数字化对智能电网、可再生能源和需求响应的影响:当前应用的最新回顾

IF 7.1 Q1 ENERGY & FUELS
Mou Mahmood , Prangon Chowdhury , Rahbaar Yeassin , Mahmudul Hasan , Tanvir Ahmad , Nahid-Ur-Rahman Chowdhury
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引用次数: 0

摘要

去碳化、分散化和数字化对先进能源系统(AES)至关重要,其中包括智能电网、可再生能源集成和需求响应措施。数字化是改变全球社会、经济和环境进程的重要趋势。这一转变使我们从传统的电网转向分散的智能网络,从而提高了效率、可靠性和可持续性。通过整合数据和连接性,这些技术优化了能源生产、分配和消费。本文对四种密切相关的新兴技术进行了全面的文献综述:人工智能(AI)、物联网(IoT)、区块链和 AES 中的数字孪生(DT)。前人的研究结果表明,人工智能通过加强能源消耗的预测、优化和管理,极大地改进了需求响应策略。线性回归等技术能有效预测电力需求和总负荷,而支持向量回归(SVR)和强化学习(RL)等更复杂的方法则能优化设备调度和负荷预测。将物联网技术集成到能源管理系统(EMS)中,可通过实时监控和自动控制进一步提高效率和可持续性。此外,DT 技术还有助于模拟能源情景,优化住宅和商业智能电网中的能源消耗。我们的研究结果还强调了区块链在创建去中心化能源交易平台、促进点对点交易以及通过智能合约增强信任方面的作用。从本综述中获得的见解强调了这些新兴技术在支持去中心化、智能化能源网络中的重要作用,为利益相关者提供了有价值的战略,以应对不断发展的数字能源领域的复杂性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Impacts of digitalization on smart grids, renewable energy, and demand response: An updated review of current applications

Impacts of digitalization on smart grids, renewable energy, and demand response: An updated review of current applications
Decarbonization, decentralization, and digitalization are essential for advanced energy systems (AES), which encompass smart grids, renewable energy integration, and demand response initiatives. Digitalization is a significant trend that transforms societal, economic, and environmental processes globally. This shift moves us from traditional power grids to decentralized, intelligent networks that enhance efficiency, reliability, and sustainability. By integrating data and connectivity, these technologies optimize energy production, distribution, and consumption. This article presents a comprehensive literature review of four closely related emerging technologies: Artificial Intelligence (AI), Internet of Things (IoT), Blockchain, and Digital Twin (DT) in AES. Our findings from the previous works indicate that AI significantly improves Demand Response strategies by enhancing the prediction, optimization, and management of energy consumption. Techniques like linear regression effectively predict power demand and aggregated loads, while more complex methods such as Support Vector Regression (SVR) and reinforcement learning (RL) optimize appliance scheduling and load forecasting. The integration of IoT technologies into Energy Management Systems (EMS) further enhances efficiency and sustainability through real-time monitoring and automated control. Additionally, DT technology aids in simulating energy scenarios and optimizing consumption in both residential and commercial smart grids. Our findings also emphasize blockchain’s role in creating decentralized energy trading platforms, facilitating peer-to-peer transactions, and enhancing trust through smart contracts. The insights gained from this review highlight the essential role of these emerging technologies in supporting decentralized, intelligent energy networks, offering valuable strategies for stakeholders to navigate the complexities of the evolving digital energy landscape.
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来源期刊
CiteScore
8.80
自引率
3.20%
发文量
180
审稿时长
58 days
期刊介绍: Energy Conversion and Management: X is the open access extension of the reputable journal Energy Conversion and Management, serving as a platform for interdisciplinary research on a wide array of critical energy subjects. The journal is dedicated to publishing original contributions and in-depth technical review articles that present groundbreaking research on topics spanning energy generation, utilization, conversion, storage, transmission, conservation, management, and sustainability. The scope of Energy Conversion and Management: X encompasses various forms of energy, including mechanical, thermal, nuclear, chemical, electromagnetic, magnetic, and electric energy. It addresses all known energy resources, highlighting both conventional sources like fossil fuels and nuclear power, as well as renewable resources such as solar, biomass, hydro, wind, geothermal, and ocean energy.
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