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Impact of Demand Response on Reliability Enhancement in Distribution Networks

Author

Listed:
  • Mohammad Reza Mansouri

    (Department of Electrical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht 73711-13119, Iran)

  • Mohsen Simab

    (Department of Electrical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht 73711-13119, Iran)

  • Bahman Bahmani Firouzi

    (Department of Electrical Engineering, Marvdasht Branch, Islamic Azad University, Marvdasht 73711-13119, Iran)

Abstract

This paper presents an innovative instantaneous pricing scheme for optimal operation and improved reliability for distribution systems (DS). The purpose of the proposed program is to maximize the operator’s expected profit under various risk-taking conditions, such that the customers pay the minimum cost to supply energy. Using the previous information of the energy consumption for each customer, a customer baseline load (CBL) is defined; the energy price for consumption costs higher and lower than this level would be different. The proposed scheme calculates the difference between the baseline load and the consumption curve with the electricity market price instead of calculating the total consumption of the customers with the unstable price of the electricity market, which is uncertain. In the proposed tariff, the developed cost and load models are included in the distribution system operation problem, and the objective function is modeled as a mixed integer linear programming (MILP) problem. Also, the effect of demand response (DR) and elasticity on the load curve, the final profit of the distribution system operator, and payment risk and operation costs are examined. Since there are various uncertainties in the smart distribution grid, the calculations being time-consuming and volumetric is important in the evaluation of reliability indices. Thus, when computation volume can be decreased and computation speed can be increased, analytical reliability analysis methods can be used, as they were in the present work. Finally, the changes in the reliability indices were calculated for the ratio of the customers’ sensitivity to the price and the customers’ participation in the proposed tariff using an analytical method based on Monte Carlo simulation (MCS). The results showed the efficiency of the proposed method in increasing the operator profit, reducing the operation costs, and enhancing the reliability indices.

Suggested Citation

  • Mohammad Reza Mansouri & Mohsen Simab & Bahman Bahmani Firouzi, 2021. "Impact of Demand Response on Reliability Enhancement in Distribution Networks," Sustainability, MDPI, vol. 13(23), pages 1-35, November.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:23:p:13201-:d:690421
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    References listed on IDEAS

    as
    1. Lee, Junghun & Yoo, Seunghwan & Kim, Jonghun & Song, Doosam & Jeong, Hakgeun, 2018. "Improvements to the customer baseline load (CBL) using standard energy consumption considering energy efficiency and demand response," Energy, Elsevier, vol. 144(C), pages 1052-1063.
    2. Zhihong Xu & Yan Gao & Muhammad Hussain & Panhong Cheng, 2020. "Demand Side Management for Smart Grid Based on Smart Home Appliances with Renewable Energy Sources and an Energy Storage System," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-20, April.
    3. Gilani, Mohammad Amin & Kazemi, Ahad & Ghasemi, Mostafa, 2020. "Distribution system resilience enhancement by microgrid formation considering distributed energy resources," Energy, Elsevier, vol. 191(C).
    4. Shojaabadi, Saeed & Abapour, Saeed & Abapour, Mehdi & Nahavandi, Ali, 2016. "Simultaneous planning of plug-in hybrid electric vehicle charging stations and wind power generation in distribution networks considering uncertainties," Renewable Energy, Elsevier, vol. 99(C), pages 237-252.
    5. Nozhati, Saeed & Sarkale, Yugandhar & Chong, Edwin K.P. & Ellingwood, Bruce R., 2020. "Optimal stochastic dynamic scheduling for managing community recovery from natural hazards," Reliability Engineering and System Safety, Elsevier, vol. 193(C).
    6. Madia Safdar & Ghulam Amjad Hussain & Matti Lehtonen, 2019. "Costs of Demand Response from Residential Customers’ Perspective," Energies, MDPI, vol. 12(9), pages 1-16, April.
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    Cited by:

    1. Sepideh Radhoush & Maryam Bahramipanah & Hashem Nehrir & Zagros Shahooei, 2022. "A Review on State Estimation Techniques in Active Distribution Networks: Existing Practices and Their Challenges," Sustainability, MDPI, vol. 14(5), pages 1-16, February.

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