Contingency-Constrained Unit Commitment with High Penetration of Intermittent Renewables

可靠性是电力系统的一个主要问题，工程师们一直在努力在各种不确定的情况下保持供电，包括发电机或输电线路突然故障引起的突发事件。北美电力可靠性公司（NERC） N-1规则要求此类单一故障不应导致系统不可用，该规则已嵌入到某些版本的机组承诺过程中。由此产生的应急约束单元承诺（CCUC）非常复杂，现在风能或太阳能等间歇性可再生能源的急剧增加使这个问题变得更加复杂。例如，在西班牙，风力发电在短短几个小时内下降相当于关闭四个核机组的情况并不罕见，这给系统运营商带来了重大挑战。因此，考虑间歇性可再生能源存在的突发事件是一个关键而紧迫的问题。与ISO-NE，南加州爱迪生公司和阿尔斯通电网公司合作，该提案是关于间歇性可再生能源高渗透率的ccucc。考虑到不同类型的不确定性及其大小、大量的偶然性、混合整数优化的组合性质以及电力系统的规模，该问题具有挑战性。我们的重点将放在层段ccucc的新配方和先进的优化方法上。拟议的研究将对管理其他不确定性产生更广泛的影响，例如N-2突发事件，以及其他复杂问题，例如微电网能源优化。对于这些问题，与不确定天气、电力需求和微电网相关的间歇性可再生能源相关的困难可以通过区间公式进行建模。由此产生的混合整数线性优化问题可以受益于我们先进的优化方法。此外，该研究将通过课程、研讨会、出版物和在线材料对研究生、本科生和专业人员进行教育。它还将通过他们的老师使高中生受益。参加了康涅狄格大学的达芬奇暑期项目将积极寻求代表人数不足的群体的参与。具体而言，任务1是在考虑N-1突发事件和间歇性可再生能源的情况下，制定能源与储备协同优化的ccucc。这种新颖的想法在于使用间隔来描述基于发电转移因子（gsf）的输电突发事件，而不是一次分析一个突发事件。线流是净节点注入的加权和，其中权重为gsf。当传输线跳闸时，gsf将发生变化。因此，各种传输偶然性可以用不同的gsf值来描述，并通过区间来建模。通过识别和消除冗余的偶然性传输约束，缩小gsf的范围，以及改进区间算法，例如使用仿射算法，可以缓解该建模的保守性。发电机突发事件和间歇性可再生能源将被纳入区间优化框架。为了有效地解决这个问题，任务二是开发新一代的优化方法。关键是将利用可分离性的最新替代拉格朗日松弛（SLR）与利用线性的分支-切割（branch-and-cut）进行协同整合。虽然SLR需要迭代，但通过新颖地利用跨迭代的子问题凸包的不变性，将大大提高总体CPU时间和解决方案质量。此外，将探讨我们的方法的并行实施。该方法将使用ISO-NE和CAISO的实际数据集进行测试。

Reliability is a major concern for power systems, and engineers have been striving hard to keep lights on under all kinds of uncertainty, including contingencies caused by a sudden failure of a generator or a transmission line. The North American Electric Reliability Corporation (NERC) N-1 rule requires that no such single failure should result in system infeasibility, and this rule has been embedded in certain versions of the unit commitment process. The resulting contingency-constrained unit commitment(CCUC) is extremely complex, and the issue is now compounded by the drastic increase in intermittent renewables such as wind or solar. In Spain, for example, it is not uncommon that a drop of wind generation within just a few hours is equivalent to the shutdown of four nuclear units, and this has presented major challenges to system operators. Consideration of contingencies in the presence of intermittent renewables is thus a critical and urgent issue. In collaboration with ISO-NE, Southern California Edison and Alstom Grid, this proposal is on CCUC with high penetration of intermittent renewables. The problem is challenging in view of different types of uncertainties and their magnitudes, the large number of contingencies, the combinatorial nature of mixed-integer optimization, and the largeness of power systems. Our focus will be on novel formulations of interval CCUC, and on advanced optimization methods. The proposed research will have the broader impacts on managing other uncertainties, e.g., N-2 contingencies, and on other complex problems such as microgrid energy optimization. For these problems, difficulties associated with uncertain weather, electricity demand and microgrid-associated intermittent renewables can be modeled by the interval formulation. The resulting mixed-integer linear optimization problems can then benefit from our advanced optimization methods. Furthermore, the research will educate graduate and undergraduate students and professionals through courses, seminars, publications and online materials. It will also benefit high school students through their teachers? involvement in the daVinci summer project at UConn. The participations of under-represented groups will be actively sought.Specifically, Task 1 is on formulating CCUC for the co-optimization of energy and reserve considering N-1 contingencies and intermittent renewables. The novel idea lies in using intervals to describe transmission contingencies based on generation shift factors (GSFs), as opposed to analyzing contingencies one at a time. A line flow is a weighted sum of net nodal injections, where weights are GSFs. When a transmission line is tripped, GSFs will be changed. Various transmission contingencies can thus be described by varying values of GSFs, and be modeled by intervals. Conservativeness of this modeling will be alleviated through shrinking ranges of GSFs based on identifying and eliminating redundant contingency transmission constraints, and through improving interval arithmetic, e.g., by using affine arithmetic. Generator contingencies and intermittent renewables will then be incorporated within the interval optimization framework. To effectively solve the problem, Task 2 is on developing a new generation of optimization methods. The key is to synergistically integrate our latest surrogate Lagrangian relaxation(SLR), which exploits separability, with branch-and-cut, which exploits linearity. While SLR requires iterations, by novel exploitation of the invariant nature of subproblem convex hulls across iterations, the overall CPU time and solution quality will be drastically improved. In addition, parallel implementation of our approach will be explored. The method will be tested using realistic data sets from ISO-NE and CAISO.