Market Insight: Ten Applications Of AI In Renewable Power Generation And Grid Management
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Executive Summary
Artificial intelligence (AI), along with its subdomain of machine learning (ML), and associated advances in automation, has rapidly gained pace in recent years. Model building and training techniques have matured and computer hardware has adapted along with industry to take advantage of them. In the power sector, AI-powered automation will enable a more decentralized, electrified and green energy system, based on renewables such as solar photovoltaics, hydroelectric power and wind – although current real-world use cases are still nascent. Existing applications of such technologies in power generation and distribution grid management for renewables are varied, providing value across the different stages of electricity production, transmission, distribution, storage and consumption. This report gives an overview of ten compelling use cases for AI-driven technologies in power generation and grid management for renewables. It will help senior operations, maintenance and reliability decision-makers, as well as engineers involved with power utilities, better understand the potential applications of AI for the efficient integration of renewables in the current energy system.
Table of contents
AI Is Key To Integrating Renewables With The GridAn Ever-Evolving Power Sector Raises The Potential For AI Applications
Ten Ways AI Will Help Bring About An Efficient, Stable And Green Electrical Grid
Organisations mentioned
AspenTech, AVEVA, Bentley Systems, GE Digital, GlidePath, Google, Habitat Energy, Hitachi Energy, Hornsdale Power Reserve, IBM, Institute of Electrical and Electronics Engineers (IEEE), International Energy Agency (IEA), Mercados EMI, Meta, neoen, Next Kraftwerke, NVIDIA, OpenAI, Opus One Solutions, Organisation for Economic Co-operation and Development (OECD), Schneider Electric, Shell, Siemens, Siemens Gamesa Renewable Energy, Statista, Teollisuuden Voima, TeslaAbout the authors
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