AI in GRID 101
1. Introduction to AI in Grid Management https://lnkd.in/gU3cbtkF
2. Digital Twin Creation and Implementation https://lnkd.in/geUU_ZfR
3. AI in Grid Planning and Optimization https://lnkd.in/gxSPbDjj
4. AI-Driven Load Forecasting https://lnkd.in/gQF742jX
5. Load Allocation and Distribution Optimization https://lnkd.in/gcetCUcJ
6. AI in Load Flow Analysis https://lnkd.in/gR2BrJbd
7. AI for Short Circuit Analysis and Protection https://lnkd.in/gF2Kqpyk
8. AI in Distributed Energy Resource (DER) Integration https://lnkd.in/g67dXf3G
9. Time Series Analysis for Grid Operations https://lnkd.in/gmkfZEnh
10. AI for Grid Resilience and Cybersecurity https://lnkd.in/gMfqUE3r
https://lnkd.in/gqZMMWCz
#DOE #ENERGY #IEEE #springler #fabledSky #IAENG #OxfordAcademic #Frontiers #EnergyInformatics #opal-rt #NVIDIA #AIforEnergy #AI #RAG #RAFT #AIAgents #ElectricUtilities #AEP #DukeEnergy #Entergy #Eversource #Exelon #GeorgiaPower #SCE #PG&E #SDG&E #SouthernCompany #HECO #PGE #SCE #EOn
AI in Grid 101:
1. Introduction to AI in Grid Management
- Reference: "AI for Energy" by the U.S. Department of Energy (2024) Summary: This report explores how artificial intelligence can improve the grid and enable the clean energy transition in the United States, identifying priority use cases, challenges, and recommendations for AI applications in grid planning, permitting, operations, and resilience
2. Digital Twin Creation and Implementation
- Reference: "Digital Twins in Power Systems: A Proposal for a Definition" by IEEE (2023) Summary: This paper provides a comprehensive definition and framework for digital twins in power systems, facilitating a common understanding and implementation strategy
3. AI in Grid Planning and Optimization
- Reference: "Artificial Intelligence Models in Power System Analysis" (2020) Summary: This chapter discusses various AI techniques applicable to power system planning and optimization, including load forecasting and network management
4. AI-Driven Load Forecasting
- Reference: "A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Models" (2020) Summary: This paper reviews state-of-the-art electric load forecasting technologies, focusing on machine learning algorithms and their combinations for constructing hybrid models
5. Load Allocation and Distribution Optimization
- Reference: "AI in Smart Grid Management" by Fabled Sky Research Summary: This article explores the integration of AI into smart grid management, highlighting its role in enhancing efficiency, reliability, and sustainability, including load allocation strategies
6. AI in Load Flow Analysis
- Reference: "Artificial Neural Network Based Load Flow Analysis for Power System" (2021) Summary: This paper presents an ANN-based load flow analysis using multilayer perceptron neural networks trained to compute voltage magnitude and angles in power systems
7. AI for Short Circuit Analysis and Protection
- Reference: "Applications of Artificial Intelligence in Power System Operation and Control" (2022) Summary: This paper evaluates the use of AI technology in power systems, including fault detection and system protection mechanisms
8. AI in Distributed Energy Resource (DER) Integration
- Reference: "AI, Data Analytics, and Mechanism Design for DER Integration Toward Net Zero" (2022) Summary: This research topic highlights the state of the art in DER integration, focusing on AI and optimization techniques to manage DERs in the grid
9. Time Series Analysis for Grid Operations
- Reference: "A Scoping Review of Deep Neural Networks for Electric Load Forecasting" (2021) Summary: This review examines the use of deep neural networks in electric load forecasting, emphasizing time series analysis techniques
10. AI for Grid Resilience and Cybersecurity
- Reference: "AI for Energy" by the U.S. Department of Energy (2024)
- Summary: This report discusses how AI can enhance grid resilience and addresses challenges related to cybersecurity in the energy sector.