Grid Resilience through Risk-Managed Power Flow Optimization
This project developed a risk-managed optimization framework to improve grid stability under renewable energy uncertainty. By integrating risk-aware dispatch modeling, it enables utilities to proactively mitigate violations and enhance reliability.
Challenge
Managing renewable generation fluctuations while ensuring grid stability.
Approach (Methodology & Analysis)
1. Development of a Risk-Managed Framework for Power Systems
Proposed a Steady-State Risk Analysis and Mitigation Framework to assess the impact of renewable energy uncertainty on power system reliability.
Integrated Risk-Managed Steady-State Analysis (RMSA) and Risk-Managed Steady-State Optimization (RMSO) to quantify violation risks and optimize grid operations.
2. Risk-Managed Steady-State Analysis (RMSA) for Grid Viability
Developed a worst-case scenario analysis to evaluate system operating limit violations due to renewable power fluctuations.
Compared Monte Carlo Simulation (MCS) results with RMSA estimates to validate accuracy and computational efficiency.
Used bus voltage magnitude and line flow exceedance as primary performance metrics for grid security assessment.
3. Risk-Managed Steady-State Optimization (RMSO) for Grid Stability
Designed a nonlinear optimization model to redispatch power generation and mitigate worst-case violations.
Used the Interior Point OPTimizer (IPOPT) to ensure feasible solutions while minimizing deviations from scheduled dispatch.
Evaluated RMSO across hundreds of grid scenarios, including a 2030 synthetic New York power system model, to assess real-world feasibility.
4. Application to Large-Scale Transmission Systems
Applied the Solver for Uncertainty in Power and Energy Resources (SUPER) to automate RMSA and RMSO simulations.
Assessed the effectiveness of the model by analyzing New York’s Independent System Operator (NYISO) grid, identifying violations in 6 critical locations.
Key Findings & Insights
RMSA Provides Faster and More Accurate Risk Predictions
Up to 21x speedup over Monte Carlo Simulation, making it suitable for real-time operations.
Predicts worst-case power deviations with high accuracy, reducing the need for computationally expensive contingency simulations.
RMSO Prevents Grid Violations with Minimal Dispatch Adjustments
Successfully redispatched power generation in 100% of test cases without exceeding system operating limits.
Only 0.003% deviation in generation schedules, ensuring minimal disruption to planned dispatch.
Data-Driven Decision-Making is Critical for Grid Reliability
Worst-case risk modeling can preemptively identify vulnerabilities, allowing grid operators to prioritize risk mitigation efforts.
The RMSO framework demonstrated cost-effective redispatching, avoiding expensive last-minute real-time energy procurement.