An optimized demand response framework for enhancing power system reliability under wind power and EV-induced...

Area of Science:

• Power Systems Engineering
• Renewable Energy Integration
• Smart Grids

Background:

• Integrating wind energy and electric vehicles (EVs) creates significant operational challenges for power systems due to inherent variability and complex load dynamics.
• Existing demand response (DR) strategies often lack the adaptability to effectively manage the uncertainties introduced by renewable generation and flexible EV charging patterns.

Purpose of the Study:

• To develop and validate a novel, optimized demand response (DR) framework for enhancing power system reliability in the context of high wind energy penetration and widespread electric vehicle (EV) adoption.
• To dynamically adjust DR incentives based on real-time wind power fluctuations, demand elasticity, and EV charging behaviors to improve grid stability and cost-effectiveness.

Main Methods:

• A real-time uncertainty model using a statistical mean-standard deviation relationship was developed to quantify wind power fluctuations.
• An optimized DR framework was designed to dynamically allocate incentives hour-by-hour, considering wind volatility, demand elasticity, and EV charging patterns.
• System reliability was assessed using a well-being-based probabilistic approach, categorizing system states into healthy (P(H)), marginal (P(M)), and risk (P(R)).

Main Results:

• The proposed framework improved the healthy system state probability (P(H)) from 95.1% (no DR) and 97.2% (non-optimized DR) to 97.44%.
• Unsupplied energy was reduced from 52,230 MWh to 51,900 MWh, indicating enhanced grid reliability.
• Demand response incentive costs were lowered by 5.6%, demonstrating improved cost-efficiency.

Conclusions:

• The integrated approach of uncertainty-driven DR optimization and probabilistic well-being assessment offers a practical solution for managing renewable energy variability.
• The framework effectively enhances power system resilience and cost-efficiency in grids with high renewable energy penetration and significant EV integration.
• Adaptive tuning of DR incentives to real-time grid conditions, particularly wind fluctuations, is a key innovation not addressed in prior research.