Impact of Artificial Intelligence on the Planning and Operation of Distributed Energy Systems in Smart Grids

被引:21
作者
Arevalo, Paul [1 ,2 ]
Jurado, Francisco [2 ]
机构
[1] Univ Cuenca, Fac Engn, Dept Elect Engn Elect & Telecommun DEET, Balzay Campus, Cuenca 010107, Azuay, Ecuador
[2] Univ Jaen, Dept Elect Engn, EPS Linares, Jaen 23700, Spain
关键词
artificial intelligence in smart grids; distributed energy systems optimization; renewable energy integration; demand response; POWER; GENERATION; PREDICTION; MANAGEMENT; NETWORKS; AI;
D O I
10.3390/en17174501
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
This review paper thoroughly explores the impact of artificial intelligence on the planning and operation of distributed energy systems in smart grids. With the rapid advancement of artificial intelligence techniques such as machine learning, optimization, and cognitive computing, new opportunities are emerging to enhance the efficiency and reliability of electrical grids. From demand and generation prediction to energy flow optimization and load management, artificial intelligence is playing a pivotal role in the transformation of energy infrastructure. This paper delves deeply into the latest advancements in specific artificial intelligence applications within the context of distributed energy systems, including the coordination of distributed energy resources, the integration of intermittent renewable energies, and the enhancement of demand response. Furthermore, it discusses the technical, economic, and regulatory challenges associated with the implementation of artificial intelligence-based solutions, as well as the ethical considerations related to automation and autonomous decision-making in the energy sector. This comprehensive analysis provides a detailed insight into how artificial intelligence is reshaping the planning and operation of smart grids and highlights future research and development areas that are crucial for achieving a more efficient, sustainable, and resilient electrical system.
引用
收藏
页数:22
相关论文
共 108 条
[1]   Reinforcement Learning Based EV Charging Management Systems-A Review [J].
Abdullah, Heba M. ;
Gastli, Adel ;
Ben-Brahim, Lazhar .
IEEE ACCESS, 2021, 9 :41506-41531
[2]   Transmission Network Planning in Super Smart Grids: A Survey [J].
Adnan, Muhammad ;
Ghadi, Yazeed ;
Ahmed, Ijaz ;
Ali, Mansoor .
IEEE ACCESS, 2023, 11 :77163-77227
[3]   A novel socio-economic-environmental model to maximize prosumer satisfaction in smart residential complexes [J].
Afzali, Peyman ;
Yeganeh, Armin ;
Derakhshan, Fatemeh .
ENERGY AND BUILDINGS, 2024, 308
[4]   Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm [J].
Ahmad, Tanveer ;
Madonski, Rafal ;
Zhang, Dongdong ;
Huang, Chao ;
Mujeeb, Asad .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2022, 160
[5]   Reinforcement learning for the optimization of electric vehicle virtual power plants [J].
Al-Gabalawy, Mostafa .
INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS, 2021, 31 (08)
[6]   A Review of Strategies to Increase PV Penetration Level in Smart Grids [J].
Aleem, Sk Abdul ;
Hussain, S. M. Suhail ;
Ustun, Taha Selim .
ENERGIES, 2020, 13 (03)
[7]   Microgrid-Level Energy Management Approach Based on Short-Term Forecasting of Wind Speed and Solar Irradiance [J].
Alhussein, Musaed ;
Haider, Syed Irtaza ;
Aurangzeb, Khursheed .
ENERGIES, 2019, 12 (08)
[8]   State-of-the-Art Artificial Intelligence Techniques for Distributed Smart Grids: A Review [J].
Ali, Syed Saqib ;
Choi, Bong Jun .
ELECTRONICS, 2020, 9 (06) :1-28
[9]   Security Threats and Promising Solutions Arising from the Intersection of AI and IoT: A Study of IoMT and IoET Applications [J].
Alrubayyi, Hadeel ;
Alshareef, Moudy Sharaf ;
Nadeem, Zunaira ;
Abdelmoniem, Ahmed M. ;
Jaber, Mona .
FUTURE INTERNET, 2024, 16 (03)
[10]   Unleashing the potential of sixth generation (6G) wireless networks in smart energy grid management: A comprehensive review [J].
Alsharif, Mohammed H. ;
Jahid, Abu ;
Kannadasan, Raju ;
Kim, Mun-Kyeom .
ENERGY REPORTS, 2024, 11 :1376-1398