Solar Energy Research Center Research Articles

Creating Cloud Segmentation Data Set Using Sky Images of Afyonkarahisar Region

The use of sky images in solar radiation intensity estimation has been one of the most studied topics in the literature since it improves the estimation results. The first step in processing sky images with image processing methods is to separate the pixels in the images as clouds or sky. This process is known as cloud segmentation in the literature. In this study, the sky is photographed using the sky imaging system installed at Afyon Kocatepe University Solar and Wind Energy Application and Research Center at times with different clouding characteristics and cloudiness rates in Afyonkarahisar Region. The photographs are divided into 25 parts, and small sky patterns are obtained. The pixels in the obtained sky patterns are manually segmented, and a cloud segmentation dataset is created for future studies. Since the resulting dataset contains high-resolution images and prelabeled data, it can be used to obtain more accurate results for the segmentation process and allows learning algorithms to learn faster. The dataset can be used by researchers in studies such as solar energy forecasting, meteorology, and weather forecasting, and the dataset in this paper will be shared with researchers upon request.

Hybrid System Modeling for Renewable Energy Sources

The main goal of this study is to design optimize and design a hybrid wind/PV solar power system to provide the premises of the Libyan Center for Solar Energy Research Center (LCSERS) with the required energy and investigates its technical and economic feasibility. HOMER simulation program is used to design the off-grid and assess the feasible solution and economic cost. The power systems are optimized based on the electricity load, climatic data sources, the economics of the power components, and other parameters in which the total Net Present Cost (NPC) must be minimized to select an economically feasible power system. Moreover, other parameters like a renewable fraction, capacity shortage, Cost of Energy (COE), and excess electricity, were also considered to check the technical capability. Sensitivity analysis of the most influential variables has been considered in four scenarios of capacity shortage. In the off-grid hybrid system, the best option is the fourth scenario, where the capacity shortage is 5% of the 60,385.6 kWh/yr electric load, peaking at 43.45 kw, because the lowest COE is 0.222 $ and the NPC is 168,173 $. The system consists of a 20 kW PV, one turbine of 25 kW, and 72 Hoppecke batteries of 1500.Ah each. The annual share of wind energy was 77%, and solar energy was 22.9%. The estimated excess of electricity was 58.3%.

Researchers’ perspective within responsible implementation with socio-technical approaches. An example from solar energy research centre in Chile

Adopting and contextualizing alternative policy approaches within any scientific discipline or context requires considering the multiple trans-boundary and trans-disciplinary characteristics of the different research and innovations paths and actors, disciplines, and global contexts. However, the implications of the stakeholder's perspective, which provides relevant outcomes for an integration of a technological system in a process where the actors' opinion forms a fundamental element of the methodology, are not sufficiently explored in recent literature. Therefore, the general question this paper deals with is how to incorporate alternative policy approaches - such as responsible research and innovation (RRI) - in the discipline of innovation in renewable energy in a non-European context. This is achieved using the implications of the stakeholder's perspective and the socio-technical transition approach as an evolution of innovation theories. Based on an analysis of the results of an original survey involving researchers, we examine the deployment of socio-technical approaches, obtaining the imprints of the responsible approaches in the strategies for innovation in the field of solar technology in Chile. The survey process, consistent in a series of semi-structured interviews and an online survey, was undertaken with 10 researchers (answering 9 with final N = 8) for the interview process and 78 targeted researchers (with 44 completing the surveys with average final N = 34) from the Chilean Solar Research Centre (SERC-Chile). Findings showed that systemic approaches were found prevailing when it comes to considerate social dimensions, which were described in terms of economic, institutional, and regulatory features. However, a growing deployment of socio-technical approaches was found in terms of the incorporation of new actors, engagement with the community, real appropriation, cultural integration, social licence to operate, and the valorisation of alternative approaches, such transdisciplinarity and co-construction of knowledge, resulting in implications for innovation and energy policy.

Procedure for the quantification of the degradation index of Photovoltaic Generators

A procedure is presented for the quantification of the degradation index of Photovoltaic Generators, based on the quantification of the operational losses inherent in the system, which allows maintaining the nominal operating conditions and by the warranty terms of the photovoltaic generator. A photovoltaic generator connected to the network with a nominal power of 7.5 kWp, installed in the Solar Energy Research Center of Santiago de Cuba, is used to evaluate and validate the procedure. The starting point is the mathematical model of the photovoltaic generator, then the operational losses of the photovoltaic generator are quantified and the mathematical model is adjusted to real conditions, through a polynomial adjustment. The results obtained show that the photovoltaic generator presents deviations in terms of the nominal power generation, because the operational losses are 7% with respect to the values ​​given by the manufacturer.

Fault Diagnostic Methodology for Grid-Connected Photovoltaic Systems

This research focuses on the design of a fault diagnosis methodology to contribute to the improvement of efficiency, maintainability and availability indicators of Grid-Connected Photovoltaic Systems. To achieve this, we start from the study of the mathematical model of the photovoltaic generator, then, a procedure is performed to quantify the operational losses of the photovoltaic generator and adjust the mathematical model of this to the real conditions of the system, through a polynomial adjustment. A real system of nominal power 7.5 kWp installed in the Solar Energy Research Center of the province of Santiago de Cuba is used to evaluate the proposed methodology. Based on the results obtained, the proposed approach is validated to demonstrate that it successfully supervises the system. The methodology was able to detect and identify 100% of the simulated failures and the tests carried out had a maximum false alarm rate of 0.22%, evidencing its capacity.

Economic Effect of Dust Particles on Photovoltaic Plant Production

The performance of photovoltaic panels decreases depending on the different factors to which they are subjected daily. One of the phenomena that most affects their energy production is dust deposition. This is particularly acute in desert climates, where the level of solar radiation is extreme. In this work, the effect of dust soiling is examined on the electricity generation of an experimental photovoltaic pilot plant, installed at the Solar Energy Research Center (CIESOL) at the University of Almería. An average reduction of 5% of the power of a photovoltaic plant due to dust contamination has been obtained, this data being used to simulate the economic effect in plants of 9 kWp and 1 and 50 MWp. The economic losses have been calculated, and are capable of being higher than 150,000 €/year in industrial plants of 50 MWp. A cleaning strategy has also been presented, which represents a substantial economic outlay over the years of plant operation.

Energy balance and performance assessment of PV systems installed at a positive-energy building (PEB) solar energy research centre

The energy balance and performance of all Photovoltaic (PV) systems installed at Fotovoltaica/UFSC solar energy laboratory (www.fotovoltaica.ufsc.br) in Florianópolis, Brazil (27° S; 48° W) were demonstrated over time, from Aug/2017 to Feb/2020. The laboratory was designed as a zero-energy building (ZEB) with PV systems installed on rooftops and façades, not maximising annual generation, but to achieve a compromise between aesthetic appeal and the energy output. Further PV systems were installed on the same site in the shapes of a carport, an electric bus (eBus) shelter and charging station, and different ground-mounted PV systems fixed at latitude tilt and single-axis tracking. Monthly analyses were carried out during the period, such as the solar irradiation availability on site, changes in the number of building occupants, the increase of installed capacity of PV systems, and energy generation and consumption. With 111 kWp, the total PV generation in the period could supply 148% of building energy needs and 97% of the Fotovoltaica/UFSC Laboratory (building plus eBus). However, there were some downtime events in certain PV systems due to R&D activities, which affected total energy production. If systems had operated at their optimal performance all the time, the laboratory could have generated 38% more energy, which means 134% of building plus eBus consumption. Therefore, the Fotovoltaica/UFSC Laboratory can be considered not only a ZEB, but a positive-energy building (PEB). The issues discussed in this paper apply not only to the particular case-study presented; they demonstrate the potential of BIPV in enabling energy positive buildings.

Development and Results from Application of PCM-Based Storage Tanks in a Solar Thermal Comfort System of an Institutional Building—A Case Study

An important element of a solar installation is the storage tank. When properly selected and operated, it can bring numerous benefits. The presented research relates to a project that is implemented at the Solar Energy Research Center of the University of Almeria in Spain. In order to improve the operation of the solar cooling and heating system of the Center, it was upgraded with two newly designed storage tanks filled with phase change materials (PCM). As a result of design works, commercial material S10 was selected for the accumulation of cold, and S46 for the accumulation of heat, in an amount of 85% and 15%, respectively. The article presents in detail the process of selecting the PCM material, designing the installation, experimental research, and exergy analysis. Individual tasks were carried out by research groups cooperating under the PCMSOL EUROPEAN PROJECT. Results of tests conducted on the constructed installation indicate that daily energy saving when using a solar chiller with PCM tanks amounts to 40% during the cooling season.

Optimal Model for Energy Management Strategy in Smart Building with Energy Storage Systems and Electric Vehicles

The aim of this work was to develop an optimal model for an energy management strategy in a real micro-grid, which involves a smart building, a photovoltaic system with storage, and a plug-in full electric vehicle. A controller based on a mathematical algorithm was the core of each strategy, which directly acted on a relay board managing the interconnection between the different elements comprising the micro-grid. The development of an optimization model involving binary variables required an efficient code that achieved solutions in a short time. The analyzed case-study corresponded to the solar energy research center (CIESOL) smart building, a bioclimatic building, that is located at the University of Almería (Spain), designated to research in renewable energies. Using the methodologies described in this work, the total cost of the smart building energy consumption was minimized by decreasing the power supplied from the grid, especially at peak hours. Highlighting the use of a simple model that provided better performance than the current state of the art methodologies. The optimal model for energy management strategy demonstrated the advantages of using classical optimization techniques to solve this specific optimization problem, compared to a rule-based controller. The linear modeling was capable of producing a simple algorithm with less code development and a reduction in the computational effort.

Evaluation of five various technologies of PV panels for Si production using Sahara sand silica source

The Sahara Solar Breeder (SSB) project is a joint Japanese-Algerian universities project to utilise the plentiful sand and solar energy in the Sahara desert. In the context of the SSB project, a Sahara Solar Energy Research Center was created at the University of Saida in Algeria. This centre is equipped with the meteorological monitoring system, the outside photovoltaic panels evaluation systems and the underground temperature measurement system. In this paper, a description of these types of equipment of this centre is presented. Also, some important data such as irradiance, temperature, energy, I–V curves and underground temperature measurement are presented and analysed. Finally, a discussion on the usefulness of these data for the SSB project will be discussed.
 Keywords: Sahara Solar Breeder project, solar irradiation, air temperature, underground temperature, PV module, I–V curve

Microservices and Machine Learning Algorithms for Adaptive Green Buildings

In recent years, the use of services for Open Systems development has consolidated and strengthened. Advances in the Service Science and Engineering (SSE) community, promoted by the reinforcement of Web Services and Semantic Web technologies and the presence of new Cloud computing techniques, such as the proliferation of microservices solutions, have allowed software architects to experiment and develop new ways of building open and adaptable computer systems at runtime. Home automation, intelligent buildings, robotics, graphical user interfaces are some of the social atmosphere environments suitable in which to apply certain innovative trends. This paper presents a schema for the adaptation of Dynamic Computer Systems (DCS) using interdisciplinary techniques on model-driven engineering, service engineering and soft computing. The proposal manages an orchestrated microservices schema for adapting component-based software architectural systems at runtime. This schema has been developed as a three-layer adaptive transformation process that is supported on a rule-based decision-making service implemented by means of Machine Learning (ML) algorithms. The experimental development was implemented in the Solar Energy Research Center (CIESOL) applying the proposed microservices schema for adapting home architectural atmosphere systems on Green Buildings.

Production of candle using solar thermal technology

A beautiful method based on solar energy to heat and melts the paraffin or bees wax has been developed to manufacture the candles by a linear fresnel reflecting solar concentrating collector and a bucket. The solar method has several advantages over the conventional candle making methods. This process is likely to generate the employment particularly in the rural complex areas in Bangladesh. This paper reports a few modifications made on the solar system to enhance the daily candle production. Candles are used by all section of the society for various purposes. Candles are made from paraffin or bees wax which is heated by Fresnel reflecting concentrating collector and method to make candles. A few other materials are also added to make good quality of candles. So far the older methods based on conventional fuels are in practice for candle production. These old manual methods are troublesome, unhygienic and needs continuous attendance and care during the melting of wax besides having other drawbacks. To overcome these drawbacks, a completely new system for candle production has been developed at Solar Energy Research Centre, Department of Physics, Jagannath University, Dhaka in Bangladesh. This system is based on solar energy which accumulates sufficient solar heat by Fresnel reflecting solar concentrating collector to melt wax for making candles. The new solar system, besides saving the fuels, has several advantages over the conventional candle making methods and also obviates the continuous attendance during wax melting. The reports about the solar system and there after the few modifications made over it to improve the candle production. It is shown that in the semi-cloudy day the efficiency of the solar candle production machine is 41.52% and 59.75% for single glazed and double glazed respectively. In the clear day the efficiency of the solar candle machine is 43.135% and 63.775% for single glazed and double glazed system respectively.

Industrial food chamber cooling and power system integrated with renewable energy as an example of power grid sustainability improvement

To meet the demands of a more resource-efficient future, innovative solutions are needed to generate sustainable energy conversion technologies and processes. Renewable energies offer a potent and viable solution to counter the effects of this problem since they are inexhaustible, pollution-free and affordable energy resources. This work compares different food chamber's cooling and power systems (FCP). The main purpose is to find a more favorable, design for South Spain from energy management and environmental perspective. The further comparison versus the conventional FCP system is made between the two possible technologies. Hence we investigate here the solar-assisted FCP system working with thermal energy in latent “phase change materials” (PCM) or sensible heat storage. We will provide new, advanced eco-design solutions using PCMs to store cold in industrial food chambers - those characterized by intense cooling demand. The energy performance of both compared systems has been assessed considering as a benchmark the energy infrastructure operating today in the Solar Energy Research Center (CIESOL). The results of the comparison demonstrated that only the solar-assisted PCM-based FCP system presents flexibility potential to handle its generation and demand according to local climate conditions, grid requirements and final refrigeration load.

Exergy analysis of a solar-assisted air-conditioning system: Case study in southern Spain

This work describes the exergy analysis applied to a single effect LiBr-H2O refrigeration system working together with a flat-plate-collector array. The main objective was to properly match the refrigeration cycle and the heat source. I used the experimental data collected from a solar-assisted air-conditioning system operating in the Solar Energy Research Center (Ciesol). The system employs a flat-plate-collector array, a hot-water single-effect LiBr-H2O absorption chiller, a cooling tower, two hot storage tanks, an auxiliary heater, as well as two chilled water storage tanks. The findings demonstrate that, from the exergetic point of view, I can achieve best results by supplying the absorption chiller with water within a temperature range of 70–80°C supporting it with the action of the chilled water storage tanks. As a result of this analysis, I recognized that there is great optimization potential for the control system of the studied system. At the same time I have ensured a proper balance between the refrigeration cycle and the heat source, making the solar-assisted air-conditioning system less energy intensive and safeguarding environmental protection.

Online 3-h forecasting of the power output from a BIPV system using satellite observations and ANN

Photovoltaic (PV) systems are the reference technology in the solar-based electricity generation market. Rapid changes in solar radiation can alter PV power output; for this reason, knowledge of future atmospheric scenarios helps system operators to control the PV production in advance, reducing the instabilities that the electrical grid may suffer in electricity integration, and managing the auto consumption power output. With this is mind, we present a model to forecast (up to 3 h ahead) the building integrated photovoltaic (BIPV) system’s power output, which is installed on the roof of the Solar Energy Research Center (CIESOL), Almería, Spain. The satellite images have been combined with Artificial Neural Networks (ANN) primarily to predict power output using the lowest number of input variables. The results, which can be considered highly satisfactory, demonstrate the ANN’s prediction accuracy with an normalized root mean square error for all sky conditions of less than 26%, and with practically no deviation. We demonstrate how beneficial matching of two already proven techniques can bring about spectacular results in energy generation prediction for the BIPV system.

Priority-based Energy Management Technique for Integration of Solar PV, Battery, and Fuel Cell Systems in an Autonomous DC Microgrid

Recent times have witnessed a significant positive trend in adoption of renewable energy sources and DC-based loads, which questions the efficiency and reliability of existing structure of electrical power system. DC microgrids are identified as potential solutions for addressing India's and other developing nations' rural electrification. This paper presents an energy management technique for isolated DC microgrids for academic readers, as well as provides an introduction to not-familiarized readers for understanding the common design challenges for implementing off-grid power systems. The principle of the proposed technique is based on law of energy conservation defined by the energy balance equation and is implemented by synthesizing the instantaneous current reference and hysteresis control with source priority, which was validated by rigors simulation studies in Matlab/Simulink. Furthermore, in order to demonstrate the modularity and scalability of the algorithm, it was tested on a prototype hardware test setup at the Solar Energy Research Centre, VIT University.

69th Southeastern Regional Meeting

The ACS Carolina-Piedmont Local Section will host the 69th Southeastern Regional Meeting (SERMACS) at the Sheraton Charlotte Hotel in Charlotte, N.C., on Nov. 7–11. The general chair is Jordan Poler of the University of North Carolina, Charlotte. The program chairs are Dan Rabinovich, also of UNC Charlotte, and David Brown of Davidson College. The meeting theme is “Humans to Hybrids” and will feature five plenary lectures, Diversity Day, an exposition, a graduate school fair, K–12 programming, multiple poster sessions and workshops, and more than 30 invited symposia. SERMACS 2017 is being held concurrently with the 2017 Solar Energy Research Center Conference and the North Carolina Photochemistry Symposium. Technical program. With more than 1,400 submitted abstracts, the technical program will cover all the traditional areas of chemistry as well as bioinorganic chemistry, polymeric nanomaterials, biological mass spectrometry, organofluorine chemistry, analytical neurochemistry, microwave chemistry, heterocyclic carbenes, ionic liquids, and the chemistry of

Guest Editorial

Guest Editorial

A Comparison of Energy Consumption Prediction Models Based on Neural Networks of a Bioclimatic Building

Energy consumption has been increasing steadily due to globalization and industrialization. Studies have shown that buildings are responsible for the biggest proportion of energy consumption; for example in European Union countries, energy consumption in buildings represents around 40% of the total energy consumption. In order to control energy consumption in buildings, different policies have been proposed, from utilizing bioclimatic architectures to the use of predictive models within control approaches. There are mainly three groups of predictive models including engineering, statistical and artificial intelligence models. Nowadays, artificial intelligence models such as neural networks and support vector machines have also been proposed because of their high potential capabilities of performing accurate nonlinear mappings between inputs and outputs in real environments which are not free of noise. The main objective of this paper is to compare a neural network model which was designed utilizing statistical and analytical methods, with a group of neural network models designed benefiting from a multi objective genetic algorithm. Moreover, the neural network models were compared to a naïve autoregressive baseline model. The models are intended to predict electric power demand at the Solar Energy Research Center (Centro de Investigación en Energía SOLar or CIESOL in Spanish) bioclimatic building located at the University of Almeria, Spain. Experimental results show that the models obtained from the multi objective genetic algorithm (MOGA) perform comparably to the model obtained through a statistical and analytical approach, but they use only 0.8% of data samples and have lower model complexity.

Initial stabilization of a statistical sample of forty-four monocrystalline photovoltaic modules

The paper discusses the methodology and presents the results of the stabilization process for a statistic of forty-four monocrystalline silicon photovoltaic (PV) modules. The modules have been exposed to real sunlight in three different sessions and have been measured prior exposure and after each exposure session using a pulsed sun simulator. The first session of exposure shows the highest power loss in the modules, with an average 1.8%. During this initial stabilization step, 17 modules had a loss in the maximum power above 2% (registered losses from 2.1% to a maximum of 2.8%). The overall power losses are in the range 1.7–3.4%, with an average loss of 2.4% for the considered module population. Visual inspection and the analysis of the measured I–V curves revealed some imperfections in the modules. However, those imperfections do not impact on the actual performance at the present time. After the stabilization, the modules have been installed in the Northeast Solar Energy Research Center (NSERC) array, located at Brookhaven National Laboratory. The array will be used to analyze module degradation in the Northeast environmental conditions and to study the grid impact of utility-level PV plants.