IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v201y2020ics036054422030774x.html
   My bibliography  Save this article

Multi-area multi-source automatic generation control in deregulated power system

Author

Listed:
  • Ghasemi-Marzbali, Ali

Abstract

The restructuring of the interconnected power systems under the Independent System Operator (ISO) managing to get an unforced and independent electricity market is the ultimate goal for the federal energy regulatory commission (FERC). Therefore, it is converted a common topic of general studies for the period of last years. Therefore, FERC presents various ancillary services such as frequency control based on load following under automatic generation control (AGC) in the deregulated environment. This paper presents a multi-area multi-source AGC in a deregulated power system that operates under deregulation platform on the bilateral policy having the thermal power plants with reheat turbine, gas, diesel and hydro power units in the form of a four-area interconnected power system with various of the possible contracts. Since damping of frequency oscillation under unexpected faults is very important to grantee system stability, therefore, the optimal tuning of proportional-integral-derivative controller with low pass filter is converted a single objective function optimization problem considering system parameter’s uncertainties and eigenvalues. The important benefit of this approach is its high insensitivity to large load changes and disturbances in the existence of unit parameter discrepancy and model’s nonlinearities. To cope with the disadvantage of the conventional controllers in the AGC power system, the proposed optimization problem is solved by a new modified virus colony search (MVCS). Simulations are carried out for a large-scale AGC power system under the deregulated regime with the various possible contracts and load distributions.

Suggested Citation

  • Ghasemi-Marzbali, Ali, 2020. "Multi-area multi-source automatic generation control in deregulated power system," Energy, Elsevier, vol. 201(C).
    • Handle: RePEc:eee:energy:v:201:y:2020:i:c:s036054422030774x
    DOI: 10.1016/j.energy.2020.117667
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S036054422030774X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.117667?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ghasemi, A. & Shayeghi, H. & Moradzadeh, M. & Nooshyar, M., 2016. "A novel hybrid algorithm for electricity price and load forecasting in smart grids with demand-side management," Applied Energy, Elsevier, vol. 177(C), pages 40-59.
    2. Brown, David P., 2018. "Capacity payment mechanisms and investment incentives in restructured electricity markets," Energy Economics, Elsevier, vol. 74(C), pages 131-142.
    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. Ghasemi-Marzbali, Ali & Shafiei, Mohammad & Ahmadiahangar, Roya, 2023. "Day-ahead economical planning of multi-vector energy district considering demand response program," Applied Energy, Elsevier, vol. 332(C).
    2. Dillip Kumar Mishra & Daria Złotecka & Li Li, 2022. "Significance of SMES Devices for Power System Frequency Regulation Scheme considering Distributed Energy Resources in a Deregulated Environment," Energies, MDPI, vol. 15(5), pages 1-32, February.
    3. Yin, Linfei & Zhang, Bin, 2021. "Time series generative adversarial network controller for long-term smart generation control of microgrids," Applied Energy, Elsevier, vol. 281(C).
    4. Chandan Kumar Shiva & Vedik Basetti & Sumit Verma, 2022. "Quasi-oppositional atom search optimization algorithm for automatic generation control of deregulated power systems," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(4), pages 1845-1863, August.
    5. Ajay Kumar & Deepak Kumar Gupta & Sriparna Roy Ghatak & Bhargav Appasani & Nicu Bizon & Phatiphat Thounthong, 2022. "A Novel Improved GSA-BPSO Driven PID Controller for Load Frequency Control of Multi-Source Deregulated Power System," Mathematics, MDPI, vol. 10(18), pages 1-41, September.
    6. Hossam Hassan Ali & Ahmed Fathy & Abdullah M. Al-Shaalan & Ahmed M. Kassem & Hassan M. H. Farh & Abdullrahman A. Al-Shamma’a & Hossam A. Gabbar, 2021. "A Novel Sooty Terns Algorithm for Deregulated MPC-LFC Installed in Multi-Interconnected System with Renewable Energy Plants," Energies, MDPI, vol. 14(17), pages 1-27, August.
    7. Kaleem Ullah & Abdul Basit & Zahid Ullah & Sheraz Aslam & Herodotos Herodotou, 2021. "Automatic Generation Control Strategies in Conventional and Modern Power Systems: A Comprehensive Overview," Energies, MDPI, vol. 14(9), pages 1-43, April.
    8. Yin, Linfei & Luo, Shikui & Ma, Chenxiao, 2021. "Expandable depth and width adaptive dynamic programming for economic smart generation control of smart grids," Energy, Elsevier, vol. 232(C).

    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. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
    2. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Prediction and explanation of the formation of the Spanish day-ahead electricity price through machine learning regression," Applied Energy, Elsevier, vol. 239(C), pages 610-625.
    3. Zhou, Chenyu & Shen, Yun & Wu, Haixin & Wang, Jianhong, 2022. "Using fractional discrete Verhulst model to forecast Fujian's electricity consumption in China," Energy, Elsevier, vol. 255(C).
    4. Bajo-Buenestado, Raúl, 2017. "Welfare implications of capacity payments in a price-capped electricity sector: A case study of the Texas market (ERCOT)," Energy Economics, Elsevier, vol. 64(C), pages 272-285.
    5. Ethem Çanakoğlu & Esra Adıyeke, 2020. "Comparison of Electricity Spot Price Modelling and Risk Management Applications," Energies, MDPI, vol. 13(18), pages 1-22, September.
    6. Zhang, Wenbin & Tian, Lixin & Wang, Minggang & Zhen, Zaili & Fang, Guochang, 2016. "The evolution model of electricity market on the stable development in China and its dynamic analysis," Energy, Elsevier, vol. 114(C), pages 344-359.
    7. Steinhäuser, J. Micha & Eisenack, Klaus, 2020. "How market design shapes the spatial distribution of power plant curtailment costs," Energy Policy, Elsevier, vol. 144(C).
    8. Jayadev, Gopika & Leibowicz, Benjamin D. & Kutanoglu, Erhan, 2020. "U.S. electricity infrastructure of the future: Generation and transmission pathways through 2050," Applied Energy, Elsevier, vol. 260(C).
    9. 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.
    10. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
    11. Yang, Wendong & Wang, Jianzhou & Niu, Tong & Du, Pei, 2019. "A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting," Applied Energy, Elsevier, vol. 235(C), pages 1205-1225.
    12. Klaus Eisenack & Mathias Mier, 2019. "Peak-load pricing with different types of dispatchability," Journal of Regulatory Economics, Springer, vol. 56(2), pages 105-124, December.
    13. Yu, Mengmeng & Hong, Seung Ho, 2017. "Incentive-based demand response considering hierarchical electricity market: A Stackelberg game approach," Applied Energy, Elsevier, vol. 203(C), pages 267-279.
    14. Cui, Jia & Yu, Renzhe & Zhao, Dongbo & Yang, Junyou & Ge, Weichun & Zhou, Xiaoming, 2019. "Intelligent load pattern modeling and denoising using improved variational mode decomposition for various calendar periods," Applied Energy, Elsevier, vol. 247(C), pages 480-491.
    15. Lee, Eunjung & Lee, Kyungeun & Lee, Hyoseop & Kim, Euncheol & Rhee, Wonjong, 2019. "Defining virtual control group to improve customer baseline load calculation of residential demand response," Applied Energy, Elsevier, vol. 250(C), pages 946-958.
    16. Nasir Ayub & Muhammad Irfan & Muhammad Awais & Usman Ali & Tariq Ali & Mohammed Hamdi & Abdullah Alghamdi & Fazal Muhammad, 2020. "Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler," Energies, MDPI, vol. 13(19), pages 1-21, October.
    17. Mbungu, Nsilulu T. & Bansal, Ramesh C. & Naidoo, Raj M. & Bettayeb, Maamar & Siti, Mukwanga W. & Bipath, Minnesh, 2020. "A dynamic energy management system using smart metering," Applied Energy, Elsevier, vol. 280(C).
    18. Sirin, Selahattin Murat & Erten, Ibrahim, 2022. "Price spikes, temporary price caps, and welfare effects of regulatory interventions on wholesale electricity markets," Energy Policy, Elsevier, vol. 163(C).
    19. Zhang, Jinliang & Siya, Wang & Zhongfu, Tan & Anli, Sun, 2023. "An improved hybrid model for short term power load prediction," Energy, Elsevier, vol. 268(C).
    20. Sana Mujeeb & Nadeem Javaid & Manzoor Ilahi & Zahid Wadud & Farruh Ishmanov & Muhammad Khalil Afzal, 2019. "Deep Long Short-Term Memory: A New Price and Load Forecasting Scheme for Big Data in Smart Cities," Sustainability, MDPI, vol. 11(4), pages 1-29, February.

    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:eee:energy:v:201:y:2020:i:c:s036054422030774x. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.