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The Effect of Different Non-linear Demand Response Models Considering Incentive and Penalty on Transmission Expansion Planning

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  • Amir Sadegh Zakeri
  • Oveis Asgari Gashteroodkhani
  • Iman Niazazari
  • Hossein Askarian-Abyaneh

Abstract

The Transmission Expansion Planning (TEP) problem involves adding new lines to the existing electrical transmission network in order to meet the electrical demand requirements. Demand Response (DR) plays an important role in solving the TEP problem due to the delay in the investment costs. Researchers usually focus on the linear model of DR, while the focus on nonlinear models including power, exponential and logarithmic of DR is small. In this paper and in order to understand which model gives the realistic results, the linear model of DR is studied simultaneously with nonlinear models including power, exponential and logarithmic of DR. Moreover, the effect of incentive and penalty which has been neglected in the studies, is investigated. The study is investigated based on the viewpoint of different participants of the market including Independent System Operator (ISO), Customers and Utilities. In order to prioritize and select the most effective DR program, five characteristics including Peak Reduction, Energy Consumption, Load Factor, Peak to Valley and Customer’s Total Cost are extracted from the load curve. Then, using the weighting coefficients obtained by Entropy technique and implementing the TOPSIS and AHP technique, different DR programs are prioritized.

Suggested Citation

  • Amir Sadegh Zakeri & Oveis Asgari Gashteroodkhani & Iman Niazazari & Hossein Askarian-Abyaneh, 2019. "The Effect of Different Non-linear Demand Response Models Considering Incentive and Penalty on Transmission Expansion Planning," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 3(1), January.
    • Handle: RePEc:epw:ejece0:v:3:y:2019:i:1:id:19057
    DOI: 10.24018/ejece.2019.3.1.57
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    References listed on IDEAS

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    1. Nigim, K. & Munier, N. & Green, J., 2004. "Pre-feasibility MCDM tools to aid communities in prioritizing local viable renewable energy sources," Renewable Energy, Elsevier, vol. 29(11), pages 1775-1791.
    2. 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.
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    Cited by:

    1. Iman Niazazari & Oveis Asgari Gashteroodkhani & Amir Niaz Azari, 2019. "A Novel Economic Dispatch in Power Grids Based on Enhanced Firework Algorithm," European Journal of Electrical Engineering and Computer Science, European Open Science, vol. 3(4), June.

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