La presente Tesis analiza, propone y aplica metodologías de representación reducida (discretas) para variables multi-dimensionales de entrada en modelos de optimización de sistemas eléctricos de potencia. Estas variables, que se presentan a la vez en distintos puntos físicos del sistema de potencia, bien pueden aparecer en el lado derecho (términos constantes) de las restricciones o como coeficientes de la función objetivo de los problemas de optimización económica de sistemas de potencia. En modelos de sistemas de mediana a gran envergadura no es posible considerar todas las variables de entrada en forma desagregada. Por ejemplo, en modelos de optimización de mediano y largo plazo se agrupan instantes de características similares en cuanto a las variables de entrada, representando cada grupo con un sólo valor para las variables. Del mismo modo, en modelos complejos y/o de gran envergadura de programación estocástica se reducen los escenarios para obtener una formulación cuya resolución sea computacionalmente factible. Lo anterior lleva en forma casi natural a intentar encontrar metodologías que permitan realizar la agrupación temporal o la reducción de escenarios, problemas que son equivalentes, de forma óptima. En este contexto, “óptima” se refiere a que la solución a los problemas simplificados sea lo más similar posible a la solución del problema de optimización original. Para encontrar las representaciones reducidas se propone usar técnicas de clustering y otras basadas en reducción de escenarios. El desempeño de las técnicas propuestas es contrastado con las metodologías actualmente usadas en la industria y la academia en tres casos de estudio: (1) la expansión del Sistema Interconectado Central (SIC) usando programación estocástica para considerar la variabilidad hidrológica; (2) el dimensionamiento óptimo de la interconexión entre sistemas aislados con características de carga diversas; y (3) la operación de mediano plazo del Sistema Interconectado Norte Grande (SING) considerando niveles elevados de penetración eólica. En los tres casos las metodologías propuestas superaron a las actualmente usadas en términos de los errores en los resultados, en especial en lo que respecta a los flujos en los últimos dos casos de estudio. La razón para esta superioridad recae en que los métodos propuestos son capaces de considerar la estructura real de los datos durante la construcción de las representaciones reducidas de mejor forma que los métodos actualmente utilizados. El caso de estudio más que llevó al problema de optimización más complejo fue la expansión del SIC, puesto que se resolvió el problema de expansión conjunta de generación y transmisión considerando la variabilidad hidrológica. Los resultados dieron lugar a planes de expansión que disminuyen los costos totales cerca de un 5% respecto a los planes indicativos de expansión determinados por la autoridad (plan de expansión de generación de la Comisión Nacional de Energía, CNE, y plan de expansión de transmisión del Centro de Despacho Económico de Carga del SIC, CDEC-SIC).

Many countries around the world are experiencing an energy transition towards low-carbon economies, in order to reduce greenhouse gas emissions and combat climate change. One of the main sectors called to lead this transition is the electricity sector, mainly through the massive incorporation of renewable energies and the decommission of coal-fired power plants. In Chile, according to the long-term Chilean energy policy, the target is to reach net zero emissions by 2040 and supply 100% of the electrical demand from renewable energies by 2050. The Chilean commitment to combat climate change already started in 2011 by promoting the introduction of renewable energies in the electrical sector. Since then, the installed capacity of variable renewable energies (VRE) increased from 4% of the total capacity in 2011, to more than 40% in 2023. Under this global trend, the transmission infrastructure is key for achieving a cost-effective and secure energy transition. Indeed, to have an adequate transmission capacity is fundamental to accommodate new renewables and facilitate their integration into the market, supply the growing demand in a cost-effective way and enhance competition to ensure market efficiency. Increasing transmission capacity is not an easy task, due to growing difficulties in acquiring new rights-of way, the opposition of local communities to the construction of new lines and long processes of environmental licensing, among others. The aforementioned challenges considerably extend the development time of new transmission line projects. Delays in the commission of expansion projects can have significant technical and economic repercussions. Key concerns include unexpected curtailment of renewable energy sources (RES), where new RES investments can experience unexpected, diminished returns if the transmission capacity if not available on time for the delivery of their energy into the grid, increasing energy costs for consumers and greater transmission losses caused by the sub optimal operation of the transmission network due to underdevelopment. Flexible technological solutions can alleviate these issues by enhancing the network capacity and reliability in shorter time and without the need of installing new transmission lines. Amongst these alternative technological solutions, we can find line reconductoring, HVAC to HVDC reconversion, Flexible AC Transmission Systems (FACTS), Battery Energy Storage System (BESS) installed as grid boosters and others. What all these solutions have in common is shorter development times, lower investment cost and less challenging regulatory processes compared to the investment in new transmission lines. Consequently, nowadays there are plenty of technological options to increase the transmission capacity. However, the main challenge consists of determining which technology, when and where should be incorporated into the system to minimize total system costs while maintaining security, social and/or environmental constraints. To answer this question, a usual approach is to address this challenge as an optimization problem, the transmission network expansion planning (TNEP) problem. The TNEP is usually formulated as a mixed-integer linear problem (MILP), where integer variables are related to the investment decisions, while continuous variables are related to the power system operation. In more complex formulations, integer variables can also comprise operational variables such as the generator's unit commitment. The TNEP can also be formulated as a nonlinear problem (MINLP), for example to consider power system losses and/or AC power flows [6]. Furthermore, the use of stochastic and/or robust models has increased in the last years, due to the growing uncertainty involved in the network planning. In the past, when power systems were vertically integrated and the generation matrix was dominated by conventional fossil-fuel based generation, there was little uncertainty regarding future generation capacity and power feed-in. Therefore, the majority of TNEP formulations were deterministic. However, the deregulation of the electricity markets, along with the massive introduction of renewable energies, brought new uncertainties regarding future generation capacity and availability. The level of uncertainty regarding future generation capacity increased even more with the introduction of renewable energies, because of their short construction lead times (1 - 2 years), compared to conventional fossil-fuel based generation (3-5 years), thus increasing the need of adopting stochastic or robust TNEP formulations. Furthermore, it is expected that the use of stochastic and robust model will increase in the future, in order to incorporate new sources of uncertainties, for examples, those related to long-term effects of the climate change. In all cases, the TNEP problem is NP-hard, which means that it cannot be solved in polynomial time. For large-scale realistic-size power system models, solving the TNEP problem demands significant amounts of computational resources and could even require several days to be solved using a state-of-the-art optimization algorithm. Furthermore, the appearance of emergent technologies that increase the operational flexibility of power systems have introduced new challenges for the TNEP. Examples of such technologies are FACTS, storage devices, special protection schemes, energy storage-based grid booster and HVDC links, as well technological option to increase the use of existing transmission network assets such as line uprating and HVDC conversion. For each new technology, novel formulations capable of capturing their value in the power system are required, which usually means having more complex formulations, more variables, and constraints, thus resulting in models which are even more challenging to solve. In particular, proper models of energy storage systems require the introduction of time-coupling constraints to account for the energy balance, thus significantly increasing the complexity of the TNEP problem. Note that, to capture the benefits of short-term storage systems such as batteries, requires increasing the time resolution of the models (for example, representative days instead of isolated operating conditions), which in turn increase the computational burden of the TNEP formulation.

Durante los últimos años, los sistemas eléctricos de potencia (SEP) alrededor del mundo han experimentado un crecimiento constante de las energías renovables no convencionales (ERNC). Los números son claros, entre los años 2010 y 2021, la capacidad instalada de las ERNC a nivel global aumentó en un 150%, pasando de 1.2 TW el año 2010 a 3.0 TW al año 2021. De hecho, solo en el año 2021 la capacidad instalada aumentó un 9.1% respecto al año anterior. De particular importancia ha sido el crecimiento de las tecnologías de generación variable con convertidor (TGVCC) como la solar fotovoltaica y eólica de velocidad variable, cuya capacidad instalada respecto al año anterior aumentó en un 19% y 13% respectivamente. En Chile, el aumento de las TGVCC ha sido similar al resto del mundo. El país cuenta actualmente con 5,1 GW de capacidad instalada en centrales fotovoltaicas y 3,7 GW en generación eólica, que en conjunto representan un 30,0% de la capacidad instalada total de generación en el país. Las soluciones para mitigar los problemas de estabilidad en redes débiles con altos niveles de TGVCC cubren un amplio espectro de alternativas que van desde refuerzos de red clásicos hasta cambios en el sistema de control de los convertidores. Adicionalmente, la robustez en áreas débiles de una red se puede mejorar localmente mediante la incorporación de equipos basados en convertidores como SVC, STATCOM, BESS, etc. En este contexto, entre los dispositivos que más se han investigado en el último tiempo se encuentran los sistemas de almacenamiento de energía, en particular, las baterías (BESS). Diversos estudios y experiencias practicas han mostrado que los BESS son una buena alternativa para mejorar la estabilidad de los SEP debido a sus tiempos rápidos de respuesta y capacidad de inyectar potencia activa y reactiva de forma independiente. Entre las aplicaciones más estudiadas se encuentran la incorporación de lazos de control para que los BESS puedan aportar a la estabilidad de frecuencia y tensión en los SEP. En estos trabajos se destaca la incorporación de inercia virtual control de reactivos, respuesta rápida de frecuencia, regulación de frecuencia y regulación de tensión. Si bien las diversas maneras en que los BESS pueden aportar a la estabilidad de los SEP han sido ampliamente investigadas durante los últimos años, la localización óptima de estos equipos en escenarios con altos niveles de TGVCC aún no ha sido muy investigada. Tradicionalmente, los trabajos en esta línea se centran en localizar los BESS considerando criterios económicos buscando, por ejemplo, minimizar los costos de un sistema en el marco de la operación o de la planificación. Si bien este tipo de trabajos aprovechan las ventajas económicas que los BESS pueden entregar, no consideran sus posibles aportes a la estabilidad de los SEP. En el contexto anterior, en este trabajo se propone una metodología para determinar la localización óptima de BESS en sistemas eléctricos débiles con bajos SCL, considerando criterios de estabilidad.

Effective bidding on multiple electricity products under uncertainty would allow a more profitable market participation for hybrid power plants with variable energy resources and storage systems, therefore aiding the decarbonization process. This study deals with the effective bidding of a photovoltaic plant with an energy storage system (PV-ESS) participating in multi-timescale electricity markets by providing energy and ancillary services (AS) products. The energy management system (EMS) aims to maximize the plant’s profits by efficiently bidding in the day ahead and realtime markets while considering the awarded products adequate delivery. EMS’s bidding decisions are usually obtained from traditional mathematical optimization frameworks. However, since the addressed problem is a multi-stage stochastic program, it is often intractable and suffers the curse of dimensionality. This document presents a novel multi-agent deep reinforcement learning (MADRL) framework for efficient multi-timescale bidding. Two agents based on multi-view artificial neural networks with recurrent layers (MVANNs) are adjusted to map environment observations to actions. Such mappings use as inputs available information related to electricity market products, bidding decisions, solar generation, stored energy, and time representations to bid in both electricity markets. Sustained by a price-taker assumption, the physically and financially constrained EMS’s environment is simulated by employing historical data. A shared cumulative reward function with a finite time horizon is used to adjust both MVANNs’ weights simultaneously during the learning phase. We compare the proposed MADRL framework against scenariobased two-stage robust and stochastic optimization methods. Results are provided for one year round market participation of the hybrid plant at a 1-minute resolution. The proposed method achieved statistically significant higher profits, less variable incomes from both electricity markets, and better provision of awarded products by achieving smaller and less variable energy imbalances through time.

The f0(1500) meson resonance is one of several contenders to have significant mixing with the lightest glueball. This resonance is well established from several previous experiments. Here we present the first photoproduction data for the f0(1500) via decay into the K0 SK0 S channel using the CLAS detector. The reaction γp → fJp → K0 SK0 Sp, where J = 0,2, was measured with photon energies from 2.7–5.1 GeV. A clear peak is seen at 1500 MeV in the background subtracted invariant mass spectra of the two kaons. This is enhanced if the measured fourmomentum transfer to the proton target is restricted to be less than 1.0 GeV2 . By comparing data with simulations, it can be concluded that the peak at 1500 MeV is produced primarily at low t, which is consistent with a t-channel production mechanism.

A measurement of the electroproduction of photons off protons in the deeply inelastic regime was performed at Jefferson Lab using a nearly 6 GeV electron beam, a longitudinally polarized proton target, and the CEBAF Large Acceptance Spectrometer. Target-spin asymmetries for ep → e0 p0 γ events, which arise from the interference of the deeply virtual Compton scattering and the Bethe-Heitler processes, were extracted over the widest kinematics in Q2, xB, t, and ϕ, for 166 four-dimensional bins. In the framework of generalized parton distributions, at leading twist the t dependence of these asymmetries provides insight into the spatial distribution of the axial charge of the proton, which appears to be concentrated in its center. These results also bring important and necessary constraints for the existing parametrizations of chiral-even generalized parton distributions.

A new high gain control law is proposed for stably invertible linear systems. The continuous-time case is first studied to set ideas. The extension to the sampled-data case is made difficult by the presence of sampling zeros. For continuous-time systems having relative degree greater than or equal to two, these zeros converge, as the sampling rate approaches zero, to either marginally stable or unstable locations. A methodology which specifically addresses the sampling zero issue is developed. The methodology uses an approximate model which includes, when appropriate, the asymptotic sampling zeros. The core idea is supported by simulation studies. Also, a preliminary theoretical analysis is provided for degree two, showing that the design based on the approximate model stabilizes the true system for the continuous and sampled-data cases.

Although Urban Heat Island (UHI) is a fundamental effect modifying the urban climate, being widely studied, the relative weight of the parameters involved in its generation is still not clear. This paper investigates the hierarchy of importance of eight parameters responsible for UHI intensity in the Mediterranean context. Sensitivity analyses have been carried out using the Urban Weather Generator model, considering the range of variability of: 1) city radius, 2) urban morphology, 3) tree coverage, 4) anthropogenic heat from vehicles, 5) building’s cooling set point, 6) heat released to canyon from HVAC systems, 7) wall construction properties and 8) albedo of vertical and horizontal surfaces. Results show a clear hierarchy of significance among the considered parameters; the urban morphology is the most important variable, causing a relative change up to 120% of the annual average UHI intensity in the Mediterranean context. The impact of anthropogenic sources of heat such as cooling systems and vehicles is also significant. These results suggest that urban morphology parameters can be used as descriptors of the climatic performance of different urban areas, easing the work of urban planners and designers in understanding a complex physical phenomenon, such as the UHI.