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: 1549572
• 负责人: Shmuel Oren
• 金额: $ 15万
• 依托单位: University of California-Berkeley
• 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=1549572&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.

由于风能资源的不确定性和多变性，将风能资源大规模整合到电力基础设施的发电组合中，给系统运营商带来了新的挑战。风能和其他可再生能源资源的间歇性，加上当前电力系统储存的局限性，对将可再生资源整合到电网中，同时保持可接受的服务可靠性构成了严峻的挑战。有效地部署常规和灵活的资源需要新的方法，明确考虑到提前一天的机组组合的不确定性。该项目建议使用拓扑控制作为一种资源机制，通过输电线路切换来调动电网的灵活性，以响应普遍的可再生发电条件重新引导潮流，从而克服因间歇性而造成的可靠性损失。然而，这种拓扑控制将需要新的算法创新，这将允许系统操作员通过将拓扑控制作为一种资源行动来计划他们的提前一天的机组组合。该项目追求一种新的输电线路切换问题公式，以响应变量生成，这在计算上是可行的。由于电网的拓扑结构由一组离散的传输链路组成，因此选择使用哪种链路就成了一个组合优化问题。此外，可再生发电中间歇性的存在要求传输网络适应所观察到的特定发电情景。这导致了一个两阶段随机优化模型，在该模型中，第一阶段的选择与慢坡发电机有关，而第二阶段模型既包括快斜坡发电机又包括间歇发电机。在这种情况下的追索行为导致了第二阶段的混合整数规划(MIP)，这违反了在电力系统运行中非常成功的常见分解算法的凸性要求。这一建议表明，一种新的模型，即两阶段随机MIP(SMIP)，具有一种非常特殊的结构，可以利用这种结构来解决现实中的随机机组组合问题，尽管一般的两阶段SMIP模型是非常困难的。该提案概述了一种并行-串联相结合的近似策略，该策略似乎很有希望，并可能改变人们普遍持有的观点，即高渗透率的可再生能源将对可靠性产生不利影响。该项目在两个层面上是一个高风险、高回报的项目：a)它可以导致可靠的可再生能源整合的真正可持续的方法，以及b)通过将SMIP分解成更小的部分来解决超大规模SMIP的算法进步，而不牺牲最优化将在解决这些非常具有挑战性的优化问题方面迈出重要的一步。