Renewable Energy Monitoring Research Articles

Development and Evaluation of an Arduino-Based Data Logging System Integrated with Microsoft Excel for Monitoring On-Grid Photovoltaic Systems

This paper presents the development and evaluation of an Arduino-based data logging system integrated with Microsoft Excel for monitoring on-grid photovoltaic (PV) systems. The system combines open-source hardware and software to enable real-time data acquisition, logging, and analysis of key performance metrics such as solar irradiance, temperature, voltage, and current levels. Leveraging the versatility of Arduino microcontrollers and the accessibility ofMicrosoft Excel, the proposed system offers a cost-effective and user-friendly solution for PV system monitoring. The integration of the MS Data Streamer add-in for Excel facilitates seamless data logging and visualization, empowering PV system owners, researchers, and practitioners with actionable insights for optimizing system performance and contributing to a sustainable energy future. Experimental validation of the system demonstrates its effectiveness in accurately measuring and logging sensor data, highlighting its potential for widespread adoption in renewable energy monitoring applications.

Smart IoT Approach for Renewable Energy Monitoring System

Numerous factories, commercial establishments, and private residences require an uninterrupted power supply. The primary source of this supply is often the grid. Industries, businesses, and residential areas needing 24-hour power inevitably require backup systems to ensure continuous operation of essential electronic devices. Typically, diesel generators are employed as backup power sources, utilizing diesel oil to generate electrical energy. While this approach has been successfully employed for many years, it carries a significant drawback. Diesel oil falls under the category of non-renewable energy sources, unable to be regenerated and requiring an extensive period for renewal. Furthermore, diesel generators emit substantial amounts of carbon dioxide into the atmosphere, contributing to air pollution in the vicinity. To address these challenges, a prototype implementing a smart Internet of Things (IoT) approach has been developed. This study introduces the utilization of an Arduino Nano microcontroller, allowing users to control their power supply source, choosing between the grid, solar panels, or water generation. This control process is facilitated through a smartphone application, granting users the ability to manage it remotely, enhancing monitoring capabilities. The system incorporates two environmentally friendly power sources: solar panels and a mini water turbine, both considered safe for ecological balance. The outcomes of this research demonstrate that users can effectively utilize the generated electricity supply for approximately 12 hours, thereby reducing reliance on the grid and harnessing the potential of renewable energy sources.

MULTI-MODAL GEOSPATIAL AND THEMATIC DATA TO FOSTER GREEN DEAL APPLICATIONS

Abstract. The Urban Data Space for Green Deal - USAGE - project is founded by the European Union (EU) to support the green transition of cities. Within USAGE, a series of geospatial, thematic and other datasets have been newly acquired or created to test and evaluate solutions (i) to better understand issues and trends on how our planet and its climate are changing and (ii) to address the role that humans play in these changes, e.g., with behaviour adaptation and mitigation actions. The paper aims to provide some relevant datasets collected in two urban areas, reporting processing methodologies and applications of analysis-ready and decision-ready geospatial data. The shared data are unique urban datasets due to their resolutions and sensors type and could boost progresses of geospatial procedures to create and use data useful for climate change adaptation, renewable energy monitoring and management, etc.

Lora Based Renewable Energy Monitoring System

The world economy is growing rapidly, and global energy demands are predicted to increase even more in the future. Energy is expected to get more expensive, in turn affecting the economic development. Energy demand can be reduced by employing efficient Energy Management Systems (EMS). The development of wireless communication technology in the last decade has made wireless communication protocols exclusive in the domain of sensor networks. Existing trends have encouraged the use and implementation of many radio-based protocols due to fact that short-range the radio transmission is inexpensive, secure and easily available. Therefore, the objective of this project is to design and implement a LoRa based Wireless Sensor Network for conventional energy monitoring system capable of intelligently monitoring parameters such as wind and solar.

Forecasting solar photosynthetic photon flux density under cloud cover effects: novel predictive model using convolutional neural network integrated with long short-term memory network

Forecast models of solar radiation incorporating cloud effects are useful tools to evaluate the impact of stochastic behaviour of cloud movement, real-time integration of photovoltaic energy in power grids, skin cancer and eye disease risk minimisation through solar ultraviolet (UV) index prediction and bio-photosynthetic processes through the modelling of solar photosynthetic photon flux density (PPFD). This research has developed deep learning hybrid model (i.e., CNN-LSTM) to factor in role of cloud effects integrating the merits of convolutional neural networks with long short-term memory networks to forecast near real-time (i.e., 5-min) PPFD in a sub-tropical region Queensland, Australia. The prescribed CLSTM model is trained with real-time sky images that depict stochastic cloud movements captured through a total sky imager (TSI-440) utilising advanced sky image segmentation to reveal cloud chromatic features into their statistical values, and to purposely factor in the cloud variation to optimise the CLSTM model. The model, with its competing algorithms (i.e., CNN, LSTM, deep neural network, extreme learning machine and multivariate adaptive regression spline), are trained with 17 distinct cloud cover inputs considering the chromaticity of red, blue, thin, and opaque cloud statistics, supplemented by solar zenith angle (SZA) to predict short-term PPFD. The models developed with cloud inputs yield accurate results, outperforming the SZA-based models while the best testing performance is recorded by the objective method (i.e., CLSTM) tested over a 7-day measurement period. Specifically, CLSTM yields a testing performance with correlation coefficient r = 0.92, root mean square error RMSE = 210.31 μ mol of photons m−2 s−1, mean absolute error MAE = 150.24 μ mol of photons m−2 s−1, including a relative error of RRMSE = 24.92% MAPE = 38.01%, and Nash Sutcliffe’s coefficient ENS = 0.85, and Legate and McCabe’s Index LM = 0.68 using cloud cover in addition to the SZA as an input. The study shows the importance of cloud inclusion in forecasting solar radiation and evaluating the risk with practical implications in monitoring solar energy, greenhouses and high-value agricultural operations affected by stochastic behaviour of clouds. Additional methodological refinements such as retraining the CLSTM model for hourly and seasonal time scales may aid in the promotion of agricultural crop farming and environmental risk evaluation applications such as predicting the solar UV index and direct normal solar irradiance for renewable energy monitoring systems.

재생에너지 기반 Hybrid 전력망의 주파수 추정

The frequency is one of the very important factors in power grid. It is essential that the frequency of a power grid should be maintained close to its nominal frequency. Recently, under the leadership of the government, interest in the expansion and distribution of renewable energy sources (RES) is increasing. Therefore, technology for stable operation in preparation for grid connection variability of RES is required. There is growing interest in the rapid measurement and accurate estimation of grid frequency for renewable energy monitoring and stability, using GPS based time-synchronous PMU (Phasor Measurement Unit) for wide area blackout prevention. In this paper, FRDWT (Fast Recursive Discrete Wavelet Transform), GCDFT (Gain Compensator Discrete Fourier Transform) and PAD (Phase Angle Difference) are selected as optimal frequency measurement methods for frequency estimation of hybrid power grid based on renewable energy. This study is to evaluate the performance of the proposed technique using domestic 345kV power grid model data by EMTP-RV S/W.

변전소의 재생에너지 모니터링을 위한 동기페이저 시스템 구축

변전소의 재생에너지 모니터링을 위한 동기페이저 시스템 구축

On harvesting residual thermal energy from photovoltaic module back surface

In Brazil, photovoltaic (PV) plants have been increasing their importance in the country’s electricity matrix mainly because its annual irradiation can achieve up to 2400 kWh/m2. On the other hand, the high temperature levels along the PV modules impose a negative effect on the electrical efficiency. For instance, in a PV plant in the Brazilian Northeast region, the PV modules temperature can reach 70 °C in a sunny day while the ambient temperature is around 30 °C. This difference can be used to generate heat as a residual thermal energy, but, in general, it is wasted to the surrounding environment. In other context, currently, there is an increasing development of Renewable Energy Monitoring Systems (REMS) for monitoring PV plants. In this paper, we present an experimental study on the potential of harvesting PV-module residual thermal energy using thermoelectric generators (TEGs) for powering low-power wireless sensor for REMS to monitor the whole PV plant itself. From the experimental results, 3.3 V is achieved from stepping up about 220 mV harvested from monthly average temperature difference of 15 °C. In addition, it is shown that 528 mW can be harvested every burst of 30 ms, which is enough to power low-power devices.

Hybrid Renewable Energy Monitoring System

Hybrid Renewable Energy Monitoring System

A Design for a Data and Information Service to Address the Knowledge Needs of the Water-Energy-Food (W-E-F) Nexus and Strategies to Facilitate Its Implementation

Food security is essential to sustain human societies. Food production is often limited by scarcities in one or more of these resources. An integrated Water-Energy-Food (W-E-F) Nexus planning and management approach promises improved resource efficiencies, new business opportunities, more coherent resource and environmental policies, and economies of scale for the data and information services underpinning better decision-making. This paper distils discussions on data and information from four regional workshops held as part of a Future Earth W-E-F Nexus Cluster project. The workshops aimed to enhance integrated management through better governance; collecting, analyzing, and communicating data and information; and integrating both for better planning and decision-making. Opportunities for enhancing food, energy, and water sustainability decisions through a data and information system are the focus of this paper. Data from each sector are needed to provide a full overview of the W-E-F Nexus. Relevant datasets and potential satellite products for water include water quality, watershed use change, snow and glacier monitoring, wetlands monitoring, water reservoir mapping, irrigation water management, aquifer monitoring, and measurements of critical water variables like precipitation, soil moisture, and evapotranspiration. Food and agricultural products include land use change maps, crop type mapping, crop acreage mapping, early warning indicators, yield assessment, precision agriculture, land degradation, clear-cut and burnt area maps, and soil moisture. Energy products include geological mapping, habitat assessment maps, logistic planning support, mine waste monitoring, renewable energy monitoring and mapping, and crop yield estimates for biofuels. However, specific priorities for W-E-F Nexus data products await further guidance from the W-E-F Nexus community. Data and information, along with modern technologies, can play a role in facilitating the paradigm shifts that benefit the W-E-F Nexus: explicitly assessing environmental services, meeting the growing urban food demand, valuing water and other resources used to produce food and energy for export, promoting resource use efficiency through integrated planning and management, and strengthening links between the W-E-F Nexus and the appropriate Sustainable Development Goals (SDGs). This paper explores the hypothesis that a properly defined system that integrates and consolidates W-E-F Nexus information and data could support the implementation of a W-E-F Nexus approach.

IoT embedded linux system based on Raspberry Pi applied to real-time cloud monitoring of a decentralized photovoltaic plant

In this project we propose, describe, implement and test the Renewable Energy Monitoring System (REMS), a new concept on data acquisition and transmission systems (DATS) applied to real-time cloud monitoring of a decentralized photovoltaic (PV) plant. To achieve this latest design, we went through various systems projects alongside the evolution of technology. From this practical experience and in agreement with Brazil’s policy of diversifying the electricity generation matrix, our proposal focuses on a multi-user remote system using Raspberry Pi and Internet of Things (IoT) concept. REMS is capable of sensing and modifying monitoring process management via remote firmware update through the developed Analog/Digital Converter Embedded System (ADCES) as well as communicating with a personal developed cloud server profile via RPi Embedded Linux System (ELS), thus not requiring a dedicated PC. The measured variables are PV voltage and current, ambient and PV module temperature, solar irradiance, and relative humidity.

Modeling baseline conditions of ecological indicators: Marine renewable energy environmental monitoring

Ecological indicators are often collected to detect and monitor environmental change. Statistical models are used to estimate natural variability, pre-existing trends, and environmental predictors of baseline indicator conditions. Establishing standard models for baseline characterization is critical to the effective design and implementation of environmental monitoring programs. An anthropogenic activity that requires monitoring is the development of Marine Renewable Energy sites. Currently, there are no standards for the analysis of environmental monitoring data for these development sites. Marine Renewable Energy monitoring data are used as a case study to develop and apply a model evaluation to establish best practices for characterizing baseline ecological indicator data. We examined a range of models, including six generalized regression models, four time series models, and three nonparametric models. Because monitoring data are not always normally distributed, we evaluated model ability to characterize normal and non-normal data using hydroacoustic metrics that serve as proxies for ecological indicator data. The nonparametric support vector regression and random forest models, and parametric state-space time series models generally were the most accurate in interpolating the normal metric data. Support vector regression and state-space models best interpolated the non-normally distributed data. If parametric results are preferred, then state-space models are the most robust for baseline characterization. Evaluation of a wide range of models provides a comprehensive characterization of the case study data, and highlights advantages of models rarely used in Marine Renewable Energy environmental monitoring. Our model findings are relevant for any ecological indicator data with similar properties, and the evaluation approach is applicable to any monitoring program.

Implementation and performance evaluation of advance metering infrastructure for Borneo-Wide Power Grid

In this paper, a supervisory computer network for Borneo-Wide Power Grid system have been proposed and implemented, which includes a renewable power generation and advanced metering infrastructure. An Internet-based communication network running on multi-protocol label switching (MPLS) has been implemented for a smart power grid, with the addition of the renewable energy monitoring system. The centralized supervisory control and data acquisition systems (SCADA) are replaced by a wide area monitoring system(WAMS) comprising of a phasor measurement unit (PMU). The implemented communication network used advanced metering infrastructure that operates on worldwide interoperability for microwave access (WiMAX), wireless fidelity (Wi-Fi) and low power Wi-Fi, which are proposed for the distribution systems of Sarawak Energy. The proposed wide area network (WAN) is simulated using OPNET Modeler and the results are compared with the existing WAN used by Sarawak Energy.

The effect of turbulence on noise emissions from a micro-scale horizontal axis wind turbine

Building mounted micro wind turbines are capable of contributing a significant proportion to a building’s energy needs in the right circumstances. However the introduction of this technology in built-up areas has been limited due to a number of issues such as low wind speeds, high turbulence and noise issues. This paper presents the results of an investigation carried out to assess the effect of turbulence on the noise emissions from a micro-scale horizontal axis wind turbine (HAWT). Two experiments were devised in order to investigate the noise emissions from a Micro Wind Turbine (MWT) mounted on one of the University of Nottingham’s renewable energy monitoring platform. The first experiment measured the one-third octave sound levels to quantify the sound spectrum for the MWT. The second experiment assessed the effects of turbulence on the noise emissions from the same MWT. This paper describes in detail both of these methodologies. Results found that a doubling of the turbulence intensity from 0.3 to 0.6 resulted in an almost doubling of the sound energy level.