TY - CHAP
T1 - Artificial Intelligence Application to Flexibility Provision in Energy Management System
T2 - A Survey
AU - Adewuyi, Oludamilare Bode
AU - Folly, Komla A.
AU - Oyedokun, David T.O.
AU - Sun, Yanxia
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - Due to the complicated load and supply balance dynamics, the massive amounts of renewable energy being introduced into the energy mix pose significant challenges for utilities and their customers. The renewable energy generators’ outputs are intermittent and thus create an imbalance between the instantaneous load demand and available supply at different instances of time. Besides, the inertia in power systems is becoming lesser due to the displacement of the rotating mass of conventional generators with inverter-based generators. Thus, the challenge of meeting the flexibility needs of modern power systems is becoming significantly high in recent times. Because of this, the traditional methods of meeting the flexibility needs of power systems are becoming insufficient; this calls for developing new intelligent approaches that can handle complex situations. Different concepts of artificial intelligence (AI) are deployed as a solution provider to numerous complex power systems operational problems, especially in resource forecasting, electricity market dynamics prediction, intelligent decision-making for generator scheduling, and more. Hence, this book chapter reviews existing flexibility management techniques and some crucial areas of AI deployment in energy management systems toward meeting the flexibility needs of modern energy supply systems.
AB - Due to the complicated load and supply balance dynamics, the massive amounts of renewable energy being introduced into the energy mix pose significant challenges for utilities and their customers. The renewable energy generators’ outputs are intermittent and thus create an imbalance between the instantaneous load demand and available supply at different instances of time. Besides, the inertia in power systems is becoming lesser due to the displacement of the rotating mass of conventional generators with inverter-based generators. Thus, the challenge of meeting the flexibility needs of modern power systems is becoming significantly high in recent times. Because of this, the traditional methods of meeting the flexibility needs of power systems are becoming insufficient; this calls for developing new intelligent approaches that can handle complex situations. Different concepts of artificial intelligence (AI) are deployed as a solution provider to numerous complex power systems operational problems, especially in resource forecasting, electricity market dynamics prediction, intelligent decision-making for generator scheduling, and more. Hence, this book chapter reviews existing flexibility management techniques and some crucial areas of AI deployment in energy management systems toward meeting the flexibility needs of modern energy supply systems.
KW - Artificial intelligence (AI)
KW - Artificial neural network
KW - Battery energy storage systems (BESSs)
KW - Deep learning
KW - Demand response
KW - Demand-side management (DSM)
KW - Dynamic electricity market
KW - Energy management system
KW - Energy storage systems (ESSs)
KW - Flexibility management
KW - Generator scheduling
KW - Grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies
KW - Intelligent decision-making
KW - Peer-to-peer energy (P2P) trading
KW - Pumped hydro storage systems (PHESSs)
KW - Resource forecast
KW - System planning
KW - Variable renewable energy resources (VREs)
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85162945807&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-26496-2_4
DO - 10.1007/978-3-031-26496-2_4
M3 - Chapter
AN - SCOPUS:85162945807
T3 - EAI/Springer Innovations in Communication and Computing
SP - 55
EP - 78
BT - EAI/Springer Innovations in Communication and Computing
PB - Springer Science and Business Media Deutschland GmbH
ER -