IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v104y2016icp213-228.html
   My bibliography  Save this article

Reliability constrained congestion management with uncertain negawatt demand response firms considering repairable advanced metering infrastructures

Author

Listed:
  • Tabandeh, Abbas
  • Abdollahi, Amir
  • Rashidinejad, Masoud

Abstract

This paper presents a new framework for congestion management utilizing a reliability model of demand response resources under the smart grid environment. Demand response resources modeling is highly appertaining to uncertainty of customer's behavior. Interval data which is a core deliverable of advanced metering infrastructure, is essential for customers to participate in demand response events. Hence, a systematic method based upon frequency and duration approaches is utilized to present the multi-state model of multiple demand response resources considering repairable advanced metering infrastructures, the so-called demand response firm. Moreover, a new two-step congestion management structure with proposed demand response firm model is introduced for relieving congestion besides diminishing the risk of supplying loads. Firstly, system operator clears the electricity market based on economic and/or reliability-driven issues without considering transmission network limits. Afterwards, he/she will alleviate the existing transmission congestion considering a trade-off between demand response firms and load shedding besides generation rescheduling. In this regard, the impact of several important factors such as demand response firms' maximum achievable potential and forced outage rate of advanced metering infrastructures on the proposed framework are assessed. The simulation results discuss economic- and reliability-driven measures of the proposed demand response model in congestion management.

Suggested Citation

  • Tabandeh, Abbas & Abdollahi, Amir & Rashidinejad, Masoud, 2016. "Reliability constrained congestion management with uncertain negawatt demand response firms considering repairable advanced metering infrastructures," Energy, Elsevier, vol. 104(C), pages 213-228.
    • Handle: RePEc:eee:energy:v:104:y:2016:i:c:p:213-228
    DOI: 10.1016/j.energy.2016.03.118
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544216303656
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2016.03.118?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Nikzad, Mehdi & Mozafari, Babak & Bashirvand, Mahdi & Solaymani, Soodabeh & Ranjbar, Ali Mohamad, 2012. "Designing time-of-use program based on stochastic security constrained unit commitment considering reliability index," Energy, Elsevier, vol. 41(1), pages 541-548.
    2. Fitiwi, Desta Z. & de Cuadra, F. & Olmos, L. & Rivier, M., 2015. "A new approach of clustering operational states for power network expansion planning problems dealing with RES (renewable energy source) generation operational variability and uncertainty," Energy, Elsevier, vol. 90(P2), pages 1360-1376.
    3. Pantoš, Miloš, 2011. "Market-based congestion management in electric power systems with increased share of natural gas dependent power plants," Energy, Elsevier, vol. 36(7), pages 4244-4255.
    4. Aghaei, J. & Shayanfar, H.A. & Amjady, N., 2009. "Joint market clearing in a stochastic framework considering power system security," Applied Energy, Elsevier, vol. 86(9), pages 1675-1682, September.
    5. Esmaili, Masoud & Ebadi, Fatemeh & Shayanfar, Heidar Ali & Jadid, Shahram, 2013. "Congestion management in hybrid power markets using modified Benders decomposition," Applied Energy, Elsevier, vol. 102(C), pages 1004-1012.
    6. Heydarian-Forushani, E. & Golshan, M.E.H. & Shafie-khah, M., 2015. "Flexible security-constrained scheduling of wind power enabling time of use pricing scheme," Energy, Elsevier, vol. 90(P2), pages 1887-1900.
    7. Han, Jinil & Papavasiliou, Anthony, 2015. "Congestion management through topological corrections: A case study of Central Western Europe," Energy Policy, Elsevier, vol. 86(C), pages 470-482.
    8. Esmaili, Masoud & Shayanfar, Heidar Ali & Amjady, Nima, 2009. "Multi-objective congestion management incorporating voltage and transient stabilities," Energy, Elsevier, vol. 34(9), pages 1401-1412.
    9. Göransson, Lisa & Goop, Joel & Unger, Thomas & Odenberger, Mikael & Johnsson, Filip, 2014. "Linkages between demand-side management and congestion in the European electricity transmission system," Energy, Elsevier, vol. 69(C), pages 860-872.
    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. Tabandeh, Abbas & Hossain, M.J. & Li, Li, 2022. "Integrated multi-stage and multi-zone distribution network expansion planning with renewable energy sources and hydrogen refuelling stations for fuel cell vehicles," Applied Energy, Elsevier, vol. 319(C).
    2. Prajapati, Vijaykumar K. & Mahajan, Vasundhara, 2021. "Reliability assessment and congestion management of power system with energy storage system and uncertain renewable resources," Energy, Elsevier, vol. 215(PB).
    3. Pourramezan, Ali & Samadi, Mahdi, 2023. "A system dynamics investigation on the long-term impacts of demand response in generation investment planning incorporating renewables," Renewable and Sustainable Energy Reviews, Elsevier, vol. 171(C).
    4. Sun, M. & Teng, F. & Konstantelos, I. & Strbac, G., 2018. "An objective-based scenario selection method for transmission network expansion planning with multivariate stochasticity in load and renewable energy sources," Energy, Elsevier, vol. 145(C), pages 871-885.
    5. Barzegar, Mohammadreza & Rashidinejad, Masoud & Abdollahi, Amir & Afzali, Peyman & Bakhshai, Alireza, 2020. "An efficient reliability index for the assessment of energy efficiency considering sitting of green virtual resources in a microgrid," Energy, Elsevier, vol. 191(C).
    6. Carnero, María Carmen & Gómez, Andrés, 2017. "Maintenance strategy selection in electric power distribution systems," Energy, Elsevier, vol. 129(C), pages 255-272.
    7. Smail, Houria & Alkama, Rezak & Medjdoub, Abdellah, 2018. "Impact of large scale power plant connection on congestion in the algerian electricity transmission system," Energy, Elsevier, vol. 159(C), pages 115-120.
    8. Mohammadnejad, Mehran & Abdollahi, Amir & Rashidinejad, Masoud, 2020. "Possibilistic-probabilistic self-scheduling of PEVAggregator for participation in spinning reserve market considering uncertain DRPs," Energy, Elsevier, vol. 196(C).
    9. Khaloie, Hooman & Abdollahi, Amir & Shafie-khah, Miadreza & Anvari-Moghaddam, Amjad & Nojavan, Sayyad & Siano, Pierluigi & Catalão, João P.S., 2020. "Coordinated wind-thermal-energy storage offering strategy in energy and spinning reserve markets using a multi-stage model," Applied Energy, Elsevier, vol. 259(C).

    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. Hosseini, Seyyed Ahmad & Amjady, Nima & Shafie-khah, Miadreza & Catalão, João P.S., 2016. "A new multi-objective solution approach to solve transmission congestion management problem of energy markets," Applied Energy, Elsevier, vol. 165(C), pages 462-471.
    2. Nikoobakht, Ahmad & Aghaei, Jamshid & Mardaneh, Mohammad, 2016. "Managing the risk of uncertain wind power generation in flexible power systems using information gap decision theory," Energy, Elsevier, vol. 114(C), pages 846-861.
    3. Aghaei, Jamshid & Alizadeh, Mohammad-Iman, 2013. "Multi-objective self-scheduling of CHP (combined heat and power)-based microgrids considering demand response programs and ESSs (energy storage systems)," Energy, Elsevier, vol. 55(C), pages 1044-1054.
    4. Esmaeily, Ali & Ahmadi, Abdollah & Raeisi, Fatima & Ahmadi, Mohammad Reza & Esmaeel Nezhad, Ali & Janghorbani, Mohammadreza, 2017. "Evaluating the effectiveness of mixed-integer linear programming for day-ahead hydro-thermal self-scheduling considering price uncertainty and forced outage rate," Energy, Elsevier, vol. 122(C), pages 182-193.
    5. Heydarian-Forushani, E. & Golshan, M.E.H. & Shafie-khah, M., 2015. "Flexible security-constrained scheduling of wind power enabling time of use pricing scheme," Energy, Elsevier, vol. 90(P2), pages 1887-1900.
    6. Navon, Aviad & Kulbekov, Pavel & Dolev, Shahar & Yehuda, Gil & Levron, Yoash, 2020. "Integration of distributed renewable energy sources in Israel: Transmission congestion challenges and policy recommendations," Energy Policy, Elsevier, vol. 140(C).
    7. Nesamalar, J. Jeslin Drusila & Venkatesh, P. & Raja, S. Charles, 2016. "Energy management by generator rescheduling in congestive deregulated power system," Applied Energy, Elsevier, vol. 171(C), pages 357-371.
    8. Esmaili, Masoud & Amjady, Nima & Shayanfar, Heidar Ali, 2011. "Multi-objective congestion management by modified augmented [epsilon]-constraint method," Applied Energy, Elsevier, vol. 88(3), pages 755-766, March.
    9. Smail, Houria & Alkama, Rezak & Medjdoub, Abdellah, 2018. "Impact of large scale power plant connection on congestion in the algerian electricity transmission system," Energy, Elsevier, vol. 159(C), pages 115-120.
    10. M. Jenabi & S. M. T. Fatemi Ghomi & S. A. Torabi & Moeen Sammak Jalali, 2022. "An accelerated Benders decomposition algorithm for stochastic power system expansion planning using sample average approximation," OPSEARCH, Springer;Operational Research Society of India, vol. 59(4), pages 1304-1336, December.
    11. Gerbaulet, Clemens & von Hirschhausen, Christian & Kemfert, Claudia & Lorenz, Casimir & Oei, Pao-Yu, 2019. "European electricity sector decarbonization under different levels of foresight," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 141, pages 973-987.
    12. Bahl, Björn & Kümpel, Alexander & Seele, Hagen & Lampe, Matthias & Bardow, André, 2017. "Time-series aggregation for synthesis problems by bounding error in the objective function," Energy, Elsevier, vol. 135(C), pages 900-912.
    13. Sousa, Tiago & Morais, Hugo & Soares, João & Vale, Zita, 2012. "Day-ahead resource scheduling in smart grids considering Vehicle-to-Grid and network constraints," Applied Energy, Elsevier, vol. 96(C), pages 183-193.
    14. Motalleb, Mahdi & Thornton, Matsu & Reihani, Ehsan & Ghorbani, Reza, 2016. "A nascent market for contingency reserve services using demand response," Applied Energy, Elsevier, vol. 179(C), pages 985-995.
    15. Najafi, M. & Ehsan, M. & Fotuhi-Firuzabad, M. & Akhavein, A. & Afshar, K., 2010. "Optimal reserve capacity allocation with consideration of customer reliability requirements," Energy, Elsevier, vol. 35(9), pages 3883-3890.
    16. Junkai He & Feng Chu & Feifeng Zheng & Ming Liu, 2021. "A green-oriented bi-objective disassembly line balancing problem with stochastic task processing times," Annals of Operations Research, Springer, vol. 296(1), pages 71-93, January.
    17. Di Somma, M. & Graditi, G. & Heydarian-Forushani, E. & Shafie-khah, M. & Siano, P., 2018. "Stochastic optimal scheduling of distributed energy resources with renewables considering economic and environmental aspects," Renewable Energy, Elsevier, vol. 116(PA), pages 272-287.
    18. Cao, M.F. & Huang, G.H. & Lin, Q.G., 2010. "Integer programming with random-boundary intervals for planning municipal power systems," Applied Energy, Elsevier, vol. 87(8), pages 2506-2516, August.
    19. Morales, J.M. & Mínguez, R. & Conejo, A.J., 2010. "A methodology to generate statistically dependent wind speed scenarios," Applied Energy, Elsevier, vol. 87(3), pages 843-855, March.
    20. Saffari, Mohammadali & Crownshaw, Timothy & McPherson, Madeleine, 2023. "Assessing the potential of demand-side flexibility to improve the performance of electricity systems under high variable renewable energy penetration," Energy, Elsevier, vol. 272(C).

    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:eee:energy:v:104:y:2016:i:c:p:213-228. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.