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 language | English |
|---|---|
| Title of host publication | EAI/Springer Innovations in Communication and Computing |
| Publisher | Springer Science and Business Media Deutschland GmbH |
| Pages | 55-78 |
| Number of pages | 24 |
| DOIs | |
| Publication status | Published - 2023 |
Publication series
| Name | EAI/Springer Innovations in Communication and Computing |
|---|---|
| Volume | Part F665 |
| ISSN (Print) | 2522-8595 |
| ISSN (Electronic) | 2522-8609 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
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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