Optimized Demand Response Framework for Enhancing Power System Reliability under Wind Power and EV-Induced Uncertainty

Abstract

The integration of wind energy and electric vehicles (EVs) introduces significant uncertainty and operational complexity to modern power systems. This report presents a novel and optimized demand response (DR) framework aimed at enhancing system reliability while addressing wind generation variability and the flexible nature of EV loads. The method incorporates a real-time uncertainty model based on a statistical mean–standard deviation relationship to dynamically quantify wind power fluctuations. DR incentives are adjusted hourly according to wind volatility, demand elasticity, and EV charging patterns. System reliability is evaluated through a probabilistic well-being assessment distinguishing healthy (P(H)), marginal (P(M)), and risk (P(R)) states. This integrated approach allows adaptive tuning of DR incentives to real-time wind fluctuations, reducing reliance on costly storage or backup generation. Validation on the IEEE RTS-24 bus system under 12 EV penetration and charging scenarios demonstrates improved system reliability and cost efficiency.

Introduction

The increasing penetration of renewable energy, particularly wind power, contributes to economic efficiency and environmental sustainability, aligning with the United Nations Sustainable Development Goals (SDGs), especially SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action). However, wind energy’s inherent fluctuations and uncertainties challenge power system reliability. Traditional solutions such as battery storage and additional reserves are costly and complex.

Demand response (DR) emerges as a cost-effective alternative to manage renewable variability by shifting or reducing demand in response to dynamic pricing. This study extends previous research by optimizing DR programs across multiple sectors, including residential, commercial, agricultural, industrial, and EV loads, thereby supporting SDG 9 (Industry, Innovation, and Infrastructure) and SDG 11 (Sustainable Cities and Communities).

Mathematical Model

The proposed mathematical model minimizes total operating costs and power outages considering wind power uncertainty and EV integration. It includes constraints for generation-load balance, power flow limits, generation limits, and wind power curtailment. The system’s operational states are evaluated probabilistically as healthy, marginal, or risk, using the well-being framework.

Key Model Components

1. Generation System: Conventional and wind generators with cost coefficients and operational limits.
2. Load Buses and Demand Response: Multiple sectors participate in DR through price-sensitive and interruptible loads.
3. Transmission Network: Modeled with power flow constraints to maintain stability.
4. Well-being Framework: Probabilistic assessment of system states (P(H), P(M), P(R)) via Monte Carlo simulations.

Demand Response Optimization

The DR system minimizes consumer electricity expenses by allowing up to 30% load interruption based on dynamic pricing signals. Real-time pricing (RTP) programs adjust tariffs hourly, influencing load shifting and reduction. The objective function balances DR costs and penalties for unserved energy, enhancing system reliability and economic efficiency.

Modeling Assumptions and Limitations

• Uniform consumer response to DR signals without delay.
• Wind generation follows predictable statistical distributions.
• Fixed EV participation rates and charging patterns.
• Standard system load and capacity thresholds for reliability metrics.
• Constant price elasticity across consumers.

Wind Energy Generation Uncertainty and Fluctuation

Wind energy variability requires backup sources or storage, which incur costs. Accurate modeling of wind generation uncertainty, expressed as a relationship between standard deviation (σ) and mean predicted value (μ), enables optimal reserve allocation, reducing unnecessary costs and improving system reliability (SDG 7, SDG 13).

The study uses the relation σ = 0.231 + 0.197μ to quantify uncertainty, highlighting increased variability at higher wind speeds. This modeling supports adaptive DR incentive allocation based on real-time wind fluctuations.

Simulation Results

The framework was tested on the IEEE RTS-24 bus system with 12 EV penetration and charging scenarios, including varying proportions of off-peak, mid-peak, and peak charging. Three case studies were analyzed:

1. Without DR consideration.
2. With DR but without optimization.
3. With optimized DR considering wind uncertainty and EV integration.

Key Findings

• Optimized DR improved the probability of a healthy system state (P(H)) to 97.44%, compared to 97.2% (non-optimized DR) and 95.1% (no DR).
• Unsupplied energy was reduced from 52,230 MWh (no DR) to 51,900 MWh (optimized DR).
• DR incentive costs decreased by 5.6% through adaptive pricing aligned with wind variability and EV charging patterns.
• Load shifting under DR sometimes increased peak-valley differences due to time-of-use pricing, indicating the need for carefully calibrated incentives.

Comparison of Results

Comparative analysis across the three case studies shows that optimized DR achieves superior system reliability and cost savings. The approach effectively balances demand and supply, reduces reliance on backup generation, and enhances grid flexibility, supporting SDG 9 and SDG 11.

Performance Highlights

• Higher healthy state probabilities (P(H)) and lower risk state probabilities (P(R)) under optimized DR.
• Significant reductions in energy not served (ENS), ensuring more reliable power delivery.
• Cost-effective DR incentive allocation sensitive to real-time wind uncertainty.

Conclusion

This study introduces an optimized demand response framework that addresses wind power uncertainty and increasing EV penetration, contributing to sustainable energy systems aligned with SDG 7 (Affordable and Clean Energy), SDG 9 (Industry, Innovation, and Infrastructure), and SDG 13 (Climate Action). The framework enhances power system reliability and economic efficiency by dynamically adjusting DR incentives based on real-time wind fluctuations and EV charging behaviors.

Simulation results demonstrate improved system well-being, reduced unsupplied energy, and lower DR costs. The approach offers practical insights for grid operators and policymakers to promote smart grid technologies and consumer participation, facilitating the transition to renewable-rich, flexible, and resilient power systems.

References

References are available upon request from the corresponding author.

1. Sustainable Development Goals (SDGs) Addressed or Connected

1. SDG 7: Affordable and Clean Energy
   • The article focuses on integrating wind energy and optimizing demand response to enhance power system reliability and efficiency.
   • Promotes renewable energy use and smart grid technologies.
2. SDG 9: Industry, Innovation and Infrastructure
   • Development of advanced mathematical models and optimization frameworks for power systems.
   • Incorporation of electric vehicles and demand response programs to modernize infrastructure.
3. SDG 11: Sustainable Cities and Communities
   • Supports sustainable urban energy management through demand response and EV integration.
4. SDG 13: Climate Action
   • Addresses challenges of renewable energy variability to reduce reliance on fossil fuel backup generation.
   • Contributes to reducing greenhouse gas emissions by optimizing renewable energy use.

2. Specific Targets Under Those SDGs Identified

1. SDG 7: Affordable and Clean Energy
   • Target 7.2: Increase substantially the share of renewable energy in the global energy mix.
   • Target 7.3: Double the global rate of improvement in energy efficiency.
2. SDG 9: Industry, Innovation and Infrastructure
   • Target 9.4: Upgrade infrastructure and retrofit industries to make them sustainable, with increased resource-use efficiency and greater adoption of clean and environmentally sound technologies.
3. SDG 11: Sustainable Cities and Communities
   • Target 11.6: Reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management.
4. SDG 13: Climate Action
   • Target 13.1: Strengthen resilience and adaptive capacity to climate-related hazards and natural disasters in all countries.
   • Target 13.2: Integrate climate change measures into national policies, strategies, and planning.

3. Indicators Mentioned or Implied in the Article to Measure Progress

1. Probability of System States (Well-being Indicators)
   • P(H): Probability of the system being in a healthy state (generation exceeds demand with reserve margin).
   • P(M): Probability of the system being in a marginal state (generation meets demand without reserve margin).
   • P(R): Probability of the system being in a risk state (demand exceeds generation capacity, risk of load shedding).
2. Energy Not Supplied (ENS)
   • Measures the amount of unsupplied energy due to generation shortfalls or system failures (kWh or MWh).
3. Demand Response (DR) Participation Rate
   • Percentage of load participating in demand response programs (e.g., 5%, 15%, 30%).
4. DR Incentive Costs
   • Costs associated with providing incentives to consumers for load shifting or reduction.
5. System Operating Costs
   • Total generation cost including DR costs and penalties for unserved energy.
6. Electric Vehicle (EV) Penetration Levels
   • Percentage of EVs in the system load (e.g., 20%, 40%, 60%, 80%) and their charging patterns.
7. Wind Energy Generation Uncertainty
   • Standard deviation (σ) and mean (μ) of wind generation used to model uncertainty and variability.

4. Table: SDGs, Targets and Indicators

Source: nature.com