Artificial Intelligence Application to Flexibility Provision in Energy Management System: A Survey

Oludamilare Bode Adewuyi, Komla A. Folly, David T.O. Oyedokun, Yanxia Sun

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

3 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publicationEAI/Springer Innovations in Communication and Computing
PublisherSpringer Science and Business Media Deutschland GmbH
Pages55-78
Number of pages24
DOIs
Publication statusPublished - 2023

Publication series

NameEAI/Springer Innovations in Communication and Computing
VolumePart F665
ISSN (Print)2522-8595
ISSN (Electronic)2522-8609

Keywords

  • Artificial intelligence (AI)
  • Artificial neural network
  • Battery energy storage systems (BESSs)
  • Deep learning
  • Demand response
  • Demand-side management (DSM)
  • Dynamic electricity market
  • Energy management system
  • Energy storage systems (ESSs)
  • Flexibility management
  • Generator scheduling
  • Grid-to-vehicle (G2V) and vehicle-to-grid (V2G) technologies
  • Intelligent decision-making
  • Peer-to-peer energy (P2P) trading
  • Pumped hydro storage systems (PHESSs)
  • Resource forecast
  • System planning
  • Variable renewable energy resources (VREs)
  • machine learning

ASJC Scopus subject areas

  • Information Systems
  • Health Informatics
  • Computer Networks and Communications
  • Electrical and Electronic Engineering

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