电力系统转型规划：面向长期优化的行业对齐框架
Power System Transition Planning: An Industry-Aligned Framework for Long-Term Optimization
AI总结：
本文提出Power System Transition Planning优化问题及行业对齐框架，通过大规模多阶段随机规划建模，结合Stochastic Dual Dynamic Programming与高性能计算实现长期电力系统转型的可处理优化。
摘要：
本工作引入了Power System Transition Planning优化问题类别。其旨在将电力系统高效转变为零排放网络。与同类工作不同，本文提出的框架广泛适用于行业的决策过程。它定义了适合领域的功能边界，聚焦于电力系统的经济效率。即在决策空间中施加广泛的规划因素的同时，该模型保持了不确定性下传统电力系统规划的结构和深度，这导致了大规模多阶段随机规划公式，在现实案例中遇到不可处理性。因此，该框架同时调用高性能计算默认主义。在此全面阐述中，我们提出了一个指导模型，将其范围与现有公式进行比较，并由一个详细的问题示例支持，展示了在小型测试案例中获得的解决方案的分析价值。然后，通过解决基于与Alberta电力系统长期规划研究实践一致的现实规划构建的广泛测试案例，证明了该框架在现实应用中的可行性。该框架采用Stochastic Dual Dynamic Programming作为分解方法以实现可处理性，并利用High-Performance Computing和并行计算。
英文摘要：
This work introduces the category of Power System Transition Planning optimization problem. It aims to shift power systems to emissions-free networks efficiently. Unlike comparable work, the framework presented here broadly applies to the industry's decision-making process. It defines a field-appropriate functional boundary focused on the economic efficiency of power systems. Namely, while imposing a wide range of planning factors in the decision space, the model maintains the structure and depth of conventional power system planning under uncertainty, which leads to a large-scale multistage stochastic programming formulation that encounters intractability in real-life cases. Thus, the framework simultaneously invokes high-performance computing defaultism. In this comprehensive exposition, we present a guideline model, comparing its scope to existing formulations, supported by a fully detailed example problem, showcasing the analytical value of the solution gained in a small test case. Then, the framework's viability for realistic applications is demonstrated by solving an extensive test case based on a realistic planning construct consistent with Alberta's power system practices for long-term planning studies. The framework resorts to Stochastic Dual Dynamic Programming as a decomposition method to achieve tractability, leveraging High-Performance Computing and parallel computation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
