AI Compliance in Energy

About This Course

Aim

Program Structure

🔹 Module 1: Introduction to AI & Regulatory Landscape in Energy

1.1 Understanding AI in the Energy Ecosystem

• Definition and scope of AI in power systems, utilities, and grid operations
• Examples of AI use in generation, distribution, demand response, DERs

1.2 Global Regulatory Landscape

• EU AI Act: objectives, classifications, and impact on energy applications
• Overview of U.S., China, India, and other national AI regulations in energy
• High-risk vs. unacceptable-risk AI use-cases

1.3 Industry-Specific Risks & Compliance Pressure

• Mapping AI risks to energy sector operations (generation, retail, trading)
• Policy pressure from ESG goals, net-zero mandates, and AI oversight

🔹 Module 2: Standards & Governance Frameworks

2.1 ISO/IEC 42001 for AI Management in Utilities

• What is ISO/IEC 42001?
• Implementing ISO 42001 in the utility industry
• Intersection with ISO 27001 (cybersecurity) and ISO 9001 (quality management)

2.2 Data Governance in Energy AI Systems

• Principles of data privacy, integrity, lineage in smart-meter ecosystems
• GDPR, data anonymization, consent management for AI-driven use
• Challenges in cross-border smart energy data processing

2.3 Building Trustworthy AI

• Explainability, fairness, transparency principles (per OECD, NIST)
• Tools for ethical AI model validation and documentation in energy

🔹 Module 3: Smart Grid Stability & AI Model Assurance

3.1 AI-Driven Grid Operations

• Forecasting, load balancing, anomaly detection
• Real-time vs. batch AI in grid management

3.2 Explainability and Audit Trails

• Explainable AI (XAI) in energy: SHAP, LIME, counterfactuals
• Building traceable audit logs for ML models
• Regulatory expectations for traceability and accountability

3.3 AI Lifecycle Compliance

• MLOps best practices in compliance-sensitive environments
• Monitoring, drift detection, model versioning aligned with policy

🔹 Module 4: Carbon Accounting & AI

4.1 Regulatory Drivers

• CSRD (EU), SEC climate rules (US), and national disclosures
• Impact of AI use on carbon footprint traceability and emissions

4.2 AI for GHG Tracking and Reporting

• Automating Scope 1–3 emissions tracking with AI
• AI model design for emissions forecasting
• Ethics of estimation and proxy modeling in regulatory reports

4.3 ISO 14064-2 Integration

• Understanding the standard’s scope and purpose
• Measuring and verifying AI-enabled emissions reductions
• Challenges in quantification and audit readiness

🔹 Module 5: Cybersecurity & Critical Infrastructure Protection

5.1 NERC CIP and OT-IT Integration

• Overview of NERC CIP controls for critical infrastructure
• Importance of cybersecurity in AI-powered SCADA, EMS systems

5.2 AI Overlay in Cybersecurity

• AI models for anomaly, intrusion, and threat detection
• Model vulnerabilities and adversarial AI in the energy sector

5.3 AI-Specific Cyber-Physical Risks

• Cyber-physical convergence risks in AI-controlled systems
• Regulatory red flags and compliance gaps

🔹 Module 6: AI in Energy Markets – Fairness, Bias & Collusion

6.1 Market Trading and AI Applications

• Real-time bidding agents, price forecasting models
• Algorithmic trading and auction platforms

6.2 Collusion Detection and Market Fairness

• Regulatory expectations from market integrity
• How AI can enable or prevent anti-competitive behavior
• Detecting bid-rigging and unfair optimizations

6.3 Transparency in Market Algorithms

• Black-box vs white-box models in bidding
• Disclosure requirements for regulators and market operators

🔹 Module 7: Resilience & Incident Management for AI Systems

7.1 AI-Enabled DERMS and Grid Resilience

• Role of Distributed Energy Resource Management Systems
• AI in voltage control, storage dispatch, and islanding

7.2 Incident Response Planning

• Developing playbooks for AI failures, cybersecurity breaches
• Lessons from past grid and AI system failures

7.3 Legal and Liability Framework

• Accountability in AI-driven decisions
• Chain of responsibility in DERMS incidents
• Insurance and compliance recordkeeping

🔹 Module 8: Synthesis, Capstone & Industry Integration

8.1 Simulation and Capstone Project

• Design and assess a compliant AI-based energy system
• Create documentation: audit logs, explainability reports, governance plan

8.2 Case Studies and Guest Lectures

• Real-world failures and compliance breaches (e.g., Texas grid, Europe)
• ISO 42001 success in utility AI deployments
• SEC enforcement and AI fraud detection in carbon markets

8.3 Final Evaluation & Certification

• Final MCQ + Scenario analysis
• Peer-reviewed capstone submission
• Issuance of certificate

Who Should Enrol?

Fee Structure

We accept 20+ global currencies. View list →

What You’ll Gain