Wan Zurina Binti Ja, 2019. "Improving Dam and Reservoir Operation Rules Using Stochastic Dynamic Programming and Artificial Neural Network Integration Model," Sustainability, MDPI, vol. 11(19), pages 1-17, September.
    2. Hojat Karami & Saeed Farzin & Aylin Jahangiri & Mohammad Ehteram & Ozgur Kisi & Ahmed El-Shafie, 2019. "Multi-Reservoir System Optimization Based on Hybrid Gravitational Algorithm to Minimize Water-Supply Deficiencies," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(8), pages 2741-2760, June.
    3. Majid Mohammadi & Saeed Farzin & Sayed-Farhad Mousavi & Hojat Karami, 2019. "Investigation of a New Hybrid Optimization Algorithm Performance in the Optimal Operation of Multi-Reservoir Benchmark Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(14), pages 4767-4782, November.
    4. Sedigheh Mohamadi & Saad Sh. Sammen & Fatemeh Panahi & Mohammad Ehteram & Ozgur Kisi & Amir Mosavi & Ali Najah Ahmed & Ahmed El-Shafie & Nadhir Al-Ansari, 2020. "Zoning map for drought prediction using integrated machine learning models with a nomadic people optimization algorithm," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 104(1), pages 537-579, October.
    5. McCartney, Matthew, 2007. "Decision support systems for large dam planning and operation in Africa," IWMI Working Papers H040225, International Water Management Institute.
    6. Michelle Sapitang & Wanie M. Ridwan & Khairul Faizal Kushiar & Ali Najah Ahmed & Ahmed El-Shafie, 2020. "Machine Learning Application in Reservoir Water Level Forecasting for Sustainable Hydropower Generation Strategy," Sustainability, MDPI, vol. 12(15), pages 1-19, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Lucia de Strasser, 2017. "Calling for Nexus Thinking in Africa’s Energy Planning," ESP: Energy Scenarios and Policy 263161, Fondazione Eni Enrico Mattei (FEEM).
    2. Ruffeis, D. & Loiskandl, W. & Spendlingwimmer, R. & Schonerklee, M. & Awulachew, Seleshi Bekele & Boelee, Eline & Wallner, K., 2008. "Environmental impact analysis of two large scale irrigation schemes in Ethiopia," Conference Papers h044103, International Water Management Institute.
    3. Željka Brkić & Mladen Kuhta, 2022. "Lake Level Evolution of the Largest Freshwater Lake on the Mediterranean Islands through Drought Analysis and Machine Learning," Sustainability, MDPI, vol. 14(16), pages 1-28, August.
    4. Hao Wang & Chen Peng & Bolin Liao & Xinwei Cao & Shuai Li, 2023. "Wind Power Forecasting Based on WaveNet and Multitask Learning," Sustainability, MDPI, vol. 15(14), pages 1-22, July.
    5. Muhammad Ali Musarat & Wesam Salah Alaloul & Muhammad Babar Ali Rabbani & Mujahid Ali & Muhammad Altaf & Roman Fediuk & Nikolai Vatin & Sergey Klyuev & Hamna Bukhari & Alishba Sadiq & Waqas Rafiq & Wa, 2021. "Kabul River Flow Prediction Using Automated ARIMA Forecasting: A Machine Learning Approach," Sustainability, MDPI, vol. 13(19), pages 1-26, September.
    6. Dongyang Han & Guanghui Lv & Xuemin He, 2022. "A Research on the Ecological Operation of Reservoirs Based on the Indicators of Hydrological Alteration," Sustainability, MDPI, vol. 14(11), pages 1-13, May.
    7. Geoffrey Gasore & Arthur Santos & Etienne Ntagwirumugara & Daniel Zimmerle, 2023. "Sizing of Small Hydropower Plants for Highly Variable Flows in Tropical Run-of-River Installations: A Case Study of the Sebeya River," Energies, MDPI, vol. 16(3), pages 1-14, January.
    8. Alemayehu, Tadesse & McCartney, Matthew & Kebede, S., 2009. "Modelling to evaluate the water resource implications of planned infrastructure development in the Lake Tana sub-basin, Ethiopia," Conference Papers h042207, International Water Management Institute.
    9. Noman Khan & Fath U Min Ullah & Ijaz Ul Haq & Samee Ullah Khan & Mi Young Lee & Sung Wook Baik, 2021. "AB-Net: A Novel Deep Learning Assisted Framework for Renewable Energy Generation Forecasting," Mathematics, MDPI, vol. 9(19), pages 1-18, October.
    10. R. Hadded & I. Nouiri & O. Alshihabi & J. Maßmann & M. Huber & A. Laghouane & H. Yahiaoui & J. Tarhouni, 2013. "A Decision Support System to Manage the Groundwater of the Zeuss Koutine Aquifer Using the WEAP-MODFLOW Framework," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 1981-2000, May.
    11. Mohammad Ehteram & Ali Najah Ahmed & Ming Fai Chow & Sarmad Dashti Latif & Kwok-wing Chau & Kai Lun Chong & Ahmed El-Shafie, 2023. "Optimal operation of hydropower reservoirs under climate change," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(10), pages 10627-10659, October.
    12. Okan Mert Katipoğlu, 2023. "Prediction of Streamflow Drought Index for Short-Term Hydrological Drought in the Semi-Arid Yesilirmak Basin Using Wavelet Transform and Artificial Intelligence Techniques," Sustainability, MDPI, vol. 15(2), pages 1-24, January.
    13. Behrang Beiranvand & Parisa-Sadat Ashofteh, 2023. "A Systematic Review of Optimization of Dams Reservoir Operation Using the Meta-heuristic Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3457-3526, July.
    14. Mehrabi, Ahmad & Heidarpour, Manouchehr & Safavi, Hamid R. & Rezaei, Farshad, 2021. "Assessment of the optimized scenarios for economic-environmental conjunctive water use utilizing gravitational search algorithm," Agricultural Water Management, Elsevier, vol. 246(C).
    15. Salah L. Zubaidi & Sandra Ortega-Martorell & Patryk Kot & Rafid M. Alkhaddar & Mawada Abdellatif & Sadik K. Gharghan & Maytham S. Ahmed & Khalid Hashim, 2020. "A Method for Predicting Long-Term Municipal Water Demands Under Climate Change," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(3), pages 1265-1279, February.
    16. Ruffeis, D. & Loiskandl, W. & Spendlingwimmer, R. & Schonerklee, M. & Awulachew, Seleshi Bekele & Boelee, Eline & Wallner, K., 2008. "Environmental impact analysis of two large scale irrigation schemes in Ethiopia," IWMI Conference Proceedings 246407, International Water Management Institute.
    17. Rosalva Mendoza Ramírez & Maritza Liliana Arganis Juárez & Ramón Domínguez Mora & Luis Daniel Padilla Morales & Óscar Arturo Fuentes Mariles & Alejandro Mendoza Reséndiz & Eliseo Carrizosa Elizondo & , 2021. "Operation Policies through Dynamic Programming and Genetic Algorithms, for a Reservoir with Irrigation and Water Supply Uses," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(5), pages 1573-1586, March.
    18. 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.
    19. Dong-Her Shih & Ching-Hsien Liao & Ting-Wei Wu & Huan-Shuo Chang & Ming-Hung Shih, 2022. "WSI: A New Early Warning Water Survival Index for the Domestic Water Demand," Mathematics, MDPI, vol. 10(23), pages 1-19, November.
    20. Shamsi, Meisam & Babazadeh, Reza, 2022. "Estimation and prediction of Jatropha cultivation areas in China and India," Renewable Energy, Elsevier, vol. 183(C), pages 548-560.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:11:p:5900-:d:561020. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.