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Supply–demand balancing for power management in smart grid: A Stackelberg game approach

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Cited by:

  1. Yiqi Dong & Zuoji Dong, 2023. "Bibliometric Analysis of Game Theory on Energy and Natural Resource," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
  2. Feng, Daili & Feng, Yanhui & Qiu, Lin & Li, Pei & Zang, Yuyang & Zou, Hanying & Yu, Zepei & Zhang, Xinxin, 2019. "Review on nanoporous composite phase change materials: Fabrication, characterization, enhancement and molecular simulation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 578-605.
  3. Sajjad Ali & Imran Khan & Sadaqat Jan & Ghulam Hafeez, 2021. "An Optimization Based Power Usage Scheduling Strategy Using Photovoltaic-Battery System for Demand-Side Management in Smart Grid," Energies, MDPI, vol. 14(8), pages 1-29, April.
  4. Nassima Radouane, 2022. "A Comprehensive Review of Composite Phase Change Materials (cPCMs) for Thermal Management Applications, Including Manufacturing Processes, Performance, and Applications," Energies, MDPI, vol. 15(21), pages 1-28, November.
  5. Yao, Jingyuan & Xiao, Erliang & Jian, Xianzhong & Shu, Lingli, 2021. "Service quality and the share of renewable energy in electricity generation," Utilities Policy, Elsevier, vol. 69(C).
  6. András Kovács, 2021. "Inverse optimization approach to the identification of electricity consumer models," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 29(2), pages 521-537, June.
  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. Kai Ma & Shubing Hu & Jie Yang & Chunxia Dou & Josep M. Guerrero, 2017. "Energy Trading and Pricing in Microgrids with Uncertain Energy Supply: A Three-Stage Hierarchical Game Approach," Energies, MDPI, vol. 10(5), pages 1-16, May.
  9. Su, Huai & Zhang, Jinjun & Zio, Enrico & Yang, Nan & Li, Xueyi & Zhang, Zongjie, 2018. "An integrated systemic method for supply reliability assessment of natural gas pipeline networks," Applied Energy, Elsevier, vol. 209(C), pages 489-501.
  10. Abdulaal, Ahmed & Moghaddass, Ramin & Asfour, Shihab, 2017. "Two-stage discrete-continuous multi-objective load optimization: An industrial consumer utility approach to demand response," Applied Energy, Elsevier, vol. 206(C), pages 206-221.
  11. 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.
  12. Lu, Renzhi & Hong, Seung Ho & Zhang, Xiongfeng, 2018. "A Dynamic pricing demand response algorithm for smart grid: Reinforcement learning approach," Applied Energy, Elsevier, vol. 220(C), pages 220-230.
  13. Tengfei Ma & Junyong Wu & Liangliang Hao & Huaguang Yan & Dezhi Li, 2018. "A Real-Time Pricing Scheme for Energy Management in Integrated Energy Systems: A Stackelberg Game Approach," Energies, MDPI, vol. 11(10), pages 1-19, October.
  14. Saeid Esmaeili & Amjad Anvari-Moghaddam & Shahram Jadid, 2019. "Optimal Operational Scheduling of Reconfigurable Multi-Microgrids Considering Energy Storage Systems," Energies, MDPI, vol. 12(9), pages 1-23, May.
  15. Xiaofeng Liu & Qi Wang & Wenting Wang, 2019. "Evolutionary Analysis for Residential Consumer Participating in Demand Response Considering Irrational Behavior," Energies, MDPI, vol. 12(19), pages 1-19, September.
  16. Hoarau, Quentin & Perez, Yannick, 2019. "Network tariff design with prosumers and electromobility: Who wins, who loses?," Energy Economics, Elsevier, vol. 83(C), pages 26-39.
  17. Su, Qingyu & Chen, Cong & Huang, Xin & Li, Jian, 2022. "Interval TrendRank method for grid node importance assessment considering new energy," Applied Energy, Elsevier, vol. 324(C).
  18. Jiang, Qian & Mu, Yunfei & Jia, Hongjie & Cao, Yan & Wang, Zibo & Wei, Wei & Hou, Kai & Yu, Xiaodan, 2022. "A Stackelberg Game-based planning approach for integrated community energy system considering multiple participants," Energy, Elsevier, vol. 258(C).
  19. Hu, Maomao & Xiao, Fu & Wang, Shengwei, 2021. "Neighborhood-level coordination and negotiation techniques for managing demand-side flexibility in residential microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
  20. 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.
  21. Sun, Bo & Li, Mingzhe & Wang, Fan & Xie, Jingdong, 2023. "An incentive mechanism to promote residential renewable energy consumption in China's electricity retail market: A two-level Stackelberg game approach," Energy, Elsevier, vol. 269(C).
  22. Dong, Jingya & Song, Chunhe & Liu, Shuo & Yin, Huanhuan & Zheng, Hao & Li, Yuanjian, 2022. "Decentralized peer-to-peer energy trading strategy in energy blockchain environment: A game-theoretic approach," Applied Energy, Elsevier, vol. 325(C).
  23. Zhang, Xiaoyan & Zhu, Shanying & He, Jianping & Yang, Bo & Guan, Xinping, 2019. "Credit rating based real-time energy trading in microgrids," Applied Energy, Elsevier, vol. 236(C), pages 985-996.
  24. Gao, Hongjun & Cai, Wenhui & He, Shuaijia & Liu, Chang & Liu, Junyong, 2023. "Stackelberg game based energy sharing for zero-carbon community considering reward and punishment of carbon emission," Energy, Elsevier, vol. 277(C).
  25. Li, Xin & Chen, Hsing Hung & Tao, Xiangnan, 2016. "Pricing and capacity allocation in renewable energy," Applied Energy, Elsevier, vol. 179(C), pages 1097-1105.
  26. 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.
  27. Zhang, Wenbin & Tian, Lixin & Wang, Minggang & Zhen, Zaili & Fang, Guochang, 2016. "The evolution model of electricity market on the stable development in China and its dynamic analysis," Energy, Elsevier, vol. 114(C), pages 344-359.
  28. Kim, Jong Suk & Boardman, Richard D. & Bragg-Sitton, Shannon M., 2018. "Dynamic performance analysis of a high-temperature steam electrolysis plant integrated within nuclear-renewable hybrid energy systems," Applied Energy, Elsevier, vol. 228(C), pages 2090-2110.
  29. Ri Piao & Deok-Joo Lee & Taegu Kim, 2020. "Real-Time Pricing Scheme in Smart Grid Considering Time Preference: Game Theoretic Approach," Energies, MDPI, vol. 13(22), pages 1-19, November.
  30. 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.
  31. Wen, Lulu & Zhou, Kaile & Yang, Shanlin & Li, Lanlan, 2018. "Compression of smart meter big data: A survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 59-69.
  32. Kim, H.J. & Kim, M.K., 2023. "A novel deep learning-based forecasting model optimized by heuristic algorithm for energy management of microgrid," Applied Energy, Elsevier, vol. 332(C).
  33. 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).
  34. Zezhong Li & Xiangang Peng & Yilin Xu & Fucheng Zhong & Sheng Ouyang & Kaiguo Xuan, 2023. "A Stackelberg Game-Based Model of Distribution Network-Distributed Energy Storage Systems Considering Demand Response," Mathematics, MDPI, vol. 12(1), pages 1-21, December.
  35. Luan, Wenpeng & Tian, Longfei & Zhao, Bochao, 2023. "Leveraging hybrid probabilistic multi-objective evolutionary algorithm for dynamic tariff design," Applied Energy, Elsevier, vol. 342(C).
  36. Ma, Tengfei & Pei, Wei & Xiao, Hao & Kong, Li & Mu, Yunfei & Pu, Tianjiao, 2020. "The energy management strategies based on dynamic energy pricing for community integrated energy system considering the interactions between suppliers and users," Energy, Elsevier, vol. 211(C).
  37. Lu, Renzhi & Hong, Seung Ho, 2019. "Incentive-based demand response for smart grid with reinforcement learning and deep neural network," Applied Energy, Elsevier, vol. 236(C), pages 937-949.
  38. Zheng, Ling & Zhou, Bin & Cao, Yijia & Wing Or, Siu & Li, Yong & Wing Chan, Ka, 2022. "Hierarchical distributed multi-energy demand response for coordinated operation of building clusters," Applied Energy, Elsevier, vol. 308(C).
  39. Zhong, Shengyuan & Zhao, Jun & Li, Wenjia & Li, Hao & Deng, Shuai & Li, Yang & Hussain, Sajjad & Wang, Xiaoyuan & Zhu, Jiebei, 2021. "Quantitative analysis of information interaction in building energy systems based on mutual information," Energy, Elsevier, vol. 214(C).
  40. Motalleb, Mahdi & Ghorbani, Reza, 2017. "Non-cooperative game-theoretic model of demand response aggregator competition for selling stored energy in storage devices," Applied Energy, Elsevier, vol. 202(C), pages 581-596.
  41. de Souza Dutra, Michael David & Alguacil, Natalia, 2020. "Optimal residential users coordination via demand response: An exact distributed framework," Applied Energy, Elsevier, vol. 279(C).
  42. Eduardo J. Salazar & Mauro Jurado & Mauricio E. Samper, 2023. "Reinforcement Learning-Based Pricing and Incentive Strategy for Demand Response in Smart Grids," Energies, MDPI, vol. 16(3), pages 1-33, February.
  43. Tang, Rui & Wang, Shengwei & Li, Hangxin, 2019. "Game theory based interactive demand side management responding to dynamic pricing in price-based demand response of smart grids," Applied Energy, Elsevier, vol. 250(C), pages 118-130.
  44. 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).
  45. Konstantakopoulos, Ioannis C. & Barkan, Andrew R. & He, Shiying & Veeravalli, Tanya & Liu, Huihan & Spanos, Costas, 2019. "A deep learning and gamification approach to improving human-building interaction and energy efficiency in smart infrastructure," Applied Energy, Elsevier, vol. 237(C), pages 810-821.
  46. Sun, Ya-Fang & Zhang, Yue-Jun & Su, Bin, 2022. "Impact of government subsidy on the optimal R&D and advertising investment in the cooperative supply chain of new energy vehicles," Energy Policy, Elsevier, vol. 164(C).
  47. Faisal Saeed & Anand Paul & Hyuncheol Seo, 2022. "A Hybrid Channel-Communication-Enabled CNN-LSTM Model for Electricity Load Forecasting," Energies, MDPI, vol. 15(6), pages 1-17, March.
  48. 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).
  49. Zhu, Lijing & Zhang, Qi & Lu, Huihui & Li, Hailong & Li, Yan & McLellan, Benjamin & Pan, Xunzhang, 2017. "Study on crowdfunding’s promoting effect on the expansion of electric vehicle charging piles based on game theory analysis," Applied Energy, Elsevier, vol. 196(C), pages 238-248.
  50. Jiang, Yanni & Zhou, Kaile & Lu, Xinhui & Yang, Shanlin, 2020. "Electricity trading pricing among prosumers with game theory-based model in energy blockchain environment," Applied Energy, Elsevier, vol. 271(C).
  51. Li, Bei & Roche, Robin & Paire, Damien & Miraoui, Abdellatif, 2019. "A price decision approach for multiple multi-energy-supply microgrids considering demand response," Energy, Elsevier, vol. 167(C), pages 117-135.
  52. Tamás Kis & András Kovács & Csaba Mészáros, 2021. "On Optimistic and Pessimistic Bilevel Optimization Models for Demand Response Management," Energies, MDPI, vol. 14(8), pages 1-22, April.
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