IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v205y2020ics0360544220310331.html
   My bibliography  Save this article

Short-term scheduling of electricity retailers in the presence of Demand Response Aggregators: A two-stage stochastic Bi-Level programming approach

Author

Listed:
  • Dadashi, Mojtaba
  • Haghifam, Sara
  • Zare, Kazem
  • Haghifam, Mahmoud-Reza
  • Abapour, Mehdi

Abstract

This article proposes a novel short-term decision-making model for electricity retailers within the electricity market and in the presence of Demand Response Aggregators (DRA). In this framework, retailers provide their required energy through the Day-ahead market, Real-time market and forward bilateral contracts. On the other hand, retailers participate in demand response (DR) programs based on the DRA’s DR offers to maximize their benefits. In this study, to cope with the uncertainty, the two-stage stochastic programming scheme is utilized. In the first stage, the participation of retailers in the Day-ahead market, as well as power purchased from the forward contracts are decided. In the second stage, the power exchanged with the Real-time market, and the DRA are determined after the realization of stochastic parameters. On the contrary, the DRA enhances its profit through submitting offers to retailers for two DR options. For solving the problem, a strategy based on the Game Theory, the Bi-Level stochastic programming approach is exploited to maximize the profit of both players in a competitive environment. The model is turned into a Single-Level problem by substituting the lower-level with its Karush–Kuhn–Tucker conditions. Finally, merits of the presented method are illustrated in a typical case study.

Suggested Citation

  • Dadashi, Mojtaba & Haghifam, Sara & Zare, Kazem & Haghifam, Mahmoud-Reza & Abapour, Mehdi, 2020. "Short-term scheduling of electricity retailers in the presence of Demand Response Aggregators: A two-stage stochastic Bi-Level programming approach," Energy, Elsevier, vol. 205(C).
    • Handle: RePEc:eee:energy:v:205:y:2020:i:c:s0360544220310331
    DOI: 10.1016/j.energy.2020.117926
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544220310331
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2020.117926?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. Deng, Tingting & Yan, Wenzhou & Nojavan, Sayyad & Jermsittiparsert, Kittisak, 2020. "Risk evaluation and retail electricity pricing using downside risk constraints method," Energy, Elsevier, vol. 192(C).
    2. Das, Saborni & Basu, Mousumi, 2020. "Day-ahead optimal bidding strategy of microgrid with demand response program considering uncertainties and outages of renewable energy resources," Energy, Elsevier, vol. 190(C).
    3. Dagoumas, Athanasios S. & Polemis, Michael L., 2017. "An integrated model for assessing electricity retailer’s profitability with demand response," Applied Energy, Elsevier, vol. 198(C), pages 49-64.
    4. Nojavan, Sayyad & Zare, Kazem & Mohammadi-Ivatloo, Behnam, 2017. "Optimal stochastic energy management of retailer based on selling price determination under smart grid environment in the presence of demand response program," Applied Energy, Elsevier, vol. 187(C), pages 449-464.
    5. Mohseni-Bonab, Seyed Masoud & Rabiee, Abbas & Mohammadi-Ivatloo, Behnam, 2016. "Voltage stability constrained multi-objective optimal reactive power dispatch under load and wind power uncertainties: A stochastic approach," Renewable Energy, Elsevier, vol. 85(C), pages 598-609.
    6. Fotouhi Ghazvini, Mohammad Ali & Soares, João & Horta, Nuno & Neves, Rui & Castro, Rui & Vale, Zita, 2015. "A multi-objective model for scheduling of short-term incentive-based demand response programs offered by electricity retailers," Applied Energy, Elsevier, vol. 151(C), pages 102-118.
    7. Ahmadi, Abdollah & Charwand, Mansour & Siano, Pierluigi & Nezhad, Ali Esmaeel & Sarno, Debora & Gitizadeh, Mohsen & Raeisi, Fatima, 2016. "A novel two-stage stochastic programming model for uncertainty characterization in short-term optimal strategy for a distribution company," Energy, Elsevier, vol. 117(P1), pages 1-9.
    8. Antonio J. Conejo & Miguel Carrión & Juan M. Morales, 2010. "Decision Making Under Uncertainty in Electricity Markets," International Series in Operations Research and Management Science, Springer, number 978-1-4419-7421-1, April.
    9. Feihu Hu & Xuan Feng & Hui Cao, 2018. "A Short-Term Decision Model for Electricity Retailers: Electricity Procurement and Time-of-Use Pricing," Energies, MDPI, vol. 11(12), pages 1-18, November.
    10. Qi Zhang & Shaohua Zhang & Xian Wang & Xue Li & Lei Wu, 2020. "Conditional-Robust-Profit-Based Optimization Model for Electricity Retailers with Shiftable Demand," Energies, MDPI, vol. 13(6), pages 1-19, March.
    11. Lu, Qing & Lü, Shuaikang & Leng, Yajun, 2019. "A Nash-Stackelberg game approach in regional energy market considering users’ integrated demand response," Energy, Elsevier, vol. 175(C), pages 456-470.
    12. Khojasteh, Meysam & Jadid, Shahram, 2015. "Decision-making framework for supplying electricity from distributed generation-owning retailers to price-sensitive customers," Utilities Policy, Elsevier, vol. 37(C), pages 1-12.
    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. Agostini, Marco & Bertolini, Marina & Coppo, Massimiliano & Fontini, Fulvio, 2021. "The participation of small-scale variable distributed renewable energy sources to the balancing services market," Energy Economics, Elsevier, vol. 97(C).
    2. Russo, Marianna & Kraft, Emil & Bertsch, Valentin & Keles, Dogan, 2022. "Short-term risk management of electricity retailers under rising shares of decentralized solar generation," Energy Economics, Elsevier, vol. 109(C).
    3. Sara Haghifam & Kazem Zare & Mehdi Abapour & Gregorio Muñoz-Delgado & Javier Contreras, 2020. "A Stackelberg Game-Based Approach for Transactive Energy Management in Smart Distribution Networks," Energies, MDPI, vol. 13(14), pages 1-34, July.
    4. Li, Yuanzheng & Huang, Jingjing & Liu, Yun & Zhao, Tianyang & Zhou, Yue & Zhao, Yong & Yuen, Chau, 2022. "Day-ahead risk averse market clearing considering demand response with data-driven load uncertainty representation: A Singapore electricity market study," Energy, Elsevier, vol. 254(PA).
    5. Gong, J.W. & Li, Y.P. & Lv, J. & Huang, G.H. & Suo, C. & Gao, P.P., 2022. "Development of an integrated bi-level model for China’s multi-regional energy system planning under uncertainty," Applied Energy, Elsevier, vol. 308(C).
    6. Li, Qiang & Zhou, Yongcheng & Wei, Fanchao & Li, Shuangxiu & Wang, Zhonghao & Li, Jiajia & Zhou, Guowen & Liu, Jinfu & Yan, Peigang & Yu, Daren, 2024. "Multi-time scale scheduling for virtual power plants: Integrating the flexibility of power generation and multi-user loads while considering the capacity degradation of energy storage systems," Applied Energy, Elsevier, vol. 362(C).
    7. Li, Songrui & Zhang, Lihui & Nie, Lei & Wang, Jianing, 2022. "Trading strategy and benefit optimization of load aggregators in integrated energy systems considering integrated demand response: A hierarchical Stackelberg game," Energy, Elsevier, vol. 249(C).
    8. Shen, Ziqi & Wei, Wei & Wu, Lei & Shafie-khah, Miadreza & Catalão, João P.S., 2021. "Economic dispatch of power systems with LMP-dependent demands: A non-iterative MILP model," Energy, Elsevier, vol. 233(C).
    9. Mohammad Hossein Nejati Amiri & Mehdi Mehdinejad & Amin Mohammadpour Shotorbani & Heidarali Shayanfar, 2023. "Heuristic Retailer’s Day-Ahead Pricing Based on Online-Learning of Prosumer’s Optimal Energy Management Model," Energies, MDPI, vol. 16(3), pages 1-21, January.
    10. Hui Zhou & Jian Ding & Yinlong Hu & Zisong Ye & Shang Shi & Yonghui Sun & Qiyu Zhang, 2022. "Economic Dispatch of Power Retailers: A Bi-Level Programming Approach via Market Clearing Price," Energies, MDPI, vol. 15(19), pages 1-17, September.
    11. Tian, Xiaoge & Chen, Weiming & Hu, Jinglu, 2023. "Game-theoretic modeling of power supply chain coordination under demand variation in China: A case study of Guangdong Province," Energy, Elsevier, vol. 262(PA).
    12. Beraldi, Patrizia & Khodaparasti, Sara, 2023. "Designing electricity tariffs in the retail market: A stochastic bi-level approach," International Journal of Production Economics, Elsevier, vol. 257(C).
    13. Ching-Jui Tien & Chia-Sheng Tu & Ming-Tang Tsai, 2022. "Risk Assessment of User Aggregators in Demand Bidding Markets," Energies, MDPI, vol. 16(1), pages 1-14, December.
    14. Lu, Xi & Xia, Shiwei & Gu, Wei & Chan, Ka Wing & Shahidehpour, Mohammad, 2021. "Two-stage robust distribution system operation by coordinating electric vehicle aggregator charging and load curtailments," Energy, Elsevier, vol. 226(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. Nojavan, Sayyad & Zare, Kazem & Mohammadi-Ivatloo, Behnam, 2017. "Optimal stochastic energy management of retailer based on selling price determination under smart grid environment in the presence of demand response program," Applied Energy, Elsevier, vol. 187(C), pages 449-464.
    2. Rom'an P'erez-Santalla & Miguel Carri'on & Carlos Ruiz, 2021. "Optimal pricing for electricity retailers based on data-driven consumers' price-response," Papers 2110.02735, arXiv.org, revised Feb 2022.
    3. Román Pérez-Santalla & Miguel Carrión & Carlos Ruiz, 2022. "Optimal pricing for electricity retailers based on data-driven consumers’ price-response," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(3), pages 430-464, October.
    4. Ghasemi, Ahmad & Jamshidi Monfared, Houman & Loni, Abdolah & Marzband, Mousa, 2021. "CVaR-based retail electricity pricing in day-ahead scheduling of microgrids," Energy, Elsevier, vol. 227(C).
    5. Mohammad Ali Fotouhi Ghazvini & João Soares & Hugo Morais & Rui Castro & Zita Vale, 2017. "Dynamic Pricing for Demand Response Considering Market Price Uncertainty," Energies, MDPI, vol. 10(9), pages 1-20, August.
    6. Qi Zhang & Shaohua Zhang & Xian Wang & Xue Li & Lei Wu, 2020. "Conditional-Robust-Profit-Based Optimization Model for Electricity Retailers with Shiftable Demand," Energies, MDPI, vol. 13(6), pages 1-19, March.
    7. Mohagheghi, Erfan & Gabash, Aouss & Alramlawi, Mansour & Li, Pu, 2018. "Real-time optimal power flow with reactive power dispatch of wind stations using a reconciliation algorithm," Renewable Energy, Elsevier, vol. 126(C), pages 509-523.
    8. Wakiyama, Takako & Zusman, Eric, 2021. "The impact of electricity market reform and subnational climate policy on carbon dioxide emissions across the United States: A path analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    9. Zhang, Yang & Yang, Qingyu & Li, Donghe & An, Dou, 2022. "A reinforcement and imitation learning method for pricing strategy of electricity retailer with customers’ flexibility," Applied Energy, Elsevier, vol. 323(C).
    10. Rezaei, Navid & Pezhmani, Yasin & Khazali, Amirhossein, 2022. "Economic-environmental risk-averse optimal heat and power energy management of a grid-connected multi microgrid system considering demand response and bidding strategy," Energy, Elsevier, vol. 240(C).
    11. Behboodi, Sahand & Chassin, David P. & Djilali, Ned & Crawford, Curran, 2018. "Transactive control of fast-acting demand response based on thermostatic loads in real-time retail electricity markets," Applied Energy, Elsevier, vol. 210(C), pages 1310-1320.
    12. 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).
    13. Russo, Marianna & Kraft, Emil & Bertsch, Valentin & Keles, Dogan, 2022. "Short-term risk management of electricity retailers under rising shares of decentralized solar generation," Energy Economics, Elsevier, vol. 109(C).
    14. Guo, Bowei & Weeks, Melvyn, 2022. "Dynamic tariffs, demand response, and regulation in retail electricity markets," Energy Economics, Elsevier, vol. 106(C).
    15. Zhang, Yuanyuan & Zhao, Huiru & Li, Bingkang & Zhao, Yihang & Qi, Ze, 2022. "Research on credit rating and risk measurement of electricity retailers based on Bayesian Best Worst Method-Cloud Model and improved Credit Metrics model in China's power market," Energy, Elsevier, vol. 252(C).
    16. Qiu, Dawei & Wang, Yi & Wang, Junkai & Jiang, Chuanwen & Strbac, Goran, 2023. "Personalized retail pricing design for smart metering consumers in electricity market," Applied Energy, Elsevier, vol. 348(C).
    17. Deng, Tingting & Yan, Wenzhou & Nojavan, Sayyad & Jermsittiparsert, Kittisak, 2020. "Risk evaluation and retail electricity pricing using downside risk constraints method," Energy, Elsevier, vol. 192(C).
    18. Dagoumas, Athanasios S. & Polemis, Michael L., 2017. "An integrated model for assessing electricity retailer’s profitability with demand response," Applied Energy, Elsevier, vol. 198(C), pages 49-64.
    19. 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.
    20. Fan, Songli & Ai, Qian & Piao, Longjian, 2018. "Bargaining-based cooperative energy trading for distribution company and demand response," Applied Energy, Elsevier, vol. 226(C), pages 469-482.

    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:205:y:2020:i:c:s0360544220310331. 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.