Enhancing peer-to-peer energy trading in Integrated Energy Systems: Gamified engagement strategies and differentiable robust optimization

被引：0
作者：
Wang, Yanjia [1 ]
Gu, Chenghong [2 ]
Xie, Da [1 ]
Alhazmi, Mohannad [3 ]
Kim, Jinsung [4 ]
Wang, Xitian [1 ]
机构：
[1] Shanghai Jiao Tong Univ, Dept Elect Informat & Elect Engn, Shanghai 200240, Peoples R China
[2] Univ Bath, Dept Elect Engn, Bath BA2 7AY, England
[3] King Saud Univ, Coll Engn, Elect Engn Dept, POB 2454, Riyadh 11421, Saudi Arabia
[4] Cornell Univ, Syst Engn, Ithaca, NY 14853 USA
来源：
ENERGY REPORTS | 2025年 / 13卷
关键词：
Integrated energy systems; Peer-to-peer energy trading; Distributionally robust optimization; Self-Determination Theory; Gamification; Renewable energy integration; Community energy management; COMMUNITY;
D O I：
10.1016/j.egyr.2025.02.034
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
The transition to low-carbon energy systems requires innovative solutions to address renewable energy variability, demand-side uncertainty, and user engagement. Integrated Energy Systems (IES) link electricity, gas, and heat to improve grid flexibility and renewable integration. This paper presents a novel framework for optimizing community energy management in IES, incorporating behavioral insights, advanced optimization, and gamification. Using Self-Determination Theory (SDT) to model user motivations, the framework integrates a differentiable Distributionally Robust Optimization (DRO) layer for uncertainty handling in energy forecasting. Gamification strategies incentivize user participation, aligning individual behavior with system goals. A multi-objective model minimizes costs, enhances flexibility, and promotes sustainability. A case study demonstrates that the framework reduces system costs approximately by 15%, cuts carbon emissions nearly by 20%, and increases user engagement scores roughly by 25% compared to conventional strategies. This work contributes: (1) integrating SDT and DRO for energy management, (2) a DRO layer for robust machine learning, (3) a multi-objective optimization framework, and (4) gamification strategies to enhance user participation. The framework bridges technical and behavioral approaches, offering actionable insights for resilient energy systems.
引用
收藏
页码：3225 / 3236
页数：12
相关论文
共 24 条
  • [1] Behi B., Arefi A., Jennings P., Pivrikas A., Gorjy A., Catalao J.P.S., Consumer engagement in virtual power plants through gamification, 2020 5th International Conference on Power and Renewable Energy, ICPRE, pp. 131-137, (2020)
  • [2] Chen S., Liu P., Li Z., Low carbon transition pathway of power sector with high penetration of renewable energy, Renew. Sustain. Energy Rev., 130, (2020)
  • [3] Dong J., Song C., Liu S., Yin H., Zheng H., Li Y., Decentralized peer-to-peer energy trading strategy in energy blockchain environment: A game-theoretic approach, Appl. Energy, 325, (2022)
  • [4] Dong W., Yang Q., Li W., Zomaya A.Y., Machine-learning-based real-time economic dispatch in sslanding microgrids in a cloud–edge computing environment, IEEE Internet Things J., 8, 17, pp. 13703-13711, (2021)
  • [5] Esmat A., de Vos M., Ghiassi-Farrokhfal Y., Palensky P., Epema D., A novel decentralized platform for peer-to-peer energy trading market with blockchain technology, Appl. Energy, 282, (2021)
  • [6] Lami B., Alferidi A., Alsolami M., Ben Slama S., Peer-to-peer energy exchange, autonomy goals, and financial rewards in Saudi Arabia households, Ain Shams Eng. J., 14, 12, (2023)
  • [7] Li Y., Bu F., Li Y., Long C., Optimal scheduling of island integrated energy systems considering multi-uncertainties and hydrothermal simultaneous transmission: A deep reinforcement learning approach, Appl. Energy, 333, (2023)
  • [8] Li Y., Wang R., Li Y., Zhang M., Long C., Wind power forecasting considering data privacy protection: A federated deep reinforcement learning approach, Appl. Energy, 329, (2023)
  • [9] Li P., Et al., Stochastic robust optimal operation of community integrated energy system based on integrated demand response, Int. J. Electr. Power Energy Syst., 128, (2021)
  • [10] Liu X., Yan Z., Wu J., Optimal coordinated operation of a multi-energy community considering interactions between energy storage and conversion devices, Appl. Energy, 248, pp. 256-273, (2019)
123