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Transmission Expansion Planning Using TLBO Algorithm in the Presence of Demand Response Resources

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  • Amir Sadegh Zakeri

    (Tehran Polytechnic, Amirkabir University of Technology, 424 Hafez Ave, 159163-4311 Tehran, Iran)

  • Hossein Askarian Abyaneh

    (Tehran Polytechnic, Amirkabir University of Technology, 424 Hafez Ave, 159163-4311 Tehran, Iran)

Abstract

Transmission Expansion Planning (TEP) involves determining if and how transmission lines should be added to the power grid so that the operational and investment costs are minimized. TEP is a major issue in smart grid development, where demand response resources affect short- and long-term power system decisions, and these in turn, affect TEP. First, this paper discusses the effects of demand response programs on reducing the final costs of a system in TEP. Then, the TEP problem is solved using a Teaching Learning Based Optimization (TLBO) algorithm taking into consideration power generation costs, power loss, and line construction costs. Simulation results show the optimal effect of demand response programs on postponing the additional cost of investments for supplying peak load.

Suggested Citation

  • Amir Sadegh Zakeri & Hossein Askarian Abyaneh, 2017. "Transmission Expansion Planning Using TLBO Algorithm in the Presence of Demand Response Resources," Energies, MDPI, vol. 10(9), pages 1-15, September.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:9:p:1376-:d:111552
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    References listed on IDEAS

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    1. Moghaddam, M. Parsa & Abdollahi, A. & Rashidinejad, M., 2011. "Flexible demand response programs modeling in competitive electricity markets," Applied Energy, Elsevier, vol. 88(9), pages 3257-3269.
    2. Munoz, F.D. & Hobbs, B.F. & Watson, J.-P., 2016. "New bounding and decomposition approaches for MILP investment problems: Multi-area transmission and generation planning under policy constraints," European Journal of Operational Research, Elsevier, vol. 248(3), pages 888-898.
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    Cited by:

    1. Motta, Vinicius N. & Anjos, Miguel F. & Gendreau, Michel, 2024. "Survey of optimization models for power system operation and expansion planning with demand response," European Journal of Operational Research, Elsevier, vol. 312(2), pages 401-412.
    2. Yuhong Wang & Lei Chen & Hong Zhou & Xu Zhou & Zongsheng Zheng & Qi Zeng & Li Jiang & Liang Lu, 2021. "Flexible Transmission Network Expansion Planning Based on DQN Algorithm," Energies, MDPI, vol. 14(7), pages 1-21, April.
    3. Muhammad Saeed Uz Zaman & Syed Basit Ali Bukhari & Khalid Mousa Hazazi & Zunaib Maqsood Haider & Raza Haider & Chul-Hwan Kim, 2018. "Frequency Response Analysis of a Single-Area Power System with a Modified LFC Model Considering Demand Response and Virtual Inertia," Energies, MDPI, vol. 11(4), pages 1-20, March.
    4. Deb, Sanchari & Gao, Xiao-Zhi & Tammi, Kari & Kalita, Karuna & Mahanta, Pinakeswar, 2021. "A novel chicken swarm and teaching learning based algorithm for electric vehicle charging station placement problem," Energy, Elsevier, vol. 220(C).

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