IDEAS home Printed from https://ideas.repec.org/r/eee/appene/v202y2017icp581-596.html

Non-cooperative game-theoretic model of demand response aggregator competition for selling stored energy in storage devices

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. 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).
  2. Motalleb, Mahdi & Annaswamy, Anuradha & Ghorbani, Reza, 2018. "A real-time demand response market through a repeated incomplete-information game," Energy, Elsevier, vol. 143(C), pages 424-438.
  3. Mei, Jie & Chen, Chen & Wang, Jianhui & Kirtley, James L., 2019. "Coalitional game theory based local power exchange algorithm for networked microgrids," Applied Energy, Elsevier, vol. 239(C), pages 133-141.
  4. 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).
  5. Jeseok Ryu & Jinho Kim, 2020. "Non-Cooperative Indirect Energy Trading with Energy Storage Systems for Mitigation of Demand Response Participation Uncertainty," Energies, MDPI, vol. 13(4), pages 1-14, February.
  6. Duan, Jiandong & Liu, Fan & Yang, Yao, 2022. "Optimal operation for integrated electricity and natural gas systems considering demand response uncertainties," Applied Energy, Elsevier, vol. 323(C).
  7. Motalleb, Mahdi & Siano, Pierluigi & Ghorbani, Reza, 2019. "Networked Stackelberg Competition in a Demand Response Market," Applied Energy, Elsevier, vol. 239(C), pages 680-691.
  8. Chen, Yang & Park, Byungkwon & Kou, Xiao & Hu, Mengqi & Dong, Jin & Li, Fangxing & Amasyali, Kadir & Olama, Mohammed, 2020. "A comparison study on trading behavior and profit distribution in local energy transaction games," Applied Energy, Elsevier, vol. 280(C).
  9. Homa Rashidizadeh-Kermani & Hamid Reza Najafi & Amjad Anvari-Moghaddam & Josep M. Guerrero, 2018. "Optimal Decision-Making Strategy of an Electric Vehicle Aggregator in Short-Term Electricity Markets," Energies, MDPI, vol. 11(9), pages 1-20, September.
  10. Elkazaz, Mahmoud & Sumner, Mark & Thomas, David, 2021. "A hierarchical and decentralized energy management system for peer-to-peer energy trading," Applied Energy, Elsevier, vol. 291(C).
  11. Meena, Nand K. & Yang, Jin & Zacharis, Evan, 2019. "Optimisation framework for the design and operation of open-market urban and remote community microgrids," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
  12. Debin Fang & Qiyu Ren & Qian Yu, 2018. "How Elastic Demand Affects Bidding Strategy in Electricity Market: An Auction Approach," Energies, MDPI, vol. 12(1), pages 1-13, December.
  13. Kakkar, Riya & Agrawal, Smita & Tanwar, Sudeep, 2024. "A systematic survey on demand response management schemes for electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 203(C).
  14. Barbero, Mattia & Casals, Lluc Canals & Corchero, Cristina, 2020. "Comparison between economic and environmental drivers for demand side aggregator," Utilities Policy, Elsevier, vol. 65(C).
  15. Wu, Qiong & Xie, Zhun & Ren, Hongbo & Li, Qifen & Yang, Yongwen, 2022. "Optimal trading strategies for multi-energy microgrid cluster considering demand response under different trading modes: A comparison study," Energy, Elsevier, vol. 254(PC).
  16. Li, Yang & Wang, Bin & Yang, Zhen & Li, Jiazheng & Chen, Chen, 2022. "Hierarchical stochastic scheduling of multi-community integrated energy systems in uncertain environments via Stackelberg game," Applied Energy, Elsevier, vol. 308(C).
  17. Jinseok Kim & Hyungseop Hong & Ki-Il Kim, 2018. "Adaptive Optimized Pattern Extracting Algorithm for Forecasting Maximum Electrical Load Duration Using Random Sampling and Cumulative Slope Index," Energies, MDPI, vol. 11(7), pages 1-23, July.
  18. Lüth, Alexandra & Zepter, Jan Martin & Crespo del Granado, Pedro & Egging, Ruud, 2018. "Local electricity market designs for peer-to-peer trading: The role of battery flexibility," Applied Energy, Elsevier, vol. 229(C), pages 1233-1243.
  19. Zhang, Chenghua & Wu, Jianzhong & Zhou, Yue & Cheng, Meng & Long, Chao, 2018. "Peer-to-Peer energy trading in a Microgrid," Applied Energy, Elsevier, vol. 220(C), pages 1-12.
  20. Bin Zhang & Li Sun & Mengyao Yang & Kin-Keung Lai & Bhagwat Ram, 2023. "A Robust Optimization Approach for Smart Energy Market Revenue Management," Energies, MDPI, vol. 16(19), pages 1-14, October.
  21. Arega Getaneh Abate & Dogan Keles & Salim Hassi & Xiufeng Liu & Xiao-Bing Zhang, 2025. "Bidding strategies for energy storage players in 100% renewable electricity market: A game-theoretical approach," Papers 2509.26568, arXiv.org, revised Nov 2025.
  22. Sobhani, Seyed Omid & Sheykhha, Siamak & Madlener, Reinhard, 2020. "An integrated two-level demand-side management game applied to smart energy hubs with storage," Energy, Elsevier, vol. 206(C).
  23. 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.
  24. Ren, Xiaoxiao & Wang, Jinshi & Yang, Sifan & Zhao, Quanbin & Jia, Yifan & Ou, Kejie & Hu, Guangtao & Yan, Junjie, 2025. "A novel multi-objective Stackelberg game model for multi-energy dynamic pricing and flexible scheduling in distributed multi-energy system," Energy, Elsevier, vol. 325(C).
  25. Yu, L. & Li, Y.P. & Huang, G.H. & Fan, Y.R. & Yin, S., 2018. "Planning regional-scale electric power systems under uncertainty: A case study of Jing-Jin-Ji region, China," Applied Energy, Elsevier, vol. 212(C), pages 834-849.
  26. Wang, Haiyang & Zhang, Chenghui & Li, Ke & Liu, Shuai & Li, Shuzhen & Wang, Yu, 2021. "Distributed coordinative transaction of a community integrated energy system based on a tri-level game model," Applied Energy, Elsevier, vol. 295(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.