IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i7p2522-d782805.html
   My bibliography  Save this article

Appraising the Optimal Power Flow and Generation Capacity in Existing Power Grid Topology with Increase in Energy Demand

Author

Listed:
  • Gideon Ude Nnachi

    (Department of Electrical Engineering, Faculty of Engineering and the Built Environment, Tshwane University of Technology Private, Bag X680, Pretoria 0001, South Africa
    These authors contributed equally to this work.)

  • Yskandar Hamam

    (Department of Electrical Engineering, Faculty of Engineering and the Built Environment, Tshwane University of Technology Private, Bag X680, Pretoria 0001, South Africa
    These authors contributed equally to this work.)

  • Coneth Graham Richards

    (Department of Electrical Engineering, Faculty of Engineering and the Built Environment, Tshwane University of Technology Private, Bag X680, Pretoria 0001, South Africa
    These authors contributed equally to this work.)

Abstract

Several socioeconomic factors such as industrialization, population growth, evolution of modern technologies, urbanization and other social activities do heavily influence the increase in energy demand. A thorough understanding of the effects of energy demand to power grid is highly essential for effective planning and operation of a power system network in terms of the available generation and transmission line capacities. This paper presents an optimal power flow (OPF) with the aim to determine the exact nodes through which the network capacities can be increased. The problem is formulated as a Direct Current (DC) OPF model, which is a linearized version of an Alternating Current (AC) OPF model. The DC-OPF model was solved as a single period OPF problem. The model was tested in several case studies using the topology of the IEEE test systems, and the computation speeds of the different cases were compared. The results suggested dual variables of the problem’s constraints as an extra tool for the network designer to see where to increase the network capacities.

Suggested Citation

  • Gideon Ude Nnachi & Yskandar Hamam & Coneth Graham Richards, 2022. "Appraising the Optimal Power Flow and Generation Capacity in Existing Power Grid Topology with Increase in Energy Demand," Energies, MDPI, vol. 15(7), pages 1-23, March.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:7:p:2522-:d:782805
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/7/2522/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/7/2522/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hua, Haochen & Qin, Yuchao & Hao, Chuantong & Cao, Junwei, 2019. "Optimal energy management strategies for energy Internet via deep reinforcement learning approach," Applied Energy, Elsevier, vol. 239(C), pages 598-609.
    2. Singh, Antriksh & Willi, David & Chokani, Ndaona & Abhari, Reza S., 2014. "Optimal power flow analysis of a Switzerland׳s transmission system for long-term capacity planning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 596-607.
    3. Carleton Coffrin & Pascal Van Hentenryck, 2014. "A Linear-Programming Approximation of AC Power Flows," INFORMS Journal on Computing, INFORMS, vol. 26(4), pages 718-734, November.
    4. Yang, Ting & Zhao, Liyuan & Li, Wei & Zomaya, Albert Y., 2021. "Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning," Energy, Elsevier, vol. 235(C).
    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. Zhu, Ziqing & Hu, Ze & Chan, Ka Wing & Bu, Siqi & Zhou, Bin & Xia, Shiwei, 2023. "Reinforcement learning in deregulated energy market: A comprehensive review," Applied Energy, Elsevier, vol. 329(C).
    2. Zhang, Bin & Wu, Xuewei & Ghias, Amer M.Y.M. & Chen, Zhe, 2023. "Coordinated carbon capture systems and power-to-gas dynamic economic energy dispatch strategy for electricity–gas coupled systems considering system uncertainty: An improved soft actor–critic approach," Energy, Elsevier, vol. 271(C).
    3. Harri Aaltonen & Seppo Sierla & Ville Kyrki & Mahdi Pourakbari-Kasmaei & Valeriy Vyatkin, 2022. "Bidding a Battery on Electricity Markets and Minimizing Battery Aging Costs: A Reinforcement Learning Approach," Energies, MDPI, vol. 15(14), pages 1-19, July.
    4. Zhou, Yanting & Ma, Zhongjing & Zhang, Jinhui & Zou, Suli, 2022. "Data-driven stochastic energy management of multi energy system using deep reinforcement learning," Energy, Elsevier, vol. 261(PA).
    5. Xue, Lin & Wang, Jianxue & Zhang, Yao & Yong, Weizhen & Qi, Jie & Li, Haotian, 2023. "Model-data-event based community integrated energy system low-carbon economic scheduling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 182(C).
    6. Sander Claeys & Marta Vanin & Frederik Geth & Geert Deconinck, 2021. "Applications of optimization models for electricity distribution networks," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 10(5), September.
    7. Josif Figueroa & Kush Bubbar & Greg Young-Morris, 2024. "An Open-Source Tool for Composite Power System Reliability Assessment in Julia™," Energies, MDPI, vol. 17(20), pages 1-15, October.
    8. Yi, Zonggen & Luo, Yusheng & Westover, Tyler & Katikaneni, Sravya & Ponkiya, Binaka & Sah, Suba & Mahmud, Sadab & Raker, David & Javaid, Ahmad & Heben, Michael J. & Khanna, Raghav, 2022. "Deep reinforcement learning based optimization for a tightly coupled nuclear renewable integrated energy system," Applied Energy, Elsevier, vol. 328(C).
    9. Zhu, Jiaoyiling & Hu, Weihao & Xu, Xiao & Liu, Haoming & Pan, Li & Fan, Haoyang & Zhang, Zhenyuan & Chen, Zhe, 2022. "Optimal scheduling of a wind energy dominated distribution network via a deep reinforcement learning approach," Renewable Energy, Elsevier, vol. 201(P1), pages 792-801.
    10. Neumann, Fabian & Hagenmeyer, Veit & Brown, Tom, 2022. "Assessments of linear power flow and transmission loss approximations in coordinated capacity expansion problems," Applied Energy, Elsevier, vol. 314(C).
    11. Zhang, Yijie & Ma, Tao & Elia Campana, Pietro & Yamaguchi, Yohei & Dai, Yanjun, 2020. "A techno-economic sizing method for grid-connected household photovoltaic battery systems," Applied Energy, Elsevier, vol. 269(C).
    12. Ahmad, Tanveer & Chen, Huanxin, 2019. "Deep learning for multi-scale smart energy forecasting," Energy, Elsevier, vol. 175(C), pages 98-112.
    13. Zeyue Sun & Mohsen Eskandari & Chaoran Zheng & Ming Li, 2022. "Handling Computation Hardness and Time Complexity Issue of Battery Energy Storage Scheduling in Microgrids by Deep Reinforcement Learning," Energies, MDPI, vol. 16(1), pages 1-20, December.
    14. Kandasamy, Jeevitha & Ramachandran, Rajeswari & Veerasamy, Veerapandiyan & Irudayaraj, Andrew Xavier Raj, 2024. "Distributed leader-follower based adaptive consensus control for networked microgrids," Applied Energy, Elsevier, vol. 353(PA).
    15. Fathy, Ahmed, 2023. "Bald eagle search optimizer-based energy management strategy for microgrid with renewable sources and electric vehicles," Applied Energy, Elsevier, vol. 334(C).
    16. Wu, Yuankai & Tan, Huachun & Peng, Jiankun & Zhang, Hailong & He, Hongwen, 2019. "Deep reinforcement learning of energy management with continuous control strategy and traffic information for a series-parallel plug-in hybrid electric bus," Applied Energy, Elsevier, vol. 247(C), pages 454-466.
    17. Xiaowei Fan & Yongtao Chen & Ruimiao Wang & Jiaxin Luo & Jingang Wang & Decheng Cao, 2023. "Integrated Energy Microgrid Economic Dispatch Optimization Model Based on Information-Gap Decision Theory," Energies, MDPI, vol. 16(8), pages 1-24, April.
    18. Sarid, A. & Tzur, M., 2018. "The multi-scale generation and transmission expansion model," Energy, Elsevier, vol. 148(C), pages 977-991.
    19. Pattupara, Rajesh & Kannan, Ramachandran, 2016. "Alternative low-carbon electricity pathways in Switzerland and it’s neighbouring countries under a nuclear phase-out scenario," Applied Energy, Elsevier, vol. 172(C), pages 152-168.
    20. Saber, Hossein & Mazaheri, Hesam & Ranjbar, Hossein & Moeini-Aghtaie, Moein & Lehtonen, Matti, 2021. "Utilization of in-pipe hydropower renewable energy technology and energy storage systems in mountainous distribution networks," Renewable Energy, Elsevier, vol. 172(C), pages 789-801.

    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:jeners:v:15:y:2022:i:7:p:2522-:d:782805. 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.