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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
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    References listed on IDEAS

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    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)

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