Contingency-Constrained Unit Commitment with Post-Contingency Corrective Recourse

We consider the problem of minimizing costs in the generation unit commitment problem, a cornerstone in electric power system operations, while enforcing an N-k-e reliability criterion. This reliability criterion is a generalization of the well-known $N$-$k$ criterion, and dictates that at least $(1-e_ j)$ fraction of the total system demand must be met following the failures of $k$ or fewer system components. We refer to this problem as the Contingency-Constrained Unit Commitment problem, or CCUC. We present a mixed-integer programming formulation of the CCUC that accounts for both transmission and generation element failures. We propose novel cutting plane algorithms that avoid the need to explicitly consider an exponential number of contingencies. Computational studies are performed on several IEEE test systems and a simplified model of the Western US interconnection network, which demonstrate the effectiveness of our proposed methods relative to current state-of-the-art

Contingency-Constrained Unit Commitment With Intervening Time for System Adjustments

The N-1-1 contingency criterion considers the con- secutive loss of two components in a power system, with intervening time for system adjustments. In this paper, we consider the problem of optimizing generation unit commitment (UC) while ensuring N-1-1 security. Due to the coupling of time periods associated with consecutive component losses, the resulting problem is a very large-scale mixed-integer linear optimization model. For efficient solution, we introduce a novel branch-and-cut algorithm using a temporally decomposed bilevel separation oracle. The model and algorithm are assessed using multiple IEEE test systems, and a comprehensive analysis is performed to compare system performances across different contingency criteria. Computational results demonstrate the value of considering intervening time for system adjustments in terms of total cost and system robustness.Comment: 8 pages, 5 figure

Enhanced Reserve Procurement Policies for Power Systems with Increasing Penetration Levels of Stochastic Resources

abstract: The uncertainty and variability associated with stochastic resources, such as wind and solar, coupled with the stringent reliability requirements and constantly changing system operating conditions (e.g., generator and transmission outages) introduce new challenges to power systems. Contemporary approaches to model reserve requirements within the conventional security-constrained unit commitment (SCUC) models may not be satisfactory with increasing penetration levels of stochastic resources; such conventional models pro-cure reserves in accordance with deterministic criteria whose deliverability, in the event of an uncertain realization, is not guaranteed. Smart, well-designed reserve policies are needed to assist system operators in maintaining reliability at least cost. Contemporary market models do not satisfy the minimum stipulated N-1 mandate for generator contingencies adequately. This research enhances the traditional market practices to handle generator contingencies more appropriately. In addition, this research employs stochastic optimization that leverages statistical information of an ensemble of uncertain scenarios and data analytics-based algorithms to design and develop cohesive reserve policies. The proposed approaches modify the classical SCUC problem to include reserve policies that aim to preemptively anticipate post-contingency congestion patterns and account for resource uncertainty, simultaneously. The hypothesis is to integrate data-mining, reserve requirement determination, and stochastic optimization in a holistic manner without compromising on efficiency, performance, and scalability. The enhanced reserve procurement policies use contingency-based response sets and post-contingency transmission constraints to appropriately predict the influence of recourse actions, i.e., nodal reserve deployment, on critical transmission elements. This research improves the conventional deterministic models, including reserve scheduling decisions, and facilitates the transition to stochastic models by addressing the reserve allocation issue. The performance of the enhanced SCUC model is compared against con-temporary deterministic models and a stochastic unit commitment model. Numerical results are based on the IEEE 118-bus and the 2383-bus Polish test systems. Test results illustrate that the proposed reserve models consistently outperform the benchmark reserve policies by improving the market efficiency and enhancing the reliability of the market solution at reduced costs while maintaining scalability and market transparency. The proposed approaches require fewer ISO discretionary adjustments and can be employed by present-day solvers with minimal disruption to existing market procedures.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

Quantification of Intra-hour Security-constrained Flexibility Region

Rapid growth of renewable energy sources (RES) in the generation capacity mix poses substantial challenges on the operation of power systems in various time scales. Particularly in the intra-hour time scale, the interplay among variability and uncertainty of RES, unexpected transmission/generation outages, and short dispatch lead time cause difficulties in generation-load balancing. This paper proposes a method to quantify the intra-hour flexibility region. A robust security-constrained multi-period optimal power flow (RSC-OPF) model is first constructed to quantify the frequency, magnitude, and intensity of insufficient flexibility. The randomness of RES is captured by uncertainty sets in this model. The N-k contingency, spinning reserve, and corrective control limit constraints are included. This model is then cast into a two-stage robust optimization (RO) model and solved by the column-and-constraint generation (C&CG) method. The emergency measures with a least number of affected buses are derived and subsequently assessed by the post-optimization sensitivity analysis. Finally, the operational flexibility region is determined by continuous perturbation on the RES penetration level and the forecast error. The IEEE 14-bus system and a realistic Chinese 157-bus system are used to demonstrate the proposed method.postprin

Real-time grid topology modeling and optimization for power transmission systems

Accurately modeling and tactfully switching power grid topology are not only crucial for routine power system operational tasks but also play a critical role in system emergency responses under extreme events. The modern power grids have recently witnessed more frequent occurrences of unintentional topology changes. These changes can be caused by misoperations of substation protection systems, malicious cyber attacks, or natural disasters. Although strategically altering the grid topology through transmission switching can effectively relieve network congestion and thus has the potential to mitigate the impact of these events, the optimal decision is in general difficult to attain due to the uncertainty and variability therein. Therefore, this motivates us to devise efficient algorithms for achieving real-time power grid topology monitoring and optimization. This dissertation first focuses on efficient modeling and monitoring of the bus split event, which is a type of grid topology change caused by circuit breakers in substations. We perform sensitivity analysis to evaluate the grid-wide impact of such events under the bus-branch representation, for which a synchrophasor data enabled identification problem is presented by matching the changes in the measurements. Inspired by this, we next explore the transmission switching problem that can incorporate the substation-level topology changes. Furthermore, to perform reliable and cost-effective transmission switching under the renewable uncertainty, we study the distributionally robust chance-constrained problem, which can provide superior robustness guarantees over the traditional chance-constrained formulation. Finally, to provide effective system responses under extreme weather events, we will also investigate scalable optimization and learning algorithms for quick power grid restoration.Electrical and Computer Engineerin

Day-Ahead Contingency-Constrained Unit Commitment With Co-Optimized Post-Contingency Transmission Switching

This paper addresses the incorporation of transmission switching in the contingency-constrained unit commitment problem within the context of co-optimized electricity markets for energy and reserves. The proposed generation scheduling model differs from existing formulations due to the joint consideration of four major complicating factors. First, transmission switching actions are considered both in the pre- and post-contingency states, thereby requiring binary post-contingency variables. Secondly, generation scheduling and transmission switching actions are co-optimized. In addition, the time-coupled operation of generating units is precisely characterized. Finally, practical features of modern power systems, such as uncertain nodal net injections and the operation of energy storage, are also considered. The proposed model is cast as a challenging mixed-integer program for which the off-the-shelf software customarily used for simpler models may lead to intractability even for moderately-sized instances. In order to circumvent this computational issue, this paper presents an enhanced and novel application of an exact nested column-and-constraint generation algorithm featuring the inclusion of valid constraints to improve the overall computational performance. Numerical simulations based on the IEEE 118- and 300-bus systems demonstrate the effective performance of the proposed approach as well as its economic and operational advantages over existing models disregarding post-contingency transmission switching.Este documento aborda la incorporación de la conmutación de transmisión en el problema de compromiso de unidades con restricciones de contingencia en el contexto de mercados eléctricos cooptimizados para energía y reservas. El modelo de programación de generación propuesto difiere de las formulaciones existentes debido a la consideración conjunta de cuatro factores principales de complicación. En primer lugar, las acciones de conmutación de transmisión se consideran tanto en el estado anterior como posterior a la contingencia, por lo que se requieren variables binarias posteriores a la contingencia. En segundo lugar, se cooptimizan las acciones de programación de generación y conmutación de transmisión. Además, se caracteriza con precisión la operación acoplada en el tiempo de las unidades generadoras. Finalmente, también se consideran las características prácticas de los sistemas de potencia modernos, como las inyecciones netas nodales inciertas y la operación de almacenamiento de energía. El modelo propuesto se presenta como un desafiante programa de enteros mixtos para el cual el software comercial que se usa habitualmente para modelos más simples puede conducir a la dificultad incluso para instancias de tamaño moderado. Para sortear este problema computacional, este artículo presenta una aplicación mejorada y novedosa de un algoritmo de generación de restricciones y columnas anidadas exactas que presenta la inclusión de restricciones válidas para mejorar el rendimiento computacional general. Las simulaciones numéricas basadas en los sistemas de bus IEEE 118 y 300 demuestran el rendimiento efectivo del enfoque propuesto, así como sus ventajas económicas y operativas sobre los modelos existentes que no tienen en cuenta la conmutación de transmisión posterior a la contingencia