Solar Facility Research Articles

Thermal Energy Storage Using Hybrid Nanofluid Phase Change Material (PCM) Based on Waste Sludge Incorp Rated ZnO/α-Fe2O3.

Renewable solar energy storage facilities are attracting scientists' attention since they can overcome the key issues affecting the shortage of energy. A nanofluid phase change material (PCM) is introduced as a new sort of PCM is settled by suspending small proportions of nanoparticles in melting paraffin. ZnO/α-Fe2O3 nanocrystals were prepared by a simple co-precipitation route and ultrasonically dispersed in the paraffin to be a nanofluid-PCM. The behaviors of the ZnO/α-Fe2O3 nanocrystals were verified by X-ray diffraction (XRD) analysis, and the average particle size and the morphology of the nanoparticles were explored by transmission electron microscopy (TEM). For the object of industrial ecology concept, aluminum-based waste derived from water-works plants alum sludge (AS) is dried and augmented with the ZnO/α-Fe2O3 nanocrystals as a source of multimetals such as aluminum to the composite, and it is named AS-ZnO/α-Fe2O3. The melting and freezing cycles were checked to evaluate the PCM at different weight proportions of AS-ZnO/α-Fe2O3 nanocrystals, which confirmed that their presence enhanced the heat transfer rate of paraffin. The nanofluids with AS-ZnO/α-Fe2O3 nanoparticles revealed good stability in melting paraffin. Additionally, the melting and freezing cycles of nanofluid-PCM (PCM- ZnO/α-Fe2O3 nanoparticles) were significantly superior upon supplementing ZnO/α-Fe2O3 nanoparticles. Nanofluid-PCM contained the AS-ZnO/α-Fe2O3 nanocrystals in the range of 0.25, 0.5, 1.0, and 1.5 wt%. The results showed that 1.0 wt% AS-ZnO/α-Fe2O3 nanocrystals contained in the nanofluid-PCM could enhance the performance with 93% with a heat gained reached 47 kJ.

Fuzzy-random robust flexible programming on sustainable closed-loop renewable energy supply chain

Photovoltaic (PV) systems have become a widely accepted method of harnessing solar energy in recent decades. In addition to the growing use of renewable energies are being used due to the depletion of fossil fuel supplies and the corresponding pollution generated by these non-renewable fuels. Since PV systems possess a limited lifespan, and are predicted to eventually become outdated, sustainability and recycling that should be incorporated into the structure of the solar system supply chain. The management of high and low moisture waste (H&LMW) is crucial due to its harmful environmental impacts, including sludge waste, carbon dioxide (CO2), and acid compounds. This study explores the integration of research on H&LMW and PV systems, focusing on energy generation from biogas, incineration power plants, and PV systems. In this respect, we propose a multi-objective mixed-integer programming model to construct a sustainable closed-loop renewable energy supply chain by considering forward and reverse flow through solar, biogas, and incineration power plants. Reducing CO2 with power-to-gas technology is thought to be an environmentally friendly approach. A novel fuzzy-random robust flexible programming approach based on Me measure is proposed, which overcomes the limitations of addressing the uncertainty of the parameters. Next, in order to solve the suggested multi-objective model, a novel strategy known as utility function based multi-choice conic goal programming is presented. A case study explores the sustainable challenges of a closed-loop renewable energy supply network in India’s renewable energy sector. The experimental results indicate that 9 candidate locations are selected for solar wafer production facilities, while 10, 9, and 15 locations are chosen for solar cell production, module manufacturing, and solar power plants, respectively. A sensitivity analysis reveals that the proportion of air pollution limitations possessively rises from 100% to 80% corresponding to the aggregate cost increases from INR 8021452 to INR 8056542.

Update on the ASTRI High-Temperature Solar Sodium Facility

The Australian Solar Thermal Research Institute (ASTRI) has been developing technologies designed to collect and store solar energy at high-temperature to drive more efficient power cycles, or for providing heat to high-temperature thermal industrial processes. ASTRI is pursuing two alternative pathways: one is based on the use of liquid sodium as a heat transfer fluid, and the other is based on the use of solid fluidous particles. The current work describes ASTRI’s completion of the design and construction of a nominal 1MW high-temperature solar sodium test facility, which will be operational at CSIRO’s solar field at the National Energy Centre in Newcastle, Australia, by 2024.

Suitability Index for the Placement of Solar Plants Based on Inequality Measurements and on Satellite Images

The selection of a certain location for the placement of a solar facility depends on the solar resource availability, which is generally assessed though exceedance probabilities. However, the choice of the specific exceedance probability is arbitrary and the assessment will be different depending on the choice taken. Furthermore, exceedance probabilities do not reflect seasonal variability, which affects radiation availability. Therefore, in this work we present a new index, the suitability index based on Theil (SIT), which allows us to assess with a single value the degree of suitability of a site for installing a solar plant. Obtained from the Theil index, it considers the availability of the resource and its seasonal variability, based as it is on the proportion of the given radiation in each month. As we will see, the new index is clearly more sensitive to the amount of radiation expressed in terms of the 50th percentile than to the variability, as given by the interquartile range. This is a quality to be pondered since scarcity of radiation will always be a greater disadvantage for a solar installation than high variability. The results obtained in the study, grounded in the application of satellite images, show that the index adequately reflects the radiation characteristics in the study area. The territory is broken into areas associated with such characteristics through a cluster analysis, so that geographical and economic elements can be considered when choosing the final location for a solar installation. Furthermore, the new index may include the effects of energy storage during the months in which a certain demand is exceeded.

Validation of a 3D Local-Scale Adaptive Solar Radiation Model by Using Pyranometer Measurements and a High-Resolution Digital Elevation Model.

The result of the multidisciplinary collaboration of researchers from different areas of knowledge to validate a solar radiation model is presented. The MAPsol is a 3D local-scale adaptive solar radiation model that allows us to estimate direct, diffuse, and reflected irradiance for clear sky conditions. The model includes the adaptation of the mesh to complex orography and albedo, and considers the shadows cast by the terrain and buildings. The surface mesh generation is based on surface refinement, smoothing and parameterization techniques and allows the generation of high-quality adapted meshes with a reasonable number of elements. Another key aspect of the paper is the generation of a high-resolution digital elevation model (DEM). This high-resolution DEM is constructed from LiDAR data, and its resolution is two times more accurate than the publicly available DEMs. The validation process uses direct and global solar irradiance data obtained from pyranometers at the University of Salamanca located in an urban area affected by systematic shading from nearby buildings. This work provides an efficient protocol for studying solar resources, with particular emphasis on areas of complex orography and dense buildings where shadows can potentially make solar energy production facilities less efficient.

Impact of the climate change on the site suitability for solar farms: Case study of Cameroon

It is widely agreed that in the coming decade the Earth's climate will undergo significant changes, which will considerably affect the ecosystems and populations in various ways. In this study, the climate change impact on the suitability of the recommended sustainable locations for siting solar PV facilities in Cameroon is assessed based on the downscaled climate projections and the geographic information system integrated with a multi-criteria decision-making technique. Spatial tools are used to identify the potential locations by extracting the constraint areas. The analytical hierarchy process is applied to weigh the criteria (climate, orography and vegetation) and calculate a land suitability index (LSI) to evaluate potential sites. Based on the Coordinated Regional Downscaling Experiment, the change in LSI towards the end of century is assessed considering two climate change scenarios, one optimistic and the other pessimistic. Results show that 13.7% of the study area is suitable for installing utility-size PV power plants with the highly suitable areas located mainly in the Extreme-North region of Cameroon. Under the RCP2.6 and RCP8.5 climate change scenarios, a very slight change in LSI (−5% ≤ LSI ≤ 5%) is projected for most of the potential sites across the country.

Comparative Life-Cycle Assessment of Electricity-Generation Technologies: West Texas Case Study

This comparison of five power plants in West Texas is intended to provide various decision-makers and stakeholders with a holistic picture of the life-cycle environmental impacts associated with these power plants. A key contribution of this analysis is that we assumed all power plants generate the same amount of electricity over a 30-year life, taking a 500 MW combined-cycle gas turbine (CCGT) plant as a benchmark. Also, in two cases, we added battery storage to wind and solar PV facilities to render them nearly as dispatchable as the CCGT. We included the entire supply chain supporting electricity generation, which encompassed raw material sourcing, processing, manufacturing, operations, and product end of life, also called “cradle to grave”. We report on 18 environmental impacts using ReCiPe midpoint (H) impact assessment. The supply chains are global, and impacts are felt differently by host communities across the world. The results can help stakeholders identify hotspots across numerous supply chains with the highest environmental impacts. We discuss some remedial measures and challenges to inform future analysis by the research community.

Design optimization of Solar Power Inverter using the GRA Method

Among the most crucial components of a solar-powered system is an inverter. It is an apparatus that transforms the direct current (DC) produced by solar panels into the alternating current (AC) required by the electric system. Flashing hybrid solar inverter is the best solar inverter for home which offers the following features: - Its operating voltage is between 100-290V. - It has 700 VA of power. 5kW solar inverter price budget starts at $2,000 for excellent single-phase devices and $1,000 for existing datasets (like Sungrow) (eg Fronius or SMA). The most popular size, 5kW, can accommodate arrays up to 6.6kW in power. A solar charge controller can only function effectively during the day, because when the ultraviolet irradiance is strong and, the system's Voltage rating must reach the inverter at a moderate DC voltage level in order for it to function. Your photovoltaic systems and inverter operate at night power will be off. The inverter doesn't run overnight because it doesn't want to draw electricity. Instead, it rises again in the morning when the sun shines. Your home and solar system are connected to the utility grid. By switching to solar PV-based power generation from the airport's current conventional energy source, the carbon footprint of the facility can be decreased. Power distribution solar PV power facilities may be built in regions that are required to be broad and free of obstructions around runways. Based on first year operating data, the current study seeks to evaluate the operational efficiency of a 12 MWp solar-powered airport at Cochin Airport Limited (CIAL), India. With the aid of the most well-liked PV simulation programmes, such as Deterioration in quality and Solar Gis, the plant effectiveness is precisely rendered. The software's performance metrics were discovered to perfectly resemble the observed values. Economic and ecological studies of Kochi airports powered by solar energy attest to its efficiency in lowering carbon footprint, resulting in an airport with almost no emissions that is clean, green, and sustainable.Gray correlation analysis is widely used to measure the degree of relationship between sequences through the gray correlation coefficient. Gray relational analysis has been used by many researchers to optimize control parameters with multiple responses through gray relational grading. The fundamental tenet of the GRA approach is that the chosen option must have the "greatest degree of Green relation" to the positive-ideal solution and the "minimum of grey relation" to the negative-best answer. A technique for determining whether or not variables are connected and how much they are correlated is called grey correlation coefficient analysis. The primary method of determining these curves' geometric similarity is by the construction of characteristic series curves. Type of Solar Module, No of modules per string, No of string, No of inverters and No of transformers Short circuit current (ISC), Open circuit voltage (VOC), Operating Temperature and Dimension. Stress Management in Healthcare Institutions in Work overload is got the first rank whereas is the Overtime is having the lowest rank.

A multi‐objective low‐voltage ride‐through control strategy for three‐phase grid‐interfaced solar power plant during symmetrical and asymmetrical faults†

SummaryThis paper presents a low‐voltage ride‐through capability (LVRT) solution for the photovoltaic (PV) grid‐connected inverters compliant with the Indian grid code. The LVRT strategy ensures the uninterrupted connection of solar energy to the grid, safeguarding against system shutdown in the event of various grid faults. During this time, the inverter supplies reactive power to the utility grid, effectively utilizing converter capacity and aiding the restoration of the voltage profile at the point of common coupling (PCC). The system used in this study involves a two‐stage grid‐interfaced solar PV setup with a boost converter that tracks the maximum power point (MPP) and an inverter that maintains the DC‐link voltage. A modified dynamic braking chopper control (MDBCC) has also been implemented to minimize the DC‐link voltage oscillations during asymmetrical faults while simultaneously injecting the sinusoidal current into the grid. To improve grid synchronization in the presence of unbalanced abnormalities, a modified dual second‐order generalized integrator phase‐locked loop (DSOGI‐PLL) has been employed to track the grid voltage angle precisely. Furthermore, a new control system has been developed in conjunction with the LVRT approach. The suggested system is simulated in MATLAB/Simulink to validate the efficacy of the LVRT control method during symmetrical and asymmetrical faults using a 100‐kW two‐stage grid‐interfaced solar PV facility. Finally, for experimental validation, a real‐time test bench (RTS) OPAL‐RT 4510 is used to implement the setup with its control, and the practical results are compared with the simulation outcomes.

Developing a Deep Learning and Reliable Optimization Techniques for Solar Photovoltaic Power Prediction

Due to its significant solar energy generation, solar PV power facilities have recently been utilized. Although PV power stations are highly preferable, the state’s fundamental drawback is that its output current qualities are unpredictable. Consequently, to ensure a balance and complete functioning, it would be crucial to build systems that allow accurate future projections of solar PV production in the short or medium term. Research postulates a strategy for using deep learning to estimate the short-term electricity generated by solar photovoltaic facilities. This study offers a novel method for predicting photovoltaic systems output power utilizing a Hybrid Deep Neural Network framework, making significant advancements in the field of deep learning applications to transmission system prediction issues. CNN and LSTM are combined in the postulated HDNN paradigm. Traditional deep learning techniques are employed in the initial stage. The effectiveness assessments of these techniques are instead presented in greater detail after they have been trained using firefly optimization techniques. The method with the highest reliability is chosen out of all the techniques used in previous research. Deep learning and power efficiency create a combination that appears to have a successful future, predominantlyin improving sustainable management and the digitalization of the electrical sector.

A Study on Predicting Installation Scale of Photovoltaic Panels and Hydrogen Fuel Storage Facilities to Achieve Net Zero Carbon Emissions Exploiting Idle Sites of Military Bases

In this study, the scale of renewable photovoltaic(PV) panels and hydrogen fuel storage facilities required to achieve “net zero carbon emissions” in military facilities were predicted based on actual electricity consumption. It was set up to expect the appropriate installation size of PV panel and hydrogen fuel storage facility for achieving carbon neutrality, limited to the electricity consumption in the public sector, including national defense and social security administration in Yeongcheon. The experimental results of this paper are largely composed of two parts. First, representative meteorological factors were considered to predict solar power generation in the Yeongcheon area, and solar power generation was estimated through a multiple regression model using deep learning techniques. Second, the size of solar power generation facilities and hydrogen storage facilities in military bases was estimated with the amount of solar power generation and electricity consumption. As a result of this analysis, it was calculated that a site of 155.76×104 m<sup>2</sup> for PV panels was needed and a facility capable of storing 27,657 kg of hydrogen gas was required. Through these results, it is meaningful to demonstrated the prospect that military units can lead the achievement of “carbon net zero 2050” by using PV panels and hydrogen fuel storage facilities on idle sites of military bases.

Long-term power prediction of photovoltaic panels based on meteorological parameters and support vector machine

<span>Solar energy is the most generally accessible energy in the entire globe. Proper solar panel maintenance is necessary to reduce reliance on imported energy. Continuous monitoring of the solar panel's power output is required. The deployment of internet of things (IoT) monitoring of solar panels for maintenance is the basis for the current research. A multi-variable long-term photovoltaic (PV) power production prediction approach based on support vector machine (SVM) is developed in this study with the aim of completely evaluating the influence of PV panels performance and actual operational state factors on the power generation efficiency. This study examines the use of SVM and climatic factors to forecast the long-term output of power from solar panels. A solar power facility in a semi-arid area provided the data utilized in this investigation. Temperature, humidity, wind speed, and sun radiation are some of the meteorological variables that were considered in the study. To anticipate the power generation of the panels, the SVM is trained using the climatic factors and the power generation data. The findings demonstrate that the SVM model consistently predicts the panels' long-term power generation with a high degree of accuracy.</span>

Simulation of Maximum Power Point Tracking with Fuzzy Logic Control Method on Solar Panels Using MATLAB

One of the newest sources energy is the sun. To limit the usage of fossil fuels, solar power facilities that harness sunlight are beginning to be created. Currently, research on solar panels is increasingly being carried out. Because solar panels are an unlimited source of energy. And solar panels can reduce exhaust emissions from conventional vehicles by 92%. However, the performance of solar panels is strongly influenced by several factors such as sunlight intensity and ambient temperature. So that solar panels can reach the maximum power point, the Maximum power point tracking (MPPT) method is employed to maximize the performance of solar panels. In this research, an mppt system will be created using fuzzy logic control in matlab simulink. Fuzzy logic control is a method that can control the system against load changes. To analyze this fuzzy logic-based mppt system, simulations are carried out using matlab simulink. Simulink is software used to model, simulate, and analyze dynamic systems. From the simulations that have been carried out, solar panels without mppt have a duty cycle change time of 0.1 seconds while with mppt the duty cycle change is 0.7 seconds, where the duty cycle change time without mppt is faster but the operating value is unstable while power tracking with mppt the duty cycle change time is longer but the operating value is more stable.

Capacitorless DC-DC Regulator as a Candidate Topology for Photovoltaic Solar Facilities

Linear-assisted DC/DC converters (or linear-switching hybrid DC/DC converters) consist of a voltage linear regulator (classic NPN or nMOS topologies and LDO) connected in parallel with a switching DC/DC converter. They are a good candidate for energy processing in photovoltaic solar facilities. In order to control these hybrid structures, different strategies exist, allowing fixing the switching frequency as a function of some parameters of the linear regulator. This article compares two control strategies that, although can be applied to the same circuital structure of linear-assisted converter, are sensibly different. The first one, reported in previous literature, cancels completely the average current through the linear regulator in steady state to achieve a reduction of the losses. Thus the efficiency of the whole system increases and almost equals the one of the standalone switching converter. The proposed approach, in spite of a slightly increment of linear regulator’s losses, reduces the output ripple due to the crossover distortion of linear regulator output stage.

India's Odyssey to Sustainable Energy Triumph-Assessing the Impact of Access to Energy and Renewable Power in the Indian Electricity Sector

Purpose: The purpose of this paper is to comprehensively examine the challenges hindering India's sustainable energy development, focusing on access to electricity, competition within the energy market, and the pivotal role of technology transfer and energy storage in overcoming growth hurdles. Theoretical Reference: The theoretical framework revolves around the intersection of Indian legislation and energy access, emphasising the significance of competition law in fostering a fair and inclusive energy landscape. The paper also draws on the theoretical foundations of technology transfer and energy storage as catalysts for sustainable growth in the renewable energy sector. Method: The methodology involves an in-depth analysis of India's renewable energy sector, exploring the impact of monopolies, anti-competitive practices, and the rapid expansion of solar power facilities. The study considers the role of competition law, technology transfer, and energy storage as key components in navigating the challenges of sustainable energy development. Result and Conclusion: The results underscore the interconnected nature of technology, energy storage, and competition legislation in India's energy revolution. The conclusion highlights public policy imperatives, emphasising demand-side management, resolution of contractual disputes, and the imperative need for efficient and cost-effective energy storage systems. The paper concludes with a call for a policy framework encouraging private sector participation and capacity building in the power sector. Implication of Research: The implications of this research extend to shaping public policy imperatives in the electricity sector, emphasising the need for demand-side management, resolution of disputes, and a policy framework to promote private sector participation. The research highlights the significance of these imperatives in developing efficient and cost-effective energy storage systems and achieving 'Round the Clock' renewable power. Originality/Value: The originality and value of this research lie in its comprehensive examination of the interconnected challenges and solutions in India's renewable energy sector. The findings contribute to the discourse on public policy imperatives, technology transfer, and competition law, offering insights that can guide policymakers, regulators, and businesses in fostering a competitive, sustainable, and resilient energy system. The study provides valuable guidance for maximising the benefits of renewable energy, ultimately contributing to increased energy access, sustainability, and economic growth in India.

Modeling and Simulation of Solar Irradiance Conversion Using the Pyranometer App

Solar irradiance is the amount of light energy that the sun emits and reaches the earth. The Pyranometer app is used to gather sample data. This predictive model calculated the electricity a solar module generates based on solar irradiance. It can calculate the voltage, current, and power a solar module can produce from solar irradiance. The study used a developmental-evaluative design using simulation modeling created using Matlab® and Simulink®. The evaluation of the model's accuracy was confirmed on a parallel run with an existing renewable solar energy facility to assess functionality, performance, robustness, workmanship, and the overall recommendation of the model. Findings revealed promising results that the designed model could, with reliable accuracy, simulate a solar module's performance from a given amount of irradiance.

Evaluation and performance comparison of different models for global solar radiation forecasting: a case study on five cities

AbstractRecently, solar energy has emerged as the most promising renewable energy source to meet the world’s energy demands. However, to harness the potential of solar energy, accurate data on solar radiation are crucial. It is considered the first step in assessing solar resources for various applications and achieving energy sustainability goals. Due to the unavailability of solar radiation measurements in many parts of the world, several models have been developed to predict global solar radiation (GSR) at these locations. Thus, this study aims to evaluate the proficiency of several GSR models at five new locations and determine the most suitable one for GSR prediction. The study has further developed solar radiation models for these new locations, as well as general ones for the entire region, which does not have any GSR models despite the existence of many planned solar energy facilities in this area. Additionally, the study investigates the effect of changing the length of the validation dataset on models’ performance and accuracy, as well as assesses the introduced models’ generalization capability. To achieve these objectives, the observed data of GSR for approximately 37 years at studied locations are used to develop and validate the proposed models. The study’s findings reveal that Model 1 provides the best performance at all locations, with accuracy, coefficient of determination ($$R^{2}$$ R 2 ), ranging from 95 to 98%, except for the coastal location, where it is from 91 to 95%. The remaining performance indicators of the best models, such as RMSE, MABE, MAPE, and $$r$$ r , are good, and their values range from 0.7863 to 1.9097 (MJ m−2 day−1), from 0.6430 to 1.7060 (MJ m−2 day−1), from 3.4319 to 10.0890 (%), and from 0.9914 to 0.9981, respectively. The length of the validation dataset has a slight effect on the models’ performance, ranging from about 1% to 2%. Therefore, Model 1 is the recommended solar radiation model, which can provide precise and rapid estimates of global solar radiation. This approach could be used in the design and performance evaluation of many solar applications. The primary benefit of this approach in the current investigation is that temperature data are continuously and effortlessly recorded for various purposes.

Good fences make good neighbors: Stakeholder perspectives on the local benefits and burdens of large-scale solar energy development in the United States

In order to meet decarbonization goals, the number of large-scale solar (LSS) facilities in the US is expected to increase considerably. The advantages of LSS over fossil-fueled power generation are numerous and well documented. However, residents living nearby proposed and existing LSS sites have voiced a number of concerns about LSS, including its possible impacts to farmland and agricultural production, biodiversity, stormwater runoff, home and property values, as well as concerns about solar panels' toxicity and safety. While rapid expansion of LSS currently relies on officials permitting and residents being willing to host these systems, the appetite for LSS in some communities may be waning. Here we examine the perceived benefits and burdens of recent LSS developments, conducting 54 interviews with a broad set of stakeholders including residents, officials and developers at seven LSS sites across the US. We focus on identifying residents' most common concerns regarding LSS systems across states, site types, landscapes and ownership structures. We find concerns are associated with either LSS development processes or impacts, and center on the type and amount of information provided, the community's influence over project design, the efficacy of community subscription efforts, as well as projects' economic, environmental, and visual and landscape impacts. Importantly, we also investigate strategies that have been employed to improve perceptions and project outcomes, which include increasing in-person engagement, more explicit discussion of project tradeoffs, third-party intermediaries acting as community champions, and explicit requirements for meaningful local economic benefits.

If you build it, will they come? Insect community responses to habitat establishment at solar energy facilities in Minnesota, USA

Global declines in insect populations have important implications for biodiversity and food security. To offset these declines, habitat restoration and enhancement in agricultural landscapes could mutually safeguard insect populations and their pollination services for crop production. The expansion of utility-scale solar energy development in agricultural landscapes presents an opportunity for the dual use of the land for energy production and biodiversity conservation through the establishment of grasses and forbs planted among and between the photovoltaic solar arrays (‘solar-pollinator habitat’). We conducted a longitudinal field study across 5 years (2018–2022) to understand how insect communities responded to newly established habitat on solar energy facilities in agricultural landscapes by evaluating (1) temporal changes in flowering plant abundance and diversity; (2) temporal changes in insect abundance and diversity; and (3) the pollination services of solar-pollinator habitat by comparing pollinator visitation to agricultural fields near solar-pollinator habitat with other agricultural field locations. We found increases over time for all habitat and biodiversity metrics: floral rank, flowering plant species richness, insect group diversity, native bee abundance, and total insect abundance, with the most noticeable temporal increases in native bee abundance. We also found positive effects of proximity to solar-pollinator habitat on bee visitation to nearby soybean (Glycine max) fields. Bee visitation to soybean flowers adjacent to solar-pollinator habitat were comparable to bee visitation to soybeans adjacent to grassland areas enrolled in the Conservation Reserve Program, and greater than bee visitation to soybean field interior and roadside soybean flowers. Our observations highlight the relatively rapid (<4 year) insect community responses to grassland restoration activities and provide support for solar-pollinator habitat as a feasible conservation practice to safeguard biodiversity and increase food security in agricultural landscapes.

Development of Rotary Solar Receiver and Solar Simulator Facility for Concentrated Solar Power Applications

A prototype rotary solar receiver and a solar simulator facility have been designed, built and commissioned by Odqa Renewable Energy Technologies in conjunction with The Oxford Thermofluids Institute, University of Oxford for the validation of concentrated solar power technologies. The key features of the rotary solar thermal receiver include the rotating absorber surface which incorporates a high temperature heat transfer design. The innovative design also includes a compact solar power inlet aperture for very high incident heat flux to reduce the radiant heat loss from the high temperature surface. Air is used as the heat transfer fluid as this has system integration advantages over other types of heat transfer media, such as CO2, steam, molten salt, or solid particles. A 30kWe Xenon-arc lamp high heat flux incident light has also been developed to enable experimental validation of the solar receiver technologies. The Xenon-arc lamp array consists of three lamps combined with a custom designed, 5-degree-of-freedom focusing mechanism to achieve precise focus and maximise the heat flux. The receiver has successfully produced working fluid temperatures reaching 1200°C, demonstrating the concept viability for high temperature processes.