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Incentive-based demand response considering hierarchical electricity market: A Stackelberg game approach

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  1. 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).
  2. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
  3. Kireem Han & Joohyung Lee & Junkyun Choi, 2017. "Evaluation of Demand-Side Management over Pricing Competition of Multiple Suppliers Having Heterogeneous Energy Sources," Energies, MDPI, vol. 10(9), pages 1-16, September.
  4. Li, Jiamei & Ai, Qian & Yin, Shuangrui & Hao, Ran, 2022. "An aggregator-oriented hierarchical market mechanism for multi-type ancillary service provision based on the two-loop Stackelberg game," Applied Energy, Elsevier, vol. 323(C).
  5. Bhatti, Bilal Ahmad & Broadwater, Robert, 2020. "Distributed Nash Equilibrium Seeking for a Dynamic Micro-grid Energy Trading Game with Non-quadratic Payoffs," Energy, Elsevier, vol. 202(C).
  6. Amna Malik & Zain Ali & Ahmed Bilal Awan & Ahmed G. Abo-Khalil & Guftaar Ahmad Sardar Sidhu, 2018. "Achieving Cost Minimization and Fairness in Multi-Supplier Smart Grid Environment," Energies, MDPI, vol. 11(6), pages 1-17, May.
  7. Xu, Jiuping & Liu, Tingting, 2020. "Technological paradigm-based approaches towards challenges and policy shifts for sustainable wind energy development," Energy Policy, Elsevier, vol. 142(C).
  8. Eissa, M.M., 2018. "First time real time incentive demand response program in smart grid with “i-Energy” management system with different resources," Applied Energy, Elsevier, vol. 212(C), pages 607-621.
  9. 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.
  10. 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.
  11. Okur, Özge & Heijnen, Petra & Lukszo, Zofia, 2021. "Aggregator’s business models in residential and service sectors: A review of operational and financial aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 139(C).
  12. 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).
  13. Bastami, Houman & Shakarami, Mahmoud Reza & Doostizadeh, Meysam, 2021. "A decentralized cooperative framework for multi-area active distribution network in presence of inter-area soft open points," Applied Energy, Elsevier, vol. 300(C).
  14. Hua, Weiqi & Jiang, Jing & Sun, Hongjian & Teng, Fei & Strbac, Goran, 2022. "Consumer-centric decarbonization framework using Stackelberg game and Blockchain," Applied Energy, Elsevier, vol. 309(C).
  15. Qiu, Haifeng & You, Fengqi, 2020. "Decentralized-distributed robust electric power scheduling for multi-microgrid systems," Applied Energy, Elsevier, vol. 269(C).
  16. 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).
  17. Suchitra Dayalan & Sheikh Suhaib Gul & Rajarajeswari Rathinam & George Fernandez Savari & Shady H. E. Abdel Aleem & Mohamed A. Mohamed & Ziad M. Ali, 2022. "Multi-Stage Incentive-Based Demand Response Using a Novel Stackelberg–Particle Swarm Optimization," Sustainability, MDPI, vol. 14(17), pages 1-25, September.
  18. Paraskevas Koukaras & Paschalis Gkaidatzis & Napoleon Bezas & Tommaso Bragatto & Federico Carere & Francesca Santori & Marcel Antal & Dimosthenis Ioannidis & Christos Tjortjis & Dimitrios Tzovaras, 2021. "A Tri-Layer Optimization Framework for Day-Ahead Energy Scheduling Based on Cost and Discomfort Minimization," Energies, MDPI, vol. 14(12), pages 1-24, June.
  19. 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.
  20. Meng, Yan & Fan, Shuai & Shen, Yu & Xiao, Jucheng & He, Guangyu & Li, Zuyi, 2023. "Transmission and distribution network-constrained large-scale demand response based on locational customer directrix load for accommodating renewable energy," Applied Energy, Elsevier, vol. 350(C).
  21. 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).
  22. 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.
  23. Zhang, Xiongfeng & Lu, Renzhi & Jiang, Junhui & Hong, Seung Ho & Song, Won Seok, 2021. "Testbed implementation of reinforcement learning-based demand response energy management system," Applied Energy, Elsevier, vol. 297(C).
  24. 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.
  25. Huang, Yujing & Wang, Yudong & Liu, Nian, 2022. "A two-stage energy management for heat-electricity integrated energy system considering dynamic pricing of Stackelberg game and operation strategy optimization," Energy, Elsevier, vol. 244(PA).
  26. Teng, Sin Yong & Máša, Vítězslav & Touš, Michal & Vondra, Marek & Lam, Hon Loong & Stehlík, Petr, 2022. "Waste-to-energy forecasting and real-time optimization: An anomaly-aware approach," Renewable Energy, Elsevier, vol. 181(C), pages 142-155.
  27. Haider, Rabab & Annaswamy, Anuradha M., 2022. "A hybrid architecture for volt-var control in active distribution grids," Applied Energy, Elsevier, vol. 312(C).
  28. 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).
  29. Zhang, Menglin & Ai, Xiaomeng & Fang, Jiakun & Yao, Wei & Zuo, Wenping & Chen, Zhe & Wen, Jinyu, 2018. "A systematic approach for the joint dispatch of energy and reserve incorporating demand response," Applied Energy, Elsevier, vol. 230(C), pages 1279-1291.
  30. Zheng, Shunlin & Sun, Yi & Li, Bin & Qi, Bing & Zhang, Xudong & Li, Fei, 2021. "Incentive-based integrated demand response for multiple energy carriers under complex uncertainties and double coupling effects," Applied Energy, Elsevier, vol. 283(C).
  31. 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).
  32. Yongxiu He & Wei Xiong & Binyou Yang & Hai-yan Yang & Jiu-fang Zhou & Ming-li Cui & Yan Li, 2022. "Combined game model and investment decision making of power grid-distributed energy system," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(6), pages 8667-8690, June.
  33. Bo wang & Nana Deng & Wenhui Zhao & Zhaohua Wang, 2022. "Residential power demand side management optimization based on fine-grained mixed frequency data," Annals of Operations Research, Springer, vol. 316(1), pages 603-622, September.
  34. 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.
  35. Gabriel Santos & Tiago Pinto & Zita Vale & Rui Carvalho & Brígida Teixeira & Carlos Ramos, 2021. "Upgrading BRICKS—The Context-Aware Semantic Rule-Based System for Intelligent Building Energy and Security Management," Energies, MDPI, vol. 14(15), pages 1-14, July.
  36. 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).
  37. Zeng, Huibin & Shao, Bilin & Dai, Hongbin & Tian, Ning & Zhao, Wei, 2023. "Incentive-based demand response strategies for natural gas considering carbon emissions and load volatility," Applied Energy, Elsevier, vol. 348(C).
  38. 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).
  39. Kaijun Lin & Junyong Wu & Di Liu & Dezhi Li & Taorong Gong, 2018. "Energy Management of Combined Cooling, Heating and Power Micro Energy Grid Based on Leader-Follower Game Theory," Energies, MDPI, vol. 11(3), pages 1-21, March.
  40. Tsaousoglou, Georgios & Giraldo, Juan S. & Paterakis, Nikolaos G., 2022. "Market Mechanisms for Local Electricity Markets: A review of models, solution concepts and algorithmic techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
  41. Fernandez, Edstan & Hossain, M.J. & Nizami, M.S.H., 2018. "Game-theoretic approach to demand-side energy management for a smart neighbourhood in Sydney incorporating renewable resources," Applied Energy, Elsevier, vol. 232(C), pages 245-257.
  42. Bhatti, Bilal Ahmad & Broadwater, Robert, 2019. "Energy trading in the distribution system using a non-model based game theoretic approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  43. Malik, Anam & Haghdadi, Navid & MacGill, Iain & Ravishankar, Jayashri, 2019. "Appliance level data analysis of summer demand reduction potential from residential air conditioner control," Applied Energy, Elsevier, vol. 235(C), pages 776-785.
  44. Gao, Jianwei & Ma, Zeyang & Guo, Fengjia, 2019. "The influence of demand response on wind-integrated power system considering participation of the demand side," Energy, Elsevier, vol. 178(C), pages 723-738.
  45. Park, Keonwoo & Moon, Ilkyeong, 2022. "Multi-agent deep reinforcement learning approach for EV charging scheduling in a smart grid," Applied Energy, Elsevier, vol. 328(C).
  46. 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).
  47. Wang, Lu & Gu, Wei & Wu, Zhi & Qiu, Haifeng & Pan, Guangsheng, 2020. "Non-cooperative game-based multilateral contract transactions in power-heating integrated systems," Applied Energy, Elsevier, vol. 268(C).
  48. Zhang, Tianyang & Pota, Himanshu & Chu, Chi-Cheng & Gadh, Rajit, 2018. "Real-time renewable energy incentive system for electric vehicles using prioritization and cryptocurrency," Applied Energy, Elsevier, vol. 226(C), pages 582-594.
  49. Li, Bo & Li, Xu & Su, Qingyu, 2022. "A system and game strategy for the isolated island electric-gas deeply coupled energy network," Applied Energy, Elsevier, vol. 306(PA).
  50. 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.
  51. 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).
  52. 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).
  53. 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.
  54. Li, Longxi, 2021. "Coordination between smart distribution networks and multi-microgrids considering demand side management: A trilevel framework," Omega, Elsevier, vol. 102(C).
  55. 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.
  56. Mohseni, Soheil & Brent, Alan C. & Kelly, Scott & Browne, Will N. & Burmester, Daniel, 2021. "Strategic design optimisation of multi-energy-storage-technology micro-grids considering a two-stage game-theoretic market for demand response aggregation," Applied Energy, Elsevier, vol. 287(C).
  57. 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.
  58. Zixu Liu & Xiaojun Zeng & Fanlin Meng, 2018. "An Integration Mechanism between Demand and Supply Side Management of Electricity Markets," Energies, MDPI, vol. 11(12), pages 1-23, November.
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