IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i4p1104-d502165.html
   My bibliography  Save this article

Modeling and Investigation of Demand Response Uncertainty on Reliability Assessment

Author

Listed:
  • Jen-Hao Teng

    (Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan)

  • Chia-Hung Hsieh

    (Department of Electrical Engineering, National Sun Yat-Sen University, Kaohsiung 80424, Taiwan)

Abstract

Demand Response (DR) provides an opportunity for customers to reduce their loads during times of high prices and therefore to shave the peak loads. The power outputs of large-scale generator units can be predicted and controlled easily; therefore, the pricing and reliability of conventional power utilities can be assessed straightforwardly. However, the customer loads are very variable and difficult to predict and control; therefore, the integration of DR might cause uncertainty issues on pricing and reliability and is essential to be further investigated. A novel uncertainty model for load reduction is proposed in this paper. The probability intensities of load reduction are first estimated from the measured load reduction variations. A multi-state probability model is then proposed for load reduction and the Markov process is used to calculate the state probabilities. A stochastic analysis scheme using Monte Carlo simulation for pricing and reliability taking the DR uncertainty into account is then investigated. Several cases are designed to compare the effects of DR uncertainty. Simulation results show that the proposed uncertainty model can be integrated into conventional economic dispatch to precisely evaluate the DR uncertainty on system operation and reliability.

Suggested Citation

  • Jen-Hao Teng & Chia-Hung Hsieh, 2021. "Modeling and Investigation of Demand Response Uncertainty on Reliability Assessment," Energies, MDPI, vol. 14(4), pages 1-17, February.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:4:p:1104-:d:502165
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/4/1104/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/4/1104/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Jiadong & Wang, Jianhui & Liu, Cong & Ruiz, Juan P., 2013. "Stochastic unit commitment with sub-hourly dispatch constraints," Applied Energy, Elsevier, vol. 105(C), pages 418-422.
    2. Behboodi, Sahand & Chassin, David P. & Crawford, Curran & Djilali, Ned, 2016. "Renewable resources portfolio optimization in the presence of demand response," Applied Energy, Elsevier, vol. 162(C), pages 139-148.
    3. Moslehi, Salim & Reddy, T. Agami, 2018. "Sustainability of integrated energy systems: A performance-based resilience assessment methodology," Applied Energy, Elsevier, vol. 228(C), pages 487-498.
    4. Luis Alejandro Arias & Edwin Rivas & Francisco Santamaria & Victor Hernandez, 2018. "A Review and Analysis of Trends Related to Demand Response," Energies, MDPI, vol. 11(7), pages 1-24, June.
    5. 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.
    Full references (including those not matched with items on IDEAS)

    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. Kocaman, Ayse Selin & Ozyoruk, Emin & Taneja, Shantanu & Modi, Vijay, 2020. "A stochastic framework to evaluate the impact of agricultural load flexibility on the sizing of renewable energy systems," Renewable Energy, Elsevier, vol. 152(C), pages 1067-1078.
    2. Ovidiu Ivanov & Samiran Chattopadhyay & Soumya Banerjee & Bogdan-Constantin Neagu & Gheorghe Grigoras & Mihai Gavrilas, 2020. "A Novel Algorithm with Multiple Consumer Demand Response Priorities in Residential Unbalanced LV Electricity Distribution Networks," Mathematics, MDPI, vol. 8(8), pages 1-24, July.
    3. Mazur, Christoph & Hoegerle, Yannick & Brucoli, Maria & van Dam, Koen & Guo, Miao & Markides, Christos N. & Shah, Nilay, 2019. "A holistic resilience framework development for rural power systems in emerging economies," Applied Energy, Elsevier, vol. 235(C), pages 219-232.
    4. Nolan, Sheila & Neu, Olivier & O’Malley, Mark, 2017. "Capacity value estimation of a load-shifting resource using a coupled building and power system model," Applied Energy, Elsevier, vol. 192(C), pages 71-82.
    5. Mujjuni, F. & Betts, T. & To, L.S. & Blanchard, R.E., 2021. "Resilience a means to development: A resilience assessment framework and a catalogue of indicators," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    6. Xie, Kaigui & Dong, Jizhe & Singh, Chanan & Hu, Bo, 2016. "Optimal capacity and type planning of generating units in a bundled wind–thermal generation system," Applied Energy, Elsevier, vol. 164(C), pages 200-210.
    7. Wei, Wei & Liu, Feng & Wang, Jianhui & Chen, Laijun & Mei, Shengwei & Yuan, Tiejiang, 2016. "Robust environmental-economic dispatch incorporating wind power generation and carbon capture plants," Applied Energy, Elsevier, vol. 183(C), pages 674-684.
    8. Ahmadi, Somayeh & Saboohi, Yadollah & Vakili, Ali, 2021. "Frameworks, quantitative indicators, characters, and modeling approaches to analysis of energy system resilience: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    9. Michael Schoepf & Martin Weibelzahl & Lisa Nowka, 2018. "The Impact of Substituting Production Technologies on the Economic Demand Response Potential in Industrial Processes," Energies, MDPI, vol. 11(9), pages 1-13, August.
    10. Goudarzi, Arman & Swanson, Andrew G. & Van Coller, John & Siano, Pierluigi, 2017. "Smart real-time scheduling of generating units in an electricity market considering environmental aspects and physical constraints of generators," Applied Energy, Elsevier, vol. 189(C), pages 667-696.
    11. Philipsen, Rens & Morales-España, Germán & de Weerdt, Mathijs & de Vries, Laurens, 2019. "Trading power instead of energy in day-ahead electricity markets," Applied Energy, Elsevier, vol. 233, pages 802-815.
    12. Poulin, Craig & Kane, Michael B., 2021. "Infrastructure resilience curves: Performance measures and summary metrics," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    13. Sivaneasan, Balakrishnan & Kandasamy, Nandha Kumar & Lim, May Lin & Goh, Kwang Ping, 2018. "A new demand response algorithm for solar PV intermittency management," Applied Energy, Elsevier, vol. 218(C), pages 36-45.
    14. Ji, Haoran & Wang, Chengshan & Li, Peng & Song, Guanyu & Yu, Hao & Wu, Jianzhong, 2019. "Quantified analysis method for operational flexibility of active distribution networks with high penetration of distributed generators," Applied Energy, Elsevier, vol. 239(C), pages 706-714.
    15. Ke, Xinda & Wu, Di & Rice, Jennie & Kintner-Meyer, Michael & Lu, Ning, 2016. "Quantifying impacts of heat waves on power grid operation," Applied Energy, Elsevier, vol. 183(C), pages 504-512.
    16. Jun Liu & Jinchun Chen & Chao Wang & Zhang Chen & Xinglei Liu, 2020. "Market Trading Model of Urban Energy Internet Based on Tripartite Game Theory," Energies, MDPI, vol. 13(7), pages 1-24, April.
    17. Tahir, Muhammad Faizan & Chen, Haoyong & Khan, Asad & Javed, Muhammad Sufyan & Cheema, Khalid Mehmood & Laraik, Noman Ali, 2020. "Significance of demand response in light of current pilot projects in China and devising a problem solution for future advancements," Technology in Society, Elsevier, vol. 63(C).
    18. Liu, Min & Qin, Jianjun & Lu, Da-Gang & Zhang, Wei-Heng & Zhu, Jiang-Sheng & Faber, Michael Havbro, 2022. "Towards resilience of offshore wind farms: A framework and application to asset integrity management," Applied Energy, Elsevier, vol. 322(C).
    19. Anwar, Ghazanfar Ali & Zhang, Xiaoge, 2024. "Deep reinforcement learning for intelligent risk optimization of buildings under hazard," Reliability Engineering and System Safety, Elsevier, vol. 247(C).
    20. Sylwia Mrozowska & Jan A. Wendt & Krzysztof Tomaszewski, 2021. "The Challenges of Poland’s Energy Transition," Energies, MDPI, vol. 14(23), pages 1-22, December.

    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:jeners:v:14:y:2021:i:4:p:1104-:d:502165. 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.