面向极端天气不确定性的电力公司长期韧性投资规划综合框架
A Comprehensive Framework for Long-Term Resiliency Investment Planning under Extreme Weather Uncertainty for Electric Utilities
AI总结：
本文提出一个四部分框架，结合极端天气不确定性、电网数字孪生、蒙特卡洛模拟和多目标优化方法，探讨电网感知优化方法在投资规划中的有效性。
摘要：
电力公司必须在未来几年进行大规模资本投资以应对需求爆炸性增长、老化资产和极端天气威胁。目前电力公司已有严格的资本规划框架，有机会将其扩展以解决不确定性下的多目标优化问题。本文提出一个四部分框架：1) 将极端天气作为不确定性来源；2) 利用电网数字孪生；3) 使用蒙特卡洛模拟捕捉变异性；4) 应用多目标优化方法寻找最优投资组合。我们使用此框架研究电网感知优化方法是否优于无模型方法。发现鉴于基于模型的元启发式优化方法的计算复杂性，简单现值排名方法在仅有限电网知识的情况下能够找到更优的投资组合。
英文摘要：
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.
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