A Comprehensive Framework for Long-Term Resiliency Investment Planning under Extreme Weather Uncertainty for Electric Utilities

Abstract

Electric utilities must make massive capital investments in the coming years to respond to explosive growth in demand, aging assets and rising threats from extreme weather. Utilities today already have rigorous frameworks for capital planning, and there are opportunities to extend this capability to solve multi-objective optimization problems in the face of uncertainty. This work presents a four-part framework that 1) incorporates extreme weather as a source of uncertainty, 2) leverages a digital twin of the grid, 3) uses Monte Carlo simulation to capture variability and 4) applies a multi-objective optimization method for finding the optimal investment portfolio. We use this framework to investigate whether grid-aware optimization methods outperform model-free approaches. We find that, in fact, given the computational complexity of model-based metaheuristic optimization methods, the simpler net present value ranking method was able to find more optimal portfolios with only limited knowledge of the grid.

Figures (4)

• Figure 1: This work compares a model-free and a model-based approach to solving a two-stage capital prioritization problem for electric utilities. Model-free approach: The potential investments are ranked by NPV, which is calculated using a baseline simulation from the digital twin to estimate operational costs and benefits, and then scheduled on a FIFO basis. Model-based approach: The NSGA-II algorithm deb2002 finds the Pareto frontier of optimized portfolios over two objectives, using the digital twin as an evaluation function. An ILP method is used to optimize the schedule of implementing the investments. Digital twin of the grid: The digital twin computes power flows over the grid layout given known generation, load and extreme weather events. Multiple simulations can be run to obtain summary statistics and confidence intervals for KPIs across different categories including resilience, growth and affordability.
• Figure 2: Test case 1 results: Comparison of optimized portfolio and schedule for model-free vs model-based approaches. NPV + FIFO: Lines 7 and 5 are buried, addressing the two most critical lines on the left-most feeder branch. NSGA-II + ILP: Gantt chart ordering prioritizes projects with the largest marginal benefit. Portfolio includes both burying some overhead lines (not line 7) and upgrading others.
• Figure 3: Test case 2 results: The NPV + FIFO portfolio reduced the median unserved energy in Feeder 5 subnet as compared to the NSGA-II + ILP portfolio, and vice versa for Feeder 3.
• Figure 4: Test case 2 results:NPV + FIFO: Lines 8 and 39 are buried, addressing the two most critical overhead lines on radial branches. NSGA-II + ILP: The optimized portfolio did not include line 39 and instead buried line 19 which was not critical as it was located on a loop branch.