IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i7p1610-d1108222.html
   My bibliography  Save this article

Coordinated Economic Operation of Hydrothermal Units with HVDC Link Based on Lagrange Multipliers

Author

Listed:
  • Ali Ahmad

    (Department of Electrical Engineering, University of Central Punjab, Lahore 54000, Pakistan
    Department of Electrical Engineering, University of Engineering and Technology, Lahore 54890, Pakistan)

  • Syed Abdul Rahman Kashif

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

  • Arslan Ashraf

    (Department of Electrical Engineering, University of Central Punjab, Lahore 54000, Pakistan)

  • Muhammad Majid Gulzar

    (Department of Control & Instrumentation Engineering, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
    Interdisciplinary Research Center for Renewable Energy and Power Systems (IRC-REPS), King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

  • Mohammed Alqahtani

    (Department of Industrial Engineering, King Khalid University, Abha 62529, Saudi Arabia)

  • Muhammad Khalid

    (Interdisciplinary Research Center for Renewable Energy and Power Systems (IRC-REPS), King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
    Electrical Engineering Department, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia
    SDAIA-KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia)

Abstract

Coordinated operation of hydrothermal scheduling with HVDC links considering network constraints becomes a vital issue due to their remote location and recent induction in the existing power system. The nonlinear and complex nature of the problem introduces many variables and constraints which results in a heavy computational burden. A widespread approach for handling these complexities is to reformulate the problem by several linearization methods. In this paper, a Lagrange multipliers-based method is proposed for the solution of hydrothermal economic scheduling including HVDC link. This method solves equality constraint optimization problems. The linear programming approach is embedded with the Lagrange method to consider both equality and inequality constraints. The proposed technique has been used on piecewise linear variables and constraints of the system considering generation, water volume, and line power flow limits. The formulated method efficiently minimizes the operational cost of thermal units and maximizes the utilization of hydro units while meeting all generation, water volume, and the HVDC link constraints. The method was successfully implemented in two scenarios of a case study. In the first scenario, hydrothermal scheduling was performed on the typical network without an HVDC line limit and equal nodal prices were found with minimal thermal generation cost of $278,822.3. In the second scenario, the proposed method optimally dispatches units to meet the HVDC line limit and minimizes thermal generation cost to $279,025.4 while satisfying hydro, thermal, and other operating constraints. Both scenarios are implemented for a 24 h period. The results have been presented to illustrate the performance of the proposed method.

Suggested Citation

  • Ali Ahmad & Syed Abdul Rahman Kashif & Arslan Ashraf & Muhammad Majid Gulzar & Mohammed Alqahtani & Muhammad Khalid, 2023. "Coordinated Economic Operation of Hydrothermal Units with HVDC Link Based on Lagrange Multipliers," Mathematics, MDPI, vol. 11(7), pages 1-19, March.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:7:p:1610-:d:1108222
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/7/1610/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/11/7/1610/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yury V. Kazantsev & Gleb V. Glazyrin & Alexandra I. Khalyasmaa & Sergey M. Shayk & Mihail A. Kuparev, 2022. "Advanced Algorithms in Automatic Generation Control of Hydroelectric Power Plants," Mathematics, MDPI, vol. 10(24), pages 1-18, December.
    2. P. M. R. Bento & S. J. P. S. Mariano & M. R. A. Calado & L. A. F. M. Ferreira, 2020. "A Novel Lagrangian Multiplier Update Algorithm for Short-Term Hydro-Thermal Coordination," Energies, MDPI, vol. 13(24), pages 1-19, December.
    3. Muhammad Majid Gulzar & Sadia Murawwat & Daud Sibtain & Kamal Shahid & Imran Javed & Yonghao Gui, 2022. "Modified Cascaded Controller Design Constructed on Fractional Operator ‘β’ to Mitigate Frequency Fluctuations for Sustainable Operation of Power Systems," Energies, MDPI, vol. 15(20), pages 1-17, October.
    4. Nemati, Mohsen & Braun, Martin & Tenbohlen, Stefan, 2018. "Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming," Applied Energy, Elsevier, vol. 210(C), pages 944-963.
    5. Jian, Jinbao & Pan, Shanshan & Yang, Linfeng, 2019. "Solution for short-term hydrothermal scheduling with a logarithmic size mixed-integer linear programming formulation," Energy, Elsevier, vol. 171(C), pages 770-784.
    6. Shehab Al-Sakkaf & Mahmoud Kassas & Muhammad Khalid & Mohammad A. Abido, 2019. "An Energy Management System for Residential Autonomous DC Microgrid Using Optimized Fuzzy Logic Controller Considering Economic Dispatch," Energies, MDPI, vol. 12(8), pages 1-25, April.
    7. Muhammad Majid Gulzar, 2023. "Maximum Power Point Tracking of a Grid Connected PV Based Fuel Cell System Using Optimal Control Technique," Sustainability, MDPI, vol. 15(5), pages 1-18, February.
    8. Umar Salman & Khalid Khan & Fahad Alismail & Muhammad Khalid, 2021. "Techno-Economic Assessment and Operational Planning of Wind-Battery Distributed Renewable Generation System," Sustainability, MDPI, vol. 13(12), pages 1-24, June.
    9. Khalid, Muhammad & Ahmadi, Abdollah & Savkin, Andrey V. & Agelidis, Vassilios G., 2016. "Minimizing the energy cost for microgrids integrated with renewable energy resources and conventional generation using controlled battery energy storage," Renewable Energy, Elsevier, vol. 97(C), pages 646-655.
    10. Daud Sibtain & Muhammad Majid Gulzar & Kamal Shahid & Imran Javed & Sadia Murawwat & Muhammad Majid Hussain, 2022. "Stability Analysis and Design of Variable Step-Size P&O Algorithm Based on Fuzzy Robust Tracking of MPPT for Standalone/Grid Connected Power System," Sustainability, MDPI, vol. 14(15), pages 1-17, July.
    11. Yousef Alhumaid & Khalid Khan & Fahad Alismail & Muhammad Khalid, 2021. "Multi-Input Nonlinear Programming Based Deterministic Optimization Framework for Evaluating Microgrids with Optimal Renewable-Storage Energy Mix," Sustainability, MDPI, vol. 13(11), pages 1-15, May.
    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. Muhammad Anique Aslam & Syed Abdul Rahman Kashif & Muhammad Majid Gulzar & Mohammed Alqahtani & Muhammad Khalid, 2023. "A Novel Multi Level Dynamic Decomposition Based Coordinated Control of Electric Vehicles in Multimicrogrids," Sustainability, MDPI, vol. 15(16), pages 1-29, August.

    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. Luay Elkhidir & Khalid Khan & Mohammad Al-Muhaini & Muhammad Khalid, 2022. "Enhancing Transient Response and Voltage Stability of Renewable Integrated Microgrids," Sustainability, MDPI, vol. 14(7), pages 1-21, March.
    2. Fontenot, Hannah & Dong, Bing, 2019. "Modeling and control of building-integrated microgrids for optimal energy management – A review," Applied Energy, Elsevier, vol. 254(C).
    3. 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.
    4. Hao, Ran & Lu, Tianguang & Ai, Qian & Wang, Zhe & Wang, Xiaolong, 2020. "Distributed online learning and dynamic robust standby dispatch for networked microgrids," Applied Energy, Elsevier, vol. 274(C).
    5. Marek Krok & Paweł Majewski & Wojciech P. Hunek & Tomasz Feliks, 2022. "Energy Optimization of the Continuous-Time Perfect Control Algorithm," Energies, MDPI, vol. 15(4), pages 1-13, February.
    6. Nie, Qingyun & Zhang, Lihui & Tong, Zihao & Dai, Guyu & Chai, Jianxue, 2022. "Cost compensation method for PEVs participating in dynamic economic dispatch based on carbon trading mechanism," Energy, Elsevier, vol. 239(PA).
    7. Saqib Iqbal & Kamyar Mehran, 2022. "A Day-Ahead Energy Management for Multi MicroGrid System to Optimize the Energy Storage Charge and Grid Dependency—A Comparative Analysis," Energies, MDPI, vol. 15(11), pages 1-19, June.
    8. Muhammad Riaz & Aamir Hanif & Haris Masood & Muhammad Attique Khan & Kamran Afaq & Byeong-Gwon Kang & Yunyoung Nam, 2021. "An Optimal Power Flow Solution of a System Integrated with Renewable Sources Using a Hybrid Optimizer," Sustainability, MDPI, vol. 13(23), pages 1-12, December.
    9. Awol Seid Ebrie & Chunhyun Paik & Yongjoo Chung & Young Jin Kim, 2023. "Environment-Friendly Power Scheduling Based on Deep Contextual Reinforcement Learning," Energies, MDPI, vol. 16(16), pages 1-12, August.
    10. P. M. R. Bento & S. J. P. S. Mariano & M. R. A. Calado & L. A. F. M. Ferreira, 2020. "A Novel Lagrangian Multiplier Update Algorithm for Short-Term Hydro-Thermal Coordination," Energies, MDPI, vol. 13(24), pages 1-19, December.
    11. Clarke, Will Challis & Brear, Michael John & Manzie, Chris, 2020. "Control of an isolated microgrid using hierarchical economic model predictive control," Applied Energy, Elsevier, vol. 280(C).
    12. Abdul Rauf & Mahmoud Kassas & Muhammad Khalid, 2022. "Data-Driven Optimal Battery Storage Sizing for Grid-Connected Hybrid Distributed Generations Considering Solar and Wind Uncertainty," Sustainability, MDPI, vol. 14(17), pages 1-27, September.
    13. Panda, Debashish & Ramteke, Manojkumar, 2019. "Preventive crude oil scheduling under demand uncertainty using structure adapted genetic algorithm," Applied Energy, Elsevier, vol. 235(C), pages 68-82.
    14. Suroso Isnandar & Jonathan F. Simorangkir & Kevin M. Banjar-Nahor & Hendry Timotiyas Paradongan & Nanang Hariyanto, 2024. "A Multiparadigm Approach for Generation Dispatch Optimization in a Regulated Electricity Market towards Clean Energy Transition," Energies, MDPI, vol. 17(15), pages 1-28, August.
    15. Esmeralda López-Garza & René Fernando Domínguez-Cruz & Fernando Martell-Chávez & Iván Salgado-Tránsito, 2022. "Fuzzy Logic and Linear Programming-Based Power Grid-Enhanced Economical Dispatch for Sustainable and Stable Grid Operation in Eastern Mexico," Energies, MDPI, vol. 15(11), pages 1-18, June.
    16. Vitor Fernão Pires & Armando Pires & Armando Cordeiro, 2023. "DC Microgrids: Benefits, Architectures, Perspectives and Challenges," Energies, MDPI, vol. 16(3), pages 1-20, January.
    17. Alla Ndiaye & Fabrice Locment & Alexandre De Bernardinis & Manuela Sechilariu & Eduardo Redondo-Iglesias, 2022. "A Techno-Economic Analysis of Energy Storage Components of Microgrids for Improving Energy Management Strategies," Energies, MDPI, vol. 15(4), pages 1-15, February.
    18. Lei Zhang & Rui Tang, 2023. "Dispatch for a Continuous-Time Microgrid Based on a Modified Differential Evolution Algorithm," Mathematics, MDPI, vol. 11(2), pages 1-21, January.
    19. Aguilar, Diego & Quinones, Jhon J. & Pineda, Luis R. & Ostanek, Jason & Castillo, Luciano, 2024. "Optimal scheduling of renewable energy microgrids: A robust multi-objective approach with machine learning-based probabilistic forecasting," Applied Energy, Elsevier, vol. 369(C).
    20. Fahad R. Albogamy & Ghulam Hafeez & Imran Khan & Sheraz Khan & Hend I. Alkhammash & Faheem Ali & Gul Rukh, 2021. "Efficient Energy Optimization Day-Ahead Energy Forecasting in Smart Grid Considering Demand Response and Microgrids," Sustainability, MDPI, vol. 13(20), pages 1-29, October.

    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:jmathe:v:11:y:2023:i:7:p:1610-:d:1108222. 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.