David Alfonso-Solar

Accurate prediction of maximum voltage is essential for the safe, efficient, and sustainable design of photovoltaic systems, as it defines the maximum allowable number of modules in series. This study examines how the choice of meteorological year affects voltage estimations in high-power PV systems. A comparison is made between maximum voltage results derived from typical meteorological (TMY) years and those based on inter-hourly historical data. The results reveal notable differences, with TMY often underestimating extreme voltage levels. To address this, the study introduces the Extreme Meteorological Year (EMY) model, which uses historical voltage percentiles to better estimate peak voltages and mitigate overvoltage risk. This model has been applied successfully in real PV plant designs. Its performance is assessed using monitoring data from seven PV projects in different regions. The EMY model demonstrates improved accuracy and safety in predicting maximum voltages compared to traditional datasets. Its percentile-based structure enables adaptation to different design criteria, enhancing reliability and supporting more sustainable photovoltaic deployment. Overall, the study underscores the importance of selecting appropriate meteorological data for voltage prediction and presents EMY as a robust tool for improving PV system design.

This study presents a methodology for estimating the carbon footprint of urban neighborhoods as a necessary step in proposing and evaluating potential GHG reduction measures to enhance the sustainability of cities. Additionally, this method has been applied to Benicalap, a district in Valencia, Spain. This research employs the Datadis, QGIS, and HOMER tools to assess emissions across Scopes 1, 2, and 3. Tailored mitigation strategies are proposed, primarily focusing on reducing emissions in Scopes 1 and 2. While previous studies have extensively examined CO₂ emissions at broader geographic scales, like nations, regions, and cities, this study emphasizes the importance of neighborhood-level analysis to address localized environmental challenges effectively. The results reveal that Benicalap’s emissions contribute 28.69 ktCO₂ (15.56%) to Scope 1, 13.71 ktCO₂ (7.43%) to Scope 2, and 142 ktCO₂ (77%) to Scope 3. By 2030, targeted interventions could reduce emissions from Scopes 1 and 2 by 19,885 ktCO₂, representing a 50.69% reduction. Among the proposed measures, sustainable transportation improvements and photovoltaic deployment stand out, contributing to 25.39% and 24.87% of the reduction, respectively. Enhancements in public lighting and nature-based solutions would offer a minor decrease of 0.43%. These insights underscore the need for strategic, localized interventions to achieve meaningful emission reductions and support sustainable urban development efforts.

En la actualidad, el suministro de energía de manera sostenible y el problema derivado de la emisión de gases de efecto invernadero son motivo de preocupación a nivel mundial. Una de las alternativas para resolver estos problemas es el uso de las fuentes de energía renovables (ER) para satisfacer la creciente demanda de energía en los diversos sectores económicos; sin dejar de lado la preocupación y mejora en la eficiencia energética (EE) y la cultura del consumo razonable de la energía en los diversos sectores consumidores. La fuente de energía renovable de mayor importancia y de gran escala es la proveniente del sol, debemos tener presente que la radiación solar que incide sobre la tierra es el motor de otras de las fuentes de energías renovables, como son los vientos, las mareas, las lluvias e incluso la biomasa. Por lo tanto, la energía solar es una fuente abundante y la podemos aprovechar de forma directa o indirecta. En México uno de nuestros grandes retos es la transición energética de fuentes convencionales de energía a fuentes de energía renovable, en un contexto en que son escasos los instrumentos de fomento por parte de los actores del sector energético y de un marco regulatorio y entorno político complicado.

In recent years, the modeling and simulation of lithium-ion batteries have garnered attention due to the rising demand for reliable energy storage. Accurate charge cycle predictions are fundamental for optimizing battery performance and lifespan. This study compares particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms in modeling a commercial lithium-ion battery, emphasizing the voltage behavior and the current delivered to the battery. Bio-inspired optimization tunes parameters to reduce the root mean square error (RMSE) between simulated and experimental outputs. The model, implemented in MATLAB/Simulink, integrates electrochemical parameters and estimates battery behavior under varied conditions. The assessment of terminal voltage revealed notable enhancements in the model through both the PSO and GWO algorithms compared to the non-optimized model. The GWO-optimized model demonstrated superior performance, with a reduced RMSE of 0.1700 (25 °C; 3.6 C, 455 s) and 0.1705 (25 °C; 3.6 C, 10,654 s) compared to the PSO-optimized model, achieving a 42% average RMSE reduction. Battery current was identified as a key factor influencing the model analysis, with optimized models, particularly the GWO model, exhibiting enhanced predictive capabilities and slightly lower RMSE values than the PSO model. This offers practical implications for battery integration into energy systems. Analyzing the execution time with different population values for PSO and GWO provides insights into computational complexity. PSO exhibited greater-than-linear dynamics, suggesting a polynomial complexity of O(n^k), while GWO implied a potential polynomial complexity within the range of O(n^k) or O(2ⁿ) based on execution times from populations of 10 to 1000.

Nowadays, the measurement of heat stress indices is of principal importance due to the escalating impact of global warming. As temperatures continue to rise, the well-being and health of individuals are increasingly at risk, which can lead to a detrimental effect on human performance and behavior. Hence, monitoring and assessing heat stress indices have become necessary for ensuring the safety and comfort of individuals. Thermal comfort indices, such as wet-bulb globe temperature (WBGT), Tropical Summer Index (TSI), and Predicted Heat Strain (PHS), as well as parameters like mean radiant temperature (MRT), are typically used for assessing and controlling heat stress conditions in working and urban environments. Therefore, measurement and monitoring of these parameters should be obtained for any environment in which people are constantly exposed. Modern cities collect and publish this relevant information following the Smart City concept. To monitor large cities, cost-effective solutions must be developed. This work presents the results of a Heat Stress Monitoring (HSM) system prototype network tested in the Benicalap-Ciutat Fallera district in Valencia, Spain. The scope of this work is to design, commission, and test a low-cost prototype that is able to measure heat stress indices. The Heat Stress Monitoring system comprises a central unit or receiver and several transmitters communicating via radiofrequency. The transmitter accurately measures wind speed, air temperature, relative humidity, atmospheric pressure, solar irradiation, and black globe temperature. The receiver has a 4G modem that sends the data to an SQL database in the cloud. The devices were tested over one year, showing that radio data transmission is reliable up to 700 m from the receiver. The system’s power supply, composed of a Photovoltaic panel and Lithium-ion batteries, provided off-grid capabilities to the transmitter, with a tested backup autonomy of up to 36 days per charge. Then, indicators such as WBGT, TSI, and MRT were successfully estimated using the data collected by the devices. The material cost of a 12-point network is around EUR 2430 with a competitive price of EUR 190 per device.

The electrification of final energy uses is a key strategy to reach the desired scenario with zero greenhouse gas emissions. Many of them can be electrified with more or less difficulty, but there is a part that is difficult to electrify at a competitive cost: heavy road transport, maritime and air transport, and some industrial processes are some examples. For this reason, the possibility of using other energy vectors rather than electricity should be explored. Hydrogen can be considered a real alternative, especially considering that this transition should not be carried out immediately because, initially, the electrification would be carried out in those energy uses that are considered most feasible for this conversion. The Canary Islands’ government is making considerable efforts to promote a carbon-free energy mix, starting with renewable energy for electricity generation. Still, in the early–mid 2030s, it will be necessary to substitute heavy transport fossil fuel. For this purpose, HOMER software was used to analyze the feasibility of hydrogen production using surplus electricity produced by the future electricity system. The results of previous research on the optimal generation MIX for Grand Canary Island, based exclusively on renewable sources, were used. This previous research considers three possible scenarios where electricity surplus is in the range of 2.3–4.9 TWh/year. Several optimized scenarios using demand-side management techniques were also studied. Therefore, based on the electricity surpluses of these scenarios, the optimization of hydrogen production and storage systems was carried out, always covering at least the final hydrogen demand of the island. As a result, it is concluded that it would be possible to produce 3.5 × 10⁴ to 7.68 × 10⁴ t of H₂/year. In these scenarios, 3.15 × 10⁵ to 6.91 × 10⁵ t of water per year would be required, and there could be a potential production of 2.8 × 10⁵ to 6.14 × 10⁵ t of O₂ per year.