IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v239y2022ipcs0360544221024403.html
   My bibliography  Save this article

Psychological insights for incentive-based demand response incorporating battery energy storage systems: A two-loop Stackelberg game approach

Author

Listed:
  • Lin, Jin
  • Dong, Jun
  • Dou, Xihao
  • Liu, Yao
  • Yang, Peiwen
  • Ma, Tongtao

Abstract

Demand-side resources play a significant role in enhancing energy efficiency and decarbonization. Performing demand curtailment will psychologically disturb end-customers' comfort and affect decision-making. The penetration of battery energy storage systems (BESSs) in electricity grids introduces another response resource to the grid operator (GO). Therefore, it's important to investigate the effect of different customer psychological factors (CPFs) on incentive-based demand response (IBDR) strategy in the system with diversified response resources including BESSs. Behavioral economics (BE) interprets individual behavior from psychology and provides insights to behavior modeling. Therefore, this paper applied BE to incorporate CPFs, such as the endowment effect and time-discounting effect. Furthermore, to bring the value of CPFs to the system level, an IBDR model considering CPFs and BESSs (CE-IBDR) is proposed by following the Stackelberg game theory. Upon the participation of load aggregators (LAs) and BESSs operator (EO) in IBDR, the model extends two-party hierarchical levels to four-party spinning from the GO, EOs, LAs and end-customers by extending the two-party Stackelberg game to a two-loop Stackelberg game. Results show that without incorporating CPFs into the model will result deviation in interpreting customer behavior. BESSs is preferred reponse resource than load reduction due to the pressure to incentive end-customers with high endowment valuation.

Suggested Citation

  • Lin, Jin & Dong, Jun & Dou, Xihao & Liu, Yao & Yang, Peiwen & Ma, Tongtao, 2022. "Psychological insights for incentive-based demand response incorporating battery energy storage systems: A two-loop Stackelberg game approach," Energy, Elsevier, vol. 239(PC).
  • Handle: RePEc:eee:energy:v:239:y:2022:i:pc:s0360544221024403
    DOI: 10.1016/j.energy.2021.122192
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221024403
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2021.122192?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. Yu, Mengmeng & Hong, Seung Ho, 2017. "Incentive-based demand response considering hierarchical electricity market: A Stackelberg game approach," Applied Energy, Elsevier, vol. 203(C), pages 267-279.
    2. Xu, Bo & Wang, Jiexin & Guo, Mengyuan & Lu, Jiayu & Li, Gehui & Han, Liang, 2021. "A hybrid demand response mechanism based on real-time incentive and real-time pricing," Energy, Elsevier, vol. 231(C).
    3. Good, Nicholas, 2019. "Using behavioural economic theory in modelling of demand response," Applied Energy, Elsevier, vol. 239(C), pages 107-116.
    4. Lu, Qing & Lü, Shuaikang & Leng, Yajun & Zhang, Zhixin, 2020. "Optimal household energy management based on smart residential energy hub considering uncertain behaviors," Energy, Elsevier, vol. 195(C).
    5. Penelope Buckley & Daniel Llerena, 2018. "Demand response as a common pool resource game: Nudges or peak pricing," Post-Print hal-02088180, HAL.
    6. Chen, Yongbao & Zhang, Lixin & Xu, Peng & Di Gangi, Alessandra, 2021. "Electricity demand response schemes in China: Pilot study and future outlook," Energy, Elsevier, vol. 224(C).
    7. Penelope Buckley & Daniel Llerena, 2018. "Demand response as a common pool resource game: Nudges versus prices," Post-Print hal-02088148, HAL.
    8. Lu, Xiaoxing & Li, Kangping & Xu, Hanchen & Wang, Fei & Zhou, Zhenyu & Zhang, Yagang, 2020. "Fundamentals and business model for resource aggregator of demand response in electricity markets," Energy, Elsevier, vol. 204(C).
    9. Delgado, Laura & Shealy, Tripp, 2018. "Opportunities for greater energy efficiency in government facilities by aligning decision structures with advances in behavioral science," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3952-3961.
    10. Sorrell, Steve, 2015. "Reducing energy demand: A review of issues, challenges and approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 74-82.
    11. Mier, Mathias & Weissbart, Christoph, 2020. "Power markets in transition: Decarbonization, energy efficiency, and short-term demand response," Energy Economics, Elsevier, vol. 86(C).
    12. Kahneman, Daniel & Knetsch, Jack L & Thaler, Richard H, 1990. "Experimental Tests of the Endowment Effect and the Coase Theorem," Journal of Political Economy, University of Chicago Press, vol. 98(6), pages 1325-1348, December.
    13. Penelope Buckley & Daniel Llerena, 2018. "Demand response as a common pool resource game: Nudges or peak pricing," Post-Print hal-02088189, HAL.
    14. Stede, Jan & Arnold, Karin & Dufter, Christa & Holtz, Georg & von Roon, Serafin & Richstein, Jörn C., 2020. "The role of aggregators in facilitating industrial demand response: Evidence from Germany," Energy Policy, Elsevier, vol. 147(C).
    15. Buckley, P. & Llerena, D., 2018. "Demand response as a common pool resource game: Nudges or peak prices," Working Papers 2018-01, Grenoble Applied Economics Laboratory (GAEL).
    16. Penelope Buckley & Daniel Llerena, 2018. "Demand response as a common pool resource game: Nudges versus prices," Working Papers hal-01704457, HAL.
    17. Shahryari, E. & Shayeghi, H. & Mohammadi-ivatloo, B. & Moradzadeh, M., 2018. "An improved incentive-based demand response program in day-ahead and intra-day electricity markets," Energy, Elsevier, vol. 155(C), pages 205-214.
    18. Zhou, Hou Sheng & Passey, Rob & Bruce, Anna & Sproul, Alistair B., 2021. "Aggregated impact of coordinated commercial-scale battery energy storage systems on network peak demand, and financial outcomes," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    19. Zhang, Lizhi & Kuang, Jiyuan & Sun, Bo & Li, Fan & Zhang, Chenghui, 2020. "A two-stage operation optimization method of integrated energy systems with demand response and energy storage," Energy, Elsevier, vol. 208(C).
    20. Carmichael, R. & Gross, R. & Hanna, R. & Rhodes, A. & Green, T., 2021. "The Demand Response Technology Cluster: Accelerating UK residential consumer engagement with time-of-use tariffs, electric vehicles and smart meters via digital comparison tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
    21. Penelope Buckley & Daniel Llerena, 2018. "Demand response as a common pool resource game: Nudges or peak pricing," Post-Print hal-02088223, HAL.
    22. Cao, Jing & Dai, Hancheng & Li, Shantong & Guo, Chaoyi & Ho, Mun & Cai, Wenjia & He, Jianwu & Huang, Hai & Li, Jifeng & Liu, Yu & Qian, Haoqi & Wang, Can & Wu, Libo & Zhang, Xiliang, 2021. "The general equilibrium impacts of carbon tax policy in China: A multi-model comparison," Energy Economics, Elsevier, vol. 99(C).
    23. Penelope Buckley & Daniel Llerena, 2018. "Demand response as a common pool resource game: Nudges or peak pricing," Post-Print hal-02485480, HAL.
    24. Motlagh, Omid & Berry, Adam & O'Neil, Lachlan, 2019. "Clustering of residential electricity customers using load time series," Applied Energy, Elsevier, vol. 237(C), pages 11-24.
    25. Sun, Yannan & Hao, Weituo & Chen, Yan & Liu, Bing, 2020. "Data-driven occupant-behavior analytics for residential buildings," Energy, Elsevier, vol. 206(C).
    26. Jan Stede & Karin Arnold & Christa Dufter & Georg Holtz & Serafin von Roon & Jörn C. Richstein, 2020. "The Role of Aggregators in Facilitating Industrial Demand Response: Evidence from Germany," Discussion Papers of DIW Berlin 1840, DIW Berlin, German Institute for Economic Research.
    27. Honarmand, Mohammad Esmaeil & Hosseinnezhad, Vahid & Ghazizadeh, Mohammad Sadegh & Wang, Fei & Siano, Pierluigi, 2019. "A peak-load-reduction-based procedure to manage distribution network expansion by applying process-oriented costing of incoming components," Energy, Elsevier, vol. 186(C).
    28. Gerd Gigerenzer & Reinhard Selten (ed.), 2002. "Bounded Rationality: The Adaptive Toolbox," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262571641, December.
    29. Rana, Md Masud & Romlie, Mohd Fakhizan & Abdullah, Mohd Faris & Uddin, Moslem & Sarkar, Md Rasel, 2021. "A novel peak load shaving algorithm for isolated microgrid using hybrid PV-BESS system," Energy, Elsevier, vol. 234(C).
    30. Sirin, Selahattin Murat & Gonul, Mustafa Sinan, 2016. "Behavioral aspects of regulation: A discussion on switching and demand response in Turkish electricity market," Energy Policy, Elsevier, vol. 97(C), pages 591-602.
    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. Dewangan, Chaman Lal & Vijayan, Vineeth & Shukla, Devesh & Chakrabarti, S. & Singh, S.N. & Sharma, Ankush & Hossain, Md. Alamgir, 2023. "An improved decentralized scheme for incentive-based demand response from residential customers," Energy, Elsevier, vol. 284(C).
    2. Liu, Di & Qin, Zhaoming & Hua, Haochen & Ding, Yi & Cao, Junwei, 2023. "Incremental incentive mechanism design for diversified consumers in demand response," Applied Energy, Elsevier, vol. 329(C).
    3. Tao, Peng & Xu, Fei & Dong, Zengbo & Zhang, Chao & Peng, Xuefeng & Zhao, Junpeng & Li, Kangping & Wang, Fei, 2022. "Graph convolutional network-based aggregated demand response baseline load estimation," Energy, Elsevier, vol. 251(C).
    4. Tang, Hong & Wang, Shengwei, 2023. "Game-theoretic optimization of demand-side flexibility engagement considering the perspectives of different stakeholders and multiple flexibility services," Applied Energy, Elsevier, vol. 332(C).
    5. Qi Huang & Aihua Jiang & Yu Zeng & Jianan Xu, 2022. "Community Flexible Load Dispatching Model Based on Herd Mentality," Energies, MDPI, vol. 15(13), pages 1-18, June.
    6. Zheng, Shunlin & Qi, Qi & Sun, Yi & Ai, Xin, 2023. "Integrated demand response considering substitute effect and time-varying response characteristics under incomplete information," Applied Energy, Elsevier, vol. 333(C).
    7. Liu, Zhengguang & Guo, Zhiling & Chen, Qi & Song, Chenchen & Shang, Wenlong & Yuan, Meng & Zhang, Haoran, 2023. "A review of data-driven smart building-integrated photovoltaic systems: Challenges and objectives," Energy, Elsevier, vol. 263(PE).

    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. 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).
    2. Good, Nicholas, 2019. "Using behavioural economic theory in modelling of demand response," Applied Energy, Elsevier, vol. 239(C), pages 107-116.
    3. Lin, Jin & Dong, Jun & Liu, Dongran & Zhang, Yaoyu & Ma, Tongtao, 2022. "From peak shedding to low-carbon transitions: Customer psychological factors in demand response," Energy, Elsevier, vol. 238(PA).
    4. Attallah, May & Abildtrup, Jens & Stenger, Anne, 2022. "Non-monetary incentives for sustainable biomass harvest: An experimental approach," Resource and Energy Economics, Elsevier, vol. 69(C).
    5. Antinyan, Armenak & Horváth, Gergely & Jia, Mofei, 2020. "Curbing the consumption of positional goods: Behavioral interventions versus taxation," Journal of Economic Behavior & Organization, Elsevier, vol. 179(C), pages 1-21.
    6. Adrian Tantau & András Puskás-Tompos & Laurentiu Fratila & Costel Stanciu, 2021. "Acceptance of Demand Response and Aggregators as a Solution to Optimize the Relation between Energy Producers and Consumers in order to Increase the Amount of Renewable Energy in the Grid," Energies, MDPI, vol. 14(12), pages 1-19, June.
    7. Adrian Tantau & András Puskás-Tompos & Costel Stanciu & Laurentiu Fratila & Catalin Curmei, 2021. "Key Factors Which Contribute to the Participation of Consumers in Demand Response Programs and Enable the Proliferation of Renewable Energy Sources," Energies, MDPI, vol. 14(24), pages 1-22, December.
    8. Máximo A. Domínguez-Garabitos & Víctor S. Ocaña-Guevara & Félix Santos-García & Adriana Arango-Manrique & Miguel Aybar-Mejía, 2022. "A Methodological Proposal for Implementing Demand-Shifting Strategies in the Wholesale Electricity Market," Energies, MDPI, vol. 15(4), pages 1-28, February.
    9. Golmohamadi, Hessam, 2022. "Demand-side management in industrial sector: A review of heavy industries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    10. Xu, Bo & Wang, Jiexin & Guo, Mengyuan & Lu, Jiayu & Li, Gehui & Han, Liang, 2021. "A hybrid demand response mechanism based on real-time incentive and real-time pricing," Energy, Elsevier, vol. 231(C).
    11. Leandra Scharnhorst & Thorben Sandmeier & Armin Ardone & Wolf Fichtner, 2021. "The Impact of Economic and Non-Economic Incentives to Induce Residential Demand Response—Findings from a Living Lab Experiment," Energies, MDPI, vol. 14(8), pages 1-24, April.
    12. Morales-España, Germán & Martínez-Gordón, Rafael & Sijm, Jos, 2022. "Classifying and modelling demand response in power systems," Energy, Elsevier, vol. 242(C).
    13. Chen, Ting & Vandendriessche, Frederik, 2023. "Enabling independent flexibility service providers to participate in electricity markets: A legal analysis of the Belgium case," Utilities Policy, Elsevier, vol. 81(C).
    14. Clement A. Tisdell, 2017. "Bounded Rationality, Satisficing and the Evolution of Economic Thought," Economic Theory, Applications and Issues Working Papers 264873, University of Queensland, School of Economics.
    15. Wen, Lulu & Zhou, Kaile & Li, Jun & Wang, Shanyong, 2020. "Modified deep learning and reinforcement learning for an incentive-based demand response model," Energy, Elsevier, vol. 205(C).
    16. Liu, Jia & Zeng, Peter Pingliang & Xing, Hao & Li, Yalou & Wu, Qiuwei, 2020. "Hierarchical duality-based planning of transmission networks coordinating active distribution network operation," Energy, Elsevier, vol. 213(C).
    17. Fioriti, Davide & Frangioni, Antonio & Poli, Davide, 2021. "Optimal sizing of energy communities with fair revenue sharing and exit clauses: Value, role and business model of aggregators and users," Applied Energy, Elsevier, vol. 299(C).
    18. Schwidtal, Jan Marc & Agostini, Marco & Coppo, Massimiliano & Bignucolo, Fabio & Lorenzoni, Arturo, 2023. "Optimized operation of distributed energy resources: The opportunities of value stacking for Power-to-Gas aggregated with PV," Applied Energy, Elsevier, vol. 334(C).
    19. Das, Laya & Garg, Dinesh & Srinivasan, Babji, 2020. "NeuralCompression: A machine learning approach to compress high frequency measurements in smart grid," Applied Energy, Elsevier, vol. 257(C).
    20. Maic Rakitta & Jannis Wernery, 2021. "Cognitive Biases in Building Energy Decisions," Sustainability, MDPI, vol. 13(17), pages 1-21, September.

    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:239:y:2022:i:pc:s0360544221024403. 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.