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This paper presents a new control strategy for reducing the switching losses produced by the use of high parasitic capacitance solar arrays in the sequential switching shunt regulator. Instead of dividing the solar array into equal sections, the proposed strategy is based on two different sections types, low-capacitance and high-capacitance ones. In order to reduce the switching losses and to maintain the original closed-loop response, a novel parallel power processing control strategy is implemented. With this new technique the low-capacitance sections are the only ones that switch at high frequency to regulate the bus while the high-capacitance sections are only connected or disconnected under high load power changes. In addition, the control closed loop delay associated to the time needed to charge the parasitic capacitance has been modelled and a controller modification is proposed to reduce AC performance degradation.

Whether renewable energy sources (RES) will provide sufficient energy surplus to entirely power complex modern societies is under discussion. We contribute to this debate by estimating the current global average energy return on energy invested (EROI) for the five RES technologies with the highest potential of electricity generation from the comprehensive and internally consistent estimations of their material requirements at three distinct energy system boundaries: standard farm-gate (EROIst), final at consumer point-of-use (EROIfinal), and extended (including indirect investments, EROIext). EROIst levels found fall within the respective literature ranges. Expanding the boundaries closer to the system level, we find that only large hydroelectricity would currently have a high EROIext ~ 6.5:1, while the rest of variable RES would be below 3:1: onshore wind (2.9:1), offshore wind (2.3:1), solar Photovoltaic (PV) (1.8:1), and solar Concentrated Solar Power (CSP) (<1:1). These results indicate that, very likely, the global average EROIext levels of variable RES are currently below those of fossil fuel-fired electricity. It remains unknown if technological improvements will be able to compensate for factors, which will become increasingly important as the variable RES scale-up. Hence, without dynamically accounting for the evolution of the EROI of the system, the viability of sustainable energy systems cannot be ensured, especially for modern societies pursuing continuous economic growth.

Battery systems are extensively used in smart energy systems in many different applications, such as Frequency Containment Reserve or Self-Consumption Increase. The behavior of a battery in a particular operation scenario is usually summarized using different key performance indicators (KPIs). Some of these indicators such as efficiency indicate how much of the total electric power supplied to the battery is actually used. Other indicators, such as the number of charging-discharging cycles or the number of charging-discharging swaps, are of relevance for deriving the aging and degradation of a battery system. Obtaining these indicators is very time-demanding: either a set of lab experiments is run, or the battery system is simulated using a battery simulation model. This work instead proposes a machine learning (ML) estimation of battery performance indicators derived from time series input data. For this purpose, a random forest regressor has been trained using the real data of electricity grid frequency evolution, household power demand, and photovoltaic power generation. The results obtained in the research show that the required KPIs can be estimated rapidly with an average relative error of less than 10%. The article demonstrates that the machine learning approach is a suitable alternative to obtain a very fast rough approximation of the expected behavior of a battery system and can be scaled and adapted well for estimation queries of entire fleets of battery systems.

CO2 is the most emitted greenhouse gas and is mainly produced by human activity. In fact, about 75% is emitted in cities and 40% of global carbon emissions is produced by the building sector. Therefore, buildings are very important in terms of CO2 emissions. This importance is also reflected in the works that have been developed on this subject. This manuscript reviews the research that has shown or calculated the amounts of CO2 emitted in buildings. For a better understanding of the scope of the investigations, a classification is presented. With this, it is intended to help researchers interested in this area by summarizing the studies carried out to date on the amounts of CO2 emitted depending on the type of building.

This research addresses the increasing importance of understanding how Artificial Intelligence can facilitate the transition of companies to a Circular Economy model. This study focuses on energy management, examining its impact on efficiency and emissions across a multi-case analysis of 18 projects in diverse sectors. The findings indicate that Artificial Intelligence positively influences both variables, with variations across applications and sectors. Notably, Artificial Intelligence significantly enhances energy efficiency in four out of six sectors, achieving over 5% improvement in half of the projects. Regarding emissions, positive effects are observed in 15 out of 18 projects, resulting in over 5% reductions in seven cases. Artificial Intelligence plays a pivotal role in emissions reduction in the Design and Energy sectors, with some projects achieving over 20% reductions. Additionally, this study explores how improved energy efficiency positively affects strategic business variables, such as cost, quality, and delivery time. The impact on emissions contributes to reducing occupational risks, particularly those associated with chemical and biological agents. Although managers are satisfied, measures need to be taken to overcome the lack of employee acceptance. These findings are of great interest to the stakeholders involved in the integration of Artificial Intelligence into companies.

Healthcare buildings exhibit a different electrical load predictability depending on their size and nature. Large hospitals behave similarly to small cities, whereas primary care centers are expected to have different consumption dynamics. In this work, we jointly analyze the electrical load predictability of a large hospital and that of its associated primary care center. An unsupervised load forecasting scheme using combined classic methods of principal component analysis (PCA) and autoregressive (AR) modeling, as well as a supervised scheme using orthonormal partial least squares (OPLS), are proposed. Both methods reduce the dimensionality of the data to create an efficient and low-complexity data representation and eliminate noise subspaces. Because the former method tended to underestimate the load and the latter tended to overestimate it in the large hospital, we also propose a convex combination of both to further reduce the forecasting error. The analysis of data from 7 years in the hospital and 3 years in the primary care center shows that the proposed low-complexity dynamic models are flexible enough to predict both types of consumption at practical accuracy levels.

A fault detection innovation to wind turbines’ pitch actuators is an important subject to guarantee the efficiency wind energy conversion and long lifetime operation of these rotatory machines. Therefore, a recent and effective fault detection algorithm is conceived to detect faults on wind turbine pitch actuators. This approach is based on the interval observer framework theory that has proved to be an efficient tool to measure dynamic uncertainties in dynamical systems. It is evident that almost any fault in any actuator may affect its historical-time behavior. Hence, and properly conceptualized, a fault detection system can be successfully designed based on interval observer dynamics. This is precisely our main contribution. Additionally, we realize a numerical analysis to evaluate the performance of our approach by using a dynamic model of a pitch actuator device with faults. The numerical experiments support our main contribution.