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

由于风力资源的不确定性和可变性，将风力资源大规模整合到电力基础设施的发电组合中对系统运营商提出了新的挑战。风能和其他可再生能源的间歇性，以及当前电力系统中存储的限制，对将可再生能源整合到电网中，同时保持可接受的服务可靠性提出了严峻的挑战。常规和灵活的资源的有效部署需要新的方法，明确占日前单位承诺的不确定性。本项目建议使用拓扑控制作为一种追索机制，通过输电线路切换调动电网的灵活性，根据当前的可再生能源发电条件重新定向潮流，从而克服由于不稳定性而导致的可靠性损失。然而，这样的拓扑控制将需要新的算法创新，这将允许系统运营商计划他们的一天前的单位承诺，将拓扑控制作为一个追索行动。该项目追求一种新的问题制定传输线切换响应变量发电，有望计算可行。希望该项目的成果将有助于促进可再生能源发电的增长，同时保持电网的效率和可靠性，因为电网的拓扑结构由一组离散的传输链路组成，选择使用哪些链路成为一个组合优化问题。此外，在可再生能源发电中存在的不稳定性要求输电网络适应所观察到的特定发电场景。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是与缓慢斜坡发电机，而第二阶段的模型包括快速斜坡以及间歇性发电机。在此设置中的追索权行动导致在第二阶段的混合整数规划（MIP），这违反了凸性要求的常见的分解算法，已经非常成功的电力系统操作。这一建议表明，一个新的模型，这是一个两阶段的随机MIP（SMIP），具有一个非常特殊的结构，可以利用，使现实的随机机组组合问题可以解决，即使一般类的两阶段SMIP模型是众所周知的是非常困难的。该提案概述了一种组合的并行-串行近似策略，该策略似乎很有前途，并且可以改变人们普遍认为的可再生能源的高渗透率将对可靠性产生不利影响的观念。该项目在两个层面上是一项高风险、高回报的事业：a）它可能导致一种真正可持续的方法，以实现可靠的可再生能源整合，以及B）通过分解成较小的部分来解决超大规模SMIP的算法进步，而不会牺牲最优性，这将为解决这些非常具有挑战性的优化问题迈出重要一步。

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.