Seismic Damage Estimation in Buried Pipelines Due to Future Earthquakes – The Case of the Mexico City Water System

Since the mid-70s, there have been advances in the development of models to better understand how earthquakes affect buried pipelines. These natural events can cause damage due to two phenomena: seismic wave propagation and permanent ground deformation. The combined effect of both phenomena in pipeline damage estimation is a subject still complex to address, especially if the objective is to estimate damage due to future earthquakes. In this chapter, the damage assessment methods only consider the impact of seismic wave propagation. The effects of permanent ground deformation phenomena, like ground subsidence, landslides, and ground rupture, are omitted. The exceptional damage caused by the 1985 Michoacan earthquake in Mexico City has encouraged researchers to develop sophisticated tools to estimate ground motion in the Valley of Mexico from Pacific coastal earthquakes, including the important site effects largely observed in the city. These tools have helped to better understand how earthquakes affect buildings and other structures like pipeline systems. The most remarkably case of pipeline damage caused by the 1985 seismic event is the extensive damage suffered by the Mexico City Water System (MCWS) that left almost 3.5 million people without water, and caused water service disruptions over a period of two months. The 1985 MCWS damage scenario has been extensively analyzed for developing models to better understand how seismic wave propagation affects buried pipelines; some of those models are employed in the future damage prediction methods described in this manuscript. Fragility functions are typically the tools most used to assess seismic damage in buried pipelines. These functions relate pipeline damage with seismic intensity. Pipeline damage is generally expressed as a linear pipe repair density. Seismic intensity is usually quantified through a seismic parameter. There are many seismic parameters used as arguments of fragility functions; the most important of these are described in Section 2. Section 3 describes the most important fragility functions proposed until now, including the two employed in the seismic damage estimation for the MCWS presented in Section 4. Finally, Section 5 contains a summary of the most important conclusions of this work

New perspectives on the damage estimation for buried pipeline systems due to seismic wave propagation

Over the past three decades, seismic fragility fonnulations for buried pipeline systems have been developed following two tendencies: the use of earthquake damage scenarios from several pipeline systems to create general pipeline fragility functions; and, the use of damage scenarios from one pipeline system to create specific-system fragility functions. In this paper, the advantages and disadvantages of both tendencies are analyzed and discussed; in addition, a summary of what can be considered the new challenges for developing better pipeline seismic fragility formulations is discussed. The most important conclusion of this paper states that more efforts are needed to improve the estimation of transient ground strain -the main cause of pipeline damage due to seismic wave propagation; with relevant advances in that research field, new and better fragility formulations could be developed

Tight Big-Ms for Optimal Transmission Switching

This paper addresses the Optimal Transmission Switching (OTS) problem in electricity networks, which aims to find an optimal power grid topology that minimizes system operation costs while satisfying physical and operational constraints. Existing methods typically convert the OTS problem into a Mixed-Integer Linear Program (MILP) using big-M constants. However, the computational performance of these approaches relies significantly on the tightness of these big-Ms. In this paper, we propose an iterative tightening strategy to strengthen the big-Ms by efficiently solving a series of bounding problems that account for the economics of the OTS objective function through an upper-bound on the generating cost. We also discuss how the performance of the proposed tightening strategy is enhanced if reduced line capacities are considered. Using the 118-bus test system we demonstrate that the proposed methodology outperforms existing approaches, offering tighter bounds and significantly reducing the computational burden of the OTS problem

Cost-driven screening of network constraints for the unit commitment problem

In an attempt to speed up the solution of the unit commitment (UC) problem, both machine-learning and optimization-based methods have been proposed to lighten the full UC formulation by removing as many superfluous line-flow constraints as possible. While the elimination strategies based on machine learning are fast and typically delete more constraints, they may be over-optimistic and result in infeasible UC solutions. For their part, optimization-based methods seek to identify redundant constraints in the full UC formulation by exploring the feasibility region of an LP-relaxation. In doing so, these methods only get rid of line-flow constraints whose removal leaves the feasibility region of the original UC problem unchanged. In this paper, we propose a procedure to substantially increase the line-flow constraints that are filtered out by optimization-based methods without jeopardizing their appealing ability of preserving feasibility. Our approach is based on tightening the LP-relaxation that the optimization-based method uses with a valid inequality related to the objective function of the UC problem and hence, of an economic nature. The result is that the so strengthened optimization-based method identifies not only redundant line-flow constraints but also inactive ones, thus leading to more reduced UC formulations.The work of Álvaro Porras was supported in part by the Spanish Ministry of Science, Innovation and Universities through the university teacher training program with Fellowship under Grant FPU19/03053. This work was supported in part by the Spanish Ministry of Science and Innovation under Grant AEI/10.13039/501100011033 through project PID2020-115460GB-I00, in part by the European Research Council (ERC) through the European Union’s Horizon 2020 Research and Innovation Programme under Grant 755705, in part by the Junta de Andalucía (JA), and in part by the European Regional Development Fund (FEDER) through the research project under Grant P20_00153

Tight big-Ms for optimal transmission switching

This paper addresses the Optimal Transmission Switching (OTS) problem in electricity networks, which aims to find an optimal power grid topology that minimizes system operation costs while satisfying physical and operational constraints. Existing methods typically convert the OTS problem into a Mixed-Integer Linear Program (MILP) using big-M constants. However, the computational performance of these approaches relies significantly on the tightness of these big-Ms. In this paper, we propose an iterative tightening strategy to strengthen the big-Ms by efficiently solving a series of bounding problems that account for the economics of the OTS objective function through an upper-bound on the generating cost. We also discuss how the performance of the proposed tightening strategy is enhanced if reduced line capacities are considered. Using the 118-bus test system we demonstrate that the proposed methodology outperforms existing approaches, offering tighter bounds and significantly reducing the computational burden of the OTS problem.Funding for open access charge: Universidad de Málaga / CBU

Unifying Chance-Constrained and Robust Optimal Power Flow for Resilient Network Operations.

Política de acceso abierto tomada de: https://v2.sherpa.ac.uk/id/publication/37938 (accepted version, pathway c)Uncertainty in renewable energy generation has the potential to adversely impact the operation of electric networks. Numerous approaches to manage this impact have been proposed, ranging from stochastic and chance-constrained programming to robust optimization. However, these approaches either tend to be conservative or leave the system vulnerable to low probability, high impact uncertainty realizations. To address this issue, we propose a new formulation for stochastic optimal power flow that explicitly distinguishes between “normal operation”, in which automatic generation control (AGC) is sufficient to guarantee system security, and “adverse operation”, in which the system operator is required to take additional actions, e.g., manual reserve deployment. The new formulation has been compared with the classical ones in a case study on the IEEE-118 and IEEE-300 bus systems. We observe that our consideration of extreme scenarios enables solutions that are more secure than typical chance-constrained formulations, yet less costly than solutions that guarantee robust feasibility with only AGC.Ministerio de Ciencia e Innovación de España (AEI/10.13039/501100011033): proyectos PID2020-115460GB-I00 y PID2023-148291NB-I00, y ayuda FPU19/03053. Consejo Europeo de Investigaciones Científicas (ERC), programa Horizonte 2020 de la UE, ayuda No 755705. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Applied Mathematics program (USA): Contract Number DE-AC02-06CH11347. Universidad de Málaga SCBI, Centro de Supercomputación

Learning-based State Estimation in Distribution Systems with Limited Real-Time Measurements.

The task of state estimation in active distribution systems faces a major challenge due to the integration of different measurements with multiple reporting rates. As a result, distribution systems are essentially unobservable in real time, indicating the existence of multiple states that result in identical values for the available measurements. Certain existing approaches utilize historical data to infer the relationship between real-time available measurements and the state. Other learning-based methods aim to estimate the measurements acquired with a delay, generating pseudo-measurements. Our paper presents a methodology that utilizes the outcome of an unobservable state estimator to exploit information on the joint probability distribution between real-time available measurements and delayed ones. Through numerical simulations conducted on a realistic distribution grid with insufficient real-time measurements, the proposed procedure showcases superior performance compared to existing state forecasting approaches and those relying on inferred pseudo-measurements.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tec

Potencial competitivo: industria de calzado en el departamento de Santander - Colombia

En cuanto al comportamiento de la industria del calzado en el departamento de Santander, en Colombia, se evidencian factores que fortalecen su desarrollo, haciendo de este un sector atractivo. Con el fin de analizar este comportamiento, este artículo analiza los diferentes factores que desempeñan un papel importante en la industria, como lo son la infraestructura, las políticas macroeconómicas, las condiciones geográficas, entre otras, con el objetivo de establecer las limitaciones que tiene esta industria y las ventajas que se pueden aprovechar

Análisis del estado del proceso de educación inclusiva de niños con discapacidad cognitiva en los colegios distritales Alemania Unificada, el porvenir y Marco Fidel Suárez.

La presente investigación tiene como propósito analizar el estado del proceso de educación inclusiva de niños y niñas con discapacidad cognitiva en los Colegios Distritales Alemania Unificada, El Porvenir y Marco Fidel Suárez a fin de brindar recomendaciones que permitan movilizar acciones encaminadas a facilitar el proceso de educación inclusiva de estudiantes con discapacidad cognitiva. Esta investigación se fundamenta en un diseño de investigación cualitativa de tipo descriptiva, donde a través de grupos focales se indaga por proceso de la educación inclusiva en tres colegios distritales, de acuerdo con las categorías de análisis, políticas de inclusión, prácticas pedagógicas y percepción, recopilando la información en matrices de procesamiento, para luego ser comprendidas y analizadas, con el fin de generar una guía metodológica que permita conocer el proceso de educación inclusiva llevada a cabo en los colegios de la presente investigación.The purpose of this research is to analyze the state of process of inclusive education of children with cognitive disabilities in the Alemania Unificada, El Porvenir and Marco Fidel Suárez District Colleges, in order to provide recommendations for mobilizing actions aimed at facilitating the process of inclusive education of students with cognitive disabilities. This research is based on a qualitative research design of descriptive type, where through focus groups is investigated by inclusive education process in three district schools, according to the categories of analysis, inclusion policies, pedagogical practices and perception, collecting information in processing matrices, to be understood and analyzed later, in order to generate a methodological guide that allows to know the process of inclusive education carried out in the schools of the present investigation