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

合作研究：大规模储能和可再生资源的下一代电力容量和输电扩展模型

• 批准号: 1234057
• 负责人: Andrew Liu
• 金额: $ 20.85万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-15 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1234057&HistoricalAwards=false
• 关键词: Collaborative; Research; Next; Generation; Electricity

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.

该项目的目的是使用多阶段和多尺度随机编程方法在未来的不确定性下为长期电力基础设施扩展提供最佳解决方案，并结合电力市场运营的精细时间和空间分辨率。 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，以最小化成本并同时确保系统可靠性的挑战。拟议的框架使用此处的建模方法的混合和拭目以待的建模方法，将将电力系统的工程和操作详细信息纳入长期扩展模型中，否则这些模型在计算上是不切实际的。最终的模型是一个多阶段随机混合构成程序。该模型的多尺度功能使其具有高度结构化和分解。提议解决该模型的算法将基于强大的重新印象和嵌套的Dantzig-Wolfe分解，这些分解有望极大地提高计算速度，因此，促进了分解方法的最新方法，以解决与类似结构的大规模问题。如果该项目成功，它将为规划机构提供最先进的电力系统建模工具。通过既定的工业合作，拟议的工作具有改变电力领域的当前计划实践的巨大潜力，从而为消费者节省了大量资金，改善了系统的可靠性和可持续的电力系统。预计此类系统将具有可再生能源，能源存储和需求侧资源的高渗透率。建模和计算框架还将为表现出多尺度特征的电力部门以外的广泛战略计划问题提供有效的方法。研究结果将融入课程开发中，并广泛传播，以鼓励对广泛的科学界进行测试和协作。该项目还包括详细的计划，通过既定的机构多元化和外展计划，使代表性不足的群体参与本研究的各个方面。