EAGER: Renewables: Collaborative Proposal on Stochastic Unit Commitment with Topology Control Recourse for Networks with High Penetration of Distributed Renewable Resources

EAGER：可再生能源：分布式可再生资源高渗透率网络的随机单位承诺与拓扑控制资源的协作提案

• 批准号: 1548847
• 负责人: Suvrajeet Sen
• 金额: $ 15万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1548847&HistoricalAwards=false
• 关键词: EAGER; Renewables; Collaborative; Proposal; Stochastic

The massive integration of wind resources into the generation mix of the electric power infrastructure poses new challenges to system operators due to the uncertainty and variability of these resources. The intermittent nature of wind and other renewable energy resources, together with limitations of storage in current power systems, poses serious challenges for integrating renewable resources into the power grid, while maintaining acceptable service reliability. Efficient deployment of conventional and flexible resources requires new methods that explicitly account for uncertainty in day-ahead unit commitment. This project proposes to use topology control as a recourse mechanism which mobilizes flexibility of the grid through transmission line switching to redirect power flow in response to prevailing renewable generation conditions, thus overcoming loss of reliability due to intermittency. However, such topology control will require new algorithmic innovations which will allow system operators to plan their day-ahead unit-commitment by incorporating topology control as a recourse action. The project pursues a novel problem formulation for transmission line switching in response to variable generation that holds promise for being computationally feasible. It is hoped that the outcomes of the project will help to facilitate growth of renewable generation while maintaining grid efficiency and reliability.Because the topology of the grid consists of a discrete set of transmission links, the choice of which links to use becomes a combinatorial optimization problem. Moreover, the presence of intermittency in renewable generation requires the transmission network to adapt to the specific generation scenario being observed. This leads to a two-stage stochastic optimization model in which the first stage choices are associated with slow-ramping generators, while the second stage model includes the fast-ramping as well as intermittent generators. The recourse action in this setup leads to a mixed-integer program (MIP) in the second stage, which violates the convexity requirements of common decomposition algorithms which have been highly successful in power system operations. This proposal shows that a new model, which is a two stage stochastic MIP (SMIP), possesses a very special structure which can be exploited so that realistic stochastic unit commitment problems can be solved, even though the general class of two-stage SMIP models are known to be extremely difficult. The proposal outlines a combined parallel-serial approximation strategy which appears promising, and could transform the commonly-held notion that high penetration of renewable energy will have an adverse effect on reliability. The project is a high-risk, high return undertaking on two levels: a) it could lead to a truly sustainable approach to reliable renewable integration, and b) the algorithmic advance of solving very large scale SMIP by decomposing into smaller pieces, without sacrificing optimality would provide a major step in the solution of these very challenging optimization problems.

由于这些资源的不确定性和可变性，将风资资源大量整合到电力基础设施的生成组合中为系统操作员带来了新的挑战。风和其他可再生能源的间歇性质，以及当前电力系统中存储的局限性，对将可再生资源整合到电网中构成了严重的挑战，同时保持可接受的服务可靠性。有效的传统和灵活资源的部署需要新的方法，以明确解释日常单位承诺的不确定性。该项目建议将拓扑控制用作求助机制，该机制通过传输线转换为重定向功率流动，以响应于盛行的可再生生成条件，从而克服了由于间歇性而造成的可靠性丧失。但是，这种拓扑控制将需要新的算法创新，这将使系统操作员能够通过将拓扑控制作为追索权来计划其日益投入的单位承担。该项目为变量生成而进行了新的问题制定，以响应变量生成，这有望在计算上可行。希望该项目的结果将有助于促进可再生生成的增长，同时保持网格效率和可靠性。由于网格的拓扑由一组离散的传输链接组成，因此使用的链接使用的选择成为组合优化问题。此外，可再生生成中间歇性的存在需要传输网络适应要观察到的特定生成方案。这导致了一个两阶段的随机优化模型，其中第一阶段的选择与缓慢的发电机相关联，而第二阶段模型包括快速升压和间歇性发电机。该设置中的追索行动导致第二阶段的混合企业计划（MIP），这违反了在电力系统操作中非常成功的常见分解算法的凸面要求。该提案表明，一个新模型是两个阶段的随机MIP（SMIP），具有非常特殊的结构，可以利用该结构，以便可以解决现实的随机单位承诺问题，即使一般的两阶段SMIP模型也可以解决已知非常困难。该提案概述了一个合并的并行近似策略，该策略似乎很有希望，并且可以改变通常控制的概念，即可再生能源的高渗透对可靠性会产生不利影响。该项目是一个高风险，高回报的两个层面：a）它可能导致真正可靠的可靠性整合方法，b）通过将算法分解为较小的零件，而无需牺牲，可以解决非常大规模的SMIP的算法进步。最佳性将为解决这些非常具有挑战性的优化问题提供重大步骤。