arxiv_ml 90% Match Research Paper Power System Operators,Energy Policy Makers,Resilience Engineers,Operations Researchers 1 month ago

Enhancing Electricity-System Resilience with Adaptive Robust Optimization and Conformal Uncertainty Characterization

ai-safety › robustness

📄 Abstract

Abstract: Extreme weather is straining electricity systems, exposing the limitations of reactive responses, and prompting the need for proactive resilience planning. Most existing approaches to enhance electricity system resilience employ simplified uncertainty models and decouple proactive and reactive decisions. This paper proposes a novel tri-level optimization model that integrates proactive actions, adversarial disruptions, and reactive responses. Conformal prediction is used to construct distribution-free system-disruption uncertainty sets with coverage guarantees. The tri-level problem is solved by using duality theory to derive a bi-level reformulation and employing Bender's decomposition. Numerical experiments demonstrate that our approach outperforms conventional robust and two-stage methods.

Key Contributions

Proposes a novel tri-level optimization model integrating proactive actions, adversarial disruptions, and reactive responses for electricity system resilience. It uses conformal prediction for uncertainty sets and solves the problem via duality and Bender's decomposition, outperforming conventional methods.

Business Value

Significantly enhances the reliability and security of electricity grids against extreme weather and other disruptions, reducing economic losses and ensuring continuous power supply.

Paper Metadata

Innovation Type

Methodological

Deployment Feasibility

High, the optimization framework can be integrated into grid management systems, though requires significant data and computational resources.

Limitations Addressed

Simplified uncertainty models and decoupled decision-making in existing resilience approaches for electricity systems.

Performance Gains

Outperforms conventional robust and two-stage methods.

Technical Tags

Electricity SystemsResilienceRobust OptimizationConformal PredictionTri-level OptimizationBender's DecompositionDuality TheoryUncertainty SetsAdversarial DisruptionsProactive Actions

Research Topics

Power Systems EngineeringOptimizationRobustnessMachine LearningRisk Management

Methods & Architectures

Tri-level OptimizationConformal PredictionDuality TheoryBender's DecompositionRobust Optimization

Applications & Tasks

Power Systems Energy Infrastructure Critical Infrastructure Protection Enhancing System ResilienceModeling Adversarial DisruptionsOptimizing Proactive and Reactive Decisions Integrating proactive actions, adversarial disruptions, and reactive responsesConstructing distribution-free uncertainty setsSolving tri-level optimization problems

Datasets & Benchmarks

Benchmarks

Numerical experiments demonstrate outperformance against conventional robust and two-stage methods.

Related Fields

Operations ResearchElectrical EngineeringComputer ScienceRisk Analysis

Keywords

Electricity System ResilienceRobust OptimizationConformal PredictionTri-level OptimizationAdversarial AttacksExtreme WeatherPower GridUncertainty QuantificationBender's DecompositionEnergy Security

Academic Context

#Power Systems Engineering#Optimization#Robustness#Machine Learning#Risk Management

Commercial Potential

Potential Products

Grid resilience planning softwareReal-time grid management optimization tools

Target Industries

UtilitiesEnergyGovernmentCritical Infrastructure

Use Case Examples

Planning grid upgrades to withstand hurricane impactsOptimizing power restoration strategies after natural disasters

Competitive Edge

Offers a more comprehensive and robust approach to resilience planning compared to existing methods by explicitly modeling adversarial disruptions and integrating proactive/reactive decisions.

Market Opportunity

Growing market for grid modernization and resilience solutions driven by climate change and security concerns.

Revenue Models

Software licensingconsulting services for grid operators.

Resource Requirements

Compute Needs

High, due to the complexity of tri-level optimization and Bender's decomposition.

Data Requirements

Detailed grid topology, load data, weather forecasts, potential disruption scenarios.

Deployment Constraints

Requires accurate modeling of system dynamics and potential threats; computational cost for real-time application might be a concern.

Scalability

Scalability depends on the efficiency of the decomposition methods and the size of the electricity system being modeled.

Regulatory Considerations

Compliance with grid reliability standards and security regulations.

Production Readiness

Maturity Level

Research

Time to Market

2-5 years for integration into operational systems.

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