Enhancing local energy sharing reliability within peer-to-peer prosumer communities: A cellular automata and deep learning approach

IF 4.8 2区 工程技术 Q2 ENERGY & FUELS Sustainable Energy Grids & Networks Pub Date : 2024-08-13 DOI:10.1016/j.segan.2024.101504
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Abstract

This study introduces a significant advancement in peer-to-peer (P2P) energy trading systems within smart grids, addressing a crucial gap in existing research by incorporating optimal energy storage capacities to accommodate varying energy demands resulting from lifestyle changes. Through a two-level optimization approach, aimed at maximizing self consumption and optimizing energy flow within the grid, we propose a novel energy management strategy. Our contribution lies in the introduction of a new layer of deep learning and rules control, forming a self-energy sharing system for each prosumer. This architecture, termed the smart node, integrates deep learning techniques, to predict and customize energy services through dynamic adjustment of lower and upper bounds of battery capacities. Additionally, we leverage cellular automaton (CA) approaches to establish sustainable consensus among P2P network users, enhancing the adaptability and efficiency of the energy management system. The results show that the proposed algorithm could reduce the energy consumed by the P2P community from the utility by around 20% and maximize the collective self-consumption by around 8% compared to conventional energy trading in microgrids.

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增强点对点专业消费者社区内的本地能源共享可靠性:细胞自动机和深度学习方法
本研究介绍了智能电网中点对点(P2P)能源交易系统的一项重大进展,通过纳入最佳能源存储容量来适应生活方式改变所带来的不同能源需求,从而弥补了现有研究中的一个重要空白。我们提出了一种新颖的能源管理策略,通过两级优化方法实现自我消费最大化和电网内能源流最优化。我们的贡献在于引入了一个新的深度学习和规则控制层,为每个消费者形成了一个自我能源共享系统。这种被称为智能节点的架构整合了深度学习技术,通过动态调整电池容量的上下限来预测和定制能源服务。此外,我们还利用蜂窝自动机(CA)方法在 P2P 网络用户之间建立可持续的共识,从而提高能源管理系统的适应性和效率。研究结果表明,与微电网中的传统能源交易相比,所提出的算法可将 P2P 社区从公用事业部门消耗的能源减少约 20%,并将集体自我消耗最大化约 8%。
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来源期刊
Sustainable Energy Grids & Networks
Sustainable Energy Grids & Networks Energy-Energy Engineering and Power Technology
CiteScore
7.90
自引率
13.00%
发文量
206
审稿时长
49 days
期刊介绍: Sustainable Energy, Grids and Networks (SEGAN)is an international peer-reviewed publication for theoretical and applied research dealing with energy, information grids and power networks, including smart grids from super to micro grid scales. SEGAN welcomes papers describing fundamental advances in mathematical, statistical or computational methods with application to power and energy systems, as well as papers on applications, computation and modeling in the areas of electrical and energy systems with coupled information and communication technologies.
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