Agentic-AI based Mathematical Framework for Commercialization of Energy Resilience in Electrical Distribution System Planning and Operation

2025-08-06

AI Summary

Distribution networks exhibit insufficient resilience under extreme weather and cyberattacks, while existing methods lack market mechanism support and dynamic reconfiguration capabilities. Method: This paper proposes a resilience-enhancement framework integrating a dual-agent Proximal Policy Optimization (PPO) algorithm with a market-based incentive mechanism. It introduces the first agent-based, AI-driven mathematical model for resilience value transformation, enabling coordinated strategic-level (investment planning) and tactical-level (real-time reconfiguration) optimization to achieve dynamic, adaptive deployment of distributed energy resources (DERs) under both normal and emergency conditions. A multi-objective reward function jointly optimizes load restoration speed, system robustness, and user satisfaction. Results: Experiments yield an average resilience score of 0.85±0.08, a benefit–cost ratio of 0.12±0.01, and show that 85% of disaster scenarios favor a four-DER configuration; a 200× reward multiplier ensures market sustainability.

Technology Category

• Search and Optimization: Distributed Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Multiagent Systems: Adversarial Agents

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

The increasing vulnerability of electrical distribution systems to extreme weather events and cyber threats necessitates the development of economically viable frameworks for resilience enhancement. While existing approaches focus primarily on technical resilience metrics and enhancement strategies, there remains a significant gap in establishing market-driven mechanisms that can effectively commercialize resilience features while optimizing their deployment through intelligent decision-making. Moreover, traditional optimization approaches for distribution network reconfiguration often fail to dynamically adapt to both normal and emergency conditions. This paper introduces a novel framework integrating dual-agent Proximal Policy Optimization (PPO) with market-based mechanisms, achieving an average resilience score of 0.85 0.08 over 10 test episodes. The proposed architecture leverages a dual-agent PPO scheme, where a strategic agent selects optimal DER-driven switching configurations, while a tactical agent fine-tunes individual switch states and grid preferences under budget and weather constraints. These agents interact within a custom-built dynamic simulation environment that models stochastic calamity events, budget limits, and resilience-cost trade-offs. A comprehensive reward function is designed that balances resilience enhancement objectives with market profitability (with up to 200x reward incentives, resulting in 85% of actions during calamity steps selecting configurations with 4 DERs), incorporating factors such as load recovery speed, system robustness, and customer satisfaction. Over 10 test episodes, the framework achieved a benefit-cost ratio of 0.12 0.01, demonstrating sustainable market incentives for resilience investment. This framework creates sustainable market incentives

Problem

• Develop market-driven mechanisms for energy resilience commercialization
• Dynamic adaptation of distribution networks to emergencies
• Balance resilience enhancement with market profitability

Innovation

• Dual-agent PPO for dynamic resilience optimization
• Market-driven mechanisms for resilience commercialization
• Custom simulation with stochastic events and constraints