IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v229y2018icp186-200.html
   My bibliography  Save this article

Hybrid probabilistic-possibilistic approach for capacity credit evaluation of demand response considering both exogenous and endogenous uncertainties

Author

Listed:
  • Zeng, Bo
  • Wei, Xuan
  • Zhao, Dongbo
  • Singh, Chanan
  • Zhang, Jianhua

Abstract

As a featured smart-grid technology, demand response (DR) provides utility companies with unprecedented flexibility to improve the reliability of electricity service in future power systems. However, due to the uncertainties arising from the demand side, the extent to which DR can be utilized for capacity support poses a major question to the utilities. To address this issue, this paper proposes a new methodological framework to assess the potential reliability value of DR in smart grids. The framework is established on the concept of capacity credit (CC), and it accommodates different types of uncertainties (i.e., probabilistic and possibilistic) accrued from physical and anthropogenic factors in DR programs. The capability of DR during operation is considered as a synthesized result of multiple facets, i.e., users’ load characteristics, participation levels, and load recoveries, and different models are developed to represent each component. To characterize the stochastic nature of demand responsiveness, the fuzzy theory is introduced, and possibilistic models are proposed to describe the human-related uncertainties under incomplete information. In addition, considering that in reality, DR operation could affect the comfort of customers, the dynamics of demand-side participation have also been incorporated in our study, in which two utility-based indices are defined to quantify the effect of such interdependency. Using a probabilistic propagation technique, the different types of uncertainties involved can be normalized and systematically addressed under the same framework. Then, the relevant models can be applied to the CC evaluation procedures, wherein two dispatching schemes (i.e., reliability-driven and coordinated management) are considered to study the effect of DR operation on its CC. The proposed methodology is tested on a modified RTS system, and the obtained results confirm its effectiveness.

Suggested Citation

  • Zeng, Bo & Wei, Xuan & Zhao, Dongbo & Singh, Chanan & Zhang, Jianhua, 2018. "Hybrid probabilistic-possibilistic approach for capacity credit evaluation of demand response considering both exogenous and endogenous uncertainties," Applied Energy, Elsevier, vol. 229(C), pages 186-200.
    • Handle: RePEc:eee:appene:v:229:y:2018:i:c:p:186-200
    DOI: 10.1016/j.apenergy.2018.07.111
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261918311462
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2018.07.111?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. Siano, Pierluigi, 2014. "Demand response and smart grids—A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 461-478.
    2. Jiang, Yibo & Xu, Jian & Sun, Yuanzhang & Wei, Congying & Wang, Jing & Ke, Deping & Li, Xiong & Yang, Jun & Peng, Xiaotao & Tang, Bowen, 2017. "Day-ahead stochastic economic dispatch of wind integrated power system considering demand response of residential hybrid energy system," Applied Energy, Elsevier, vol. 190(C), pages 1126-1137.
    3. Kwag, Hyung-Geun & Kim, Jin-O, 2014. "Reliability modeling of demand response considering uncertainty of customer behavior," Applied Energy, Elsevier, vol. 122(C), pages 24-33.
    4. Wang, Fei & Xu, Hanchen & Xu, Ti & Li, Kangping & Shafie-khah, Miadreza & Catalão, João. P.S., 2017. "The values of market-based demand response on improving power system reliability under extreme circumstances," Applied Energy, Elsevier, vol. 193(C), pages 220-231.
    5. Li, Gengfeng & Bie, Zhaohong & Xie, Haipeng & Lin, Yanling, 2016. "Customer satisfaction based reliability evaluation of active distribution networks," Applied Energy, Elsevier, vol. 162(C), pages 1571-1578.
    6. Nan, Sibo & Zhou, Ming & Li, Gengyin, 2018. "Optimal residential community demand response scheduling in smart grid," Applied Energy, Elsevier, vol. 210(C), pages 1280-1289.
    7. Joung, Manho & Kim, Jinho, 2013. "Assessing demand response and smart metering impacts on long-term electricity market prices and system reliability," Applied Energy, Elsevier, vol. 101(C), pages 441-448.
    8. Roger Flage & Terje Aven & Enrico Zio & Piero Baraldi, 2014. "Concerns, Challenges, and Directions of Development for the Issue of Representing Uncertainty in Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 34(7), pages 1196-1207, July.
    9. Nolan, Sheila & O’Malley, Mark, 2015. "Challenges and barriers to demand response deployment and evaluation," Applied Energy, Elsevier, vol. 152(C), pages 1-10.
    10. Aalami, H.A. & Moghaddam, M. Parsa & Yousefi, G.R., 2010. "Demand response modeling considering Interruptible/Curtailable loads and capacity market programs," Applied Energy, Elsevier, vol. 87(1), pages 243-250, January.
    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. Motta, Vinicius N. & Anjos, Miguel F. & Gendreau, Michel, 2024. "Survey of optimization models for power system operation and expansion planning with demand response," European Journal of Operational Research, Elsevier, vol. 312(2), pages 401-412.
    2. Yao, Hongmin & Qin, Wenping & Jing, Xiang & Zhu, Zhilong & Wang, Ke & Han, Xiaoqing & Wang, Peng, 2022. "Possibilistic evaluation of photovoltaic hosting capacity on distribution networks under uncertain environment," Applied Energy, Elsevier, vol. 324(C).
    3. Han, Rushuai & Hu, Qinran & Cui, Hantao & Chen, Tao & Quan, Xiangjun & Wu, Zaijun, 2022. "An optimal bidding and scheduling method for load service entities considering demand response uncertainty," Applied Energy, Elsevier, vol. 328(C).
    4. Sampath Kumar & M Sushama, 2020. "Strategic demand response framework for energy management in distribution system based on network loss sensitivity," Energy & Environment, , vol. 31(8), pages 1385-1402, December.
    5. Wen, Lei & Song, Qianqian, 2023. "ELCC-based capacity value estimation of combined wind - storage system using IPSO algorithm," Energy, Elsevier, vol. 263(PB).
    6. Liu, Wenxia & Huang, Yuchen & Li, Zhengzhou & Yang, Yue & Yi, Fang, 2020. "Optimal allocation for coupling device in an integrated energy system considering complex uncertainties of demand response," Energy, Elsevier, vol. 198(C).
    7. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N., 2022. "Demand response-integrated investment and operational planning of renewable and sustainable energy systems considering forecast uncertainties: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    8. Zeng, Bo & Zhao, Dongbo & Singh, Chanan & Wang, Jianhui & Chen, Chen, 2019. "Holistic modeling framework of demand response considering multi-timescale uncertainties for capacity value estimation," Applied Energy, Elsevier, vol. 247(C), pages 692-702.
    9. Zhu, Xi & Zeng, Bo & Dong, Houqi & Liu, Jiaomin, 2020. "An interval-prediction based robust optimization approach for energy-hub operation scheduling considering flexible ramping products," Energy, Elsevier, vol. 194(C).
    10. Maolin Cheng, 2019. "A Grey CES Production Function Model and Its Application in Calculating the Contribution Rate of Economic Growth Factors," Complexity, Hindawi, vol. 2019, pages 1-8, April.
    11. Chen, Tao & Pipattanasomporn, Manisa & Rahman, Imran & Jing, Zejia & Rahman, Saifur, 2020. "MATPLAN: A probability-based planning tool for cost-effective grid integration of renewable energy," Renewable Energy, Elsevier, vol. 156(C), pages 1089-1099.

    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. Zeng, Bo & Zhao, Dongbo & Singh, Chanan & Wang, Jianhui & Chen, Chen, 2019. "Holistic modeling framework of demand response considering multi-timescale uncertainties for capacity value estimation," Applied Energy, Elsevier, vol. 247(C), pages 692-702.
    2. Dehnavi, Ehsan & Abdi, Hamdi, 2016. "Optimal pricing in time of use demand response by integrating with dynamic economic dispatch problem," Energy, Elsevier, vol. 109(C), pages 1086-1094.
    3. Adhikari, Rajendra & Pipattanasomporn, M. & Rahman, S., 2018. "An algorithm for optimal management of aggregated HVAC power demand using smart thermostats," Applied Energy, Elsevier, vol. 217(C), pages 166-177.
    4. Yu, Mengmeng & Hong, Seung Ho, 2016. "Supply–demand balancing for power management in smart grid: A Stackelberg game approach," Applied Energy, Elsevier, vol. 164(C), pages 702-710.
    5. Lee, Eunjung & Lee, Kyungeun & Lee, Hyoseop & Kim, Euncheol & Rhee, Wonjong, 2019. "Defining virtual control group to improve customer baseline load calculation of residential demand response," Applied Energy, Elsevier, vol. 250(C), pages 946-958.
    6. Märkle-Huß, Joscha & Feuerriegel, Stefan & Neumann, Dirk, 2018. "Large-scale demand response and its implications for spot prices, load and policies: Insights from the German-Austrian electricity market," Applied Energy, Elsevier, vol. 210(C), pages 1290-1298.
    7. Neda Hajibandeh & Miadreza Shafie-khah & Sobhan Badakhshan & Jamshid Aghaei & Sílvio J. P. S. Mariano & João P. S. Catalão, 2019. "Multi-Objective Market Clearing Model with an Autonomous Demand Response Scheme," Energies, MDPI, vol. 12(7), pages 1-16, April.
    8. Hou, Lingxi & Li, Weiqi & Zhou, Kui & Jiang, Qirong, 2019. "Integrating flexible demand response toward available transfer capability enhancement," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    9. Cheng, Lin & Wan, Yuxiang & Tian, Liting & Zhang, Fang, 2019. "Evaluating energy supply service reliability for commercial air conditioning loads from the distribution network aspect," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    10. Seungmi Lee & Jinho Kim, 2018. "Analytical Assessment for System Peak Reduction by Demand Responsive Resources Considering Their Operational Constraints in Wholesale Electricity Market," Energies, MDPI, vol. 11(12), pages 1-15, November.
    11. Neves, Diana & Pina, André & Silva, Carlos A., 2015. "Demand response modeling: A comparison between tools," Applied Energy, Elsevier, vol. 146(C), pages 288-297.
    12. Xu, Bing & Nayak, Amar & Gray, David & Ouenniche, Jamal, 2016. "Assessing energy business cases implemented in the North Sea Region and strategy recommendations," Applied Energy, Elsevier, vol. 172(C), pages 360-371.
    13. Zeng, Bo & Wu, Geng & Wang, Jianhui & Zhang, Jianhua & Zeng, Ming, 2017. "Impact of behavior-driven demand response on supply adequacy in smart distribution systems," Applied Energy, Elsevier, vol. 202(C), pages 125-137.
    14. Hu, Maomao & Xiao, Fu & Wang, Lingshi, 2017. "Investigation of demand response potentials of residential air conditioners in smart grids using grey-box room thermal model," Applied Energy, Elsevier, vol. 207(C), pages 324-335.
    15. Meyabadi, A. Fattahi & Deihimi, M.H., 2017. "A review of demand-side management: Reconsidering theoretical framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 367-379.
    16. Sadeghianpourhamami, N. & Demeester, T. & Benoit, D.F. & Strobbe, M. & Develder, C., 2016. "Modeling and analysis of residential flexibility: Timing of white good usage," Applied Energy, Elsevier, vol. 179(C), pages 790-805.
    17. Zeng, Huibin & Shao, Bilin & Dai, Hongbin & Yan, Yichuan & Tian, Ning, 2023. "Natural gas demand response strategy considering user satisfaction and load volatility under dynamic pricing," Energy, Elsevier, vol. 277(C).
    18. Ussama Assad & Muhammad Arshad Shehzad Hassan & Umar Farooq & Asif Kabir & Muhammad Zeeshan Khan & S. Sabahat H. Bukhari & Zain ul Abidin Jaffri & Judit Oláh & József Popp, 2022. "Smart Grid, Demand Response and Optimization: A Critical Review of Computational Methods," Energies, MDPI, vol. 15(6), pages 1-36, March.
    19. Neda Hajibandeh & Mehdi Ehsan & Soodabeh Soleymani & Miadreza Shafie-khah & João P. S. Catalão, 2017. "The Mutual Impact of Demand Response Programs and Renewable Energies: A Survey," Energies, MDPI, vol. 10(9), pages 1-18, September.
    20. Davarzani, Sima & Pisica, Ioana & Taylor, Gareth A. & Munisami, Kevin J., 2021. "Residential Demand Response Strategies and Applications in Active Distribution Network Management," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(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:appene:v:229:y:2018:i:c:p:186-200. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.