Agrivoltaic (APV) systems integrate energy production with crop cultivation, offering a promising solution to land-use conflicts and enhancing resource utilisation efficiency. However, traditional APV designs often struggle to effectively coordinate the dynamic lighting needs of crops with the power generation efficiency of photovoltaic (PV) systems. To address this challenge, this study developed a dynamic tracking photovoltaic greenhouse (TPVG) system, which served as the basis for a real-time model predicting greenhouse shadows by integrating solar geometry and ray-tracing algorithms. In addition, indicators of daily irradiance variation coefficient, crop yield, land equivalent ratio, and energy consumption for TPVG are compared with those of a non-tracking photovoltaic greenhouse (NTPVG) via experiments and simulations. The results show that the daily average coefficient of variation (CV) of indoor irradiance decreased by 8-12%, while power generation increased by 10.11-13.76%. In addition, compared to NTPVG, strawberry yield increases by 20.75%, inter-crop retention rate improves by 8.1%, and the land equivalent ratio (LER) ultimately reaches 1.77. Although the initial investment for TPVG is 29.46% higher than for NTPVG, the levelized cost of strawberries (LCOS) is 20.6% lower, and the payback period (PBP) is 6.88 years, 0.52 years longer than for NTPVG. Moreover, TPVG demonstrates superior self-sufficiency (SS) rates across multiple latitudinal regions, with the annual average SS rate in mid- and low-latitude regions remaining above 0.94. Therefore, the research provides a validated and efficient design framework for the APV field, offering actionable insights for optimising the synergistic production of food and energy globally.

A solar-powered electric vehicle (SPEV) utilises photovoltaic modules (PVMs) on its roof baffle to convert solar energy into electricity, effectively increasing the driving distance of electric vehicles (EVs). However, commercial PVMs have low energy conversion efficiency, resulting in insufficient drive power for SPEVs. A flat-roof vehicle-integrated photovoltaics (VIPV) supplies power to low-voltage loads, while a high-voltage battery bank charges the electric drive-reconstructed on-board charger (OBC) to address this problem. Therefore, this paper presents a novel multiport converter that can be integrated into the SPEV and connects to low-voltage (LV) loads via a nonisolated DC-DC converter operating in six modes. In addition, a multiport converter was presented and controlled by strategies that integrated proportional-integral (PI) control with maximum power point tracking (MPPT). The results indicate that the maximum efficiencies of the PVM supplying power solely to the load and to the battery are 98.92% and 95.88%, respectively. The maximum efficiency of the battery supplying power to the load is 98.86%, whereas the maximum efficiency of the PVM and battery combination to supply power to the load is 95.06%. The vehicle-specific power (VSP) indicates that when the speeds of cars, vans, and buses equipped with PVMs are less than 90 km/h, 95 km/h, and 115 km/h, respectively, PVMs with greater installed capacities yield greater gains for SPEVs. Conversely, when the speeds of cars, vans, and buses exceed these threshold values, smaller PVM capacities enhance VSP. Finally, the simulations show that PVMs integrated with buses, cars, and vans can reduce electricity costs by 13.04% to 32.93% and increase the driving range by 15% to 26.34%.

Photovoltaic (PV) power generation is affected by intermittent solar radiation, leading to fluctuations in output power and reducing the stability and reliability of PV systems. In general, a hybrid energy storage system (HESS) combined with a PV system is employed to smooth PV power fluctuation. However, the traditional algorithms for balancing the capacity of HESS, power fluctuations, and life cycle costs (LCC) remain challenging. To address this challenge, a modified method of variable cut-off frequency low-pass filtering (VFLPF) is developed to determine the capacity of battery and supercapacitor (SC) in HESS, as well as balance the relationship between PV power fluctuations and total energy system capacity (TESC). Additionally, ZE and Zp are adopted as metrics for evaluating the total energy system capacity and reference power, respectively, specifically employed to mitigate the maximum fluctuation rate (MFR) in PV systems. Moreover, the presented algorithm is validated by the photovoltaic module combined with the hybrid energy storage system (PVM-HESS) and the photovoltaic array combined with the hybrid energy storage system (PVA-HESS). In the presented PVM-HESS and PVA-HESS, the metrics ZE are respectively reduced by 2.65-2.94 % and 20-54.10 % compared to the traditional fixed cut-off frequency low-pass filtering (FFLPF) strategies of S.I (cut-off frequency of 160 Hz) and S.II (cut-off frequency of 750 Hz), while the Zp is reduced by 3.3-45.62 % and 3.31-78 %, respectively. The life cycle costs determined by the presented algorithm are reduced by 7.71 % and 11.10-21.52 %. Furthermore, a three-port DC-DC converter of 450 W is designed to connect the DC Bus with the PV module and the hybrid energy storage system, in which the MFR of PV output power is reduced by 23.77 %. Finally, a 20 kWp PVA combined with the hybrid energy storage system optimises power allocation and capacity configuration while reducing the MFRs throughout the year by 0.07-64.53 %. In conclusion, the presented algorithm and strategy are an effective technical solution for reducing total energy capacity and reference power when smoothing power fluctuations, as well as providing theoretical support for coordinated operation of storage energy equipment in a hybrid energy storage system.

Efficiency and power output of photovoltaic (PV) modules increase gradually, while the output voltage of a photovoltaic module (PVM) is relatively lower and is not suitable for the input source. In addition, the output performance of ageing PVMs is nonuniform, although the nominal parameters are the same, resulting in the generation of the PVMs connected in series being limited by the lowest current module. However, existing solutions often fail to simultaneously address the dual challenges of efficient voltage boosting and power mismatch mitigation in ageing PV strings. To bridge this gap, this paper not only presents the mathematical models of nonuniform performance as power mismatch metrics but also proposes an improved quadratic boost converter (QBC) and explores comprehensive control strategies for PV arrays. Based on the theories of continuous conduction mode (CCM) of the QBC, mathematical models of the voltage converter ratio and small signal transfer functions are formulated. In addition, a prototype rated at 550 W is developed, with an input DC voltage range of 18-60 V and an output voltage of 300-500 V. Besides, a perturb and observed algorithm is implemented by the digital signal processor as maximum power point tracking (MPPT), and experimental maximum efficiency of the QBC is 97.43%, with a voltage converter ratio of 7.25. Moreover, three control methods, including typical control with MPPT (TCM), boost converter control with MPPT (BCM), and boost converter control without MPPT (BCWOTM), are simulated in PV strings with different power degradation. Standard deviation (STD) values of the PV strings are 0 W, 1.46 W, 4.40 W and 4.33 W. Compared to TCM, daily power generation controlled by BCM and BCWOTM increases from 2.05% to 9.59%, and 0.40% to 3.16%, respectively. Conversely, when the STD of PVMs is less than 1.54 W and solar irradiation is below 1.08 kWh/(m2 & sdot;d), TCM is suitable for controlling PVMs connected in series. Moreover, the daily performance ratio (PR) values controlled by BCM and BCWOTM are stabilised from 0.938 to 0.995 and 0.882 to 0.937, respectively. In conclusion, the proposed QBC and the comprehensive control study provide theoretical guidance for designing and operating photovoltaic strings and arrays.

Recently, there has been an increasing emphasis on generating energy from renewable sources, resulting in the installation of photovoltaic (PV) modules on the roofs of agricultural greenhouses. The optimal combination involves integrating a photovoltaic greenhouse with vertical growing of edible mushrooms. This synergistic approach allows for increased planting capacity and enhanced exploitation of solar radiation. However, there is very little progress in the evaluation of spatial light for such photovoltaic planting systems. This study examined the amount of daylight accessible in a photovoltaic greenhouse for mushroom vertical cultivation in Kunming, China. The spatial intensity of daylight was simulated with RADIANCE, a software application that simulates solar and light rays by ray-tracing. The findings indicated that the average disparity in sunlight intensity between each layer of the shelf of edible mushroom, when arranged in an east-west or north-south orientation, was below 9% at noon. However, the latter configuration resulted in a more even distribution of daylight in the greenhouse. The shading percentage remains relatively consistent during the four significant days of spring equinox, summer solstice, autumn equinox, and winter solstice. On the summer solstice, the shadow range is primarily concentrated in the greenhouse due to the elevated position of the sun. This leads to a lack of consistent lighting across different heights within the greenhouse. As the solar altitude angle decreases, the intensity of light decreases but the light inhomogeneity increases. These findings have the potential to be used as design techniques for supporting vertical planting in PV greenhouse planning.

Developing heat storage materials capable of operating above 600 degrees C is a significant challenge in solar thermal power systems. In this study, a low-temperature ultrasonic-magnetic activation approach was employed to fabricate a phase change material for high-temperature thermal energy storage with an Al@Al2O3 core-shell structure, utilizing the unique cavitation effect of ultrasonic waves. The specific surface area and pore volume of samples prepared via ultrasound-assisted hydrothermal synthesis increased by approximately 87.67 % and 91.3 %, respectively. In addition, the prepared Al@Al2O3 microcapsules exhibited a melting point of approximately 662 degrees C and a latent heat of phase transition of 269.13 J/g. After 100 melting-freezing cycles, the microcapsules demonstrated good thermal cycling stability, with a retained latent heat of 237.11 J/g. These findings suggest that the newly developed Al@Al2O3 microcapsules can be utilized for high-temperature heat storage due to their stable performance and ease of fabrication.

The application of facility agriculture led by greenhouse is considered as a good approach to regulate the ideal growing conditions for crops and boost productivity. To make up for the energy consumption of this modern agriculture, photovoltaic greenhouses have been emphasized. For agricultural greenhouses (whether ordinary or photovoltaic ones), it is of great significance to regulate the micro-environment in agricultural greenhouses to promote crop growth and yield while saving energy. By searching and classifying papers in the literature database with different keywords, it is found that most studies predominantly concentrate on one or two specific types of micro-environmental changes, such as light and heat or heat and humidity, with limited attention to the interaction of micro-environmental factors within greenhouses. Furthermore, the current research methodologies are relatively simplistic, typically employing only one or two approaches, such as measurement, simulation, or machine learning. Thus, the previous research on the theoretical and technological developments of light, thermal and humidity environment in greenhouse were reviewed, and the influence factors of micro-climate in both photovoltaic and ordinary greenhouse were summarized, which are more comprehensive than the previous state-of-the-art. Additionally, the results of recent studies are used to describe the way that greenhouse technology will develop in the future. A reference for future improvement of greenhouse design and diversified utilization of photovoltaic technology are provided, these new materials and technologies can ensure agricultural production while increasing energy efficiency, which have positive significance for achieving peak carbon emission.

This study analyzed the effect of light intensity on the yield and nutritional value of Hericium erinaceus (Lion's mane mushroom) in a photovoltaic (PV) greenhouse to evaluate the environmental benefits of renewable energy use in agriculture. The light intensity in the PV greenhouse was measured and simulated at different times of day, during different seasons, and under varying weather conditions. It was concluded that the average light intensity was suitable for the cultivation of edible mushrooms. The maximum light intensity did not exceed 800 lux during the year. An experiment was conducted to assess the yield and nutritional value of Hericium erinaceus under various light conditions, including natural light, low light, and supplemental red and blue LED light. Low-light conditions improved mushroom growth, whereas supplemental blue light at night significantly increased the levels of some amino acids, particularly glutamate and leucine, which are essential for physiological functions. Blue LED light increased the content of phenolics, whereas low-light conditions increased the flavonoid content. The fat content was relatively low under sunlight conditions. The annual off-grid greenhouse PV system generated 3,036.5 kWh of electricity, meeting the greenhouse's operational demand of 2,674.7 kWh and resulting in substantial energy savings. The carbon dioxide emissions were reduced by 1,313.57 kg per year.

To increase the accuracy as well as effectiveness of predicting the level of CO2 in mushroom cultivating greenhouses, two optimized prediction models of long and short term memory neural networks (VMD-SSA-LSTM and VMD-DBO-LSTM) are proposed. To start with, time series data on greenhouse CO2 concentrations were decomposed to get intrinsic mode function (IMF) at various time scales. The sparrow search algorithm (SSA) or dung beetle optimization Algorithm (DBO) is then used to optimize the amount of hidden layer neurons, discover the best learning rate, find the optimal iteration times, and improve prediction accuracy. Finally, the SSA or DBO optimized LSTM network is applied to represent the dynamic time of the multi-variable feature series, resulting in CO2 concentration predictions. The model for forecasting presented in this research was used to forecast CO2 concentrations in an experimental greenhouse at Yunnan Normal University. A comparison experiment between the LSTM, EMD-LSTM and VMD-LSTM models is carried out, indicating that the VMD-SSA-LSTM and VMD-DBO-LSTM models outperform the others in terms of prediction accuracy, while VMD-DBO-LSTM model is faster in calculation. The results were compared to actual data, revealing mean absolute errors, mean absolute percentage errors, root-mean-square errors and R2 of the model optimized by SSA are 2.3488 ppm, 0.4593%, and 2.9958 ppm on sunny days, and 6.6212 ppm, 1.1721%, and 8.2909 ppm on cloudy days, respectively. The results of the model optimized by DBO are 2.6365 ppm, 0.5140%, 3.3014 ppm and 0.9919 on sunny days, and 5.1328 ppm, 0.8990%, 6.8016 ppm and 0.9942 on cloudy days, respectively.

A photovoltaic greenhouse (PVGH) has emerged as a promising agricultural production technology for providing a suitable crop microclimate. However, few studies have investigated the effectiveness of the hybrid cooling method in regulating the microclimate of a PVGH based on crop growth. This paper addresses this need by proposing a block-cooling model of the hybrid cooling method based on the relationships among irradiance, cooling load, and energy changes caused by cooling equipment. In addition, to improve the accuracy of microenvironment control in a PVGH, decoupled temperature and humidity control is proposed and realised via the proportion-integration-differentiation (PID) controller. Moreover, the maximum power point (MPP) and state of charge (SOC) of the PV and battery bank system are controlled by proportion-integration (PI) regulators via DC-DC converters. The results show that the hybrid cooling method maintains the indoor humidity and temperature at 48.37-61.77 % and 16.87-22.50 degrees C, respectively. The range of standard deviation (SD) values is from 0.85 degrees C to 1.48 degrees C. Self-consumption (SC) and self-sufficiency (SS) range from 0.68 to 0.85 and from 0.48 to 0.85, respectively. The levelized cost of energy (LCOE) and the LCOE of PV self-consumption electricity are 1.24 CNY/ kWh and 1.38 CNY/kWh, respectively. When the hybrid cooling method is used for PVGH, greenhouse gas (GHG) emissions are 0.194 t/year, which reduces GHG emissions by up to 4.01 t/year. In conclusion, this paper scientifically divides the usage times of fan, air conditioning, and spray combined with fan cooling methods in the hybrid cooling method based on the periods corresponding to different irradiance levels, providing a comprehensive system optimisation reference.

Photovoltaic-battery water pumping systems (PVBWPSs) can provide fresh water and irrigation in off-grid areas. Previous research has focused on direct current (DC) voltage versus frequency to control the speed of a pump. However, the use of photovoltaic (PV) modules with batteries to create a high-performance hybrid system with fixed and variable frequencies of supply power remains challenging, particularly in an off-grid water pumping system with limited power and water supplies. Based on a conventional frequency conversion mode and power balance, this work addresses fixed and variable frequencies under changing solar irradiance conditions for a PV system and a PV system combined with a battery (PVB) mode to improve energy utilisation. According to DC power balance and centrifugal pump theories, a mathematical model of the power supply frequency in the PVBWPS is presented, as well as the loss of load probability (LLP) and pumping coefficient (Cp), through which the performance metrics are obtained. The formulated models are validated through the experimental PVBWPS, which includes 2.19 kWp PV modules, a 9.6 kWh battery bank, and a 0.75 kW centrifugal pump. The experimental results show that the root mean square error (RMSE) and maximum relevant error (RE) of the frequency are 0.14 Hz and 0.74%, respectively. Consequently, the output performances are revealed via software simulation. The calculated results indicate that the maximum pumping volume for fixed-power-frequency operation is 48 Hz, which is 27.56 m3 on a sunny day and 17.63 m3 on a cloudy day. On a rainy day, the maximum pumping volume is 3.27 m3 at 41 Hz. Similarly, the Cp values reach maxima of 2.51 m3/kWh and 2.11 m3/kWh at 48 Hz in both sunny weather and cloudy weather, respectively, while on rainy days, the Cp peaks at 0.77 m3/kWh at 41 Hz. Moreover, every 1 Hz increase in the fixed frequency mode leads to a rise in the LLP, while the minimum change is at 46-48 Hz for cloudy and rainy days. Furthermore, the simulations revealed that for variable frequency control, the volume of water pumped in the PVB mode reached 40.19 m3, 29.36 m3, and 15.11 m3, which are increased by 4.91%, 21.83% and 103.09% compared with the variable frequency PV mode, and 45.83%, 66.53% and 362.08% higher than in PV fixed frequency mode, respectively. Compared with the PV mode, the system weighted efficiency of the variable-frequency PVB mode is increased by 2.06%, 4.98%, and 8.36% under three weather conditions. This work provides critical theoretical guidelines for the design and operation of high-performance PVBWPs.

The distinctive pharmacological characteristics of Chinese medicinal herbs are rendering them increasingly popular as functional foods. However, fresh herbs are prone to mold and decay. Currently, drying processing is primarily employed to extend the storage time and effectively preserve the bioactive constituents of these herbs. After examining of the recent literatures on drying Chinese medicinal herbs, this paper provides a comprehensive overview of various drying technologies, including shade drying, hot air drying, solar drying, heat pump drying, microwave drying, far-infrared drying, vacuum drying, vacuum freeze drying, radio frequency drying, high voltage electric field drying and some combined techniques. While, an extensive illustration for each drying method principles, advantages/disadvantages, potential improvements and deep investigation of its energetic performance and the influence on the main active components in the drying process, such as polysaccharides, polyphenols, flavonoids, saponins, volatile oils, were summarized. The results showed that the combined drying technology not only had high efficiency and low energy consumption, but also preserved the bioactive medicinal components and sensory quality at the best. This paper provides a theoretical foundation and vast technical support for enhancing the value of Chinese medicinal herbs utilization and drying industry.

Photovoltaic power generation can provide energy for greenhouses and achieve high quality and high yield of crops. In reality, solar irradiance is fluctuating and intermittent. Thus, the key to ensure efficient photovoltaic power use under greenhouse environmental conditions is to provide an accurate prediction of solar irradiance. Yet, currently studies on irradiance prediction in terms of the variable time lengths, multi-parameter and full climate conditions are rather limited. To improve the comprehensive performance of the prediction model, this paper proposes models of irradiance time series prediction such as Pyraformer, Informer, Transformer and TimesNet. Those models were tested based on the synergistic combination of weather conditions (WC), sunshine time accumulation (STA/h), instantaneous total irradiance (ITI/(W/m2)) and irradiance daily accumulation (IDA/(MJ/m2)). The model performance was rigorously evaluated with 9 prediction lengths, 5 training days, 4 seasons and 5 days for 9 weather conditions. The results showed that the Transformer model had the best overall prediction performance for STA, ITI, IDA and WC at different time steps of 10 min to 24 h. All models were suitable for predicting time series within 1 h. However, TimesNet model was not suitable for predicting time series with steps outside 1 h. On the other hand, by using the sun combination dataset, the Transformer model had the best performance at a time step of 10 min. The mean absolute error (MAE), mean-square error (MSE), root mean squared error (RMSE) and coefficient of determination (R2) of ITI were 0.118 W/m2, 0.059 W/m2, 0.243 W/m2 and 93.9 %, respectively. When exploring the minimum dataset, with the increase of data samples, the prediction effect of TimesNet showed an increasing trend. While, Transformer had the best prediction effect for the dataset with one year of use. When exploring seasonality, Pyraformer model had the best prediction effect on winter and summer, and TimesNet had the best prediction effect on autumn and spring. Local prediction of 9 climate conditions showed that the effects of snow and dust storm were not ideal. The research results showed that the characterization factors that were closely linked to irradiance. The prediction scheme proposed in this paper combined the advantages of different time steps, different factor combination datasets, different data volumes and seasonality, which greatly improves the generalization ability of the model. This study can provide a reference for irradiance prediction and more refined management of photovoltaic (PV) greenhouse.

Fast and accurate acquisition of positive sequence components of unbalanced grid voltage is an essential require-ment to ensure the safety operation of the grid-connected inverter.To improve the extraction speed of positive sequence components of unbalanced voltage,this study proposes a sampling period delay filter(SPDF)to quickly separate positive and negative sequence components by delaying two sampling periods of grid voltage in dq frame.With the SPDF method,only one coordinate transformation is required and the computational burden can be reduced apparently.Then,the noise immunity performance of the proposed SPDF algorithm is investigated;and the corresponding solution,operation period delay filter(OPDF),can guarantee the desired fast response performance under the premise of limiting the amplified noise within the acceptable range.Finally,the feasibility and priority of the above two algorithms have been verified by the simulation and experimental results.

Photovoltaic (PV) greenhouses are widely used to regulate internal irradiance and air temperature to create optimal climatic conditions for plant growth. Therefore, comprehensive multidomain energy models of PV greenhouses are more likely needed to improve the system performance. However, few studies investigate optical-electrical-thermal characteristics and PV greenhouse evaluation indicators based on crop growth requirements. The aim of this work is to develop coupled optical-electrical-thermal models to assess the energy performance of the PV greenhouse with different PV roof coverage ratios during the summer months. In addition, the models of photosynthetically active radiation (PAR) and net photosynthetic rate (PN) are presented based on the irradiance and temperature of the PV greenhouse to evaluate the dynamic response of crops to interior climate conditions. The models are validated with experiments on the PV greenhouse in Kunming, with a total area of 26.25 m(2). The electricity supply and demand systems include PV modules (3.285 kWp), a battery bank (24 kWh), an air-water heat pump (AWHP), and spray combined with fan cooling. Two cooling scenarios are analysed and compared by varying the PV module coverage ratio. Scenario 1 (S.1) is AWHP cooling, and scenario 2 (S.2) is spray combined with fan cooling. The optimum interior temperature and PAR of the PV greenhouse are 22 degrees C and 300 mu mol center dot m(-2)center dot s(-1), respectively. The simulations indicate that if the proportion of the PV layout on the greenhouse roof is greater than 20%, the year-round PAR inside the greenhouse will be below the threshold value of 550 mu mol center dot m(-2)center dot s(-1) at which the strawberry P-N is saturated. For every 10% increase in PV roof coverage, the interior air temperature decreases by 0.02-0.56 degrees C corresponding to a daily cooling load reduction of 0.45-1.02 kWh/d, while the PV generation increases by 1.7-3.19 kWh/d and the maximum electricity consumption of S.1 and S.2 drops by 0.66 kWh/d and 0.52 kWh/d, respectively. Moreover, the high self-consumption-sufficiency balance (SCSB) values show that the PV and battery sizes are 2.22-2.78 kWp (40-50% coverage) and 15.72-15.75 kWh for S.1, and those in S.2 are 1.11-1.67 kWp (10-30% coverage) and 0.66-3.22 kWh. The coefficient of performance (COP) of the system reaches a maximum of 0.45-1.12 at 40% coverage in S.1 and 1.88-3.01 at 10-30% coverage in S.2. When the PV module coverage is 40% in S.1, the levelized costs of electricity (LCOE) and levelized costs of cooling (LCOC) are the lowest at 0.0760USD/kWh(el) and 0.0693USD/kWh(c). Similarly, the lowest LCOE and LCOC of S.2 are 0.0306USD/kWh(el) and 0.0210USD/kWh(c), as the PV module coverage is 30%. The study will be beneficial for applying photovoltaic modules, battery banks, and cooling technologies in the greenhouse, especially considering the dynamic crop response to internal climates and the energy performance assessment.

Accurately predicting power output details of individual photovoltaic (PV) modules is crucial for evaluating and controlling operating PV systems. Although many techniques have been developed to address this aspect, accurately detecting and predicting the power output of an individual module in a large-scale PV operating system remains challenging. To improve the accuracy and efficiency of predictive technology, a new method is proposed to extract five physical parameters based on the single-diode model (SDM) of a PV module. The proposed technique only requires the initial electrical performance of a PV module under standard conditions. In addition, the initial voltage and current, which depend on solar irradiance, the average temperature, as well as the degradation rate, are used to solve the physical parameter functions of an operating PV module. Consequently, these five parameters and the maximum power output of an operating module can be calculated with the proposed model. To determine the accuracy of the proposed approach, the relative error (RE) and mean absolute percent error (MAPE) of individual PV modules in operating PV arrays and strings are examined. The results indicate that the minimum RE of the power output of an individual PV module occurs near the maximum power point (MPP). The experimental results of both the PV arrays and strings indicate that the MAPE of power prediction for an individual module at the MPP is lower than that reported in previous research. Moreover, solar irradiance measurement accuracy and stability are the main factors influencing the error in this study. Comprehensive experimental result analysis demonstrates that the proposed real-time technique is suitable for large-scale PV farm testing integrated infrared imaging in terms of accuracy, reliability, and efficiency.

在满足温室供暖需求的条件下,通过改变系统储热量对光伏温室进行供暖实验,探究配置不同光伏容量、蓄电池容量以及储热量的系统经济性与自耗率.结果表明:要满足温室供暖时长达到10 h,且保证温室温度满足夜间草莓生长需求,水箱储热量至少需要在26.25 kW·h以上.在储热量为35.00 kW·h及光伏容量为2.19 kW的系统中,蓄电池容量至少需要14 kW·h,系统才能满足温室供暖的要求且自耗率高于0.8;储热量为35.00 kW·h,光伏容量为4.38 kW,蓄电池容量为4 kW·h的系统成本最低,为1.61元/(kW·h).

为确保光伏制冷的运行性能,该文针对离网型光伏冷库采用动态等效阻抗匹配的控制策略,通过阻抗变换器进行脉宽调制输出不同占空比,使光伏发电输出与负载相匹配,构建光伏最大功率点等效阻抗与温度、辐照度和标准测试条件下最大功率点等效阻抗之间的数学模型.基于所提的控制方法,搭建一套5.4 kW的分布式光伏直驱冷库实验系统,研究表明,采用动态等效阻抗匹配的控制方法能有效改善太阳辐射波动性对光伏冷库的影响,控制后的光电转换效率、制冰量和制冷系统利用率与常规控制的系统相比同比提高41.0％、64.1％和63.5％,为光伏冷库在优化控制方面提供了理论分析及实验参考.

针对单相离网逆变系统投切感性负载时产生的谐波,分别利用拓扑结构为LCL型和LLCL型无源滤波器的数学模型,根据负载特性设计了两种滤波器相关参数.在负载一定和变电感条件下分别仿真了两种滤波器的输出电压和电流波形,并且计算了电压谐波畸变率.