Collaborative Research: The Next-Generation Electricity Capacity and Transmission Expansion Model with Large-Scale Energy Storage and Renewable Resources

该项目的目标是利用多阶段、多尺度的随机规划方法，结合电力市场运行的精细时空分辨率，为未来不确定性下的长期电力基础设施扩建提供最佳解决方案。这项工作将解决将大规模可再生能源和储能资源整合到电网中的挑战，以最大限度地降低成本，同时确保系统可靠性。采用“此时此地”和“静观其变”的混合建模方法，拟议的框架将把电力系统的工程和操作细节纳入长期扩展模型，否则在计算上是不切实际的。所得模型是一个多阶段随机混合整数规划。该模型的多尺度特征使其具有高度结构化和可分解性。求解该模型的算法将基于强重构和嵌套dantzigg - wolfe分解，这有望大大提高计算速度，从而推动求解具有相似结构的大规模问题的分解方法的发展。如果项目成功，它将为规划当局提供最先进的电力系统建模工具。通过已建立的工业合作，拟议的工作具有巨大的潜力，可以改变电力行业目前的规划实践，从而为消费者节省大量资金，提高系统可靠性，并实现电力系统的可持续发展。这样的系统预计将具有可再生能源、能源储存和需求侧资源的高渗透率。建模和计算框架还将为电力部门以外的具有多尺度特征的广泛战略规划问题提供有效的方法。研究结果将纳入课程发展，并广泛传播，以鼓励在广泛的科学界进行测试和合作。该项目还包括详细的计划，通过建立机构多样化和外展计划，让代表性不足的群体参与这项研究的各个方面。

The objective of this project is to use a multistage and multiscale stochastic programming approach to provide optimal solutions for long-term electricity infrastructure expansion under future uncertainties, incorporating fine temporal and spatial resolution of electricity market operations. This work will address the challenges of integrating large-scale renewable energy and energy storage resources into the electricity grid, to minimize costs and simultaneously ensure system reliability. Using a hybrid of here-and-now and wait-and-see types of modeling approaches, the proposed framework will incorporate engineering and operational details of electricity systems into long-term expansion models that would otherwise be computationally impractical. The resulting model is a multistage stochastic mixed-integer program. The model's multiscale feature makes it highly structured and decomposable. Algorithms proposed to solve the model will be based on strong reformulation and nested Dantzig-Wolfe decomposition, which are expected to greatly enhance computing speed, and consequently, advance the state-of-the-art of decomposition methods for solving large-scale problems with similar structures. If the project is successful, it will provide planning authorities the most advanced electricity system modeling tools. Through established industrial collaboration, the proposed work holds great potential to transform the current planning practice in the electricity sector, resulting in substantial savings to consumers, improved system reliability, and sustainable power systems. Such systems are expected to have a high penetration of renewable energy, energy storage, and demand-side resources. The modeling and computational framework will also provide an efficient methodology for a broad class of strategic planning problems beyond the electricity sector that exhibit multiscale characteristics. The research findings will be integrated into curriculum development and be disseminated widely to encourage testing and collaboration over the broad scientific community. The project also includes detailed plans to involve underrepresented groups in all aspects of this research through established institutional diversification and outreach programs.