• Energy Research
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Abstract Integration of photovoltaics (PV) with electrical energy storage (battery) is a straightforward approach to turn intermittent power source into stable power supply. Power coupling, or power matching, between PV-device, a battery, and a load is most frequently performed with aid of maximum power point tracking (MPPT) electronics. MPPT electronics provides high flexibility as for PV and load impedances, and irradiance, however, it brings in additional cost, and complexity, power overhead, potential reliability issues, and interference signals. On the other hand, direct coupling via preselection of PV and battery parameters is a simple scalable and highly efficient alternative to MPPT for a specific set of conditions. We explore with modeling how far a directly coupled PV-battery unit can stay power-matched under various conditions, and demonstrate feasibility of excellent power matching over orders of magnitude of irradiance and a wide range of load resistances. Both a PV-harvester in an office room with low irradiance, non-demanding load, and high autonomy, and a PV-system on a roof with high irradiance, demanding load, and partial autonomy, can operate efficiently without MPPT electronics if an appropriate battery is included. This result emphasizes the role of a battery as an impedance matching element besides storage functionality in a directly matched PV-system.

Abstract Integration of photovoltaics (PV) with electrical energy storage (battery) is a straightforward approach to turn intermittent power source into stable power supply. Power coupling, or power matching, between PV-device, a battery, and a load is most frequently performed with aid of maximum power point tracking (MPPT) electronics. MPPT electronics provides high flexibility as for PV and load impedances, and irradiance, however, it brings in additional cost, and complexity, power overhead, potential reliability issues, and interference signals. On the other hand, direct coupling via preselection of PV and battery parameters is a simple scalable and highly efficient alternative to MPPT for a specific set of conditions. We explore with modeling how far a directly coupled PV-battery unit can stay power-matched under various conditions, and demonstrate feasibility of excellent power matching over orders of magnitude of irradiance and a wide range of load resistances. Both a PV-harvester in an office room with low irradiance, non-demanding load, and high autonomy, and a PV-system on a roof with high irradiance, demanding load, and partial autonomy, can operate efficiently without MPPT electronics if an appropriate battery is included. This result emphasizes the role of a battery as an impedance matching element besides storage functionality in a directly matched PV-system.

Abstract An improved hybrid vacuum and pressure-assisted infiltration technique was developed for the infiltration of phase change materials (PCM) into the graphite foam matrix. A single chamber with no moving part infiltration setup was designed and fabricated. Pelletized PCM was introduced to provide enough porosity for air removal using vacuum pumps with no need for transfer PCM after melting and infiltration. Simple design, the use of stainless steel, and the corrosion resistance feature of PCM provides a very cost-effective design and easily controllable process. Mixed alkali and alkaline chloride salts as PCM were infiltrated into the high porosity graphite foam to enhance the thermal conductivity of latent heat storage medium. Chloride based PCM with proven corrosion-resistant features, high energy storage density that enables storage and release of energy at nearly constant temperatures close to the melting temperature of 355 °C are used in this study, as an example. The composites of infiltrated graphite foam and chloride salt PCMs were studied using SEM, EDS microstructural analyses to determine the effectiveness of the infiltration process. An infiltration efficiency greater than 90% of the available porosity was achieved. The thermal conductivity of the infiltrated samples was analyzed using a laser Nano-flash thermal conductivity device. The thermal conductivity of the foam/PCM composite was shown to be larger by a factor greater than 40 times of pure chloride PCM (1–2 W/m-K). Low-cost infiltration with proven efficacy and repeatability of infiltrated PCM could be a breakthrough for 3rd generation of CSP plant applications with supercritical CO2 power cycles.

Abstract An improved hybrid vacuum and pressure-assisted infiltration technique was developed for the infiltration of phase change materials (PCM) into the graphite foam matrix. A single chamber with no moving part infiltration setup was designed and fabricated. Pelletized PCM was introduced to provide enough porosity for air removal using vacuum pumps with no need for transfer PCM after melting and infiltration. Simple design, the use of stainless steel, and the corrosion resistance feature of PCM provides a very cost-effective design and easily controllable process. Mixed alkali and alkaline chloride salts as PCM were infiltrated into the high porosity graphite foam to enhance the thermal conductivity of latent heat storage medium. Chloride based PCM with proven corrosion-resistant features, high energy storage density that enables storage and release of energy at nearly constant temperatures close to the melting temperature of 355 °C are used in this study, as an example. The composites of infiltrated graphite foam and chloride salt PCMs were studied using SEM, EDS microstructural analyses to determine the effectiveness of the infiltration process. An infiltration efficiency greater than 90% of the available porosity was achieved. The thermal conductivity of the infiltrated samples was analyzed using a laser Nano-flash thermal conductivity device. The thermal conductivity of the foam/PCM composite was shown to be larger by a factor greater than 40 times of pure chloride PCM (1–2 W/m-K). Low-cost infiltration with proven efficacy and repeatability of infiltrated PCM could be a breakthrough for 3rd generation of CSP plant applications with supercritical CO2 power cycles.

Solar photovoltaic (PV) modules are susceptible to manufacturing defects, mishandling problems or extreme weather events that can limit energy production or cause early device failure. Trained professionals use electroluminescence (EL) images to identify defects in modules, however, field surveys or inline image acquisition can generate millions of EL images, which are infeasible to analyze by rote inspection. We develop a rapid automatic computer vision pipeline (∼0.5 seconds/module) to analyze EL images and identify defects including cracks, intra-cell defects, oxygen-induced defects, and solder disconnections. Defect identification is achieved with a machine learning model (Random Forest, ResNet models and YOLO) trained on 762 manually-labeled EL images of PV modules. We compare model performance on an imbalanced real-world validation set containing 134 EL images and determine that ResNet18 and YOLO are the optimal models; we next evaluated these models on a dedicated testing set (129 module images) with resulting macro F1 scores of 0.83 (ResNet18) and 0.78 (YOLO). Using a field EL survey of a PV power plant damaged in a vegetation fire, we analyze 18,954 EL images (2.4 million cells) and inspect the spatial distribution of defects on the solar modules. The results find increased frequency of ‘crack’, ‘solder’ and ‘intra-cell’ defects on the edges of the solar module closest to the ground after fire. We also find an abnormal increase of striation rings on cells which were assumed to be caused mainly in fabrication process. Our methods are published as open-source software. It can also be used to identify other kinds of defects or process different types of solar cells with minor modification on models by transfer learning.

Solar photovoltaic (PV) modules are susceptible to manufacturing defects, mishandling problems or extreme weather events that can limit energy production or cause early device failure. Trained professionals use electroluminescence (EL) images to identify defects in modules, however, field surveys or inline image acquisition can generate millions of EL images, which are infeasible to analyze by rote inspection. We develop a rapid automatic computer vision pipeline (∼0.5 seconds/module) to analyze EL images and identify defects including cracks, intra-cell defects, oxygen-induced defects, and solder disconnections. Defect identification is achieved with a machine learning model (Random Forest, ResNet models and YOLO) trained on 762 manually-labeled EL images of PV modules. We compare model performance on an imbalanced real-world validation set containing 134 EL images and determine that ResNet18 and YOLO are the optimal models; we next evaluated these models on a dedicated testing set (129 module images) with resulting macro F1 scores of 0.83 (ResNet18) and 0.78 (YOLO). Using a field EL survey of a PV power plant damaged in a vegetation fire, we analyze 18,954 EL images (2.4 million cells) and inspect the spatial distribution of defects on the solar modules. The results find increased frequency of ‘crack’, ‘solder’ and ‘intra-cell’ defects on the edges of the solar module closest to the ground after fire. We also find an abnormal increase of striation rings on cells which were assumed to be caused mainly in fabrication process. Our methods are published as open-source software. It can also be used to identify other kinds of defects or process different types of solar cells with minor modification on models by transfer learning.