• Energy Research
• 2025-2025close
• 13. Climate action close

Background: In recent years, the integration of renewable energy sources into the grid has increased exponentially. However, one significant challenge in integrating these renewable sources into the grid is intermittency. Objective: To address this challenge, accurate PV power forecasting techniques are crucial for operations and maintenance and day-to-day operations monitoring in solar plants. Methods: In the present work, a hybrid approach that combines Deep Learning (DL) and Numerical Weather Prediction (NWP) with electrical models for PV power forecasting is proposed Results: The outcomes of the study involve evaluating the performance of the proposed model in comparison to a Physical model and a DL model for predicting solar PV power one day ahead and two days ahead. The results indicate that the prediction accuracy of PV power decreases and the error rates increase when forecasting two days ahead, as compared to one day ahead. Conclusion: The obtained results demonstrate that DL models combined with NWP and electrical models can improve PV Power forecasting compared to a Physical model and a DL model.

Los lípidos constituyen una parte importante de la dieta y participan en numerosas funciones biológicas vitales. Particularmente, el aceite de pescado es un valioso producto rico en ácidos grasos poliinsaturados Omega-3, que en la actualidad se considera atractivo por sus efectos benéficos a la salud humana. Los aceites marinos a menudo se descartan como desecho en muchas industrias pesqueras, principalmente debido a la falta de implementación de procesos innovadores de recuperación y refinado de estos recursos de forma lucrativa y sustentable. En este trabajo se aborda dicha problemática al proponer una metodología sistemática con el objetivo de integrar los aspectos económicos, ambientales y sociales en las decisiones de diseño de procesos de una refinería de aceite de pescado crudo, enfocado en la producción de concentrados de Omega-3 a partir del aceite residual de una planta procesadora de harina de atún en México. Esto puede realizarse mediante la formulación y solución de un problema de optimización multi-objetivo que incluye opciones de procesamiento convencionales, así como intensificadas dentro una superestructura. El problema MINLP (Mixed Integer Non-Linear Programing) resultante es solucionado con una combinación de métodos lexicográfico y de restricción épsilon (ε-constraint), para luego someter a una evaluación comparativa las soluciones obtenidas en forma de frente de Pareto utilizando Análisis de Ciclo de Vida y Análisis de Riesgos con la asistencia de un software de simulación de procesos (SuperPro Designer®). La metodología de búsqueda del diseño de proceso óptimo sustentable arrojó tres configuraciones factibles, destacando la selección de procesos intensificados como nano-neutralización, destilación molecular, transesterificación con ultrasonido y cromatografías argentométrica y con CO2. De los cuales, el proceso utilizando cromatografía con CO2 supercrítico resultó tener mejor desempeño en términos de indicadores de Análisis de Ciclo de Vida y seguridad industrial. A su vez, este trabajo proporciona una base de datos de propiedades de los compuestos lipídicos y técnicas de procesamiento que puede ser implementada en futuros proyectos. Además, ayuda a demostrar que la revalorización de los aceites marinos, así como de los desechos de la industria alimentaria en general, es importante para lograr un modelo de economía circular dentro del desarrollo productivo sustentable. Lipids constitute an important part of the diet and participate in numerous vital biological functions. Fish oil, in particular, is a valuable nutritious product, mainly due to its Omega-3 polyunsaturated fatty acids content that is currently attractive for its beneficial health effects. Marine oils are often discarded as waste in many seafood industries, primarily due to the lack of implementation of innovative resource recovery and oil refining processes in a lucrative and sustainable way. This work tackles this issue by proposing a systematic framework with the objective to integrate the economic, environmental and social aspects together in the process design decisions of a crude fish oil refinery focused on the production of Omega-3 concentrates from waste oil of a tuna processing plant in Mexico. This is addressed by the formulation and solution of a multi-objective optimization design problem that includes conventional and intensified processing options in a superstructure. The resulting MINLP (Mixed Integer Non-Linear Programing) problem is solved with a combination of lexicographic and ε-constraint methods. Then, the solutions provided in the form of a Pareto front are subject to benchmarking with a Life Cycle Assessment and risk analysis methodology with the assistance of a process simulation software (SuperPro Designer®). The search methodology of the sustainable optimal process design resulted in three feasible configurations, highlighting the selection of intensified processes such as nano-neutralization, molecular distillation, ultrasound assisted transesterification, and argentometric and CO2 chromatography. Among these, the process using supercritical CO2 chromatography has the best performance in terms of Life Cycle Assessment and industrial safety indicators. At the same time, this work provides a database of lipid compound properties and processing techniques that can be implemented in future projects. In addition, it helps to demonstrate that the revalorization of marine oils, as well as other waste from the food industry in general, is relevant to achieve a circular economy model within sustainable productive development. Administradores Investigadores Estudiantes

Global lakes hold about 87% of the freshwater. However, climate change has posed a severe threat to these freshwater resources. Evaporation (E) is a major water loss from lakes, and the strong coupling between lake E and changes in atmospheric conditions in a warming climate leads to temporal and spatial variability in water loss through E, making it challenging for water resource management. This dissertation examines such spatiotemporal variability in global lake E in response to climate change, investigates its environmental controls, and identifies regions with large sensitivities to climate changes. Using a state-of-science Lake, Ice, Snow, and Sediment Simulator (LISSS) that is a lake model within the Community Land Model (CLM), it is shown that the large spatial variability of global lake E is modulated by the vapor pressure difference (e_D) between lake surface and overlying air. The e_D also causes higher nighttime lake E, which contributes more to the spatial variability of global lake E than daytime lake E. The performance of the Penman method (PM) is also evaluated against observations and the LISSS modeling results in estimating global lake E. It is shown that the PM overestimates lake E due to a strong bias in the net radiation (Rn) and lake water heat storage (G). Using the LISSS simulated Rn and G in the PM, however, the PM performance is largely improved and the PM E becomes comparable to the LISSS E. The global lake E trend over 1951 - 1978 is analyzed, which shows a decreasing E trend. Such a declined global lake E was largely caused by the decreased downward shortwave solar radiation. The global lake E was switched from the decreased trend over 1951-1978 to an increased trend over 1981-2016 with an accelerated trend of 0.76 mm yr-1. The tropical, arid, and temperate climate regions lakes contribute 66% to the increasing trend despite covering only 38% of the global lake surface area. Such a change in the global lake E trend was attributed to the increased vapor pressure deficit in a warmer climate. The model projection indicates that the mean global lake E will increase by 13% by the end of the 21st century under the Representative Concentration Pathway (RCP) 8.5 emissions scenario, relative to the 1985-2000 mean global lake E. The changes in lake E are expected to be more pronounced in North America, equatorial South America, Africa, northern Europe, Siberia, and Southeast Asia due to increased interannual variability. The results in this dissertation indicate that the widespread but heterogeneous increase in the global lake E threatens the crucial socioeconomic benefits that lakes provide to human society.

The increasing wildfire activity in the past few years has been devastating, setting negative records in many states and regions around the world, especially in North America. Power systems have been impacted by wildfires in many ways, even in regions located hundreds of kilometers away from high-risk zones, depending on wind speed and direction conditions, the stemming smoke of wildfires may significantly impact the air quality and reduce the solar PV generation, and forcing several utilities to rely on PSPS programs to mitigate wildfire risks. Thus, power system operators must ensure reliability and resilience across power generation, transmission, and distribution while minimizing carbon emissions that can harm even more the air quality of the affected communities during wildfire events. Furthermore, a cost-effective power system expansion planning solution in regions with increased wildfire risk is achieved by placing ESSs and new transmission/distribution lines while taking into account their availability given the increasing number of PSPS events. This research aims to analyze the impact of wildfire activity on the electrical system's planning and operation, by analyzing the impact of the 2020 wildfire season on renewable energy in Washington state, focusing on variables that directly impact the wind and photovoltaic power. After that, efforts are made to approach the expansion planning of power transmission and distribution systems under wildfire risk, considering sitting and sizing of ESS as an alternative, with a compliance check on unbalanced power flow and system operating limits. The resulting models are a MILP optimization problem, and simulation experiments are performed to validate the effectiveness of the proposed formulation using different High Fire-Threat District Tier Zones based on real-world data from electric utilities in California.

Machine learning's application in solar-thermal desalination is limited by data shortage and inconsistent analysis. This study develops an optimized dataset collection and analysis process for the representative solar still. By ultra-hydrophilic treatment on the condensation cover, the dataset collection process reduces the collection time by 83.3%. Over 1,000 datasets are collected, which is nearly one order of magnitude larger than up-to-date works. Then, a new interdisciplinary process flow is proposed. Some meaningful results are obtained that were not addressed by previous studies. It is found that Radom Forest might be a better choice for datasets larger than 1,000 due to both high accuracy and fast speed. Besides, the dataset range affects the quantified importance (weighted value) of factors significantly, with up to a 115% increment. Moreover, the results show that machine learning has a high accuracy on the extrapolation prediction of productivity, where the minimum mean relative prediction error is just around 4%. The results of this work not only show the necessity of the dataset characteristics' effect but also provide a standard process for studying solar-thermal desalination by machine learning, which would pave the way for interdisciplinary study.

While the operating cost of electricity grids based on thermal generation was largely driven by the cost of fuel, as renewable penetration increases, ancillary services represent an increasingly large proportion of the running costs. Electric frequency is an important magnitude in highly renewable grids, as it becomes more volatile and therefore the cost related to maintaining it within safe bounds has significantly increased. So far, costs for frequency-containment ancillary services have been socialised in most countries, but it has become relevant to rethink this regulatory arrangement. In this paper, we discuss the issue of cost allocation for these services, highlighting the need to evolve towards a causation-based regulatory framework. We argue that parties responsible for creating the need for ancillary services should bear these costs. However, this would imply an important change in electricity market policy, therefore it is necessary to understand the impact on current and future investments on generation, as well as on electricity tariffs. Here we provide a mostly qualitative analysis of this issue, defining guidelines for practical implementation and further study. Published in journal Energy Policy

YouTube, a website for sharing videos online, is widely popular around the world and is also frequently used as a source of information on science and environmental subjects. For more than fifteen years, climate change advocacy groups have been actively spreading awareness about the issue through social media platforms such as YouTube. They have been creating videos that present up-to-date statistics and information on environmental policies, showcasing their ongoing campaigns, and most significantly, producing advertisements related to climate change. However, only little is known about how the audience perceive these advertisements, what role does emotion play in engaging the audience through views, likes and comments. We specifically wanted to explore the nature of emotional flow in climate change video ads and whether shifts in emotions in the ad content results in greater audience views, likes and comments, in other words, audience engagement. The study's findings indicate that including emotional shifts in advertisements leads to a significantly greater number of views, likes, and comments compared to ads that do not include emotional shifts. Additionally, emotions such as fear, guilt, anger, sorrow, and surprise were identified as crucial factors in effectively engaging audiences through views, likes and comments with climate change ads.

In the last decade, the manufacturing capacity of silicon, the dominant PV technology, has increasingly been concentrated in China. This has led to PV cost reduction of approximately 80%, while, at the same time, posing risks to PV supply chain security. Recent advancements of novel perovskite tandem PV technologies as an alternative to traditional silicon-based PV provide opportunities for diversification of the PV manufacturing capacity and for increasing the GHG emission benefit of solar PV. Against this background, we estimate the current and future cost-competitiveness and GHG emissions of a set of already commercialized as well as emerging PV technologies for different production locations (China, USA, EU), both at residential and utility-scale. We find EU and USA-manufactured thin-film tandems to have 2 to 4% and 0.5 to 2% higher costs per kWh and 37 to 40%and 32 to 35% less GHG emissions per kWh at residential and utility-scale, respectively. Our projections indicate that they will also retain competitive costs (up to 2% higher)and a 20% GHG emissions advantage per kWh in 2050.