IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v284y2023ics0360544223019862.html
   My bibliography  Save this article

Optimal bidding strategy for virtual power plant participating in combined electricity and ancillary services market considering dynamic demand response price and integrated consumption satisfaction

Author

Listed:
  • Mei, Shufan
  • Tan, Qinliang
  • Liu, Yuan
  • Trivedi, Anupam
  • Srinivasan, Dipti

Abstract

The virtual power plant (VPP) plays an important role in managing distributed energy by integrating renewable energy sources, energy storage systems and dispatchable loads. It can not only provide peak regulation services as good flexible resources, but also participate in the electricity market for additional profit. This paper presents a multimarket model to develop an optimal bidding strategy for VPP. To enhance the effectiveness of demand response, a fixed time of use price is converted into a dynamic response price. The integrated consumption satisfaction is quantified from both comfort and economy perspectives. Multi-objective optimization is carried out to maximize market profit of VPP as well as consumer satisfaction. The results show that: (1) Compared to time of use prices, VPP's profit and consumers' satisfaction level increased by 12.46% and 3.26% respectively under dynamic response prices. (2) The increase in the market profit of VPP is accompanied by a gradual decline in integrated consumption satisfaction. There are two inflection points in the process of decline, occurring at satisfaction levels of 1.01 and 0.98 respectively. (3) The multi-objective optimization strategy can achieve a win-win situation for both its operator and the internal consumers.

Suggested Citation

  • Mei, Shufan & Tan, Qinliang & Liu, Yuan & Trivedi, Anupam & Srinivasan, Dipti, 2023. "Optimal bidding strategy for virtual power plant participating in combined electricity and ancillary services market considering dynamic demand response price and integrated consumption satisfaction," Energy, Elsevier, vol. 284(C).
    • Handle: RePEc:eee:energy:v:284:y:2023:i:c:s0360544223019862
    DOI: 10.1016/j.energy.2023.128592
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223019862
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.128592?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. Löschenbrand, Markus, 2021. "Modeling competition of virtual power plants via deep learning," Energy, Elsevier, vol. 214(C).
    2. Banaei, Mohsen & Oloomi-Buygi, Majid & Zabetian-Hosseini, Seyed-Mahdi, 2018. "Strategic gaming of wind power producers joined with thermal units in electricity markets," Renewable Energy, Elsevier, vol. 115(C), pages 1067-1074.
    3. Luo, Zhe & Hong, SeungHo & Ding, YueMin, 2019. "A data mining-driven incentive-based demand response scheme for a virtual power plant," Applied Energy, Elsevier, vol. 239(C), pages 549-559.
    4. Shafiekhani, Morteza & Ahmadi, Abdollah & Homaee, Omid & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "Optimal bidding strategy of a renewable-based virtual power plant including wind and solar units and dispatchable loads," Energy, Elsevier, vol. 239(PD).
    5. Kong, Xiangyu & Lu, Wenqi & Wu, Jianzhong & Wang, Chengshan & Zhao, Xv & Hu, Wei & Shen, Yu, 2023. "Real-time pricing method for VPP demand response based on PER-DDPG algorithm," Energy, Elsevier, vol. 271(C).
    6. Kim, Jihyo & Lee, Soomin & Jang, Heesun, 2022. "Lessons from residential electricity demand analysis on the time of use pricing experiment in South Korea," Energy Economics, Elsevier, vol. 113(C).
    7. Hadayeghparast, Shahrzad & SoltaniNejad Farsangi, Alireza & Shayanfar, Heidarali, 2019. "Day-ahead stochastic multi-objective economic/emission operational scheduling of a large scale virtual power plant," Energy, Elsevier, vol. 172(C), pages 630-646.
    8. Ming, Hao & Meng, Jing & Gao, Ciwei & Song, Meng & Chen, Tao & Choi, Dae-Hyun, 2023. "Efficiency improvement of decentralized incentive-based demand response: Social welfare analysis and market mechanism design," Applied Energy, Elsevier, vol. 331(C).
    9. Fan, Linyuan & Ji, Dandan & Lin, Geng & Lin, Peng & Liu, Lixi, 2023. "Information gap-based multi-objective optimization of a virtual energy hub plant considering a developed demand response model," Energy, Elsevier, vol. 276(C).
    10. Zhang, Tianhan & Qiu, Weiqiang & Zhang, Zhi & Lin, Zhenzhi & Ding, Yi & Wang, Yiting & Wang, Lianfang & Yang, Li, 2023. "Optimal bidding strategy and profit allocation method for shared energy storage-assisted VPP in joint energy and regulation markets," Applied Energy, Elsevier, vol. 329(C).
    11. Tan, Caixia & Wang, Jing & Geng, Shiping & Pu, Lei & Tan, Zhongfu, 2021. "Three-level market optimization model of virtual power plant with carbon capture equipment considering copula–CVaR theory," Energy, Elsevier, vol. 237(C).
    12. Lu, Qing & Zhang, Yufeng, 2022. "A multi-objective optimization model considering users' satisfaction and multi-type demand response in dynamic electricity price," Energy, Elsevier, vol. 240(C).
    13. Silva, Ana R. & Pousinho, H.M.I. & Estanqueiro, Ana, 2022. "A multistage stochastic approach for the optimal bidding of variable renewable energy in the day-ahead, intraday and balancing markets," Energy, Elsevier, vol. 258(C).
    14. Wei, Congying & Xu, Jian & Liao, Siyang & Sun, Yuanzhang & Jiang, Yibo & Ke, Deping & Zhang, Zhen & Wang, Jing, 2018. "A bi-level scheduling model for virtual power plants with aggregated thermostatically controlled loads and renewable energy," Applied Energy, Elsevier, vol. 224(C), pages 659-670.
    15. Li, Jinghua & Lu, Bo & Wang, Zhibang & Zhu, Mengshu, 2021. "Bi-level optimal planning model for energy storage systems in a virtual power plant," Renewable Energy, Elsevier, vol. 165(P2), pages 77-95.
    16. Chang, Weiguang & Dong, Wei & Wang, Yubin & Yang, Qiang, 2022. "Two-stage coordinated operation framework for virtual power plant with aggregated multi-stakeholder microgrids in a deregulated electricity market," Renewable Energy, Elsevier, vol. 199(C), pages 943-956.
    17. Khan, Irfan & Zakari, Abdulrasheed & Zhang, Jinjun & Dagar, Vishal & Singh, Sanjeet, 2022. "A study of trilemma energy balance, clean energy transitions, and economic expansion in the midst of environmental sustainability: New insights from three trilemma leadership," Energy, Elsevier, vol. 248(C).
    18. Zhou, Huan & Fan, Shuai & Wu, Qing & Dong, Lianxin & Li, Zuyi & He, Guangyu, 2021. "Stimulus-response control strategy based on autonomous decentralized system theory for exploitation of flexibility by virtual power plant," Applied Energy, Elsevier, vol. 285(C).
    19. Tan, Qinliang & Ding, Yihong & Zheng, Jin & Dai, Mei & Zhang, Yimei, 2021. "The effects of carbon emissions trading and renewable portfolio standards on the integrated wind–photovoltaic–thermal power-dispatching system: Real case studies in China," Energy, Elsevier, vol. 222(C).
    20. Fusco, Andrea & Gioffrè, Domenico & Francesco Castelli, Alessandro & Bovo, Cristian & Martelli, Emanuele, 2023. "A multi-stage stochastic programming model for the unit commitment of conventional and virtual power plants bidding in the day-ahead and ancillary services markets," Applied Energy, Elsevier, vol. 336(C).
    21. Ju, Liwei & Zhao, Rui & Tan, Qinliang & Lu, Yan & Tan, Qingkun & Wang, Wei, 2019. "A multi-objective robust scheduling model and solution algorithm for a novel virtual power plant connected with power-to-gas and gas storage tank considering uncertainty and demand response," Applied Energy, Elsevier, vol. 250(C), pages 1336-1355.
    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. Zhou, Kaile & Peng, Ning & Yin, Hui & Hu, Rong, 2023. "Urban virtual power plant operation optimization with incentive-based demand response," Energy, Elsevier, vol. 282(C).
    2. Naval, Natalia & Yusta, Jose M., 2021. "Virtual power plant models and electricity markets - A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    3. Wafa Nafkha-Tayari & Seifeddine Ben Elghali & Ehsan Heydarian-Forushani & Mohamed Benbouzid, 2022. "Virtual Power Plants Optimization Issue: A Comprehensive Review on Methods, Solutions, and Prospects," Energies, MDPI, vol. 15(10), pages 1-20, May.
    4. Yang, Shenbo & Fang, Jiangpeng & Zhang, Zheyu & Lv, ShuoShuo & Lin, Hongyu & Ju, Liwei, 2024. "Two-stage coordinated optimal dispatching model and benefit allocation strategy for rural new energy microgrid," Energy, Elsevier, vol. 292(C).
    5. 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).
    6. Lau, Jat-Syu & Jiang, Yihuo & Li, Ziyuan & Qian, Qian, 2023. "Stochastic trading of storage systems in short term electricity markets considering intraday demand response market," Energy, Elsevier, vol. 280(C).
    7. Ju, Liwei & Yin, Zhe & Lu, Xiaolong & Yang, Shenbo & Li, Peng & Rao, Rao & Tan, Zhongfu, 2022. "A Tri-dimensional Equilibrium-based stochastic optimal dispatching model for a novel virtual power plant incorporating carbon Capture, Power-to-Gas and electric vehicle aggregator," Applied Energy, Elsevier, vol. 324(C).
    8. Mostafa Darvishi & Mehrdad Tahmasebi & Ehsan Shokouhmand & Jagadeesh Pasupuleti & Pitshou Bokoro & Jwan Satei Raafat, 2023. "Optimal Operation of Sustainable Virtual Power Plant Considering the Amount of Emission in the Presence of Renewable Energy Sources and Demand Response," Sustainability, MDPI, vol. 15(14), pages 1-25, July.
    9. Mohammad Mohammadi Roozbehani & Ehsan Heydarian-Forushani & Saeed Hasanzadeh & Seifeddine Ben Elghali, 2022. "Virtual Power Plant Operational Strategies: Models, Markets, Optimization, Challenges, and Opportunities," Sustainability, MDPI, vol. 14(19), pages 1-23, September.
    10. Liu, Xin & Li, Yang & Wang, Li & Tang, Junbo & Qiu, Haifeng & Berizzi, Alberto & Valentin, Ilea & Gao, Ciwei, 2024. "Dynamic aggregation strategy for a virtual power plant to improve flexible regulation ability," Energy, Elsevier, vol. 297(C).
    11. Zhao, Kaifang & Qiu, Kai & Yan, Jian & Shaker, Mir Pasha, 2023. "Technical and economic operation of VPPs based on competitive bi–level negotiations," Energy, Elsevier, vol. 282(C).
    12. Dong, Lianxin & Fan, Shuai & Wang, Zhihua & Xiao, Jucheng & Zhou, Huan & Li, Zuyi & He, Guangyu, 2021. "An adaptive decentralized economic dispatch method for virtual power plant," Applied Energy, Elsevier, vol. 300(C).
    13. Ju, Liwei & Yin, Zhe & Zhou, Qingqing & Li, Qiaochu & Wang, Peng & Tian, Wenxu & Li, Peng & Tan, Zhongfu, 2022. "Nearly-zero carbon optimal operation model and benefit allocation strategy for a novel virtual power plant using carbon capture, power-to-gas, and waste incineration power in rural areas," Applied Energy, Elsevier, vol. 310(C).
    14. Ju, Liwei & Lv, ShuoShuo & Zhang, Zheyu & Li, Gen & Gan, Wei & Fang, Jiangpeng, 2024. "Data-driven two-stage robust optimization dispatching model and benefit allocation strategy for a novel virtual power plant considering carbon-green certificate equivalence conversion mechanism," Applied Energy, Elsevier, vol. 362(C).
    15. Esfahani, Moein & Alizadeh, Ali & Amjady, Nima & Kamwa, Innocent, 2024. "A distributed VPP-integrated co-optimization framework for energy scheduling, frequency regulation, and voltage support using data-driven distributionally robust optimization with Wasserstein metric," Applied Energy, Elsevier, vol. 361(C).
    16. Gao, Hongchao & Jin, Tai & Feng, Cheng & Li, Chuyi & Chen, Qixin & Kang, Chongqing, 2024. "Review of virtual power plant operations: Resource coordination and multidimensional interaction," Applied Energy, Elsevier, vol. 357(C).
    17. Silva-Rodriguez, Lina & Sanjab, Anibal & Fumagalli, Elena & Gibescu, Madeleine, 2024. "Light robust co-optimization of energy and reserves in the day-ahead electricity market," Applied Energy, Elsevier, vol. 353(PA).
    18. Zhang, Xinyue & Guo, Xiaopeng & Zhang, Xingping, 2023. "Bidding modes for renewable energy considering electricity-carbon integrated market mechanism based on multi-agent hybrid game," Energy, Elsevier, vol. 263(PA).
    19. Lin, Xiaojie & Lin, Xueru & Zhong, Wei & Zhou, Yi, 2023. "Predictive operation optimization of multi-energy virtual power plant considering behavior uncertainty of diverse stakeholders," Energy, Elsevier, vol. 280(C).
    20. Rong Xia & Jun Dai & Xiangjie Cheng & Jiaqing Fan & Jing Ye & Qiangang Jia & Sijie Chen & Qiang Zhang, 2024. "Demand Response of Integrated Zero-Carbon Power Plant: Model and Method," Energies, MDPI, vol. 17(14), pages 1-15, July.

    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:284:y:2023:i:c:s0360544223019862. 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.