🤖 AI Summary
This study addresses the lack of long-term resilience investment planning methods in power systems that can simultaneously account for multiple objectives and uncertainties arising from extreme weather events, surging electricity demand, and aging infrastructure. To bridge this gap, the authors propose a novel four-stage framework that integrates digital twins of power grids, Monte Carlo simulations, and multi-objective optimization, systematically incorporating extreme weather modeling into investment decision-making for the first time. The work also provides a comparative evaluation of model-based and model-free approaches. Empirical results demonstrate that, under limited grid knowledge, a simple net present value ranking method outperforms computationally intensive model-based optimization techniques, offering a practical and efficient alternative for real-world planning applications.
📝 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.