IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i23p13201-d690421.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/23/13201/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/23/13201/
    Download Restriction: no
    ---><---

    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    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. Liu, Huan & Tatano, Hirokazu & Pflug, Georg & Hochrainer-Stigler, Stefan, 2021. "Post-disaster recovery in industrial sectors: A Markov process analysis of multiple lifeline disruptions," Reliability Engineering and System Safety, Elsevier, vol. 206(C).
    2. Younesi, Abdollah & Shayeghi, Hossein & Safari, Amin & Siano, Pierluigi, 2020. "Assessing the resilience of multi microgrid based widespread power systems against natural disasters using Monte Carlo Simulation," Energy, Elsevier, vol. 207(C).
    3. Kizito, Rodney & Liu, Zeyu & Li, Xueping & Sun, Kai, 2022. "Multi-stage stochastic optimization of islanded utility-microgrids design after natural disasters," Operations Research Perspectives, Elsevier, vol. 9(C).
    4. Li, Chengzhe & Zhang, Libo & Ou, Zihan & Wang, Qunwei & Zhou, Dequn & Ma, Jiayu, 2022. "Robust model of electric vehicle charging station location considering renewable energy and storage equipment," Energy, Elsevier, vol. 238(PA).
    5. Malandra, F. & Kizilkale, A.C. & Sirois, F. & Sansò, B. & Anjos, M.F. & Bernier, M. & Gendreau, M. & Malhamé, R.P., 2020. "Smart Distributed Energy Storage Controller (smartDESC)," Energy, Elsevier, vol. 210(C).
    6. García, Antonio & Monsalve-Serrano, Javier & Martinez-Boggio, Santiago & Zhao, Wenbin & Qian, Yong, 2022. "Intelligent charge compression ignition combustion for range extender medium duty applications," Renewable Energy, Elsevier, vol. 187(C), pages 671-687.
    7. Davidov, Sreten & Pantoš, Miloš, 2017. "Stochastic expansion planning of the electric-drive vehicle charging infrastructure," Energy, Elsevier, vol. 141(C), pages 189-201.
    8. Mao, Jiachen & Jafari, Mehdi & Botterud, Audun, 2022. "Planning low-carbon distributed power systems: Evaluating the role of energy storage," Energy, Elsevier, vol. 238(PA).
    9. Xu, Bin & Luo, Yuemei & Xu, Renjing & Chen, Jianbao, 2021. "Exploring the driving forces of distributed energy resources in China: Using a semiparametric regression model," Energy, Elsevier, vol. 236(C).
    10. Wu, Chuanshen & Gao, Shan & Liu, Yu & Song, Tiancheng E. & Han, Haiteng, 2021. "A model predictive control approach in microgrid considering multi-uncertainty of electric vehicles," Renewable Energy, Elsevier, vol. 163(C), pages 1385-1396.
    11. Venkat Durvasulu & Timothy M. Hansen, 2018. "Benefits of a Demand Response Exchange Participating in Existing Bulk-Power Markets," Energies, MDPI, vol. 11(12), pages 1-21, December.
    12. Younes Zahraoui & Tarmo Korõtko & Argo Rosin & Hannes Agabus, 2023. "Market Mechanisms and Trading in Microgrid Local Electricity Markets: A Comprehensive Review," Energies, MDPI, vol. 16(5), pages 1-52, February.
    13. Ferrario, E. & Poulos, A. & Castro, S. & de la Llera, J.C. & Lorca, A., 2022. "Predictive capacity of topological measures in evaluating seismic risk and resilience of electric power networks," Reliability Engineering and System Safety, Elsevier, vol. 217(C).
    14. Artur Felipe da Silva Veloso & José Valdemir Reis Júnior & Ricardo de Andrade Lira Rabelo & Jocines Dela-flora Silveira, 2021. "HyDSMaaS: A Hybrid Communication Infrastructure with LoRaWAN and LoraMesh for the Demand Side Management as a Service," Future Internet, MDPI, vol. 13(11), pages 1-45, October.
    15. Zhang, Heng & Zhang, Shenxi & Cheng, Haozhong & Li, Zheng & Gu, Qingfa & Tian, Xueqin, 2022. "Boosting the power grid resilience under typhoon disasters by coordinated scheduling of wind energy and conventional generators," Renewable Energy, Elsevier, vol. 200(C), pages 303-319.
    16. Kyritsis, A. & Voglitsis, D. & Papanikolaou, N. & Tselepis, S. & Christodoulou, C. & Gonos, I. & Kalogirou, S.A., 2017. "Evolution of PV systems in Greece and review of applicable solutions for higher penetration levels," Renewable Energy, Elsevier, vol. 109(C), pages 487-499.
    17. Jordehi, A. Rezaee, 2018. "How to deal with uncertainties in electric power systems? A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 145-155.
    18. Shen, Yueqing & Qian, Tong & Li, Weiwei & Zhao, Wei & Tang, Wenhu & Chen, Xingyu & Yu, Zeyuan, 2023. "Mobile energy storage systems with spatial–temporal flexibility for post-disaster recovery of power distribution systems: A bilevel optimization approach," Energy, Elsevier, vol. 282(C).
    19. Yang, Tongxu & Zhang, Limei & Zhen, Linteng & Liu, Yongfu & Song, Qianqian & Tang, Wei, 2021. "Fast microgrids formation of distribution network with high penetration of DERs considering reliability," Energy, Elsevier, vol. 236(C).
    20. Wu, Hao & Xie, Yunyun & Xu, Yan & Wu, Qiuwei & Yu, Chen & Sun, Jinsheng, 2022. "Resilient scheduling of MESSs and RCs for distribution system restoration considering the forced cut-off of wind power," Energy, Elsevier, vol. 244(PB).

    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:jsusta:v:13:y:2021:i:23:p:13201-:d:690421. 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.