用简单方法比复杂模型更优，助力电网应对极端天气投资决策
A Comprehensive Framework for Long-Term Resiliency Investment Planning under Extreme Weather Uncertainty for Electric Utilities
- 构建四步框架，融合极端天气不确定性与电网数字孪生
- 蒙特卡洛模拟+多目标优化，评估不同投资组合效果
- 仅凭基础电网知识的净现值排序法表现更佳
电力公司需在未来几年投入巨资应对需求激增、设备老化及极端天气威胁。当前已有严谨的资本规划框架，但可进一步拓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）采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。通过该框架，研究比较了电网感知优化方法与无模型方法的性能。结果表明，在模型基元启发式优化计算复杂度高的情况下，仅需有限电网知识的净现值排序法反而能发现更优的投资组合。
原文摘要 · Abstract (English)
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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