Area Of Photovoltaic Panels Research Articles

How Do Zooplankton Communities Respond to Environmental Factors across the Subsidence Wetlands Created by Underground Coal Mining in the North China Plain?

The degradation and loss of natural wetlands has caused severe crises for wetland taxa. Meanwhile, constructed wetlands are expanding significantly and facing dramatic environmental changes. Exploring the responses of wetland organisms, particularly zooplankton, may have important implications for the management of wetlands. Environmental and zooplankton samples were collected from 34 subsidence wetlands created by underground coal mining across the North China Plain in August 2021. We used generalized linear models and redundancy analysis to test zooplankton responses to environmental variables, with the relative importance quantified by variation partitioning. We identified 91 species, divided into 7 functional groups, with the highest density of rotifer filter feeders (RF, 2243.4 ± 499.4 ind./L). Zooplankton species richness was negatively correlated with electrical conductivity (EC), chlorophyll-a, total phosphorus, and pH. The Shannon–Weiner and Pielou evenness indices were positively correlated with transparency and negatively correlated with the photovoltaic panel area (AS). Rotifer predators (RCs) and RF densities were positively correlated with cropland area and dissolved oxygen, but negatively correlated with AS. Small crustacean filter feeders positively correlated with AS, whereas medium crustacean feeders (MCFs) positively correlated with EC. AS was the most critical variable affecting the zooplankton community. Our study showed that the spatial pattern of zooplankton communities was shaped by environmental heterogeneity across the subsidence wetlands, providing implications for the management and conservation of these constructed wetlands.

Study on Fault Monitoring Technology of Photovoltaic Panel Based on Thermal Infrared and Optical Remote Sensing

Abstract. Rapid access to the operating status of Photovoltaic (PV) panels and troubleshooting can save management and maintenance costs for the development of PV power plants, which is important for PV power plant management and power generation capacity assurance. The use of remote sensing technology to identify the faults of photovoltaic panels has developed rapidly, however, current research usually relies only on a single optical data source to identify and count the area of PV panels in a PV electric field, although there are literature on PV panel fault detection, only the surface fault identification of PV panels is tested, while the internal faults (such as panel bad points or bad lines) cannot be identified because of the limitations of optical remote sensing. In this paper, a photovoltaic panel fault monitoring technology based on multi-source remote sensing is proposed. The optical and thermal infrared hybrid data combined with deep learning technology are used to achieve rapid and accurate fault identification and localization of PV panel arrays. It can not only automatically identify PV panels that are obscured by dust and foreign objects, but also locate PV panels that have bad dots or bad lines, which greatly improves the ability and effectiveness of remote sensing PV panel fault monitoring. The high-resolution unmanned air vehicle (UAV) optical image and thermal infrared image are applied in this experiment. The Mask RCNN algorithm is used to accurately locate and number the photovoltaic panel of the optical image. Then, the fault scene classification model is established for the multi-type fault characteristics of the optical image and thermal infrared image within the panel range, so as to identify five types of faults, such as dust cover, branch cover, bird droppings cover, internal bad points and bad lines of PV panel, which effectively solves the problem that the single optical remote sensing image cannot identify the internal component faults of the photovoltaic panel under normal conditions.

Size optimization of a stand-alone solar-wind-battery hybrid system for net zero energy buildings: A case study

In this research, a solar-wind-battery hybrid system (PV/WT/BS) is proposed to supply the electricity demand of a stand-alone net zero energy building. for estimating the output power of the hybrid system, wind speed and solar irradiance are measured throughout a year and their uncertainties are estimated by applying the Monte Carlo algorithm. The wind speed Wiebull distribution is estimated, and the solar irradiance distribution is obtained using a novel approach based on the sky cloudiness parameter. Moreover, to simulate the performance of the hybrid system components according to the actual conditions, their availability is calculated using failure and repair rates. The optimal size of the proposed hybrid system is calculated considering, for the first time, the reliability and system costs. The system's size is optimized through the consideration of key decision variables, including photovoltaic panel area, rated power, wind turbine tower height, and battery capacity. Two distinct optimization methods are employed: a single-objective approach utilizing Genetic Algorithm, where the total system cost is minimized with reliability as a constraint, and a double-objective approach using NSGA-II, considering both initial cost and reliability as objectives. The optimal system size is determined through an efficient decision-making process. A practical case study is conducted, monitoring wind speed, solar irradiance, and electricity demand of a residential building at 15-minute intervals over a year. The results reveal the efficacy of the optimization methods, with the single-objective approach yielding a total system cost of €75,495 and reliability of 95.19%, while the double-objective optimization achieves a cost of €73,460 and a reliability of 91.88%.

Estimation of Rooftop Solar Photovoltaic Potential Based on High-Resolution Images and Digital Surface Models

Buildings are important components of urban areas, and the construction of rooftop photovoltaic systems plays a critical role in the transition to renewable energy generation. With rooftop solar photovoltaics receiving increased attention, the problem of how to estimate rooftop photovoltaics is under discussion; building detection from remote sensing images is one way to address it. In this study, we presented an available approach to estimate a building’s rooftop solar photovoltaic potential. A rapid and accurate rooftop extraction method was developed using object-based image classification combining normalized difference vegetation index (NDVI) and digital surface models (DSMs), and a method for the identification of suitable rooftops for solar panel installation by analysing the geographical restrictions was proposed. The approach was validated using six scenes from Beijing that were taken using Chinese Gaofen-2 (GF-2) satellite imagery and Pleiades imagery. A total of 176 roofs in six scenarios were suitable for PV installation, and the estimated photovoltaic panel area was 205,827 m2. The rooftop photovoltaic potential was estimated to total 22,551 GWh. The results indicated that the rooftop photovoltaic potential estimation method performs well.

Assessment of the ground coverage ratio of agrivoltaic systems as a proxy for potential crop productivity

AbstractThe yield of crops in both agrivoltaic (AV) and agroforestry (AF) systems is difficult to predict. The shade pattern of an AV system is not typical and is quite different from the one of AF systems. Most countries allow AV systems on croplands only if the crop productivity is maintained (e.g., in France) or slightly reduced, as in Japan and Germany, with 80% and 66% minimum relative yield (RY) required, respectively. I suggest using the Ground Coverage Ratio (GCR: ratio of area of photovoltaic panels to area of land) as an indicator of the crop potential productivity in AV systems. The GCR can easily be computed and controlled for all kinds of AV systems with panels that are either fixed (horizontal, tilted, or vertical) or mobile (on 1- or 2-axis trackers). Here, I provide a synthesis of published data for crop productivity under AV systems. Only publications that provided both the GCR of the system and the crop RYs were included. Measuring RYs requires a reliable non-AV control plot. Several publications were excluded because of doubts regarding the measurements’ validity (e.g., systems that are too small, resulting in strong edge effects, or unreliable control plots). Despite the scattering of results, a clear pattern is evidenced: RYs decrease rapidly when GCRs increase. It appears that a GCR < 25% is required to ensure that most crop RYs stay > 80%. These results are consistent with a recent meta-analysis examining the impact of shade on crops. The use of the GCR criterion to validate AV projects is a simple and cost-effective alternative to the tricky control of crop yields in the fields.

Mapping Photovoltaic Panels in Coastal China Using Sentinel-1 and Sentinel-2 Images and Google Earth Engine

Photovoltaic (PV) panels convert sunlight into electricity, and play a crucial role in energy decarbonization, and in promoting urban resources and environmental sustainability. The area of PV panels in China’s coastal regions is rapidly increasing, due to the huge demand for renewable energy. However, a rapid, accurate, and robust PV panel mapping approach, and a practical PV panel classification strategy for large-scale applications have not been established. Here, we developed a new approach that uses spectral and textural features to identify and map the PV panels there were in coastal China in 2021 using multispectral instrument (MSI) and synthetic aperture radar (SAR) images, and the Google Earth Engine (GEE), to differentiate PV panels according to their underlying surface properties. Our 10-m-spatial-resolution PV panel map had an overall accuracy of 94.31% in 2021. There was 510.78 km2 of PV panels in coastal China in 2021, which included 254.47 km2 of planar photovoltaic (PPV) panels, 170.70 km2 of slope photovoltaic (SPV) panels, and 85.61 km2 of water photovoltaic (WPV) panels. Our resultant PV panel map provides a detailed dataset for renewable layouts, ecological assessments, and the energy-related Sustainable Development Goals (SDGs).

A framework to estimate generating capacities of PV systems using satellite imagery segmentation

The growing interest in global warming has led to various efforts to rely on new renewable energy such as solar power. More accurate energy generation calculation is on-demand with the rise of publicly accessible residential photovoltaic (PV) panels. PV panel area computation is the first step towards the image-based estimation of energy generation from the residential solar arrays. Satellite image segmentation provides a low-cost and simple solution to calculate solar panel area installed on rooftops and the ground. The most relevant approach is to acquire high-quality labels rather than network architectures in many scenarios. This research aims to estimate potential solar energy from satellite images using deep learning segmentation techniques. We compared five architectures such as UNet, UNet++, Feature Pyramid Network (FPN), Pyramid Scene Parsing Network (PSPNet), and DeepLabV3, with different encoders. We converted segmented image pixels into square meters regarding the average slope of panels. We considered the average solar panel efficiency, global tilted irradiation, and the loss coefficient to estimate the annual energy output from the calculated area. UNet with the EfficientNetB7 encoder performed the best with a dice similarity coefficient of 0.9982 for segmentation and a Mean Absolute Percentage Error (MAPE) of 3.5 for energy output estimation.

Multi-objective optimization of energy-saving measures and operation parameters for a newly retrofitted building in future climate conditions: A case study of an office building in Chengdu

The energy-saving measures and suitable operation parameters will be urgent concerns in future global climate change conditions. However, a critical review revealed that most studies considered upgrading the wall insulation and glazing in the design process or the retrofit stage for existing buildings built before the twenty-first century and neglected improving system energy efficiency and operation of newly retrofitted buildings with higher-performance envelopes in the context of the carbon peak and carbon neutrality. Therefore, a real and newly retrofitted office building in Chengdu was investigated. The effect of indoor cooling and heating temperature setpoints, water supply/return temperature setpoints for the air-conditioning system, night ventilation settings in summer, and renewable energy variables (photovoltaic) are considered in the optimization. The simulation model was validated using the monitored heating and cooling energy consumption. Transient system simulation coupled with jEPlus + an evolutionary algorithm and the non-dominated sorting genetic algorithm were used to explore the optimal solution to reduce carbon emission and energy consumption while ensuring acceptable indoor thermal comfort. The sensitivity analysis showed night ventilation duration and the air change rate had significant effects on carbon emission reduction. The annual carbon emission reduction of the building was 46500–58500 kg from 2030 to 2060 using a photovoltaic panel area of 1000 m2. The indoor temperature setpoint decreased over time due to the increasing outdoor temperature in the future. Moreover, the building energy system showed a better performance when the air source heat pump cooling setpoint was increased from 7 °C to 9 °C. Decreasing the air source heat pump heating setpoint from 45 °C to 42.5 °C for 2030 and 2060, and from 45 °C to 40 °C for 2040 and 2050 were the optimal solutions. The proposed approach provides guidance for operators to optimize energy savings and operation parameters under future climate conditions.

Design optimization and optimal power management of standalone solar-hydrogen system using a new metaheuristic algorithm

Optimal configuration models and efficient energy management strategies are developed based on a standalone energy system, including hydrogen fuel cell and photovoltaic (PV) in green buildings. To optimize a hybrid scheme, integer decision variables and continuous decision variables are considered as the number of scheme components. To the optimal size of the system, the objective function is defined based on the system annual cost and loss of power supply probability (LPSP) concept for reliability. To define the optimal solution to the optimization problem, the use of three modified harmony search (MHS) algorithms are proposed, namely, MHSLD, MHSWF, and MHSLWF. These methods are compared with modified and original HS algorithms. Furthermore, three loss of power supply probability values are introduced (1 %. 2 %, and 3 %) for the optimal power management. In this regard, the effect of the PV price and reservoir tank cost variation on the justification of the systems is investigated. The simulation results reveal that the MHSWF performs with high accuracy and robustness as it obtains the minimal values of the investigated indexes for different desired reliability, βL, (1 %, 2 %, and 3 %) in all simulation runs obtained. Therefore, the ranking order of the search power of the algorithms is MHSWF > MHSLD > MHSLWF > MHS > HS. For the desired βL with 1 %, MHSWF obtained the minimal SAC values and achieved cost savings of 0.8 %, 6.1 %, 11 %, and 20.2 % as compared with four other methods, and the saving reached up to 13.2 % in the costs for other βL values. The sensitivity analyses have been performed on the results of the modeling which reveals that by the increase of the desired βL (from 1 % to 3 %), the values of PV areas, and the number of hydrogen tanks decrease by 5.4 % and 31 %, respectively, while the value of SAC decreases by 31 %.In different desired βL, by decreasing the PV panel area cost, the value of SAC based on LPSP decreases, and by reducing the hydrogen tank cost of the system by 46 %, the SAC of the system based on LPSP decreased by 39 %.

Solar Farms as the Only Power Source for the Entire Country

The presented paper shows a hypothetical large solar farm that would be the only source of electricity for the entire country. The energy crisis in Europe raises the question of whether it is possible to supply an electrical system based only on renewable energy sources. What should the surface area of the solar panels be in a hypothetical large solar farm to power the entire country? In this work, we will show what requirements must be met to make this feasible. Very important differences between the installed power capacity in a coal-fired or nuclear power plant and a solar power plant are discussed. The article presents calculations of the surface area of photovoltaic panels in that solar farm for four exemplary countries in Central Europe: Poland, Germany, the Czech Republic and the Slovak Republic. These studies are particularly important for Poland, whose electrical system is still mainly based on coal-fired power plants. The hypothetical solar farm could, in practice, take the form of dozens of solar power plants located in different parts of the country. Most importantly, the proposed solution will counteract climate change.

Energy Performance Analysis of a Multi-Story Building Using Building Information Modeling (BIM)

There is an enormous rise in building construction to meet serious demands of population increase. Besides its benefits, certain negative impacts on climate change and environments are associated with the built environment due to substantial energy requirements during operational phase. The current work aims to assess the energy consumption pattern of a residential facility based upon solar path analysis using simulation technique. A multi-story conventional building has been developed in a virtual 3D parametric environment using building information modeling. The BIM model was converted into the energy model using cloud computing. The energy model, at the proposed current orientation, was analyzed using insight 360 and solar energy analysis performed accordingly. Based upon the solar path analysis, the study observed that, at the present trajectory of solar path, provision of solar panels arrangements on 106,221 ft2 Photovoltaic panel area can produce the energy of 2,163,417 kwh/year with a payback period of 0.8 years.Keyword: Building Information Modelling, Energy analysis, Energy Optimization, Architecture 2030 Challenge

농촌 공간 환경영향요인 분석을 위한 무인항공기 적용 가능성에 관한 실험적 연구 - 홍성군 갈산면의 태양광 발전시설과 빈집을 중심으로 -

Rural spaces are increasingly valuable as areas for introducing renewable energy infrastructure to achieve carbon neutrality. Rural areas are the living grounds of rural residents, and the balance of conservation and development for rural areas is important for the introduction of reasonable facilities. In order to maintain a balance between development and preservation and to introduce reasonable renewable energy facilities, it is necessary to develop a current status survey and an effective survey method to utilize a space capable of introducing renewable energy facilities such as idle land and vacant houses. Therefore, this study was conducted to verify the readability using an unmanned aerial vehicle, and the main results are as follows. The detection of photovoltaic power generation facilities using unmanned aerial vehicles was effective in analyzing the location and area of photovoltaic panels located on the roofs of buildings, and it was possible to calculate the expected power generation by region through the area calculation of photovoltaic panels. The vacant house detection can be used to select an investigation target for an vacant house condition survey as it can identify damage to buildings that are expected to be empty houses, management status, and electricity supply facilities through aerial photos. It is judged that the unmanned aerial vehicle detection capability can be utilized as a method to improve the efficiency of investigation and supplement the data related to solar power generation facilities and vacant houses provided by public institutions. Although this study detected the status of solar power generation facilities and vacant houses through high-resolution aerial image analysis, as a follow-up study, automatic measurement methods using the temperature difference of solar power generation facilities and general characteristics of vacant houses that can be read from the air were investigated. If the deriving research is carried out, it is judged that it will be possible to contribute to the improvement of the accuracy of the detection result using the unmanned aerial vehicle and the expansion of the application range.

Combined Cooling and Power Management Strategy for a Standalone House Using Hydrogen and Solar Energy

Tropical climate is characterized by hot temperatures throughout the year. In areas subject to this climate, air conditioning represents an important share of total energy consumption. In some tropical islands, there is no electric grid; in these cases, electricity is often provided by diesel generators. In this study, in order to decarbonize electricity and cooling production and to improve autonomy in a standalone application, a microgrid producing combined cooling and electrical power was proposed. The presented system was composed of photovoltaic panels, a battery, an electrolyzer, a hydrogen tank, a fuel cell, power converters, a heat pump, electrical loads, and an adsorption cooling system. Electricity production and storage were provided by photovoltaic panels and a hydrogen storage system, respectively, while cooling production and storage were achieved using a heat pump and an adsorption cooling system, respectively. The standalone application presented was a single house located in Tahiti, French Polynesia. In this paper, the system as a whole is presented. Then, the interaction between each element is described, and a model of the system is presented. Thirdly, the energy and power management required in order to meet electrical and thermal needs are presented. Then, the results of the control strategy are presented. The results showed that the adsorption cooling system provided 53% of the cooling demand. The use of the adsorption cooling system reduced the needed photovoltaic panel area, the use of the electrolyzer, and the use of the fuel cell by more than 60%, and reduced energy losses by 7% (compared to a classic heat pump) for air conditioning.

Selection of optimal location and design of a stand-alone photovoltaic scheme using a modified hybrid methodology

Since rural systems often have no connections to backup electrical supplies, determining the optimal location and the optimal size of a stand-alone system is an important challenge. In this paper an efficient framework based on a hybrid heuristic approach is proposed to find the appropriate capacity and location for stand-alone, remote photovoltaic/battery schemes. The framework evaluates the total area of photovoltaic panels and the size of the storage scheme using a modified hybrid methodology. To determine ideal values of the decision variables, a geographic information system module is used to find the appropriate location according to various sets of conditions such as social, technical, economic, and environmental. In order to determine the appropriate capacity, an improved hybrid methodology based on harmony search and simulated annealing is utilized along with technical and economic conditions to satisfy continuously the load while minimizing the total life cycle cost and maximizing the reliability of the scheme. An actual case study is considered for South Khorasan, Iran using the framework. In order to investigate how impactful the proposed methodology is, the outcomes of the aforementioned method and other heuristic methods are compared. For improved harmony search, simulated annealing, and geographic information systems, the proposed framework shows promising results for a stand-alone photovoltaic scheme taking into account the quality of delivered power and cost in islands and areas that are hard to access.

Performance analyses of various commercial photovoltaic modules based on local spectral irradiances in Malaysia using genetic algorithm

A novel comprehensive methodology integrated with genetic algorithm (GA) has been formulated by accounting for local spectral irradiance and the specifications of commercial photovoltaic (PV) module. Despite having the same solar irradiance, variation in power conversion efficiency (PCE) of PV module can be significant in different site locations by referencing to that of standard AM1.5 spectrum. We have carried out a case study using GA for a combination of three site locations in Peninsular Malaysia and twelve types of commercial PV modules. Type-9 PV module operating in Bangi has recorded the lowest gain with 0.1%, while type-10 PV module operating in Bandar Sungai Long has shown the best gain of up to 27%. For solving multi-objective problems, MmGA and NSGAII have been applied to optimize three objectives concurrently including PCE, PV weight and PV panel area. From the simulation, type-1, type-6, and type-12 PV modules show the best in at least two objectives for the categories of m-Si, p-Si, and thin film, respectively.

Detection and Analysis of Photovoltaic Panels Based on UAV and HSV Space

To address the difficulty of image analysis in a complex environment,an image segmentation and detection method of an HSV spatial model was studied.First,a UAV was used to collect images.Second,Gaussian convolution was used to detect the gradient of the cracked image after the photovoltaic(PV)panel area was extracted.Finally,morphological image processing and the HSV spatial model were applied to extract the occlusion,and the ratio of the minimum external rectangle area to the PV panel area was calculated.This method can effectively segment and detect PV images in complex backgrounds and provides certain innovative and practical value.

Agricultural sustainability estimation of the European photovoltaic greenhouses

Abstract The integration of the photovoltaic (PV) energy in the greenhouse farm has raised concerns on the agricultural sustainability of this specific agrosystem in terms of crop planning and management, due to the shading cast by the PV panels on the canopy. The PV greenhouse (PVG) can be classified on the basis of the PV cover ratio (PVR), that is the ratio of the projected area of PV panels to the ground and the total greenhouse area. In this paper, we estimated the yield of 14 greenhouse horticultural and floricultural crops inside four commercial PVG types spread in southern Europe, with PVR ranging from 25 to 100%. The aim of the work is to identify the PVG types suitable for the cultivation of the considered species, based on the best trade-off between PV shading and crop production. The daily light integral (DLI) was used to compare the light scenarios inside the PVGs to the crop light requirements, and estimate the potential yield. The structures with a PVR of 25% were compatible with the cultivation of all considered species, including the high light demanding ones (tomato, cucumber, sweet pepper), with an estimated negligible or limited yield reduction (below 25%). The medium light species (such as asparagus) with an optimal DLI lower than 17 mol m−2 d−1 and low light crops can be cultivated inside PVGs with a PVR up to 60%. Only low light demanding floricultural species with an optimal DLI lower than 10 mol m−2 d−1, such as poinsettia, kalanchoe and dracaena, were compatible inside PVGs with a PVR up to 100%. Innovative cropping systems should be considered to overcome the penalizing light scenarios of the PVGs with high PVR, also implementing LED supplementary lighting. This paper contributes to identify the sustainable PVG types for the chosen species and the alternative crop managements in terms of transplantation period and precision agriculture techniques, aimed at increasing the crop productivity and adaptability inside the PVG agrosystems.

Photovoltaic panel anomaly detection system based on Unmanned Aerial Vehicle platform

The emergence and rapid development of the Unmanned Aerial Vehicle (UAV) Photovoltaic inspection system have become an effective means of solving the operation and maintenance of photovoltaic power plants. In order to cooperate with the current UAV platform for photovoltaic panel anomaly detection, this paper proposes a photovoltaic infrared target anomaly detection system. In this paper, the Sobel operator is used to extract the photovoltaic slab area of the image, and the canny operator is used to obtain the photovoltaic small plate area to realize the complementary advantages. At the same time, the deep learning is applied to the algorithm to learn the discriminative characteristics of the image, and the brightness statistics are used. The method is supplemented by the method to achieve anomaly detection of the photovoltaic panel area. The effectiveness of the proposed algorithm is proved by a large number of experiments.

Technical analysis of photovoltaic energy generation for supplying the electricity demand in Brazilian dairy farms

In many Brazilian dairy farms, the supply of electrical energy does not fully meet the demands of the production systems and/or presents frequent fluctuations and interruptions, making the expansion of production chain unfeasible. These and other barriers have motivating the adoption of renewable and independent energy sources, installed directly in the farms. The present study was conducted to perform a technical analysis of photovoltaic energy generation required for supplying the electricity demand in dairy farms located at Minas Gerais State, Brazil. Daily global radiations on tilted panels were estimated by mathematical models and long-term meteorological database (greater than 33 years). Electrical energy generated by grid-connected photovoltaic systems was predicted considering minimum, average, and maximum daily global radiation conditions. Electrical energy demands were monitored in small (66 cows), medium (106 cows), and large (158 cows) dairy farms. The direct use of electrical energy in operations performed at Brazilian dairy farms were 4, 28, 15, 39, and 15%, on average, for lighting, milking, water heating/pumping, milk cooling/refrigeration, and miscellaneous, respectively. The estimated energy consumptions were 0.96, 0.87, and 0.81 kWh cow−1 day−1 for farms with 66, 106, and 158 cows, respectively. The photovoltaic panel area varied considerably among small, medium, and large dairy farms in order to meet 80% of renewable fraction for the electricity demand.

Optimal integration of microalgae production with photovoltaic panels: environmental impacts and energy balance

BackgroundMicroalgae are 10 to 20 times more productive than the current agricultural biodiesel producing oleaginous crops. However, they require larger energy supplies, so that their environmental impacts remain uncertain, as illustrated by the contradictory results in the literature. Besides, solar radiation is often too high relative to the photosynthetic capacity of microalgae. This leads to photosaturation, photoinhibition, overheating and eventually induces mortality. Shadowing microalgae with solar panels would, therefore, be a promising solution for both increasing productivity during hotter periods and producing local electricity for the process. The main objective of this study is to measure, via LCA framework, the energy performance and environmental impact of microalgae biodiesel produced in a solar greenhouse, alternating optimal microalgae species and photovoltaic panel (PV) coverage. A mathematical model is simulated to investigate the microalgae productivity in raceways under meteorological conditions in Sophia Antipolis (south of France) at variable coverture percentages (0% to 90%) of CIGS solar panels on greenhouses constructed with low-emissivity (low-E) glass.ResultsA trade-off must be met between electricity and biomass production, as a larger photovoltaic coverture would limit microalgae production. From an energetic point of view, the optimal configuration lies between 10 and 20% of PV coverage. Nevertheless, from an environmental point of view, the best option is 50% PV coverage. However, the difference between impact assessments obtained for 20% and 50% PV is negligible, while the NER is 48% higher for 20% PV than for 50% PV coverage. Hence, a 20% coverture of photovoltaic panels is the best scenario from an energetic and environmental point of view.ConclusionsIn comparison with the cultivation of microalgae without PV, the use of photovoltaic panels triggers a synergetic effect, sourcing local electricity and reducing climate change impacts. Considering an economic approach, low photovoltaic panel coverage would probably be more attractive. However, even with a 10% area of photovoltaic panels, the environmental footprint would already significantly decrease. It is expected that significant improvements in microalgae productivity or more advanced production processes should rapidly enhance these performances.