Page 499 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

2 Use Case Description

2�1 Description 4.5: Manufacturing

Intended Use

This case applies deep reinforcement learning to enhance power system dispatching, focusing
on reducing transmission section flows while maintaining safety constraints.

Problem to Solve

Current dispatcher's empirical adjustments introduce subjectivity, limit scenario complexity,
prolong response times, and hinder multi-objective optimization. Growing renewable
uncertainties exacerbate these challenges.

Limitations of Existing Solutions

Traditional methods are constrained by dispatchers' inconsistent expertise, multi-objective
control complexity in multivariable optimization, and real-time operational time constraints,
resulting in delayed and suboptimal decision-making.

AI Methodology

The integration of MLP's high-dimensional state modeling with DDPG's continuous control
enables adaptive section regulation through dynamic pattern learning while preserving stability
constraints.

Benefits of the AI-Based Approach

• Efficiency: Decision-making processes can be accelerated from minute level decisions to
second.
• Versatility: More complex scenarios can be generalized.
• Multi-Objective: System balance, transmission limits, renewable energy integration, and
operational costs can be simultaneously optimized through coordinated controls.
• Scalability: Evolving grid complexities and constraints can be dynamically accommodated
through AI-driven adaptation mechanisms.

Drawbacks of the AI-Based Approach

• Safety Compliance: AI systems may compromise safety requirements under generation/
load uncertainties.
• Model Robustness: Incomplete knowledge of physics may reduce model robustness and
generalization capabilities.
• Data Dependency: Inaccessibility of high-quality data for model training demands may
limit practical application effect.

By embedding physical knowledge into AI models, these limitations can be addressed,
enhancing the robustness and reliability of the system.

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