Alghanmi Nouf Atiahallah, Alkhudhayr Hanadi
Faculty of Computing and Information Technology, Department of Information Technology, King Abdulaziz University, Rabigh, 21911, Saudi Arabia.
Faculty of Computing and Information Technology, Department of Information Systems, King Abdulaziz University, Rabigh, 21911, Saudi Arabia.
Heliyon. 2024 Aug 30;10(17):e36948. doi: 10.1016/j.heliyon.2024.e36948. eCollection 2024 Sep 15.
Peer-to-peer (P2P) energy trading is an innovative concept poised to transform energy demand management and utilization. EnergyShare AI is a powerful peer-to-peer energy exchange system that operates on a P2P model that integrates advanced machine learning with distributed energy sharing. This paper presents EnergyShare AI, a technology that connects consumers and prosumers through solar arrays, energy storage systems (ESS), and electric vehicles (EVs). Using Deep Reinforcement Learning (DRL) algorithms, Energy Share AI significantly improves energy management efficiency and substantially reduces costs. Our approach offers several advantages over traditional linear integer programming models, particularly in optimizing bidirectional energy transfer involving EVs and highlighting the critical role of ESS and photovoltaic (PV) systems in facilitating efficient P2P energy trading. Our research results show that successful P2P exchange can lead to significant cost savings and improved sustainability, thereby increasing the amount of energy transferred between different household profiles and stages of human development.
对等（P2P）能源交易是一个创新概念，有望改变能源需求管理和利用方式。EnergyShare AI是一个强大的对等能源交换系统，它基于将先进机器学习与分布式能源共享相结合的P2P模型运行。本文介绍了EnergyShare AI，这是一种通过太阳能阵列、储能系统（ESS）和电动汽车（EV）将消费者和产消者连接起来的技术。使用深度强化学习（DRL）算法，Energy Share AI显著提高了能源管理效率并大幅降低了成本。我们的方法相对于传统线性整数规划模型具有多个优势，特别是在优化涉及电动汽车的双向能源传输方面，并突出了ESS和光伏（PV）系统在促进高效P2P能源交易中的关键作用。我们的研究结果表明，成功的P2P交换可以带来显著的成本节约并提高可持续性，从而增加不同家庭类型和人类发展阶段之间的能源传输量。
