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

被引:8
|
作者
Mahmood, Mou [1 ]
Chowdhury, Prangon [2 ]
Yeassin, Rahbaar [2 ]
Hasan, Mahmudul [3 ]
Ahmad, Tanvir [2 ]
Chowdhury, Nahid-Ur-Rahman [2 ]
机构
[1] Southeast Univ SEU, Dept Comp Sci & Engn CSE, Dhaka 1208, Bangladesh
[2] Ahsanullah Univ Sci & Technol AUST, Dept Elect & Elect Engn EEE, Dhaka 1208, Bangladesh
[3] Northern Univ Bangladesh NUB, Dept Elect & Elect Engn EEE, Dhaka 1230, Bangladesh
关键词
Digitalization; Smart Grids; Internet of Things; Artificial Intelligence; Blockchain; Digital Twin; Renewable Energy; Demand Response; PARTICLE SWARM OPTIMIZATION; ARTIFICIAL NEURAL-NETWORKS; SUPPORT VECTOR MACHINES; DIRECT LOAD CONTROL; BIG DATA ANALYTICS; SIDE MANAGEMENT; WIND ENERGY; ANT COLONY; SIZE OPTIMIZATION; PREDICTION MODEL;
D O I
10.1016/j.ecmx.2024.100790
中图分类号
O414.1 [热力学];
学科分类号
摘要
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.
引用
收藏
页数:31
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