IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i2p1644-d1035837.html
   My bibliography  Save this article

A New Fast Deterministic Economic Dispatch Method and Statistical Performance Evaluation for the Cascaded Short-Term Hydrothermal Scheduling Problem

Author

Listed:
  • Muhammad Ahmad Iqbal

    (Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan)

  • Muhammad Salman Fakhar

    (Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan)

  • Noor Ul Ain

    (Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan)

  • Ahsen Tahir

    (Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan)

  • Irfan Ahmad Khan

    (Clean and Resilient Energy Systems (CARES) Lab, Electrical and Computer Engineering Department, Texas A & M University, Galveston, TX 77553, USA)

  • Ghulam Abbas

    (Department of Electrical Engineering, The University of Lahore, Lahore 54000, Pakistan)

  • Syed Abdul Rahman Kashif

    (Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan)

Abstract

The Cascaded Short-Term Hydrothermal Scheduling (CSTHTS) problem is a highly non-linear, multi-modal, non-convex, and NP-hard optimization problem that has been solved by conventional and metaheuristic algorithms in the past. As the CSTHTS problem falls under the category of applied operational research, therefore, the work is still on-going to find new algorithms and variants of the existing algorithms that would better approximate the optimal global solution in a shorter computational time. This article proposes a novel deterministic thermal economic dispatch method embedded with the improved Accelerated Particle Swarm Optimization (APSO) algorithm to infinitesimally reduce the Big O time complexity for the standard benchmark test case of the CSTHTS optimization problem. Then, it discusses and presents the importance of performing standard statistical tests to establish the supremacy of one metaheuristic algorithm over the other one in solving the CSTHTS problem. The results obtained are better than the results of the many state-of-the-art algorithms applied to solve the considered test case of the CSTHTS problem in the literature, and the superiority of the improved APSO algorithm has been established statistically using the parametric independent samples t -test and the non-parametric Mann–Whitney U-test over the other metaheuristic algorithms such as particle swarm optimization in solving the chosen test case of the CSTHTS problem.

Suggested Citation

  • Muhammad Ahmad Iqbal & Muhammad Salman Fakhar & Noor Ul Ain & Ahsen Tahir & Irfan Ahmad Khan & Ghulam Abbas & Syed Abdul Rahman Kashif, 2023. "A New Fast Deterministic Economic Dispatch Method and Statistical Performance Evaluation for the Cascaded Short-Term Hydrothermal Scheduling Problem," Sustainability, MDPI, vol. 15(2), pages 1-23, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:2:p:1644-:d:1035837
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/2/1644/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/2/1644/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Patwal, Rituraj Singh & Narang, Nitin & Garg, Harish, 2018. "A novel TVAC-PSO based mutation strategies algorithm for generation scheduling of pumped storage hydrothermal system incorporating solar units," Energy, Elsevier, vol. 142(C), pages 822-837.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gouthamkumar Nadakuditi & Harish Pulluri & Preeti Dahiya & K. S. R. Murthy & P. Srinivasa Varma & Mohit Bajaj & Torki Altameem & Walid El-Shafai & Mostafa M. Fouda, 2023. "Non-Dominated Sorting-Based Hybrid Optimization Technique for Multi-Objective Hydrothermal Scheduling," Energies, MDPI, vol. 16(5), pages 1-25, February.
    2. Ghulam Abbas & Irfan Ahmad Khan & Naveed Ashraf & Muhammad Taskeen Raza & Muhammad Rashad & Raheel Muzzammel, 2023. "On Employing a Constrained Nonlinear Optimizer to Constrained Economic Dispatch Problems," Sustainability, MDPI, vol. 15(13), pages 1-23, June.
    3. Khush Bakht & Syed Abdul Rahman Kashif & Muhammad Salman Fakhar & Irfan Ahmad Khan & Ghulam Abbas, 2023. "Accelerated Particle Swarm Optimization Algorithms Coupled with Analysis of Variance for Intelligent Charging of Plug-in Hybrid Electric Vehicles," Energies, MDPI, vol. 16(7), pages 1-23, April.

    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. Sakthivel, V.P. & Thirumal, K. & Sathya, P.D., 2022. "Short term scheduling of hydrothermal power systems with photovoltaic and pumped storage plants using quasi-oppositional turbulent water flow optimization," Renewable Energy, Elsevier, vol. 191(C), pages 459-492.
    2. Basu, Mousumi, 2022. "Fuel constrained short-term hydrothermal generation scheduling," Energy, Elsevier, vol. 239(PD).
    3. Zhang, Rongda & Wei, Jing & Zhao, Xiaoli & Liu, Yang, 2022. "Economic and environmental benefits of the integration between carbon sequestration and underground gas storage," Energy, Elsevier, vol. 260(C).
    4. Pavan Kumar Singh & Nitin Singh & Richa Negi, 2021. "Short-Term Wind Power Prediction Using Hybrid Auto Regressive Integrated Moving Average Model and Dynamic Particle Swarm Optimization," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 15(2), pages 111-138, April.
    5. Kim, H.J. & Kim, M.K., 2023. "A novel deep learning-based forecasting model optimized by heuristic algorithm for energy management of microgrid," Applied Energy, Elsevier, vol. 332(C).
    6. Hu, Yusha & Li, Jigeng & Hong, Mengna & Ren, Jingzheng & Lin, Ruojue & Liu, Yue & Liu, Mengru & Man, Yi, 2019. "Short term electric load forecasting model and its verification for process industrial enterprises based on hybrid GA-PSO-BPNN algorithm—A case study of papermaking process," Energy, Elsevier, vol. 170(C), pages 1215-1227.
    7. Ahmed A. Ewees & Mohammed A. A. Al-qaness & Laith Abualigah & Diego Oliva & Zakariya Yahya Algamal & Ahmed M. Anter & Rehab Ali Ibrahim & Rania M. Ghoniem & Mohamed Abd Elaziz, 2021. "Boosting Arithmetic Optimization Algorithm with Genetic Algorithm Operators for Feature Selection: Case Study on Cox Proportional Hazards Model," Mathematics, MDPI, vol. 9(18), pages 1-22, September.
    8. Chen, Shuixia & Wang, Jian-qiang & Zhang, Hong-yu, 2019. "A hybrid PSO-SVM model based on clustering algorithm for short-term atmospheric pollutant concentration forecasting," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 41-54.
    9. Anand, Himanshu & Narang, Nitin & Dhillon, J.S., 2019. "Multi-objective combined heat and power unit commitment using particle swarm optimization," Energy, Elsevier, vol. 172(C), pages 794-807.
    10. Feng, Chen & Zheng, Yuan & Li, Chaoshun & Mai, Zijun & Wu, Wei & Chen, Huixiang, 2021. "Cost advantage of adjustable-speed pumped storage unit for daily operation in distributed hybrid system," Renewable Energy, Elsevier, vol. 176(C), pages 1-10.
    11. Chun-Yao Lee & Guang-Lin Zhuo, 2021. "A Hybrid Whale Optimization Algorithm for Global Optimization," Mathematics, MDPI, vol. 9(13), pages 1-19, June.
    12. Tang, Xiongmin & Li, Zhengshuo & Xu, Xuancong & Zeng, Zhijun & Jiang, Tianhong & Fang, Wenrui & Meng, Anbo, 2022. "Multi-objective economic emission dispatch based on an extended crisscross search optimization algorithm," Energy, Elsevier, vol. 244(PA).
    13. Pavan Kumar Singh & Nitin Singh & Richa Negi, 2021. "Short-Term Wind Power Prediction Using Hybrid Auto Regressive Integrated Moving Average Model and Dynamic Particle Swarm Optimization," International Journal of Cognitive Informatics and Natural Intelligence (IJCINI), IGI Global, vol. 15(2), pages 124-151, April.
    14. Fazli Wahid & Rozaida Ghazali & Lokman Hakim Ismail & Ali M. Algarwi Aseere, 2023. "An Optimal Neural Network for Hourly and Daily Energy Consumption Prediction in Buildings," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 14(1), pages 1-13, January.
    15. Yang, Xu & Li, Hongru, 2023. "Multi-sample learning particle swarm optimization with adaptive crossover operation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 208(C), pages 246-282.
    16. Patwal, Rituraj Singh & Narang, Nitin, 2020. "Multi-objective generation scheduling of integrated energy system using fuzzy based surrogate worth trade-off approach," Renewable Energy, Elsevier, vol. 156(C), pages 864-882.
    17. Yin, Hao & Wu, Fei & Meng, Xin & Lin, Yicheng & Fan, Jingmin & Meng, Anbo, 2020. "Crisscross optimization based short-term hydrothermal generation scheduling with cascaded reservoirs," Energy, Elsevier, vol. 203(C).
    18. Zaher Mundher Yaseen & Mohammad Ehteram & Md. Shabbir Hossain & Chow Ming Fai & Suhana Binti Koting & Nuruol Syuhadaa Mohd & Wan Zurina Binti Jaafar & Haitham Abdulmohsin Afan & Lai Sai Hin & Nuratiah, 2019. "A Novel Hybrid Evolutionary Data-Intelligence Algorithm for Irrigation and Power Production Management: Application to Multi-Purpose Reservoir Systems," Sustainability, MDPI, vol. 11(7), pages 1-28, April.
    19. Yahia, Zakaria & Pradhan, Anup, 2020. "Simultaneous and sequential stochastic optimization approaches for pumped storage plant scheduling with random breakdowns," Energy, Elsevier, vol. 204(C).
    20. Daneshvar, Mohammadreza & Mohammadi-Ivatloo, Behnam & Zare, Kazem & Asadi, Somayeh, 2020. "Two-stage stochastic programming model for optimal scheduling of the wind-thermal-hydropower-pumped storage system considering the flexibility assessment," Energy, Elsevier, vol. 193(C).

    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:15:y:2023:i:2:p:1644-:d:1035837. 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.