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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. [ABSTRACT FROM AUTHOR]

Nowadays, energy decarbonization due to integrating renewable energy sources presents important challenges to overcome. The intermittent nature of photovoltaic systems reduces power quality by producing voltage variations and frequency deviations in electrical system networks, especially in weak and isolated distribution systems in developing countries. This paper presents a power smoothing method for improving the low-pass filter and moving average for grid-connected photovoltaic systems. This novel method includes state-of-charge monitoring control of the supercapacitor's energy storage system to reduce the fluctuations of photovoltaic power at the point of common coupling. A case study for a microgrid in a high-altitude city in Ecuador is presented with exhaustive laboratory tests using real data. This research aims to improve energy power quality in electrical distribution systems to cope with the growth of renewable penetration. The results demonstrate significant power quality and stability improvements achieved through the proposed method. For instance, the power smoothing method effectively reduced power fluctuations by 16.7% with the low-pass filter, 14.05% with the ramp-rate filter, and 9.7% with the moving average filter. [ABSTRACT FROM AUTHOR]

In recent years, photovoltaic energy production has experienced significant progress, being integrated into the grid through large-scale distributed systems. The intermittent nature of solar irradiance coupled with the presence of photovoltaic failures causes fluctuations that could compromise the quality and stability of electrical grid. This paper presents a novel photovoltaic power smoothing method in a combination with moving averages and ramp rate to reduce fluctuations with hybrid storage systems (supercapacitors/batteries), the main novelty involves optimizing the number of charging/discharging cycles under PV failures. To achieve this goal, a photovoltaic failure detection method is proposed that uses machine learning to process big data by monitoring the behavior of photovoltaic. The experiments have been done under controlled conditions in the microgrid laboratory of the University of Cuenca. The results show the reduction of the supercapacitor operation with respect to other power smoothing methods. Moreover, the monitoring system is capable of detecting a failure in photovoltaic systems with a root mean squared error of 0.66 and the computational effort is reduced using the new smoothing technique. In this sense, the initial execution time is 4 times lower compared to the moving average method. [Display omitted] [ABSTRACT FROM AUTHOR]

Renewable energy sources have been widely developed in grid-connected systems. However, a challenge to overcome is the random characteristic of renewable resources such as solar irradiance, photovoltaic power fluctuations caused by cloud movement could cause instability of the utility grid. To solve this drawback, several authors have proposed various power smoothing methods for photovoltaic systems using supercapacitors. Nevertheless, sizing optimization and operability of the supercapacitor has not been properly studied. Forecasting power fluctuations is an important strategy to avoid the unnecessary operation of the supercapacitor in certain cases. In this paper, a novel power smoothing method (predictor – corrector) using supercapacitors for a grid-connected photovoltaic system is proposed, the method consists of two stages, prediction and correction. The main novelty of the new method is the use a simple k-means algorithm application model in the cycle estimation stage for supercapacitors, with the aim of selecting representative data of power fluctuations and supercapacitor charge/discharge cycles. Then, for the correction stage, the novel proposed method uses ramp rate algorithms to generate the reference signal to control the state of charge of the supercapacitor. The validation of the new proposed method has been done through exhaustive laboratory experiments under different cloudiness events. The results show that the energy losses when applying the new method are lower with respect to the moving average and ramp rate methods. Furthermore, the number of technical violations is reduced, demonstrating the feasibility of the proposed method to ensure successful mitigation of PV power fluctuations. • A novel method of power smoothing (corrective predictor) based on the moving average and ramp rate techniques is presented. • The method allows to optimize the operability of the supercapacitor by forecasting PV power fluctuations. • Extensive experiments show that energy losses are reduced by 16% than with the traditional ramp rate method. • A learning model is proposed to generate representative data of the power fluctuations and charge/discharge cycles of SC. • The new method is validated by exhaustive laboratory experiments in real conditions. [ABSTRACT FROM AUTHOR]

Photovoltaic and hydrokinetic systems are increasing their penetration in electrical distribution systems. This leads to problems of power fluctuations due to the intermittence of renewable sources that could compromise the stability and quality of the power grid. To address this issue, this paper presents a feasibility study of three power smoothing methods for a photovoltaic-hydrokinetic system using laboratory equipment to optimally replicate the real behavior of this type of hybrid system. The proposed algorithms are based on a hybrid storage system with supercapacitors and lithium-ion batteries, several analyzes are presented based on technical and economic parameters. The results demonstrate the feasibility of power smoothing methods for real systems, the comparison between the algorithms highlights the characteristics of the Enhanced Linear Exponential Smoothing Method, reducing the energy cost and regulating the point of common coupling voltage. Moreover, the sensitivity studies show that the energy exchange with the utility grid is affected according to the variations in the capacity of the batteries and the response to power smoothing can decrease or improve depending on the size of the supercapacitors. [Display omitted] • Photovoltaic and hydrokinetic systems in electrical distribution systems. • Problems of power fluctuations due to the intermittence of renewable sources. • Stability and quality of the power grid. • A hybrid storage system with supercapacitors and lithium-ion batteries. • Enhanced Linear Exponential Smoothing Method. [ABSTRACT FROM AUTHOR]

This paper analyzes the impact on an off-grid renewable hybrid system composed of photovoltaic energy, hydrokinetic turbines, batteries and biomass gasifiers, using various types of biomass in order to determine the optimal configuration of the system located in southern Ecuador. Three types of energy dispatch, charge cycle, load following and combined cycle have been proposed with the objective of determining new patterns on the behavior of sources with respect to electric demand. The biomass used as an energy resource produces electricity through a biomass gasifier that feeds a microturbine. Considering the types of biomass consumed by the gasifier, the items such as net present cost and cost of energy have been analyzed for the different types of control. Sensitivity studies indicate the increase in the cost of the system by increasing the minimum state of charge in the batteries. However, this increase reduces biomass consumption and CO 2 emissions. Finally, the variation of the cost in the components influences the total cost of the system, being the fuel and the photovoltaic system the systems that have the highest sensitivity, the results have shown that the renewable system is able to supply the demand without violating any norm. • Off-grid renewable hybrid system including biomass gasifiers in Ecuador. • Three types of energy dispatch, charge cycle, load following and combined cycle. • Electricity through a biomass gasifier that feeds a microturbine. • Net present cost and cost of energy have been analyzed. • The biomass and the photovoltaic systems have the highest sensitivity. [ABSTRACT FROM AUTHOR]

• Effects of different storage technologies on a hybrid renewable energy system. • Photovoltaic system, hydrokinetic system and diesel generator. • Five types of storage are considered. • The study is done in the HOMER Pro software. • Sensitivity analysis and variation of the minimum state of charge. This study analyzes the effects of different storage technologies on a hybrid renewable energy system composed of photovoltaic, hydrokinetic and diesel generator. Five types of storage are considered: lead acid, lithium ion, vanadium redox flow, hydrogen and hydrogen-vanadium redox flow. The analysis is mainly based on indicators such as Net Present Cost, Cost of Energy, unmet load and CO 2 emissions. HOMER software has been used in this paper with time-sampling models and sensitivity studies to determine the effects of each storage system. The results show that the vanadium redox flow batteries system presents a lower net present cost and cost of energy. The sensitivity analysis shows that the vanadium redox flow system presents a greater variation of the minimum state of charge (SOC) in relation to the net present cost. However, the system composed of hydrogen and fuel cell present the lowest variation. The two proposed strategies for energy control show the same performance. However, when using the Load Tracking Control, the variation of the minimum state of charge with respect to the net cost is greater than the variation obtained when applying the Load Cycle Control. [ABSTRACT FROM AUTHOR]