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The advancement of novel water treatment technologies requires the implementation of both accurate data measurement and recording processes. These procedures are essential for acquiring results and conducting thorough analyses to enhance operational efficiency. In addition, accurate sensor data facilitate precise control over chemical treatment dosages, ensuring optimal water quality and corrosion inhibition while minimizing chemical usage and associated costs. Under this framework, this paper describes the sensoring and monitoring solution for a hybrid system based on a cooling tower (CT) connected to mechanical vapor compression (MVC) equipment for desalination and brine concentration purposes. Sensors connected to the data commercial logger solution, Almemo 2890-9, are also discussed in detail such as temperature, relative humidity, pressure, flow rate, etc. The monitoring system allows remote control of the MVC based on a server, GateManager, and TightVNC. In this way, the proposed solution provides remote access to the hybrid system, being able to visualize gathered data in real time. A case study located in Cartagena (Spain) is used to assess the proposed solution. Collected data from temperature transmitters, pneumatic valves, level sensors, and power demand are included and discussed in the paper. These variables allow a subsequent forecasting process to estimate brine concentration values. Different sample times are included in this paper to minimize the collected data from the hybrid system within suitable operation conditions. This solution is suitable to be applied to other desalination processes and locations. [ABSTRACT FROM AUTHOR]

Batteries have been integral components in modern vehicles, initially powering starter motors and ensuring stable electrical conditions in various vehicle systems and later in energy sources of drive electric motors. Over time, their significance has grown exponentially with the advent of features such as "Start & Stop" systems, micro hybridization, and kinetic energy regeneration. This trend culminated in the emergence of hybrid and electric vehicles, where batteries are the energy source of the electric traction motors. The evolution of storage for vehicles has been driven by the need for larger autonomy, a higher number of cycles, lower self-discharge rates, enhanced performance in extreme temperatures, and greater electrical power extraction capacity. As these technologies have advanced, so have they the methods for their disposal, recovery, and recycling. However, one critical aspect often overlooked is the potential for battery reuse once they reach the end of their useful life. For each battery technology, specific regeneration methods have been developed, aiming to restore the battery to its initial performance state or something very close to it. This focus on regeneration holds significant economic implications, particularly for vehicles where batteries represent a substantial share of the overall cost, such as hybrid and electric vehicles. This paper conducts a comprehensive review of battery technologies employed in vehicles from their inception to the present day. Special attention is given to identifying common failures within these technologies. Additionally, the scientific literature and existing patents addressing regeneration methods are explored, shedding light on the promising avenues for extending the life and performance of automotive batteries. [ABSTRACT FROM AUTHOR]

Power system configuration and performance are changing very quickly. Under the new paradigm of prosumers and energy communities, grids are increasingly influenced by microgeneration systems connected in both low and medium voltage. In addition, these facilities provide little or no information to distribution and/or transmission system operators, increasing power system management problems. Actually, information is a great asset to manage this new situation. The arrival of affordable and open Internet of Things (IoT) technologies is a remarkable opportunity to overcome these inconveniences allowing for the exchange of information about these plants. In this paper, we propose a monitoring solution applicable to photovoltaic self-consumption or any other microgeneration installation, covering the installations of the so-called 'prosumers' and aiming to provide a tool for local self-consumption monitoring. A detailed description of the proposed system at the hardware level is provided, and extended information on the communication characteristics and data packets is also included. Results of different field test campaigns carried out in real PV self-consumption installations connected to the grid are described and analyzed. It can be affirmed that the proposed solution provides outstanding results in reliability and accuracy, being a popular solution for those who cannot afford professional monitoring platforms. [ABSTRACT FROM AUTHOR]

The climate emergency has intensified the search for the generation of electricity from renewable energies in order to turn cities into sustainable cities. Small-scale wind power offers new opportunities for decentralized electricity production, avoiding dependence on the grid and transmission losses. Among viable locations within the urban environment, high-rise buildings are especially promising due to the elevated height and less turbulent wind conditions. They can also be integrated into the architecture of the building or as independent units in the urban environment. In this area, this work presents a methodology for determining the annual energy production of urban wind projects. The proposal is divided into four stages: location, wind and urban indicators, turbine selection and annual production estimation, and economic/environmental analysis. The evaluation of the solution is carried out for a Spanish case study. According to the results, more than 68,000 kWh/year can be generated with an investment recovery period of less than six years. [ABSTRACT FROM AUTHOR]

This paper describes and assesses a decentralized solution based on a wireless sensor-actuator network to provide primary frequency control from demand response in power systems with high wind energy penetration and, subsequently, with relevant frequency excursions. The proposed system is able to modify the electrical power demand of a variety of thermostatically-controlled loads, maintaining minimum comfort levels and minimizing both infrastructure requirements and primary reserves from the supply side. This low-cost hardware solution avoids any additional wiring, extending the wireless sensor-actuator network technology towards small customers, which account for over a 30% share of the current power demand. Frequency excursions are collected by each individual load controller, considering not only the magnitude of the frequency deviation, but also their evolution over time. Based on these time-frequency excursion characteristics, controllers are capable of modifying the power consumption of thermostatically-controlled loads by switching them off and on, thus contributing to primary frequency control in power systems with higher generation unit oscillations as a consequence of relevant wind power integration. Field tests have been carried out in a laboratory environment to assess the load controller performance, as well as to evaluate the electrical and thermal response of individual loads under frequency deviations. These frequency deviations are estimated from power systems with a high penetration of wind energy, which are more sensitive to frequency oscillations and where demand response can significantly contribute to mitigate these frequency excursions. The results, also included in the paper, evaluate the suitability of the proposed load controllers and their suitability to decrease frequency excursions from the demand side in a decentralized manner. [ABSTRACT FROM AUTHOR]

The Weibull probability distribution has been widely applied to characterize wind speeds for wind energy resources. Wind power generation modeling is different, however, due in particular to power curve limitations, wind turbine control methods, and transmission system operation requirements. These differences are even greater for aggregated wind power generation in power systems with high wind penetration. Consequently, models based on one-Weibull component can provide poor characterizations for aggregated wind power generation. With this aim, the present paper focuses on discussing Weibull mixtures to characterize the probability density function (PDF) for aggregated wind power generation. PDFs of wind power data are firstly classified attending to hourly and seasonal patterns. The selection of the number of components in the mixture is analyzed through two well-known different criteria: the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Finally, the optimal number of Weibull components for maximum likelihood is explored for the defined patterns, including the estimated weight, scale, and shape parameters. Results show that multi-Weibull models are more suitable to characterize aggregated wind power data due to the impact of distributed generation, variety of wind speed values and wind power curtailment. [ABSTRACT FROM AUTHOR]

This paper proposes and assesses an integrated solution to monitor and diagnose photovoltaic (PV) solar modules based on a decentralized wireless sensor acquisition system. Both DC electrical variables and environmental data are collected at PV module level using low-cost and high-energy efficiency node sensors. Data is real-time processed locally and compared with expected PV module performances obtained by a PV module model based on symmetrized-shifted Gompertz functions (as previously developed and assessed by the authors). Sensor nodes send data to a centralized sink-computing module using a multi-hop wireless sensor network architecture. Such integration thus provides extensive analysis of PV installations, and avoids off-line tests or post-processing processes. In comparison with previous approaches, this solution is enhanced with a low-cost system and non-critical performance constraints, and it is suitable for extensive deployment in PV power plants. Moreover, it is easily implemented in existing PV installations, since no additional wiring is required. The system has been implemented and assessed in a Spanish PV power plant connected to the grid. Results and estimations of PV module performances are also included in the paper. [ABSTRACT FROM AUTHOR]

Due to the relevant penetration of solar PV power plants, an accurate power generation forecasting of these installations is crucial to provide both reliability and stability of current grids. At the same time, PV monitoring requirements are more and more demanded by different agents to provide reliable information regarding performances, efficiencies, and possible predictive maintenance tasks. Under this framework, this paper proposes a methodology to evaluate different LoRa-based PV monitoring architectures and node layouts in terms of short-term solar power generation forecasting. A random forest model is proposed as forecasting method, simplifying the forecasting problem especially when the time series exhibits heteroscedasticity, nonstationarity, and multiple seasonal cycles. This approach provides a sensitive analysis of LoRa parameters in terms of node layout, loss of data, spreading factor and short time intervals to evaluate their influence on PV forecasting accuracy. A case example located in the southeast of Spain is included in the paper to evaluate the proposed analysis. This methodology is applicable to other locations, as well as different LoRa configurations, parameters, and networks structures; providing detailed analysis regarding PV monitoring performances and short-term PV generation forecasting discrepancies. [ABSTRACT FROM AUTHOR]

The integration of renewables into power systems is a key transformation for mitigating climate change and reducing fossil-fuel dependence. Among the different resources, wind participation has become crucial in recent decades—both onshore and offshore wind power plants. However, assuming the useful life of the wind turbines at approximately 20 years, different solutions should be discussed to overcome the turbine's aging problem. In the coming years, some countries within the wind sector will face the decision of partially or totally repowering or dismantling their turbines. This paper reviews different repowering strategies and contributions from a multifactorial perspective. A set of categories is defined by the authors and those multifactorial parameters are then classified according to such categories: technical, economic, environmental, social, and political. From each category, the most relevant factors to be considered for repowering decision-making purposes are identified and discussed. According to the specific literature, more than 90% of the reviewed contributions are focused on onshore wind power plant repowering actions. This percentage is in line with onshore and offshore wind generation units installed in recent decades. The reviewed studies show that Germany has a major number of contributions. Regarding offshore repowering strategies, all contributions propose a multifactorial analysis, in contrast to onshore repowering strategies where only 68% of the authors carry out a multifactorial analysis. The revised repowering methodologies and the categorization of factors can also be used by the repowering market, as a useful tool in the near future. [ABSTRACT FROM AUTHOR]

It is expected that distribution power systems will soon be able to connect a variety of microgrids from residential, commercial, and industrial users, and thus integrate a variety of distributed generation technologies, mainly renewable energy sources to supply their demands. Indeed, some authors affirm that distribution networks will propose significant changes as a consequence of this massive integration of microgrids at the distribution level. Under this scenario, the control of distributed generation inverters, demand management systems, renewable resource forecasting, and demand predictions will allow better integration of such microgrid clusters to decongest power systems. This paper presents a review of microgrids connected at distribution networks and the solutions that facilitate their integration into such distribution network level, such as demand management systems, renewable resource forecasting, and demand predictions. Recent contributions focused on the application of microgrids in Low-Voltage distribution networks are also analyzed and reviewed in detail. In addition, this paper provides a critical review of the most relevant challenges currently facing electrical distribution networks, with an explicit focus on the massive interconnection of electrical microgrids and the future with relevant renewable energy source integration. [ABSTRACT FROM AUTHOR]

Energy transition requires actions from different sectors and levels, mainly focused on achieving a low-carbon and high-renewable integration society. Among the different sectors, the transport sector is responsible for more than 20% of global greenhouse gas emissions, mostly emitted in cities. Therefore, initiatives and analysis focused on electric vehicles integration powered by renewables is currently a desirable solution to mitigate climate change and promote energy transition. Under this framework, this paper proposes a multi-indicator analysis for the estimation of CO 2 emissions combining renewable integration targets, reduction emission targets and realistic renewable resource potentials. Four scenarios are identified and analyzed: (i) current situation with conventional vehicles, (ii) replacement of such conventional by electric vehicles without renewable integration, (iii) and (iv) integration of renewables to fulfill emission reduction targets for 2030 and 2050 respectively. The analysis is evaluated in the state of Maine (United States). From the results, a minimum renewable penetration of 39% and 82%, respectively, is needed to fulfill the emission reduction targets for 2030 and 2050 by considering 100% conventional vehicle replacement. Different combinations of available renewable resources can reduce emissions by more than 35%. [ABSTRACT FROM AUTHOR]

The trend in energy consumption, with a particular focus on heating and cooling demand, is an issue that is relevant to the promotion of new energy policies and more efficient energy systems. Moreover, heating and cooling energy demand is expected to rise in the next several decades, mainly due to climate change as well as increasing incomes in developing countries. In this context, the building sector is currently a relevant energy-intensive economic sector in Morocco; it accounts for 33% of the country's total energy demand (as the sector with the second highest energy demand, after the transport sector), with the residential sector accounting for 25% and the tertiary sector accounting for 8%. Aiming to reduce energy dependence and promote sustainable development, the Moroccan government recently issued a comprehensive plan to increase the share of renewables and improve energy efficiency. This strategy includes novel thermal building regulations promoted by the Moroccan Agency for Energy Efficiency. This paper analyzes the thermal behavior and heating-cooling energy demand of a residential building located in Tangier (Morocco) as a case example, based on the country's new thermal regulations and considering specific climatological conditions. A comparison with common Moroccan residential buildings as well as with those in nearby countries with similar meteorological conditions but significant differences in terms of energy demand regulation and requirements, such as Spain, is also included. Simulations were carried out using the DesingBuilder and EnergyPlus Software packages. According to the results, the last building thermal regulation requirements in Morocco need to be revised and extended in order to achieve the energy efficiency objectives established by the Moroccan government for 2030. [ABSTRACT FROM AUTHOR]

The increasing integration of photovoltaic (PV) power plants into power systems demands a high accuracy of yield prediction and measurement. With this aim, different global horizontal irradiance (GHI) estimations based on new-generation geostationary satellites have been recently proposed, providing a growing number of solutions and databases, mostly available online, in addition to the many ground-based irradiance data installations currently available. According to the specific literature, there is a lack of agreement in validation strategies for a bankable, satellite-derived irradiance dataset. Moreover, different irradiance data sources are compared in recent contributions based on a diversity of arbitrary metrics. Under this framework, this paper describes a characterization of metrics based on a principal component analysis (PCA) application to classify such metrics, aiming to provide non-redundant and complementary information. Therefore, different groups of metrics are identified by applying the PCA process, allowing us to compare, in a more extensive way, different irradiance data sources and exploring and identifying their differences. The methodology has been evaluated using satellite-based and ground-measured GHI data collected for one year in seven different Spanish locations, with a one-hour sample time. Data characterization, results, and a discussion about the suitability of the proposed methodology are also included in the paper. [ABSTRACT FROM AUTHOR]

Wind power is widely considered to be a qualified renewable, clean, ecological and inexhaustible resource that is becoming a leader in the current energy transition process. It is a mature technology solution that was quickly developed and has been massively integrated into power systems in recent years. Indeed, a remarkable number of renewable integration policies have been promoted by different governments and countries. With the aim of maximizing the power given by wind resources, the locations of both onshore and offshore wind power plants must be optimized following a sort of different criteria. Under this scenario, a number of factors and decision criteria in the evaluation and selection of locations can be identified. Moreover, the relevant wind power increasing in the power generation mix is addressed, along with a standardization of factors and decision criteria in the optimization and selection of such optimal locations. In this context, this paper describes a systematic review and meta-analysis combining most of the contributions and studies proposed during the last decade. Thus, our aim is focused on reviewing and categorizing all factors to be considered for optimal location estimation, pointing out the differences among the selected factors and the decision criteria for onshore and offshore wind power plants. In addition, our review also includes an analysis of the representative key indicators for the contributions, such as the annual frequency of publications, geographical classification, analysis by category, evaluation method and determining factors. [ABSTRACT FROM AUTHOR]