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[EN] Cities are expected to be protagonists of the energy transition and, among other challenges, the decarbonation of the residential consumption could greatly benefit from photovoltaic generation in the built environment. To assess its potential, the present study analyses the possibility to cover the electric demand of the city of Valencia with rooftop-installed PV panels. Specific types of buildings has been selected to study the potential of each one of them with respect to their demand and with a specific production/consumption model. The total potential energy production for the city of Valencia is estimated to be enough to satisfy the demand of domestic electricity. The economic investment scenario has been analysed (best- and worst-case scenarios), and the corresponding environmental benefits have been studied. The results of the present article are encouraging in the context of a global energy transition, necessary in the present times to keep human consumption within the planetary limits., This study has been supported by Climate-KIC¿s Pathfinder Programme (Task ID TC2018A_2.1.5-PRO_P066¿1B) and by the Cátedra de Transición Energética Urbana (Las Naves-UPV)

[EN] The increase in global electricity consumption has made energy efficiency a priority for governments. Consequently, there has been a focus on the efficient integration of a massive penetration of electric vehicles (EVs) into energy markets. This study presents an assessment of various strategies for EV aggregators. In this analysis, the smart charging methodology proposed in a previous study is considered. The smart charging technique employs charging power rate modulation and considers user preferences. To adopt several strategies, this study simulates the effect of these actions in a case study of a distribution system from the city of Quito, Ecuador. Different actions are simulated, and the EV aggregator costs and technical conditions are evaluated., All the authors wish to thank Manuel Alcázar Ortega and José Francisco Carbonell Carretero from Universitat Politècnica de València for their contributions to this paper. We also thank the Ministry of Electricity and Renewable Energy of Ecuador (MEER) and Empresa Eléctrica Quito (EEQ) for providing important information for this study.

The aim of this paper is to demonstrate the usefulness, practical applicability, accuracy and reliability of Stan dard IEC 61400-27-1. The objective of this standard is to specify generic simulation models for the four main types of wind turbines, as well as procedures to validate them. Given that most of the works published and related to the validation of generic wind turbine models have applied a limited number of voltage dip tests to a single wind turbine, the present paper significantly widens the scope of these studies, conducting a total of 59 voltage dip field tests in 7 different wind turbines from 3 manufacturers. The Type 3 and Type 4 wind turbines, i. e., the doubly-fed and the full-scale converter wind turbines, respectively, are the two topologies analysed, since these comprise the vast majority of the market share for new wind turbines. The field measurements carried out on the actual wind turbines are compared to the simulation results of the corresponding IEC dynamic wind turbine models, and the IEC validation methodology is applied. Based on the results obtained, several comparative analyses are performed, and the reasons for differences in the accuracy of the behaviour of the wind turbine topologies considered are also analysed.

[EN] A residential load management system equips smart meters (SMs) to measure load utilization at residencies. SM reports electricity usage based on electronic appliances. In this paper, a smart residential load management system is designed by three-fold along with the provisioning of consumer security. Load management encompasses load categorizing by Hopfield neural network, fuzzy-logic based bill payments identification and load state prediction using Markov chain. Residential load is based on three classes: active load, affordable load and inactive load. The estimation of residential load ensures to manage the load at residency either to turn off the load or intimate excess consumption of electricity. The registered consumers of a specific residency are enabled to receive load status by Internet of Things (IoT) devices. SMs at residencies periodically compute the electricity manipulation and those readings are encrypted using hybrid blowfish and elliptic curve cryptography algorithm with considering the behavior information from IoT device and partial key storage assists security, even if the device is theft. This smart residential security assisted load management (SRS-LM) system is developed in network simulator 3 and the results showed improvements in terms of power usage, load power, peak load reduction, total power consumption, power cost and computation time, This work was Supported by National Council of Science and Technology (CONACYT) through the pH.D. scholarship 705018. The authors would like to thank the University of Guadalajara, Mexico, and the Polytechnic University of Valencia, Spain, for all the collaboration and realization of this paper

[EN] The installation of fast electric vehicle charging stations (EVCS) will be essential to promote the acceptance by the users of electric vehicles (EVs). However, if EVCS are exclusively supplied by the grid, negative impacts on its stability together with possible CO2 emission increases could be produced. Introduction of hybrid renewable energy systems (HRES) for EVCS can cope with both drawbacks by reducing the load on the grid and generating clean electricity. This paper develops a weighted multicriteria methodology to design the most suitable configuration of HRES for EVCS. This method determines the local renewable resources and the EVCS electricity demand. Then, taking into account environmental, economic and technical aspects, it deduces the most adequate HRES power generation planning for EVCS. Besides, an experimental stage to validate the design deduced from the multicriteria methodology is included. Therefore, the final power generation planning for the HRES in EVCS is supported not only by a complete numerical evaluation, but also by an experimental verification of the demand being fully covered. Methodology application to Valencia (Spain) proves that an off-grid HRES with solar PV, wind and batteries support would be the most suitable configuration for the system. This solution was also experimentally verified. (C) 2021 Elsevier Ltd. All rights reserved., One of the authors (PBM) was supported by the regional public administration of Valencia under the grant ACIF/2018/106

This work presents a novel low-pass filter based on using dielectric materials, in the critical areas of the structure, to prevent high-power space condition adverse effects, like multipactor breakdown. It is shown that the use of dielectric pieces in the inverters of the filter leads to increased gaps between metallic parts in the irises. In addition, by completely filling up the air space in critical gaps, the multipactor breakdown thresholds can be increased. Moreover, they also lead to a volume reduction, increasing compactness of the structure. To demonstrate the proposed concept, a prototype has been manufactured with an aluminum housing and a Teflon dielectric material, and tested for multipactor effect at ESA-VSC Laboratory. Tests confirmed that the manufactured filter has good electrical performance with cut-off frequency 13.6 GHz, insertion losses 0.32 dB, return losses better than 15 dB, and additionally does not show multipactor breakdown up to the maximum power tested in the experimental facility (5.5 kW). This research work has been financially supported by the Spanish Ministerio de Ciencia e Innovación in the frame of the project “Green an Efficient Technologies for Advanced Telecommunication Systems (GRETAS)” with Ref. TPID2019-103982RB-C42.

The liberalization of the gas market has deeply affected the structure of the gas system. The change from a vertically integrated utility structure to a liberalized architecture has resulted in a new network asset where aggregators could play a key role. To enable aggregators’ functionalities, the gas system has moved to a smart configuration. The development of this Advanced Metering Infrastructure (AMI) allowing a two-way communication between the utility and the consumers, together with demand response programs, prove to be an advantageous solution. Utilities are allowed to manage consumers’ gas load by asking them to reduce or shift their gas usage so that rebates or back payments could be available for consumers. In this framework, aimed at ensuring the feasibility of the gas system and promoting the aggregation of small gas loads, a new agent has been identified. In this research, the potential of district heating systems as gas market aggregators, together with a novel architecture for the gas network system has been identified. Furthermore, DR benefits in natural gas systems have been assessed with a focus on the Italian peninsula, where the authors have already carried out a previous analysis in this regard.

[EN] The European Union advocates for legislative support to local energy communities. Measures include the promotion of dynamic energy allocation and discriminatory electricity tariffs such as the recent Spanish framework. However, the impact of these normative changes is not yet evaluated. This paper inquires into the impact of dynamic allocation coefficient and different electricity tariffs on the profitability of local energy communities. To do so, a linear optimisation model is developed and applied to real consumer data in Spain around a variable capacity photovoltaic generation plant. Comparing the economic performance of the static or variable power allocation under the effect of changing electricity tariffs. While both measures are beneficial, the new electricity tariffs result in larger profitability increases than the planned variable coefficients. The combination of measures allows for profitability improvements of up to 25% being complementary measures. However, installations that maximise the potential for electricity generation are still not as profitable due to the low purchase price of surplus energy. While discriminatory electricity price tariffs and variable allocation coefficients are positive measures, further measures are needed for these communities to install generation plants as large as the potential that each case allows., This work was supported in part by the Spanish public administration under the grant FPU2016/00962 and by the Catedra de Transicion Energetica Urbana (Las Naves-FVCiE-UPV)

6 páginas.- 2 figura.- 29 referencias.- 1st Conference on Information Technology for Social Good, GoodIT 2021, Rome 9-11 September 2021, New challenges such as climate change and sustainability arise in society influencing not only environmental issues but human's health directly. To face these new challenges IT technologies and their application to environmental intelligent monitoring become into a powerful tool to set new policies and blueprints to contribute to social good. In the new H2020 project, WatchPlant will provide new tools for environmental intelligence monitoring by the use of plants as "well-being"sensors of the environment they inhabit. This will be possible by equipping plants with a net of communicated wireless self-powered sensors, coupled with artificial intelligence (AI) to become plants into "biohybrid organisms"to test exposure-effects links between plant and the environment. It will become plants into a new tool to be aware of the environment status in a very early stage towards in-situ monitoring. Additionally, the system is devoted to be sustainable and energy-efficient thanks to the use of clean energy sources such as solar cells and a enzymatic biofuel cell (BFC) together with its self-deployment, self-awareness, adaptation, artificial evolution and the AI capabilities. In this concept paper, WatchPlant will envision how to face this challenge by joining interdisciplinary efforts to access the plant sap for energy harvesting and sensing purposes and become plants into "biohybrid organisms"to benefit social good in terms of environmental monitoring in urban scenarios. © 2021 Owner/Author., Project WatchPlant has received funding from the European Union’s Horizon 2020 research and innovation program under the FET grant agreement, no. 101017899

This study presents a new methodology, based on Monte-Carlo techniques to evaluate the reliability of a carbon-free electricity generation system based on renewable sources, it uses as inputs the variation of the electricity demand and the fluctuations in the renewable supply and provides the renewable system to be installed to guarantee a specific supply reliability level. Additionally, looking for a reduction of this renewable system, the methodology determines the improvements by the incorporation of nuclear power and electricity storage. The methodology is of general application, its implementation being possible under different contexts, such as different time horizons and different future energy scenarios, both for developing, emerging, and developed countries. The only requirement is to have a sufficient database from which to make predictions for future scenarios of electrical generation–demand balances. As an example of practical implementation, the electrical system reliability for the particular case of Spain in 2040 has been forecasted. When considering the fluctuations in solar and wind power contributions, very high values of the installed power from these renewable sources are needed to reach a high reliability of the system. These values decrease substantially if contributions from nuclear and storage technologies are included.

Demand prediction has been playing an increasingly important role for electricity management, and is fundamental to the corresponding decision-making. Due to the high variability of the increasing electrical load, and of the new renewable energy technologies, power systems are facing technical challenges. Thus, short-term forecasting has crucial utility for generating dispatching commands, managing the spot market, and detecting anomalies. The techniques associated with machine learning are those currently preferred by researchers for making predictions. However, there are concerns regarding limiting the uncertainty of the obtained results. In this work, a statistical methodology with a simple implementation is presented for obtaining a prediction interval with a time horizon of seven days (15-min time steps), thereby limiting the uncertainty. The methodology is based on pattern recognition and inferential statistics. The predictions made differ from those from a classical approach which predicts point values ​​by trying to minimize the error. In this study, 96 intervals of absorbed active power are predicted for each day, one for every 15 min, along with a previously defined probability associated with the real values ​​being within each obtained interval. To validate the effectiveness of the predictions, the results are compared with those from techniques with the best recent results, such as artificial neural network (ANN) long short-term memory (LSTM) models. A case study in Ecuador is analyzed, resulting in a prediction interval coverage probability (PICP) of 81.1% and prediction interval normalized average width (PINAW) of 10.13%, with a confidence interval of 80%.

[EN] Off grid electrification is key to achieving universal electricity access. Despite the potential of biomass gasifiers as a clean technology to ensure reliable systems, they are not as well looked upon as their advantages suggest. In particular, the software HOMER, which researchers and technicians use the most to simulate and design Microgrids, does not include biomass gasification among its simulation technologies. Although some authors did simulate biomass gasifiers in HOMER, the necessary parameters and procedures to calculate and include them in the tool remain unclear, non-replicable, and leave open research questions as regards modelling. Using the inbuilt Biogas power plant modules in HOMER, this paper presents a set of steps to include the technical and economic parameters to simulate fuelling an electric generator through the syngas produced in a downdraft biomass gasification plant. Two case studies of isolated rural communities in Honduras and Zambia show the viability of the procedure. These case studies also confirm the technical and economic viability of islanded biomass-photovoltaic hybrid renewable energy microgrids. In both cases, the energy demand supplied and distributed by the microgrid had a Levelized Cost of Energy lower than the alternative of extending the electric grid to the communities., We do appreciate the work of MSc Josefine Axelsson, who conducted part of the study of Mumbeji, Zambia. This is an extended and updated version of a paper originally presented at the 15th Conference on Sustainable Development of Energy, Water and Environment Systems (SDEWES 2020) held in Cologne, Germany over the period 1st to September 5, 2020 (denoted then as paper SDEWES2019.0458 - Modelling a gasifier with HOMER for the simulation of off-grid hybrid renewable energy microgrids). This work was supported in part by the Spanish public administration under the grant FPU2016/00962, by the regional public administration of Valencia under the grant ACIF/2018/106, by the Food and Agricuture Organisation under the Letter of Agreement (PO number: 332412). And finally, by the Catedra de Transicion Energetica Urbana (Las Naves-UPV).

[EN] Islands located far away from the mainland and remote communities depend on isolated microgrids based on diesel fuel, which results in significant environmental and cost issues. This is currently being addressed by integrating renewable energy sources (RESs). Thus, this paper discusses the generation planning problem in diesel-based island microgrids with RES, considering the electrification of transportation and cooking to reduce their environmental impact, and applied to the communities of Santa Cruz and Baltra in the Galapagos Islands in Ecuador. A baseline model is developed in HOMER for the existing system with diesel generation and RES, while the demand of electric vehicles and induction stoves is calculated from vehicle driving data and cooking habits in the islands, respectively. The integration of these new loads into the island microgrid is studied to determine its costs and environmental impacts, based on diesel cost sensitivity studies to account for its uncertainty. The results demonstrate the economic and environmental benefits of investing in RES for Galapagos' microgrid, to electrify the local transportation and cooking system., J.-M. Clairand would like to thank Universidad de las Americas for funding his visit to the University of Waterloo. The authors would like to thank W. Mendieta, F. Calero, and E. Vera from the University of Waterloo for their valuable comments and helpful suggestions.

Resumen: Esta investigación describe la tesis en ingeniería eléctrica contrastando el nivel educativo y la lengua en dos universidades estatales metropolitanas tradicionales y altamente selectivas de Chile y Brasil. Desde un marco sistémico-funcional, se analizó cualitativamente la Configuración Contextual, la extensión, el Potencial de Estructura Genérica de elementos iniciales y de elementos semánticos y las opciones de despersonalización en la introducción y conclusión de 20 tesis recientes. La tesis se inscribe en un contexto de asimetría de experticia y saber entre autor y director, roles predominantemente masculinos, y varía en su complejidad y aporte según el nivel educativo. Consiste en una investigación individual sistemática sobre un tema especializado, en un marco de educación formal superior, y tiene como producto un texto escrito multimodal. Su extensión ronda las 95 páginas en pregrado y el doble de extensión en posgrado. Se inicia obligatoriamente con tapa, dedicatoria, agradecimientos y resumen en lengua local. El contraste entre lenguas muestra que en portugués se utilizan más elementos obligatorios relacionados con la protocolarización e internacionalización de ejemplares multimodales. Las introducciones incluyen obligatoriamente la motivación, objetivos y organización textual, mientras que en las conclusiones son obligatorias las consideraciones finales y trabajos futuros, y optativa la síntesis de hallazgos. En ambas secciones, la estructura es más compleja y estable en posgrado. La presencia del autor es despersonalizada casi por completo mediante estrategias como la metonimia. Esta investigación hace aportes al conocimiento del género y orienta iniciativas de alfabetización disciplinar en ingeniería de pregrado y posgrado en Chile y Brasil.

[EN] Learning and training are key aspects in the evolution of societies. In the case of professional activities, the 21st century must take into account those professions which require knowledge and skills upgrading to new technological advances, such as intelligent buildings measurements for construction managers and supervisors as well as other professionals who act in different building facilities. The Evidence based Learning Outcomes, is to develop smart metering learning outcomes which reflect building sector needs and which can be integrated into existing Vocational Education Training (VET) offerings for construction site managers. These evidence based learning outcomes will address the skills needs of site managers in smart metering technologies and services, delivering a European¿wide comprehensive pedagogical structure. This article explains the collection and analysis of sector and VET evidence on the current and future smart metering site manager skills and training needs (covering power, gas, heat and water meters) in the six partnership countries., This work has been conducted within the framework of the research project COSMET "Training in Smart Meters for Construction Site Managers" funded by the European Commission within the Key Action 2: Strategic Partnerships in VET, reference number 2015-1-UK01- KA202-013406.

Wind power plants are becoming a generally accepted resource in the generation mix of many utilities. At the same time, the size and the power rating of individual wind turbines have increased considerably. Under these circumstances, the sector is increasingly demanding an accurate characterization of vertical wind speed profiles to estimate properly the incoming wind speed at the rotor swept area and, consequently, assess the potential for a wind power plant site. The present paper describes a shape-based clustering characterization and visualization of real vertical wind speed data. The proposed solution allows us to identify the most likely vertical wind speed patterns for a specific location based on real wind speed measurements. Moreover, this clustering approach also provides characterization and classification of such vertical wind profiles. This solution is highly suitable for a large amount of data collected by remote sensing equipment, where wind speed values at different heights within the rotor swept area are available for subsequent analysis. The methodology is based on z-normalization, shape-based distance metric solution and the Ward-hierarchical clustering method. Real vertical wind speed profile data corresponding to a Spanish wind power plant and collected by using a commercialWindcube equipment during several months are used to assess the proposed characterization and clustering process, involving more than 100000 wind speed data values. All analyses have been implemented using open-source R-software. From the results, at least four different vertical wind speed patterns are identified to characterize properly over 90% of the collected wind speed data along the day. Therefore, alternative analytical function criteria should be subsequently proposed for vertical wind speed characterization purposes. The authors are grateful for the financial support from the Spanish Ministry of the Economy and Competitiveness and the European Union —ENE2016-78214-C2-2-R—and the Spanish Education, Culture and Sport Ministry —FPU16/0428

During the last several decades, demand response programs have played a crucial role in the management of the electrical energy system worldwide. Thus, demand response programs, offered by either system operators or utilities, have contributed to solve different matters that appear in the habitual operation of the power grid. Currently, there are no successful experiences of demand response applications in the natural gas system. However, it seems natural that, in line with the current trends in the energy sector, demand response concepts will be eventually essential for the better and more efficient operation of natural gas systems. The research here presented leads with the analysis of demand response as a strategic tool for the gas system operator to manage better the natural gas network. Thus, this research demonstrates as demand response could represent a great opportunity for consumers to play an active role in the operation of the natural gas grid. In this paper, the ability of consumers to participate in the balancing of natural gas in the transmission network is assessed and the utilization of demand response resources to help reducing the imbalances in the natural gas system is demonstrated.

Producción Científica, The study of power quality (PQ) has gained relevance over the years due to the increase in non-linear loads connected to the grid. Therefore, it is important to study the propagation of power quality disturbances (PQDs) to determine the propagation points in the grid, and their source of generation. Some papers in the state of the art perform the analysis of punctual measurements of a limited number of PQDs, some of them using high-cost commercial equipment. The proposed method is based upon a developed proprietary system, composed of a data logger FPGA with GPS, that allows the performance of synchronized measurements merged with the full parameterized PQD model, allowing the detection and tracking of disturbances propagating through the grid using wavelet transform (WT), fast Fourier transform (FFT), Hilbert–Huang transform (HHT), genetic algorithms (GAs), and particle swarm optimization (PSO). Measurements have been performed in an industrial installation, detecting the propagation of three PQDs: impulsive transients propagated at two locations in the grid, voltage fluctuation, and harmonic content propagated to all the locations. The results obtained show that the low-cost system and the developed methodology allow the detection of several PQDs, and track their propagation within a grid with 100% accuracy., Consejo Nacional de Ciencia y Tecnología (CONACYT) - (grant 486847)

Prediction of energy production is crucial for the design and installation of PV plants. In this study, five free and commercial software tools to predict photovoltaic energy production are evaluated: RETScreen, Solar Advisor Model (SAM), PVGIS, PVSyst, and PV*SOL. The evaluation involves a comparison of monthly and annually predicted data on energy supplied to the national grid with real field data collected from three real PV plants. All the systems, located in Castile and Leon (Spain), have three different tilting systems: fixed mounting, horizontal-axis tracking, and dual-axis tracking. The last 12 years of operating data, from 2008 to 2020, are used in the evaluation. Although the commercial software tools were easier to use and their installations could be described in detail, their results were not appreciably superior. In annual global terms, the results hid poor estimations throughout the year, where overestimations were compensated by underestimated results. This fact was reflected in the monthly results: the software yielded overestimates during the colder months, while the models showed better estimates during the warmer months. In most studies, the deviation was below 10% when the annual results were analyzed. The accuracy of the software was also reduced when the complexity of the dual-axis solar tracking systems replaced the fixed installation., This research was funded by Spanish Ministry of Science and Innovation, grant number RTI2018-098900-B-I00 and the Regional Government of Castilla y León under the “Support Program for Recognized Research Groups of Public Universities of Castilla y León” (ORDEN EDU/667/2019) and “Health and Safety Program” (INVESTUN/19/BU/0004).

There are several compilations of sky classifications that refer to Meteorological Indices (MIs) (variables usually recorded at meteorological ground stations), due to the scarcity of sky scanner devices that can supply the experimental data needed to apply the CIE standard sky classification. The use of one rather than another MI is never justified, because there is no standardized criterion for their selection. In this study, forty-three MIs, traditionally used to define different sky conditions, are reviewed. Feature Selection (FS) is a key step in the design of a sky-classification algorithm using MIs as an alternative to data from sky scanners. Four procedural methods for FS -Pearson, Permutation Importance, Recursive Feature Elimination, and Boruta- are applied to an extensive data set of MIs that includes CIE standard sky classification data, which was used as a reference. The use of FS procedures significatively reduced the original set of MIs, permitting the construction of different classification trees with high performance for the sky classification. In the case of the Pearson FS method, the classification tree only used two MIs. The advantage of the Pearson FS method is that it functions independently from the machine-learning algorithm used latter for the sky classification.

[EN] Agriculture and forestry crop residues represent more than half of the world's residual biomass; these residues turn into synthesis gas (syngas) and are used for power generation. Including Syngas Gensets into hybrid renewable microgrids for electricity generation is an interesting alternative, especially for rural communities where forest and agricultural waste are abundant. However, energy demand is not constant throughout the day. The variations in the energy demand provoke changes in both gasification plant efficiency and biomass consumption. This paper presents an Artificial Neural Network (ANN) based model hybridized with a Particle Swarm Optimization (PSO) algorithm for a Biomass Gasification Plant (BGP) that allows estimating the amount of biomass needed to produce the required syngas to meet the energy demand. The proposed model is compared with two traditional models of ANNs: Feed Forward Back Propagation (FF-BP) and Cascade Forward Propagation (CF-P). ANNs are trained in MATLAB software using a set of historical real data from a BGP located in the Distributed Energy Resources Laboratory of the Universitat Polite`cnica de Vale`ncia in Spain. The model performance is validated using the Mean Squared Error (MSE) and linear regression analysis. The results show that the proposed model performs 23.2% better in terms of MSE than de other models. The tunning parameters of the optimal PSO algorithm for this application were found. Finally, the model was validated to predict the necessary biomass and syngas to cover the energy demand, The authors would like to thank Consejo Nacional de Ciencia y Tecnologia (CONACYT) for their support of funding this work under the 487628 and 486670 scholarship numbers. The authors express their sincere appreciation to Universitat Politenica de Valencia for the facilities to perform the tests for the proposed algorithm at the Renewable Energies Laboratory (LabDER) of the Institute for Energy Engineering.

Software risk prediction is the most sensitive and crucial activity of Software Development Life Cycle (SDLC). It may lead to the success or failure of a project. The risk should be predicted earlier to make a software project successful. A model is proposed for the prediction of software requirement risks using requirement risk dataset and machine learning techniques. In addition, a comparison is made between multiple classifiers that are K-Nearest Neighbour (KNN), Average One Dependency Estimator (A1DE), Na&iuml, ve Bayes (NB), Composite Hypercube on Iterated Random Projection (CHIRP), Decision Table (DT), Decision Table/Na&iuml, ve Bayes Hybrid Classifier (DTNB), Credal Decision Trees (CDT), Cost-Sensitive Decision Forest (CS-Forest), J48 Decision Tree (J48), and Random Forest (RF) achieve the best suited technique for the model according to the nature of dataset. These techniques are evaluated using various evaluation metrics including CCI (correctly Classified Instances), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE), Root Relative Squared Error (RRSE), precision, recall, F-measure, Matthew&rsquo, s Correlation Coefficient (MCC), Receiver Operating Characteristic Area (ROC area), Precision-Recall Curves area (PRC area), and accuracy. The inclusive outcome of this study shows that in terms of reducing error rates, CDT outperforms other techniques achieving 0.013 for MAE, 0.089 for RMSE, 4.498% for RAE, and 23.741% for RRSE. However, in terms of increasing accuracy, DT, DTNB, and CDT achieve better results.

[EN] Growing cities are a world-wide phenomenon and simultaneously awareness about potential dangers due to air pollution, heat, and pathogens is increasing. Integrated and permanent monitoring of environmental features in cities can help to establish an early warning system and to provide data for policy makers. In our new project `WatchPlant,¿ we propose a green approach for urban monitoring by a network of sensors tightly coupled with natural plants. We want to develop a sustainable, energy-efficient bio-hybrid system that harvests energy from living plants and utilizes methods of phytosensing, that is, using natural plants as sensors. We present our concept, here with focus on Alife-related methods operating on the gathered plant data and the bio-hybrid network. With a self-organizing network of sensors, that are alive, we hope to contribute to our future of livable green cities., Project WatchPlant has received funding from the European Union's Horizon 2020 research and innovation program under the FET grant agreement, no. 101017899. Project Biohybrids is funded by H2020 program, grant agreement no. 945773.

[EN] This paper addresses the detailed modeling and small-signal stability analysis of a multi-terminal high voltage direct current (HVDC) system including a large diode rectifier-connected off-shore wind power plant (WPP). The study covers both islanded and connected WPP operation with different control scenarios. Islanded WPP stands for non-conducting HVDC diode rectifier (DR) whereas in connected WPP operation the DR is conducting. The study of both modes is important as the WPP would be in one of these modes during normal operation. Variable off-shore ac-grid frequency and a dynamic diode rectifier model have been considered for adequate dynamic modeling. The proposed models have been validated against detailed PSCAD EMT simulations and have been used to carry out the small-signal stability analysis of the islanded and connected WPP with different scenarios. Moreover, the dynamic model is used to assess the system robustness against parametric uncertainties, communication delay and power level, ensuring the integration of the off-shore WPP into the on-shore electrical power system. This study also points out that adequate droop control of the HVDC grid has a larger impact on overall stability than the diode rectifier connected off-shore WPP controllers., This work was supported by the Spanish Ministry of Economy and EU FEDER Funds under grants DPI2014-53245-R, and DPI2017-84503-R. This project has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 691714.

[EN] The methods currently used for designing a fluidized bed reactor in gasification plants do not meet an integrated methodology that optimizes all the different parameters for its sizing and operational regime. In the case of small-scale (several tens of kWs biomass gasifiers), this design is especially complex, and, for this reason, they have usually been built in a very heuristic trial and error way. In this paper, an integrated methodology tailoring all the different parameters for the design and sizing of a small-scale fluidized bed gasification plants is presented. Using this methodology, a 40 kWth biomass gasification reactor was designed, including the air distribution system. Based on this design, with several simplified assumptions, a reactor was built and commissioned. Results from the experimental tests using this gasifier are also presented in this paper. As a result, it can be said the prototype works properly, and it produces syngas able to produce thermal energy or even electricity., This work was supported in part by the European Commission through GROW GREEN project (Agreement number: 730283-GROW GREEN-H2020-SCC-2016-2017/H2020-SCC-NBS2stage-2016. http://growgreenproject.eu/).

[EN] This paper proposes to administer a multi-step artificial intelligence approach with an ensemble of adaptive neural networks (NNs) trained on 50000 samples to identify partial discharge (PD) diagnostic measurements for in-service medium voltage (MV) power cables. To evaluate the performance of the algorithm, a case study was performed on cables deliberately selected to contain both uncomplicated measurements and disruptive irregularities representative of conditions during field testing. The experimental test results prove that the proposed cable-specific adaptation improves PD identification accuracy, with further increment through the NN ensembles. The main contribution of the approach is in both the cable-specific adaption and the NN ensemble being applied to MV cable field measurements.

[EN] Many electric utilities currently have a low level of smart meter implementation on traditional distribution grids. These utilities commonly have a problem associated with non-technical energy losses (NTLs) to unidentified energy flows consumed, but not billed in power distribution grids. They are usually due to either the electricity theft carried out by their own customers or failures in the utilities' energy measurement systems. Non-technical energy losses lead to significant economic losses for electric utilities around the world. For instance, in Latin America and the Caribbean countries, NTLs represent around 15% of total energy generated in 2018, varying between 5 and 30% depending on the country because of the strong correlation with social, economic, political, and technical variables. According to this, electric utilities have a strong interest in finding new techniques and methods to mitigate this problem as much as possible. This research presents the results of determining with the precision of the existing data-oriented methods for detecting NTL through a methodology based on data analytics, machine learning, and artificial intelligence (multivariate data, analysis methods, classification, grouping algorithms, i.e., k-means and neural networks). The proposed methodology was implemented using the MATLAB computational tool, demonstrating improvements in the probability to identify the suspected customer's measurement systems with error in their records that should be revised to reduce the NTLs in the distribution system and using the information from utilities' databases associated with customer information (customer information system), the distribution grid (geographic information system), and socio-economic data. The proposed methodology was tested and validated in a real situation as a part of a recent Ecuadorian electric project., This research received funding from the project Smart GrI+D+i from the Universidad Catolica de Cuenca in Ecuador.

Applications of renewable electricity in cities are mostly limited to photovoltaics, and they need other renewable sources, batteries, and the grid to guarantee reliability. This paper proposes a hybrid system, combining biomass and photovoltaics, to supply electricity to educational buildings. This system is reliable and provides at least 50% of electricity based on renewable sources. Buildings with small (&lt, 500 kW) installed power based on renewables, mainly biomass, are usually expensive. Besides, in urban areas, photovoltaic capacity is limited due to roof availability. This paper analyzes different configurations, meeting these constraints to obtain an economically feasible solution based on photovoltaic-biomass modelling of small size hybrid systems. The technology used for biomass energy valorization is a fluidized bed gasification power plant, which has been modelled with real data obtained from experimental tests and previous research projects. Thereby, real costs and electric efficiency are included in the model. The techno-economic feasibility analysis using HOMER software with metered real load curves from an educational building has been modelled. The results of the model show that hybrid renewable systems are very feasible in the scenario of 50% of electricity contribution, however, higher contribution (&gt, 70%) implies high electricity costs.

Building renovation plays a key role in reducing greenhouse gas emissions and achieving the climate protection goals. The district scale approach is one of the most effective approaches to accelerate this process of reducing the energy consumption in the building sector as increasing its renovation rates. In this context, the Energy in Buildings and Communities Programme of the IEA, IEA-EBC started in 2017 the project "Annex 75: Cost-Effective Building renovation at District Level Combining Energy Efficiency and Renewables" aiming to explore optimal opportunities of district renovations from a cost-benefit perspective. IEA Annex 75 is a co-operative effort of participants from 13 different countries: Austria, Belgium, China, Czech Republic, Denmark, Germany, Italy, The Netherlands, Norway, Portugal, Spain, Sweden and Switzerland. In this paper, key elements of the methodology developed in Annex 75 project are presented. This methodology aims to facilitate the identification of optimal solutions in different European countries, enabling to explore similarities and differences amongst them, with a particular focus on the balance between energy efficiency measures and renewable energy measures. After a detailed description of the developed methodology, it is also applied to a case study located in Portugal and results obtained are analysed in detail. The paper demonstrates the usefulness of the methodology for evaluating and identifying optimal solutions in renovations at district scale, as well as for successfully addressing the research questions investigated by the Annex 75 project. They also provide some insights regarding the specific case study, showing that, although district systems are not usual in the current Portuguese context, these centralised solutions in renovations at district level are cost-effective interventions that can lead to significant reductions of greenhouse gas emissions and non-renewable primary energy use. (C) 2020 Elsevier B.V. All rights reserved., The work presented in this paper has been developed by the authors as a contribution to the IEA EBC Annex 75 project. The authors would like to acknowledge all the project participants and all national funding organizations.The author Raul Briones-Llorente wishes to acknowledge the Consejeria de Educacion de la Junta de Castilla y Leon (Spain) and the Operational Programme European Social Fund for the funding of his doctoral scholarship.

[EN] The router plays an important role in communication among different processing cores in on-chip networks. Technology scaling on one hand has enabled the designers to integrate multiple processing components on a single chip; on the other hand, it becomes the reason for faults. A generic router consists of the buffers and pipeline stages. A single fault may result in an undesirable situation of degraded performance or a whole chip may stop working. Therefore, it is necessary to provide permanent fault tolerance to all the components of the router. In this paper, we propose a mechanism that can tolerate permanent faults that occur in the router. We exploit the fault-tolerant techniques of resource sharing and paring between components for the input port unit and routing computation (RC) unit, the resource borrowing for virtual channel allocator (VA) and multiple paths for switch allocator (SA) and crossbar (XB). The experimental results and analysis show that the proposed mechanism enhances the reliability of the router architecture towards permanent faults at the cost of 29% area overhead. The proposed router architecture achieves the highest Silicon Protection Factor (SPF) metric, which is 24.4 as compared to the state-of-the-art fault-tolerant architectures. It incurs an increase in latency for SPLASH2 and PARSEC benchmark traffics, which is minimal as compared to the baseline router., This work was supported by the Spanish 'Ministerio de Ciencia Innovacion y Universidades' and FEDER program in the framework of the 'Proyectos de I+D d Generacion de Conocimiento del Programa Estatal de Generacion de Conocimiento y Fortalecimiento Cientifico y Tecnologico del Sistema de I+D+i, Subprograma Estatal de Generacion de Conocimiento' (ref: PGC2018-095747-B-I00).

[EN] Wind power plants depend greatly on weather conditions, thus being considered intermittent, uncertain and non-dispatchable. Due to the massive integration of this energy resource in the recent decades, it is important that transmission and distribution system operators are able to model their electrical behaviour in terms of steady-state power flow, transient dynamic stability, and short-circuit currents. Consequently, in 2015, the International Electrotechnical Commission published Standard IEC 61400-27-1, which includes generic models for wind power generation in order to estimate the electrical characteristics of wind turbines at the connection point. This paper presents, describes and details the models for wind turbine topologies Types 1 and 2 following IEC 61400-27-1 for electrical simulation purposes, including the values for the parameters for the different subsystems. A hardware-in-the-loop combined with a real-time simulator is also used to analyse the response of such wind turbine topologies under voltage dips. The evolution of active and reactive powers is discussed, together with the wind turbine rotor and generator rotational speeds., This work was partially supported by the Spanish Ministry of Economy and Competitiveness and the European Union -FEDER Funds, ENE2016-78214-C2-1-R-; and the Spanish Ministry of Education, Culture and Sports -ref. FPU16/04282-.

[EN] Distributed energy storage may play a key role in the operation of future low-carbon power systems as they can help to facilitate the provision of the required flexibility to cope with the intermittency and volatility featured by renewable generation. Within this context, this paper addresses an optimization methodology that will allow managing distributed storage systems of different technology and characteristics in a specific distribution network, taking into account not only the technical aspects of the network and the storage systems but also the uncertainties linked to demand and renewable energy variability. The implementation of the proposed methodology will allow facilitating the integration of energy storage systems within future smart grids. This paper's results demonstrate numerically the good performance of the developed methodology., This research was funded by European Regional Development Fund (Comunidad Valenciana FEDER 2014-2020 PO, CCI number: 2014ES16RFOP013) and the ITE-IVACE collaboration agreement corresponding to the annuity 2019 (file: IMDEEA-2019-38).

Microgrids have emerged as a solution to address new challenges in power systems with the integration of distributed energy resources (DER). Inverter-based microgrids (IBMG) need to implement proper control systems to avoid stability and reliability issues. Thus, several researchers have introduced multi-objective control strategies for distributed generation on IBMG. This paper presents a review of the different approaches that have been proposed by several authors of multi-objective control. This work describes the main features of the inverter as a key component of microgrids. Details related to accomplishing efficient generation from a control systems’ view have been observed. This study addresses the potential of multi-objective control to overcome conflicting objectives with balanced results. Finally, this paper shows future trends in control objectives and discussion of the different multi-objective approaches.

Isolated microgrids, such as islands, rely on fossil fuels for electricity generation and include vehicle fleets, which poses significant environmental challenges. To address this, distributed energy resources based on renewable energy and electric vehicles (EVs) have been deployed in several places. However, they present operational and planning concerns. Hence, the aim of this paper is to propose a two-level microgrid problem. The first problem considers an EV charging strategy that minimizes charging costs and maximizes the renewable energy use. The second level evaluates the impact of this charging strategy on the power generation planning of Santa Cruz Island, Galapagos, Ecuador. This planning model is simulated in HOMER Energy. The results demonstrate the economic and environmental benefits of investing in additional photovoltaic (PV) generation and in the EV charging strategy. Investing in PV and smart charging for EVs could reduce the N P C by 13.58%, but a reduction in the N P C of the EV charging strategy would result in up to 3.12%.

[EN] Sustainable activated carbon can be obtained from the pyrolysis/activation of biomass wastes coming from different origins. Carbon obtained in this way shows interesting properties, such as high surface area, electrical conductivity, thermal and chemical stability, and porosity. These characteristics among others, such as a tailored pore size distribution and the possibility of functionalization, lead to an increased use of activated carbons in catalysis. The use of activated carbons from biomass origins is a step forward in the development of more sustainable processes enhancing material recycling and reuse in the frame of a circular economy. In this article, a perspective of different heterogeneous catalysts based on sustainable activated carbon from biomass origins will be analyzed focusing on their properties and catalytic performance for determined energy-related applications. In this way, the article aims to give the reader a scope of the potential of these tailor-made sustainable materials as a support in heterogeneous catalysis and future developments needed to improve catalyst performance. The selected applications are those related with H2 energy and the production of biomethane for energy through CO2 methanation., This research was funded by the Centro de Desarrollo Tecnologico Industrial-CDTI (ALMAGRID Project-CER-20191006), by the Instituto Valenciano de Competitividad Empresarial-IVACE-FEDER (BIO3 Project-IMDEEA/2019/44) and by the Agencia Valenciana de Investigacion-AVI (REWACER Project INNEST00/19/050).

Motor current signature analysis (MCSA) is a fault diagnosis method for induction machines (IMs) that has attracted wide industrial interest in recent years. It is based on the detection of the characteristic fault signatures that arise in the current spectrum of a faulty induction machine. Unfortunately, the MCSA method in its basic formulation can only be applied in steady state functioning. Nevertheless, every day increases the importance of inductions machines in applications such as wind generation, electric vehicles, or automated processes in which the machine works most of time under transient conditions. For these cases, new diagnostic methodologies have been proposed, based on the use of advanced time-frequency transforms&mdash, as, for example, the continuous wavelet transform, the Wigner Ville distribution, or the analytic function based on the Hilbert transform&mdash, which enables to track the fault components evolution along time. All these transforms have high computational costs and, furthermore, generate as results complex spectrograms, which require to be interpreted for qualified technical staff. This paper introduces a new methodology for the diagnosis of faults of IM working in transient conditions, which, unlike the methods developed up to today, analyzes the current signal in the slip-instantaneous frequency plane (s-IF), instead of the time-frequency (t-f) plane. It is shown that, in the s-IF plane, the fault components follow patterns that that are simple and unique for each type of fault, and thus does not depend on the way in which load and speed vary during the transient functioning, this characteristic makes the diagnostic task easier and more reliable. This work introduces a general scheme for the IMs diagnostic under transient conditions, through the analysis of the stator current in the s-IF plane. Another contribution of this paper is the introduction of the specific s-IF patterns associated with three different types of faults (rotor asymmetry fault, mixed eccentricity fault, and single-point bearing defects) that are theoretically justified and experimentally tested. As the calculation of the IF of the fault component is a key issue of the proposed diagnostic method, this paper also includes a comparative analysis of three different mathematical tools for calculating the IF, which are compared not only theoretically but also experimentally, comparing their performance when are applied to the tested diagnostic signals.

Similarly to how Steinmetz developed his theory of alternating current in single-phase sinusoidal systems, a few formal relationships between expressions of the instantaneous and Buchholz&rsquo, s apparent power in three-phase systems were identified in this paper. Based on these relationships, a methodology to express Buchholz&rsquo, s apparent power and its components in any three-phase, wye-configured system&mdash, sinusoidal or non-sinusoidal, balanced or unbalanced&mdash, through instantaneous power expressions was established. The application of the proposed method to the entire system allowed the determination of a novel quantity referred to as neutral-displacement power, which measured the impacts of the phenomena caused by the neutral path operation on the values of the source and load apparent power. These impacts were analyzed using a real-world urban installation with a neutral conductor deterioration simulation via an Excel platform as an application example.

Spain is a country with a high dependence on fossil fuels. For this reason, in 2007, it implemented a bonus system that aimed to encourage the production of renewable energies, particularly photovoltaic solar energy. These production bonuses, guaranteed by the Spanish government, led to an exponential increase in the number of companies in the market and, consequently, the MWh produced. However, in 2012, given the excessive budgetary burden involved in maintaining this “feed-in tariff” system and after several years of institutional instability, the aforementioned system of incentives for phoyovoltaic (PV) energy was eliminated. This paper has tried to analyze the consequences of this institutional change, a clear example of the “hold up” problem. For this purpose, a sample of 5354 companies, which was divided, geographically, into Spanish regions and, temporarily, into three different periods, has been taken, considering diverse economic and financial variables. The results show a notable weakening of the sector that, due to the effects of the regulatory change, has lost attractiveness and profitability for investors and is consequently suffering from stagnation, which has led to the disappearance of many companies in the sector., Raquel Fernández González gratefully acknowledge the financial support provided by the Xunta de Galicia under Grants ED431C2018/48 and ED431E2018/07 and the Ministry of Economy and Competitiveness under Grant RTI2018-099225-B-100. This research was funded by Regional Government of Castilla y León under the “Support Program for Recognized Research Groups of Public Universities of Castilla y León” (ORDEN EDU/667/2019) and the Spanish Ministry of Science, Innovation & Universities under the I + D + i state program “Challenges Research Projects” (Ref. RTI2018-098900-B-I00).

[EN] Fault diagnosis of induction motors through the analysis of the stator current is increasingly being used in maintenance systems, because it is non-invasive and has low requirements of hardware and software. Nevertheless, its industrial application faces some practical limitations. In particular, the detection of fault harmonics that are very close to the fundamental component is challenging, as in large induction motors working at very low slip, because the leakage of the fundamental can hide the fault components until the damage is severe. Several methods have been proposed to alleviate this problem, although all of them increase noticeably the complexity of the diagnostic system. In this paper, a novel method is proposed, based on the analysis of the rectified motor current. It is shown that its spectrum contains the same fault harmonics as the spectrum of the original current signal, but with a much lower frequency, and free from the fundamental component leakage. Besides, the proposed method is very easy to implement, either by software, using the absolute value of the current samples, or by hardware, using a simple rectifier. The proposed approach is presented theoretically and validated experimentally with the detection of a broken bars fault of a large induction motor., This work was supported in part by the Spanish "Ministerio de Ciencia, Innovacion yUniversidades (MCIU)," in part by the "Agencia Estatal de Investigacion (AEI)," and in part by the "Fondo Europeo de Desarrollo Regional (FEDER)" in the framework of the "Proyectos I+D+i -Retos Investigacion 2018," under Project RTI2018-102175-B-I00 (MCIU/AEI/FEDER, UE).

[EN] The phenomenon responsible for the different apparent powers measured in a subsystem of a three-phase star-configured system, based on the voltage reference point, was identified in this paper using specific components of the instantaneous powers, as a result of applying the conservation of energy principle to the entire system. The effects of the phenomenon were determined using a proposed apparent power component referred to as the neutral-displacement power, whose square is the quadratic difference between the apparent powers of a subsystem, measured using two voltage reference points. The neutral-displacement power is a component of the apparent power, which is determined using the values of the zero-sequence voltages and the line currents in that subsystem. Expressions of the proposed power were derived using the Buchholz apparent power formulations. The validation of the derived expressions was checked in the laboratory and in a real-world electrical network, using a well-known commercial analyzer and a prototype developed by the authors., This research was funded by Universidad Politecnica de Valencia, under grant Primeros Proyectos de Investigacion (PAID-06-18), Vicerrectorado de Investigacion, Innovacion y Valencia.

[EN] This paper proposes the use of principal component analysis (PCA) for the state of health (SOH) diagnosis of a battery energy storage system (BESS) that is operating in a renewable energy laboratory located in Chocó, Colombia. The presented methodology allows the detection of false alarms during the operation of the BESS. The principal component analysis model is applied to a parameter set associated to the capacity, internal resistance and open circuit voltage of a battery energy storage system. The parameters are identified from experimental data collected daily. The PCA model retains the first 5 components that collect 80.25% of the total variability. During the test under real operation contidions, PCA diagnosed a degradation of state of health fastest than the comercial battery controller. A change in the charging modes lead to a battery recovery that was also monitored by the proposed algortihm, and control actions are proposed that lead the BESS to work in normal conditions., The authors would like to acknowledge the research project "Implementacion de un programa de desarrollo e investigacion de energias renovables en el departamento del Choco, BPIN 2013000100285 (in Spanish)" and the Universidad TecnolOgica del Choco (in Spanish). The authors would like to thank the anonymous reviewers as well as the editor for their valuable comments that have greatly improved the final version of the paper.

[EN] In rural areas or in isolated communities in developing countries it is increasingly common to install micro-renewable sources, such as photovoltaic (PV) systems, by residential consumers without access to the utility distribution network. The reliability of the supply provided by these stand-alone generators is a key issue when designing the PV system. The proper system sizing for a minimum level of reliability avoids unacceptable continuity of supply (undersized system) and unnecessary costs (oversized system). This paper presents a method for the accurate sizing of stand-alone photovoltaic (SAPV) residential generation systems for a pre-established reliability level. The proposed method is based on the application of a sequential random Monte Carlo simulation to the system model. Uncertainties of solar radiation, energy demand, and component failures are simultaneously considered. The results of the case study facilitate the sizing of the main energy elements (solar panels and battery) depending on the required level of reliability, taking into account the uncertainties that affect this type of facility. The analysis carried out demonstrates that deterministic designs of SAPV systems based on average demand and radiation values or the average number of consecutive cloudy days can lead to inadequate levels of continuity of supply., This work has been supported by research funds of the Universitat Politecnica de Valencia.

The World Health Organization (WHO) warns that the presence of magnetic fields due to the circulation of industrial frequency electrical currents may have repercussions on the health of living beings. Hence, it is crucially important that we are able to quantify these fields under the normal operating conditions of the facilities, both in their premises and in their surroundings, in order to take the appropriate corrective measures and assure the safety conditions imposed, in force, by regulations. For this purpose, CRMag&reg, software has been developed. Using the simplified Maxwell equations for low frequencies, CRMag&reg, calculates and represents the magnetic flux density (MFD) that electrical currents produce in the environment. Users can easily model electrical facilities through a friendly and simple data entry. MFDs calculated by CRMag&reg, have been validated in real facilities and laboratory tests. With this software, exposure levels can be studied in any hypothetical scenario, even in inaccessible zones. This allows designers to guarantee that legal limits (occupational, general population, or precautionary levels related to epidemiological studies) are fulfilled. A real case study has been described to show how the reconfiguration of conductors in a distribution transformer substation (DTS) allows significant reductions in MFD in some points outside the facility.

In this article, a comparative analysis is made between three statistical methods (Taguchi’s Orthogonal Array Testing method, Monte Carlo and Two-Point method) by integrating the uncertainty of primary sources of renewable generation in systems of electric power. The modeling of the Institute of Electrical and Electronics Engineers test system of 13 nodes is made by integrating the distributed generation with two different sources: wind and photovoltaic. For the simulation of wind power generation, the wind speed data is from El Cabo de la Vela in the Guajira department in Colombia and for the simulation of solar power generation, the solar radiation data is from Bogota city in Colombia. Once the system of 13 nodes is modeled and incorporated to the variability of primary resource and the load in each case; the load flow can be made by using the Matpower tool in Matlab for each one of the statistical methods proposed. The voltage, power generated, and power demanded data is recovered for each method to create comparison charts, establish the advantages, and disadvantages of each one in the analysis of the distribution of power systems with distributed generation. The main results are: the Taguchi’s Orthogonal Array Testing method improves its behavior if the number of levels is increased for each variable; more iterations in the Montecarlo method produce a greater precision of the probabilities; and the two-point method is a combination between the benefits of the deterministic and the probabilistic. Keywords: Distributed generation, Solar power, Wind power, Deterministic methods, Probabilistic methods

[EN] Eighty per cent of the people without access to electricity live in rural areas. Due to high investment costs in the grid, the solution to providing electricity to these people will mainly rely on the installation of islanded hybrid microgrids. Designers need to consider a variety of factors for the optimal design of hybrid microgrids. However, many of these criteria are qualitative or uncertain. This paper provides a novel methodology to assess the influence of such criteria in the design of a Hybrid Microgrid of Renewable Energy Sources (HRES). The method combines context analysis, literature review and the Analytic Network Process (ANP) through panels of experts and surveys. The methodology ranked the criteria and helped to design a HRES in an isolated Honduran rural community in the Mesoamerican Dry Corridor. The study presents a review and classification of the main criteria and energy technologies considered for the design of HRESs in rural communities. The most influential factors turned out to be the institutional support, the possible expansion of the grid to the community and the availability of local energy resources. Regarding energy technologies, photovoltaic and wind power ranked as the preferred followed by a biomass gasifier as backup., This is an extended and updated version of a paper originally presented at the 14th Conference on Sustainable Development of Energy, Water and Environment Systems (SDEWES 2019) held in Dubrovnik, Croatia over the period 1st to 6th October 2019 (denoted then as paper SDEWES2019.0565 - Choosing the best configuration for a hybrid microgrid of renewable energy sources by means of the analytic network process). This work was supported in part by the Spanish public administration under grant FPU2016/00962, by the regional public administration of Valencia under grant ACIF/2018/106, by the Food and Agriculture Organisation under the Letter of Agreement (PO number: 332412). And finally, by the Catedra de Transicion Energetica Urbana (Las Naves-UPV).

[EN] Technological advances in solar photovoltaic generation have considerably reduced equipment costs in new facilities, which is why this research focuses on determining the generation capacity through the use of existing infrastructure, solar radiation measurements and demand, to meet the demand for electrical energy through the application of forecasting models with the use of data analytics techniques and neural networks in order to determine the technical benefit in product quality, the reduction of demand peaks, mitigate transmission losses and provide greater reliability to the electrical system of the industry and utility., This work was supported by Universitat Politecnica de Valencia, Camino de Vera, s/n 46022 Valencia, Tel. (+34) 96 387 70 00 - Spain and the Universidad Politecnica Salesiana, Calle Vieja, Cuenca 010105 - Ecuador.

Spain is among the European countries with the greatest photovoltaic potential. During the first decade of the 2000s, Spain was a European leader in installed photovoltaic power and job creation in the renewable energy sector, strongly influenced by a very favorable regulatory framework and public subsidies at a time of economic growth. That situation would be completely reversed with the regulatory changes introduced in 2012 to combat the tari deficit and the instability in the electricity supply system, problems accentuated by the economic crisis. The main objective of this study is to present an exhaustive review of the legislative changes that have a ected photovoltaic energy in Spain. Using real data on electricity production and the remuneration of a typical photovoltaic plant, we show that there was a very significant fall in the economic returns that investors had come to expect, within a system that prioritized, first and foremost, the initial investment rather than the levels of electricity production. The changes to the legislative framework a ecting a typical 100 kWp photovoltaic (PV) facility that entered into service before 2007 provoked a significant decrease of 8.7% in expected revenues, calculated from real data of production and income. These economic losses can be even higher, with a drop in revenue of almost 25% if the entire period of the installation’s useful life is analyzed. Public support for renewable energy is important for its introduction into the electricity market, but so is regulatory stability that o ers investment security and predictability for maintaining investment and development in the renewable energy sector, Regional Government of Castilla y León under the “Support Program for Recognized Research Groups of Public Universities of Castilla y León” (ORDEN EDU/667/2019) and the Spanish Ministry of Science, Innovation, and Universities under the I + D + i state program “Challenges Research Projects” (Ref. RTI2018-098900-B-I00)

Electricity consumption patterns reveal energy demand behaviors and enable strategY implementation to increase efficiency using monitoring systems. However, incorrect patterns can be obtained when the time-series components of electricity demand are not considered. Hence, this research proposes a new method for handling time-series components that significantly improves the ability to obtain patterns and detect anomalies in electrical consumption profiles. Patterns are found using the proposed method and two widespread methods for handling the time-series components, in order to compare the results. Through this study, the conditions that electricity demand data must meet for making the time-series analysis useful are established. Finally, one year of real electricity consumption is analyzed for two different cases to evaluate the effect of time-series treatment in the detection of anomalies. The proposed method differentiates between periods of high or low energy demand, identifying contextual anomalies. The results indicate that it is possible to reduce time and effort involved in data analysis, and improve the reliability of monitoring systems, without adding complex procedures.

[EN] The electricity sector presents new challenges in the operation and planning of power systems, such as the forecast of power demand. This paper proposes a comprehensive approach for evaluating statistical methods and techniques of electric demand forecast. The proposed approach is based on smoothing methods, simple and multiple regressions, and ARIMA models, applied to two real university buildings from Ecuador and Spain. The results are analyzed by statistical metrics to assess their predictive capacity, and they indicate that the Holt-Winter and ARIMA methods have the best performance to forecast the electricity demand (ED).

[EN] Environmental concerns push governments to invest in renewable energy (RE). They are natural sources with a low carbon footprint and do not pollute locally. However, it is technically difficult to deploy high penetration of RE into the utility grid, due to the generation uncertainties and high installation costs, which are some of the most critical issues in RES use in this area. To address this issue, DC microgrids arise as a solution to integrate local distributed generation (DG) and storage, and to mitigate the issues related to AC/DC and DC/AC converters. Thanks to their main advantages for the power grid and energy consumers, microgrids have gained significant interest in recent years. By another side, the electric vehicles (EVs) market is expected to grow in the coming years, which represent a new load that must be properly managed to avoid grid issues. Thus, this paper discusses the operation of DC microgrid considering the introduction of EVs. A nonlinear control is presented, including the modeling of charging of EVs. The simulated DC microgrid includes solar PV, a battery, and a supercapacitor. Significant variations from PV generation were included to highlight the performance of the methodology. The results show that the voltage fluctuations are small, which provides the DC microgrid with the required voltage stability. Moreover, it has been demonstrated that DC microgrids can be integrated in isolated locations that are not connected to the main grid in view of the RESs and EVs., This work belongs to the project SIS.JCG.19.03 from Universidad de las Américas - Ecuador