DIE-Artículos

URI permanente para esta colecciónhttps://hdl.handle.net/10953/230

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Mitigation of carbon footprint with 100% renewable energy system by 2050: The case of Galapagos islands
(Elsevier, 2022-01-17) Arévalo, Paul; Cano-Ortega, Antonio; Jurado-Melguizo, Francisco
In this paper, a technical-economic study of the 100% renewable energy sources in the Galapagos islands is done. Historical consumption data for 2011-2020 have been considered to forecast the load curve. To achieve this goal, the load is forecasting by Nonlinear autoregressive exogenous neuronal network model for 2030 and 2050. The study focuses on supplying the energy demand of the islands with renewable sources, analyzing possible scenarios based on the current electricity system. The methodology studies the capacity of renewable sources to balance supply and demand through dispatch simulations using the EnergyPLAN software. The results show energy flows, costs and long-term energy balances (2050), with 100% renewable energy from several wind and photovoltaic combinations. Moreover, The precision of the demand forecast was 98.12% with a mean square error of 0.013%. The total annual cost decreases while the capacities of the renewable sources increase to a certain point of equilibrium. As salient features of the developed approach, various sensitivity analyzes are presented that allow understanding the uncertainties, scope and limitations of the proposed models.
Neural network predictive control in renewable systems (HKT-PV) for delivered power smoothing
(Elsevier, 2024-03-12) Cano-Ortega, Antonio; Arévalo, Paul; Jurado-Melguizo, Francisco
The reduction of power fluctuations from intermittent renewable sources is one of the most pressing challenges today. Recent research has shown that prediction and control mechanisms, when combined with energy storage systems, significantly contribute to improving these techniques. However, substantial research gaps still exist regarding the optimization of energy storage system operability. This article introduces an innovative power smoothing method based on neural network predictive control, in conjunction with the exponential moving average method. The proposed approach encompasses the ability to substantially reduce energy fluctuations, optimize battery state of charge, and mitigate ramp rates, thereby preventing deep discharges that shorten battery lifespan. Furthermore, the control system's primary objective is to optimize energy exchange with the grid, surpassing the performance offered by other conventional power smoothing methods. The control system excels in optimizing energy exchange within the network, surpassing conventional methods. Extensive testing on the University of Cuenca microgrid reveals a consistently more stable and higher battery charge compared to conventional methods. Numerical results for underscore the method's effectiveness with a fluctuation suppression rate of 30.78% compared to 34.85% (low pass filter) and 36.22% (ramp rate) methods respectively. The enhanced voltage profiles at the common coupling point ensure the delivery of high-quality and stable power.
Large-scale integration of renewable energies by 2050 through demand prediction with ANFIS, Ecuador case study
(Elsevier, 2023-10-21) Arévalo, Paul; Cano-Ortega, Antonio; Jurado-Melguizo, Francisco
The growing reliance on hydroelectric power and the risk of future droughts pose significant challenges for power systems, especially in developing countries. To address these challenges, comprehensive long-term energy planning is essential. This paper proposes an optimized electrical system for 2050, using Ecuador as a case study. For forecasting electricity demand, a Neuro-Fuzzy Adaptive Inference System is employed, utilizing real historical data. Subsequently, the EnergyPlan software constructs a long-term energy consumption model, exploring three scenarios based on Ecuador's energy potential. The first scenario represents a 'business as usual' approach, mirroring the current trend in the Ecuadorian electricity system. In contrast to the second scenario, it encompasses a broader range of renewable sources, including offshore wind, pumped storage, biomass, and geothermal energy. The third scenario extends the second one by incorporating demand response systems, such as vehicle-to-grid and hydrogen-to-grid technologies. In terms of novelty, this study highlights the innovative use of the Neuro-Fuzzy Adaptive Inference System for demand forecasting, along with a comprehensive exploration of multiple scenarios to optimize the electrical system. Research findings indicate that the integration of these new renewable energy sources not only reduces electricity import costs but also ensures surplus electricity production. Consequently, it is anticipated that the 2050 electricity system will reduce its dependence on hydroelectric energy while adopting photovoltaic and wind energy with penetration rates of 65%, 11.2%, and 9%, respectively. This transition will be facilitated by a pumped storage system with a 28% penetration rate and enhanced connectivity with neighboring countries, enabling the seamless integration of electric and hydrogen vehicles
A novel experimental method of power smoothing using supercapacitors and hydrogen for hybrid system PV/HKT
(Elsevier, 2023-09-01) Arévalo, Paul; Cano-Ortega, Antonio; Jurado-Melguizo, Francisco
Nowadays, the intermittent nature of renewable energy systems represents one of the most significant challenges in isolated systems, where power fluctuations can cause instability and compromise energy quality. Although hydrogen systems and supercapacitors have been widely studied in the literature, they have been less investigated as participating agents, and further research is needed in this area. This paper presents a novel power smoothing method for an off-grid system that consist of photovoltaic panels, hydrokinetic turbines, fuel cells and a hybrid storage system (hydrogen and supercapacitors). Two well-known power smoothing methods were used to generate the power signals for the new method. The main novelty is based on controlling the state of charge of the supercapacitor using the fuel cell, for the reduction of power fluctuations and efficiently hydrogen produce. First, the capacity of the renewable system is optimized using the HOMER Pro software. Then, the optimized system was used to simulate the new method proposed in Matlab-Simulink. Finally, to validate the results obtained, extensive experiments were conducted in a laboratory test bench. The results showed that the power fluctuations index was reduced by up to 50 % in the electrolyzer and 20 % in the fuel cell, with a levelized cost of electricity of 0.19 USD/kWh. Therefore, the application of the new proposed energy smoothing method significantly improves hydrogen production.
Extraction and characterization of Cucumis melon seeds (Muskmelon seed oil) biodiesel and studying its blends impact on performance, combustion, and emission characteristics in an internal combustion engine
(Elsevier BV, 2024-05-28) Al-Bawwat, Ala’a K.; Gomaa, Mohamed R.; Cano-Ortega, Antonio; Jurado-Melguizo, Francisco; Alsbou, Eid Musa
This study examines the performance, combustion, and emissions characteristics of a single-cylinder internal combustion diesel engine when fueled with a blend of diesel and biodiesel derived from muskmelon seeds. The kinematic viscosity of the extracted muskmelon seed oil was 6.1 cSt at 40 ◦C, which is higher than the kinematic viscosity of petroleum diesel of 2.6 cSt. Muskmelon biodiesel was further analyzed using thin-layer chromatography (TLC) and high-voltage separator tests. A comparison of the fuel properties of muskmelon biodiesel with conventional diesel fuel revealed that muskmelon biodiesel could be used alone or in a diesel– biodiesel blend to fuel compression diesel engines. In this study, muskmelon seed biodiesel was blended with diesel fuel at proportions of 10 %, 20 %, and 50 % (BD10, BD20, and BD50, respectively). At a relatively low rotational speed of 1200 rpm, the brake thermal efficiency (BTE) of the engine operated with BD10 and BD20 blends were 36.1 % and 36.0 %, respectively, while the brake-specific fuel consumption (BSFC) of the two blends were 0.260 kg/kWh, and 0.262 kg/kWh, respectively. These values closely resemble those typically observed in diesel fuel engines. Indeed, the average BTE of the BD20 blend was only 3.24 % less than the average BTE of diesel fuel. Diesel fuel generates less NOx and SO2 emissions compared to biodiesel blends: BD100 emitted the most NOx pollution of all fuels tested. In addition, BD10 released significantly more SO2 emissions compared to the other fuels tested. However, the BD20 blend outperformed all other blends in terms of CO, NOx, and SO2 emissions at high engine speeds. The only exception was H2S emissions, which were higher than BD50 and BD100. BD20 also exhibited significantly reduced CO emissions compared to diesel fuel, while BD10 emitted significantly more CO emissions than the other biodiesel blends. Our findings revealed that BD20 exhibited the best engine performance and lower emissions among all fuels tested. In other words, BD20 is the ideal fuel blend for use in diesel engines and does not require any alterations to the engine. Muskmelon waste seeds represent a non-edible waste stream that can be exploited in the production of biodiesel fuel, allowing for the upcycling of a potentially problematic thermochemical conversion feedstock. This potentially valuable use for waste muskmelon seeds in the energy sector could address the wastefulness associated with this particular waste stream.
Rechargeable Li-Ion Batteries, Nanocomposite Materials and Applications
(MDPI AG, 2024-11-26) El Afia, Sara; Cano-Ortega, Antonio; Arévalo, Paul; Jurado-Melguizo, Francisco
Lithium-ion batteries (LIBs) are pivotal in a wide range of applications, including consumer electronics, electric vehicles, and stationary energy storage systems. The broader adoption of LIBs hinges on advancements in their safety, cost-effectiveness, cycle life, energy density, and rate capability. While traditional LIBs already benefit from composite materials in components such as the cathode, anode, and separator, the integration of nanocomposite materials presents significant potential for enhancing these properties. Nanocomposites, including carbon–oxide, polymer–oxide, and siliconbased variants, are engineered to optimize key performance metrics, such as electrical conductivity, structural stability, capacity, and charging/discharging efficiency. Recent research has focused on refining these composites to overcome existing limitations in energy density and cycle life, thus paving the way for the next generation of LIB technologies. Despite these advancements, challenges related to high production costs and scalability remain substantial barriers to the widespread commercial deployment of nanocomposite-enhanced LIBs. Addressing these challenges is essential for realizing the full potential of these advanced materials, thereby driving significant improvements in the performance and practical applications of LIBs across various industries.
Tracing harmonic distortion and voltage unbalance in secondary radial distribution networks with photovoltaic uncertainties by an iterative multiphase harmonic load flow
(Elsevier, 2020-03-18) de-la-Casa-Hernández, Jesús; Ruiz-Rodríguez, Francisco Javier; Jurado-Melguizo, Francisco; Sánchez-Sutil, Francisco
Secondary radial distribution networks (SRDNs) have been increasingly affected by the uncertainties of harmonic sources associated with photovoltaic (PV) systems. The quantitative assessment of uncertainty propagation causing harmonic distortion and voltage unbalance can be successively handled by probabilistic or affine formulations of harmonic load flows (HLFs). This study developed a general analytical technique (GAT) for solving iterative multiphase HLFs in SRDNs with PV uncertainties. This technique merges the point-estimate method (PEM) and complex affine arithmetic (AA), combined with Legendre series approximation (LGSA). It also models the input correlation. One advantage of this GAT is that the iterative harmonic penetration (IHP) method, modeled for HLF, accounts for the interaction of background harmonic voltage with the PV harmonic current. The first prerequisite was evidently an uncertainty model for PV harmonic current. This paper presents the results for a real unbalanced three-phase SRDN and compares them with those obtained with the Monte-Carlo simulation (MCS). These confirmed the accuracy of GAT as well as its lower computational cost. The numerical results obtained showed that the GAT outperformed the incomplete GAT (IGAT), which is solely based on PEM and Cornish-Fisher expansion, thanks to the ability of AA to bound the outputs used in the LGSA.
Survey and assessment of technical and economic features for the provision of frequency control services by household-prosumers
(Elsevier, 2020-07-04) López-Valdivia, Andrés; Ogayar, Blas; de-la-Casa-Hernández, Jesús; Sánchez-Sutil, Francisco
This paper surveys and assesses the technical and economic features that are crucial in the provision of frequency control services from aggregated household-prosumers. A framework is presented that allows policy makers to accurately assess these services. Furthermore, the role of the service aggregator in the provision from prosumers is outlined, something that highlights the value of the new distributed framework. Also discussed are the key features within the overall context of the design variables in various European countries, which are representative of different synchronous areas. This finding implies that from a regulatory point of view, important efforts are being made to harmonise the different network codes in the investigated countries. Moreover, this research compares the impact of techno-economic design variables on the financial incomes of providing frequency control services. For this purpose, we used a stochastic model of input variables that involves the statistical analysis of time series. A probabilistic method based on Monte Carlo simulation allowed the assessment of the revenues. The outcomes indicate that the RES penetration in each country together with the ratios of the power availability band and of the power availability band activation, and their associated prices are crucial factors that influence the resulting profitability.
Optimal sizing and management strategy for PV household-prosumers with self-consumption/sufficiency enhancement and provision of frequency containment reserve
(Elsevier, 2020-06-15) de-la-Casa-Hernández, Jesús; Sánchez-Sutil, Francisco; Muñoz-Rodríguez, Francisco José; Baier, Carlos
This study provides a methodology to assess the techno-economic performance of photovoltaic householdprosumers that jointly provide self-consumption/sufficiency enhancement (SCSE) and frequency containment reserve (FCR). It thus addresses the following issues: (i) battery aging; (ii) supercapacitors joined to batteries building hybrid storage systems; (iii) management strategies of SCSE and charge level in energy storage systems; (iv) an integrated system with a 1-ms simulation step and high-resolution inputs. The methodology was applied to one Spanish household-prosumer. The study compared three charge-level management strategies by using different technical and economic performance indicators and concluded that the deadband recovery was the best. Moreover, the best techno-economic indicators were achieved by broadening the storage capacity band of unrestricted operation for SCSE (30–90%). Regarding the prosumer sizing, the optimal converter-battery configuration was determined so as to minimize the total energy supply cost. Long-term performance confirmed that when FCR provision was added to the SCSE, profitability increased up to 14.01%, with a relatively low impact on battery aging. A sensitivity analysis guaranteed a cost reduction of 3.68% for the prosumer energy and of 16% for the storage system life cycle at the optimal hybrid storage sizing. This sizing involved a 1% supercapacitor hybridization and a time constant of 150 s for power splitting.
Smart regulation and efficiency energy system for street lighting with LoRa LPWAN
(Elsevier, 2021-03-21) Sánchez-Sutil, Francisco; Cano-Ortega, Antonio
Public lighting installations represent an important consumption in smart cities. Therefore, it is necessary to implement energy saving solutions. In this sense, the use of smart meters (SM) provides a fundamental tool in the development of energy saving systems, which require monitoring and control in real time. The information flow generated requires an efficient communication system. Long Range (LoRa) protocol sends information across long distances with very low energy consumption. For this reason, it is especially interesting to implement in street light (SL) installations. This research designs a control, monitoring and energy saving system for SLs composed of three devices: Gateway for Street Lights System (GWSLS), Operating and Monitoring Device for Street Lights (OMDSL), and Illumination Level Device (ILD). Street Lights Regulation (SLR) algorithm was developed to dynamically control the lighting level. Lighting levels are selected using the Artificial Bee Colony (ABC) optimization algorithm, which is fast, reliable and accurate. Measured data is sent to the GWSLS gateway by the OMDSLs installed with the LoRa network and uploaded to the cloud using Firebase.
Smart plug for monitoring and controlling electrical devices with a wireless communication system integrated in a LoRaWAN
(Elsevier, 2022-10-03) Sánchez-Sutil, Francisco; Cano-Ortega, Antonio
The application of Long-Range Wide-Area Network (LoRaWAN) in Internet of Things (IoT) monitoring applications has grown exponentially in recent history because of its cost-effectiveness, robustness to interference, low power consumption, large coverage for connectivity and licensed-free frequency band due to its adaptive data rate. In this research, an IoT-ready smart plug (SP) based on the LoRaWAN technology has been developed. Smart Plug for the LoRaWAN (SPLW) has been designed to measure and control the energy consumed by electrical loads in households, office buildings, hotels, hospitals, etc. with the purpose of achieving energy efficiency. SPLW monitors several electrical parameters (i.e., voltage, current, power, energy) and sends this information in real time to the cloud. A LoRa Bee associated to an Arduino Nano microcontroller and a PZEM-004 t sensor collects the necessary information in order to send it to the LoRaWAN gateway. This data is sent to The Things of Network (TTN) service, created specifically for LoRaWAN IoT applications. TTN offers numerous integrations, e.g. Ubidots has been used for WebApp and smartphone monitoring. Tests have been performed in six households to demonstrate the performance of SPLW in terms of its functionality, simplicity, reliability and cost.
Optimization of battery/supercapacitor-based photovoltaic household-prosumers providing self-consumption and frequency containment reserve as influenced by temporal data granularity
(Elsevier, 2021-02-01) de-la-Casa-Hernández, Jesús; Gómez-Gonzalez, Manuel; Sánchez-Sutil, Francisco; Jurado-Melguizo, Francisco
Service complementarity between a frequency containment reserve and PV self-consumption can increase incomes for household-prosumers. Moreover, battery/supercapacitor-based hybrid energy storage systems (HESSs) play a major role. Fitting power and energy management improve HESS performance, and therefore increase the profitability of the asset. Furthermore, component sizing is critical. To achieve both targets, we developed a hybrid meta-heuristic optimization algorithm that deals with the management strategies and sizing. Accordingly, a four-dimensional, non-linear, non-convex, and mixed-integer optimization problem was formulated, and a cost function was minimized by combining the Haar wavelet (WT) transform and the teaching-learning-based optimization (TLBO) method. The algorithm has a flexible design, which is adapted in terms of a number of discrete states to suit input profiles defined according to different time discretizations. The effectiveness of the algorithm was proved by using different data granularity for a PV prosumer in Spain in various service scenarios. The simulations performed in this study reflected both technical and economic impacts. The results suggest that for optimization purposes, high-resolution data should be used to consider the full range of input fluctuations. However, these results largely depend on the service scenario setup. Indeed, in some scenarios, accurate results were obtained by using coarse-grained data, which entailed a lower computational burden. In contrast, in other scenarios, it was preferable to use data with a higher resolution. The optimal combination of services significantly increased the profitability of the asset.
Power Factor Compensation Using Teaching Learning Based Optimization and Monitoring System by Cloud Data Logger
(MDPI, 2019-05-10) Cano-Ortega, Antonio; Sánchez-Sutil, Francisco; de-la-Casa-Hernández, Jesús
The main objective of this paper is to compensate power factor using teaching learning based optimization (TLBO), determine the capacitor bank optimization (CBO) algorithm, and monitor a system in real-time using cloud data logging (CDL). Implemented Power Factor Compensation and Monitoring System (PFCMS) calculates the optimal capacitor combination to improve power factor of the installation by measure of voltage, current, and active power. CBO algorithm determines the best solution of capacitor values to install, by applying TLBO in di erent phases of the algorithm. Electrical variables acquired by the sensors and the variables calculated are stored in CDL using Google Sheets (GS) to monitor and analyse the installation by means of a TLBO algorithm implemented in PFCMS, that optimizes the compensation power factor of installation and determining which capacitors are connected in real time. Moreover, the optimization of the power factor in facilities means economic and energy savings, as well as the improvement of the quality of the operation of the installation.
A Platform for Analysing Huge Amounts of Data from Households, Photovoltaics, and Electrical Vehicles: From Data to Information
(MDPI, 2022-12-01) Cano-Ortega, Antonio; García-Cumbreras, Miguel Ángel; Sánchez-Sutil, Francisco; de-la-Casa-Hernández, Jesús
Analytics is an essential procedure to acquire knowledge and support applications for determining electricity consumption in smart homes. Electricity variables measured by the smart meter (SM) produce a significant amount of data on consumers, making the data sets very sizable and the analytics complex. Data mining and emerging cloud computing technologies make collecting, processing, and analysing the so-called big data possible. The monitoring and visualization of information aid in personalizing applications that benefit both homeowners and researchers in analysing consumer profiles. This paper presents a smart meter for household (SMH) to obtain load profiles and a new platform that allows the innovative analysis of captured Internet of Things data from smart homes, photovoltaics, and electrical vehicles. We propose the use of cloud systems to enable data-based services and address the challenges of complexities and resource demands for online and offline data processing, storage, and classification analysis. The requirements and system design components are discussed.
Design and Implementation of a Smart Energy Meter Using a LoRa Network in Real Time
(MDPI, 2021-12-17) Sánchez-Sutil, Francisco; Cano-Ortega, Antonio; de-la-Casa-Hernández, Jesús
Nowadays, the development, implementation and deployment of smart meters (SMs) is increasing in importance, and its expansion is exponential. The use of SMs in electrical engineering covers a multitude of applications ranging from real-time monitoring to the study of load profiles in homes. The use of wireless technologies has helped this development. Various problems arise in the implementation of SMs, such as coverage, locations without Internet access, etc. LoRa (long range) technology has great coverage and equipment with low power consumption that allows the installation of SMs in all types of locations, including those without Internet access. The objective of this research is to create an SM network under the LoRa specification that solves the problems presented by other wireless networks. For this purpose, a gateway for residential electricity metering networks using LoRa (GREMNL) and an electrical variable measuring device for households using LoRa (EVMDHL) have been created, which allow the development of SM networks with large coverage and low consumption.
Design and Testing of a Power Analyzer Monitor and Programming Device in Industries with a LoRA LPWAN Network
(MDPI, 2021-02-11) Sánchez-Sutil, Francisco; Cano-Ortega, Antonio
Electrical installations represent an important part of the industry. In this sense, knowing the state of the electrical installation in real time through the readings of the installed power analyzers is of vital importance. For this purpose, the RS485 bus can be used, which most electrical installations already have. An alternative to the bus wiring and its distance limitation is the use of low-power wide area networks (LPWAN). The long range (LoRa) protocol is ideal for industries due to its low-power consumption and coverage of up to 10 km. In this research, a device is developed to control all the reading and programming functions of a power analyzer and to integrate the device into the LoRa LPWAN network. The power analyzer monitor and programming device (PAMPD) is inexpensive and small enough to be installed in electrical panels, together with the power analyzer, without additional wiring. The information collected is available in the cloud in real time, allowing a multitude of analysis be run and optimization in real time. The results support high efficiency in information transmission with average information loss rate of 3% and a low average transmission time of 30 ms.
Smart Public Lighting Control and Measurement System Using LoRa Network
(MDPI, 2020-01-09) Sánchez-Sutil, Francisco; Cano-Ortega, Antonio
The installation of smart meters in smart cities to monitor streetlights (SLs) provides easy access to measurements of electrical variables and lighting levels, which improves the operation of installation. The use of smart meters in cities requires temporary high-resolution data to improve the energy e ciency (EE) of SLs. Long range (LoRa) is an ideal wireless protocol for use in smart cities due to its low energy consumption, secure communications, and long range indoors and outdoors. For this purpose, we developed a low-cost new system and successfully evaluated it by developing three devices, namely the measure and control device for street lights (MCDSL), lighting level measurement device (LLMD) and gateway LoRa network (GWLN), based on the Arduino open-source electronic platform. This paper describes the hardware and software design and its implementation. Further, an algorithm has been developed to enhance the energy e ciency of public lights using MCDSL, the energy e ciency for street lights (EESL) algorithm, that use the illumination level measured on the same set of SLs with a dynamic control, which assumed di erent lighting levels throughout the night, and adjusted luminous flux based on the tra c intensity of pedestrians. It sends the acquired data through the LoRa low-power wide-area-network (LPWAN) to the cloud.
Modeling of PV Module and DC/DC Converter Assembly for the Analysis of Induced Transient Response Due to Nearby Lightning Strike
(MDPI, 2021-01-08) Formisano, Alessandro; de-la-Casa-Hernández, Jesús; Petrarca, Carlo; Sánchez-Sutil, Francisco
Photovoltaic (PV) systems are subject to nearby lightning strikes that can contribute to extremely high induced overvoltage transients. Recently, the authors introduced a 3D semianalytical method to study the electromagnetic transients caused by these strikes in a PV module. In the present paper we develop an improved model of the PV module that: (a) takes into account high-frequency effects by modelling capacitive and inductive couplings; (b) considers the electrical insulation characteristics of the module; (c) includes the connection to a DC/DC converter. The whole process involves three major steps, i.e., the magnetic-field computation, the evaluation of both common-mode- and differential-mode-induced voltages across the PV module, and the use of the calculated voltages as input to a lumped equivalent circuit of the PV module connected to the DC/DC converter. In such a framework, the influence of the PV operating condition on the resulting electrical stresses is assessed; moreover, the relevance or insignificance of some parameters, such as the module insulation or the frame material, is demonstrated. Finally, results show that the induced overvoltage are highly dependent both on the grounding of the conducting parts and on the external conditions such as lightning current waveforms and lightning channel (LC) geometry.
Energy Management Model for a Remote Microgrid Based on Demand-Side Energy Control
(MDPI, 2023-12-28) Benavides, Darío; Arévalo, Paul; Cano-Ortega, Antonio; Sánchez-Sutil, Francisco; Villa-Ávila, Edisson
The internet of things is undergoing rapid expansion, transforming diverse industries by facilitating device connectivity and supporting advanced applications. In the domain of energy production, internet of things holds substantial promise for streamlining processes and enhancing efficiency. This research introduces a comprehensive monitoring and energy management model tailored for the University of Cuenca’s microgrid system, employing internet of things and ThingSpeak as pivotal technologies. The proposed approach capitalizes on intelligent environments and employs ThingSpeak as a robust platform for presenting and analyzing data. Through the integration of internet of things devices and sensors, the photovoltaic system’s parameters, including solar radiation and temperature, are monitored in real time. The collected data undergo analysis using sophisticated models and are presented visually through ThingSpeak, facilitating effective energy management and decision making. The developed monitoring system underwent rigorous testing in a laboratory microgrid setup, where the photovoltaic system is interconnected with other generation and storage systems, as well as the electrical grid. This seamless integration enhances visibility and control over the microgrid’s energy production. The results attest to the successful implementation of the monitoring system, highlighting its efficacy in improving the supervision, automation, and analysis of daily energy production. By leveraging internet of things technologies and ThingSpeak, stakeholders gain access to real-time data, enabling them to analyze performance trends and optimize energy resources. This research underscores the practical application of internet of things in enhancing the monitoring and management of energy systems with tangible benefits for stakeholders involved.
Performance Optimization LoRa Network by Artificial Bee Colony Algorithm to Determination of the Load Profiles in Dwellings
(MDPI, 2020-01-21) Cano-Ortega, Antonio; Sánchez-Sutil, Francisco
This paper presents a system to improve the performance of the Long Range (LoRa) network using an algorithm derived from the artificial bee colony (ABC), which obtains a minimum packet lost rate (PLR) in the LoRa network and allows to more accurately determine load profiles of dwellings, with smaller a time measurement and less data transmission. The developed algorithm calculates the configuration parameters of the LoRa network, monitoring in real time the data traffc, and is implemented in gateway LoRa network monitor (GLNM). Intelligent measurement equipment has been developed to determine the dwelling load profiles. This energy measurement device for dwelling (EMDD) measures the variables and consumption of electricity in each home with measurement times that can be configured. This research also develops the GLNM gateway, which monitors and receives data from the EMDDs installed and uploads them to the cloud using Firebase. This developed system allows to perform demand forecasting studies, analysis of home consumption, optimization of electricity tariffs, etc., applied to smart grids.

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