Snowy Days Research Articles

Exploration of the Heterogeneity among Elderly Drivers by Analyzing Traffic Crash Data: A Case Study in Pennsylvania, USA

<div>With population aging and life expectancy increasing, elderly drivers have been increasing quickly in the United States and the heterogeneity among them with age is also increasingly non-ignorable. Based on traffic crash data of Pennsylvania from 2011 to 2019, this study was designed to identify this heterogeneity by quantifying the relationship between age and crash characteristics using linear regression. It is found that for elderly driver-involved crashes, the proportion leading to casualties significantly increases with age. Meanwhile, the proportions at night, on rainy days, on snowy days, and involving driving under the influence (DUI) decrease linearly with age, implying that elderly drivers tend to avoid traveling in risky scenarios.</div> <div>Regarding collision types, elderly driver-involved crashes are mainly composed of angle, rear-end, and hit-fixed-object collisions, proportions of which increase linearly, decrease linearly, and keep consistent with age, respectively. The increase in angle collisions is primarily attributed to more crashes at stop-controlled intersections. The findings suggest that it may be inappropriate to take elderly drivers as homogeneous or simply categorize them into several age groups. Instead, regarding elderly drivers, age should be taken as continuous in future studies to display their linearly changing trends. This is one of the pioneering studies exploring the heterogeneity across elderly drivers with age with solid data analysis. The findings are expected to provide new insights for agencies to develop customized countermeasures regarding elderly traffic safety in the aging society.</div>

A novel snow conditions‐compatible computational intelligence‐based PV power forecasting approach for microgrids in snow prone regions

AbstractEnergy management in a renewable energy‐based microgrid has a key role in improving energy utilisation and reducing the microgrid operation cost. The optimal energy management strategy can be significantly affected by the intermittency of renewable energies and also harsh weather conditions. In this study, a novel snow conditions‐compatible computational intelligence‐based short‐term photovoltaic (PV) power forecasting (PVPF) approach is proposed that is independent of exogenous weather forecasts. The proposed approach consists of a snow cover detection stage, a snow cover forecasting stage, and a PV power forecasting stage. This approach is then validated for a model predictive control (MPC)‐based energy management system (EMS) of a PV energy‐based grid‐connected microgrid located in a snow‐prone area. The PVPF method together with a computational intelligence‐based short‐term load demand forecasting model constitutes the forecasting block of the EMS. The forecasting block generates day‐ahead hourly forecasts based on the local measurements of the meteorological‐electrical parameters and sends them to the optimisation block where a two‐stage control method, corresponding to the tertiary and secondary control levels, is developed based on mixed‐integer linear and quadratic programming. The developed EMS is applied to a test microgrid simulated in MATLAB/Simulink and compared with a heuristic control method. The results show that the proposed approach can reduce the overall operation cost of the microgrid by 8% (24$), 15% (166$), and 13% (235$) on sunny, cloudy, and snowy days under study, respectively, compared to the heuristic controller.

Weather conditions and ski resorts’ vitality: Linear and non-linear effects

Weather is crucial to the ski industry's demand and supply sides, but its comprehensive and varied impacts on ski resorts are not well understood. Thus, this study develops a systematic evaluation system for ski resorts based on concept of vitality, which includes place, people and activities, representing the supply and demand sides with subjective and objective measures. We then examine linear and non-linear impacts of weather on ski resorts' vitality in China using u-tests and quadratic models. Results show that temperature has a significant linear relationship with the vitality of ski resorts, while wind speed and snowy days show non-linear relationships. Snowy days exhibit a U-shaped relationship with ski resort attractiveness, while wind speed displays an inverse U-shaped relationship with ski activity. The findings have implications for the climatic research in tourism field and practical implications for the management of ski resorts. Management implications●Ski industry stakeholder should prioritize their attention to climate change, given the significant correlation between climate conditions and ski resorts' vitality.●Ski resorts with favorable climate conditions and high vitality, such as those in Xinjiang and Northeast China, should develop brand strategies and capitalize on their advantages in the face of climate change.●Ski resorts that are more vulnerable to climate change and have low vitality should acknowledge it as a primary challenge and diversify their offerings with non-snow activities to attract a broader range of visitors.●Indoor ski resorts in southern China, which are less dependent on weather conditions, have emerged and may offer additional opportunities for development in the context of climate change.

Trade-offs and synergistic relationships in wind erosion in Central Asia over the last 40 years: A Bayesian Network analysis

Wind erosion may have significant effects on both the environment and human activities. Central Asia, a prominently affected region by wind erosion on a global scale, has been the focus of a significant number of studies employing fundamental linear equations to establish causal relationships among the driving factors of wind erosion. However, such approaches neglect the imperative need for comprehensive analysis accounting for the intricate non-linear interactions among multiple factors, thereby potentially leading to distorted assessment outcomes. This study evaluated the multidimensional gradients (temporal, spatial, and vertical) of wind erosion in Central Asia over the last four decades using an enhanced version of the Revised Wind Erosion Equation (RWEQ) model. The Wind Erosion Modulus – Bayesian Network (WIEM-BN) model was developed by incorporating the Wind Erosion Modulus (WIEM) calculated by the RWEQ model as the parent node of the Bayesian Network (BN) and integrating multiple factors that influence the occurrence of wind erosion. The findings revealed a synergistic relationship between precipitation and the Normalized Difference Vegetation Index (NDVI), snowy days, and the Human Influence Index (HII), with trade-off correlations between temperature and precipitation and NDVI and soil moisture. The lowest overall WIEM level in Central Asia was associated with high HII and temperature (10–19 °C) as well as moderate precipitation (180–280 mm) and NDVI (0.18–0.24). Wind erosion in Central Asia varied substantially with Land Use Cover Change (LUCC), with the following drivers receiving precedence: wind speed > windy days > elevation > surface roughness (K') > HII > snowy days. Wind erosion was primarily caused by climate conditions (R = 0.741), with human activities (R = 0.629) acting as a suppressor. This study offered strategies to reduce wind erosion by up to 647.06 t km2 yr−1 in Central Asia, including a greater emphasis on ecological conservation programs and increased plant cover.

Image Desnowing via Deep Invertible Separation

Images taken on snowy days often suffer from severe negative visual effects caused by snowflakes. The task of removing snowflakes from a snowy image is known as image desnowing, which is challenging as image details are easily mistakenly treated and thus may be significantly lost during snowflake removal. Leveraging invertible neural networks (INNs), this paper presents a deep learning-based method for single image desnowing, which can remove snowflakes accurately while preserving image details well. Interpreting desnowing as an image decomposition problem, we propose an INN composed of two asymmetric interactive paths for predicting a latent image and a snowflake layer respectively. Such an INN is able to progressively refine the features of both latent images and snowflake layers for disentanglement, while retaining all information possibly relevant to latent image reconstruction. In addition, an attentive coupling layer supervised by snowflake masks is introduced to enhance feature dismantlement and a coupling-in-coupling structure is developed for further improvement. Extensive experiments show that, the proposed method outperforms existing ones on three benchmark datasets of synthetic and real-world images, and meanwhile it also shows advantages in terms of model size and computational efficiency.

Lightweight image de-snowing: A better trade-off between network capacity and performance

The single image de-snowing task is an essential topic in computer vision, as images captured on snowy days degrade the performance of current vision-based intelligent systems. Existing methods build complex network structures with numerous parameters to pursue continuous performance improvement. Nonetheless, they generally ignore the negative impact of large memory consumption in real applications. This paper aims to address the above problem by making a trade-off between network capacity and performance. We propose two novel networks suitable for different application scenarios. For devices with small memory and requiring fast inference speed, we propose an extremely lightweight recursive network (XLRNet). XLRNet is constructed by a single recursive strategy and two novel lightweight modules. For devices with large memory and pursuing better de-snowing performance, we propose a coupled lightweight dual recursive network (CLDRNet). CLDRNet cascades two XLRNets by a novel dual recursive strategy and a novel dual coupled LSTM module (DC-LSTM). Extensive experiments demonstrate the effectiveness and superiority of our two models on three synthetic datasets and real-world datasets.

Surface hazards in North-west Europe following sudden stratospheric warming events

Sudden stratospheric warmings (SSWs) have been linked to surface temperature anomalies, but how these connect to changes in the likelihood of specific weather extremes and their associated weather patterns remains uncertain. While, on average, it is true that cold surface temperatures follow SSW events, particularly in Northern Europe, there is considerable event-to-event variability. Over the British Isles and Central Europe, only around 45% of SSWs are followed by a colder than average period and a negative phase of the North Atlantic Oscillation, cautioning against an over-generalised approach to surface anomalies associated with SSWs. Focussing on more hazardous weather, which in winter is associated with cold extremes, we use reanalysis data to consider how SSWs impact temperature-related hazards; namely the frequency of snowy days, frost days and spells of extreme cold weather in 12 major European cities. In general, SSWs are associated with an increased risk of snow across most of western Europe, and that this is particularly significant in milder, more maritime locations such as London where in reanalysis, snowfall days are 40% more likely after an SSW. However, there is considerable variation in surface temperature anomalies between SSW events; the third of SSWs with the warmest surface anomalies are statistically more likely to have a decreased risk of snow, frost and persistent cold spells compared with non-SSW time periods. These warmer events are associated with a different temperature anomaly pattern, which is consistent in both reanalysis data and large ensemble CMIP6 models. We further show that these warm surface temperature anomaly SSWs are becoming more frequent, a trend which is consistent with background global warming. The varied surface anomalies associated with SSWs highlights the need to study their impacts in a probabilistic sense, and motivates further work to enable better prediction of the impacts of a given event.

Variation Characteristics of Pavement Temperature in Winter and Its Nowcasting for Xianyang Airport Expressway, China

Based on the pavement temperature observation data of the transportation meteorological stations along the Xianyang Airport Expressway, China, as well as the datasets of precipitation and sunshine hours obtained from the nearby weather stations, the variation characteristics of local pavement temperatures are investigated for winter in this study. Results indicate that during the daytime, the pavement temperatures are always higher on sunny and cloudy days than those on rainy and snowy days, while during the nighttime, the temperatures on sunny and cloudy days are higher than those on the days with freezing rain and snow, and with the temperatures on rainy and snowy days without icing being further higher. In general, the pavement temperatures in winter features significant periodic oscillations with cycles of roughly 24 h, 12 h, 8 h, 6 h, 5 h and 4 h, which differ slightly at different times for different stations. Moreover, the nowcasting experiments on the local pavement temperatures are also carried out using a regression model via extracting the corresponding periodic features. It shows the mean absolute errors of about 0.6 °C, 1.2 °C, and 1.5 °C for lead times of 1 h, 2 h, and 3 h, respectively. The nowcasting skills are higher on rainy and snowy days, while are inferior on sunny days. For nowcasting cases initialized at nighttime (daytime), the mean absolute errors are 0.4 °C (0.7 °C) and 0.9 °C (1.4 °C) for lead times of 1 h and 2 h. Examinations suggest that the nowcasting system could be well utilized in plain areas of China, whereas it shows relatively larger biases in plateau areas with complex terrain.

Relationships between site index and ecological variables of trembling poplar forests (Populus tremula L.) in Türkiye.

This study was carried out to identify relationships between site index (dominant height at a reference age) and ecological variables of trembling poplar forests in Türkiye. Samples were collected from 78 plots differing in elevation, aspect, inclination, slope position, and site class. Physiographic factors of the sample plots were recorded, soil samples were collected from different predefined layers from soil pits, and bedrock samples were collected for identification. From three trees at the stand top height, the tree closest to the arithmetic mean height was felled, and its height and age were determined. Physical and chemical characteristics of the soil samples were analysed. Relationships of the soil properties, physiographic factors, and climate with site index were assessed with correlation, stepwise regression, and regression tree methods. Significant relationships were found between site index at 30years and elevation from the physiographic factors; the maximum temperature and the number of snowy days of the coldest month from the climate characteristics; fine earth, silt, and pH from the percentage values of soil properties at different depths; and fine earth, silt, and clay from the soil characteristics aggregated on pedon level. The height growth of trembling poplar was 11.8% according to stepwise regression analysis and 18% according to the regression tree method. The models obtained in the current study might help evaluate the potential of sites regarding the growth of trembling poplar.

Improved 3D Object Detector Under Snowfall Weather Condition Based on LiDAR Point Cloud

LiDAR sensors are now used to supplement structure information and depth information for 3D object detection in automated driving. In adverse weathers, however, LiDAR tends to collect many noisy points in rainy or snowy days, which may disturb the results of object detection. In order to enhance the performance of the detector, we improve existing LiDAR-only 3D object detectors from two aspects under real snow weather condition. Firstly, double-attention block including point-wise attention and channel attention is applied to reweight the input feature of stacked pillars for crucial information extraction. Secondly, a lightweight and effective global context based pillar feature refinement extraction block is employed to capture long-range contextual information. It aims to filter local noisy information in the feature map, especially for the data collected in adverse weather conditions. Moreover, most of the previous works tend to focus on dataset under normal weather condition, so driving scenarios in adverse weather will bring challenges to the generalization of the model. Hence, to adapt our network to diverse domains better, we design a maximum mean discrepancy (MMD) block to get the distribution of domain feature representations as well as calculate the MMD loss in training process. Accordingly, the distribution discrepancy of two domains is narrowed. The performance evaluated on Canadian Adverse Driving Condition (CADC) Dataset collected in snowfall weather condition and KITTI dataset verifies the improvement of our approach. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/jiajia0408/i3detector_snowfall</uri> .

Performance of anti-icing stone mastic asphalt: laboratory and field investigation

ABSTRACT Anti-icing asphalt pavements can delay surface icing by reducing the freezing point to a certain degree. This study conducted the laboratory and field investigation to compare the anti-icing effectiveness and mechanical performance of two anti-icing stone mastic asphalt (SMA). Two types of anti-icing modifiers (e.g. LX II and MFL) were used in SMA respectively. The electrical conductivity test, ice melting test, and outdoor snow melting test were conducted on fresh and aged LX II SMA and MFL SMA, respectively, to evaluate the anti-icing effectiveness. The effect of anti-icing additives on high-temperature stability, low-temperature cracking resistance, and water stability of SMA were also analyzed. Two in-situ pavements were constructed using LX II SMA and MFL SMA. The anti-icing effectiveness of LX II and MFL pavement were compared by the observation of ice formation and snow accumulation on pavement surface on snowy days. The results showed that after long-term aging of anti-icing SMA, the chloride in LX II and MFL was more easily released. Two anti-icing additives had no significant effect on the water stability of SMA, and only slightly reduced high-temperature stability and low-temperature cracking resistance. LX II presented better anti-icing performance than MFL in both laboratory and field conditions.

Change in Winter Precipitation Regime across Ontario, Canada

The focus of this study is to investigate the effects of climate change on the hydrologic regimes in Ontario, Canada. The variables include total precipitation, the form of precipitation (snowfall and rainfall), and the temperature during winter. The winter season is hydrologically significant for Canadian conditions. The historical data for 70 years, from 1939 to 2008, on total precipitation, snowfall, rainfall, and temperature over the winter period were analyzed using least-squares regressions, Alexandersson’s Standard Normal Homogeneity Test, and the Mann–Kendall test for 13 stations across Ontario to identify positive and negative trends and their significance. The analysis of the precipitation indices reveals no significant trend in the winter total precipitation, decreasing trends in winter snowfall, and increasing trends in winter rainfall. The snowy day analysis depicts a large scatter across the province, with the number ranging from 40 days to 80 days, which shows that the number of snowy days varies considerably over the years at all stations. The analysis showed that the change in snowy-rainy days is attributed to the significant upward trend of the daily mean winter minimum temperature for almost all the stations. Therefore, the changes in the form of precipitation during winter may affect water management including streamflow, tile drainage flow, soil erosion, sediment and nutrient transport to surface water bodies, and the effectiveness of best management practices being used for managing non-point source pollution.

Changes in rain and snow over the Tibetan Plateau based on IMERG and Ground-based observation

The latest generation of satellite precipitation estimates, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement Version 06 (IMERG V06), calibrated by monthly rain gauge data, is applied to investigate the changing characteristics of precipitation in different phases (rain, snow, and sleet) over the Tibetan Plateau (TP) from 2001 to 2020. The performance of the IMERG product in capturing the characteristics of the three precipitation phases is firstly evaluated against the ground-based observation. The results show that the IMERG product performs well in capturing the spatial distribution, magnitudes, and annual cycle of the amount and frequency of precipitation in different phases. The bias of annual precipitation amount (frequency) averaged in the 90 stations used in this study is approximately 10% (20%). Comparatively, the IMERG product shows higher skill in estimating rain than snow, and performs better in the other three seasons than in winter. In addition, the performance of the IMERG product is comparable in the southern and northern TP. Both the annual amount and frequency of precipitation have increased in most parts of the TP in the last two decades, with maximal values of 50 mm and 20 days per decade, respectively, occurring in the central part of eastern TP. Changes in the amount and frequency of precipitation are dominated by rain and snow, respectively, in the study period. In addition, light and moderate precipitation contribute approximately 70% and 90% to the increases of the regional mean precipitation amount and frequency, respectively, in the central part of eastern TP. There are no significant and uniform changes in the snowy season during the last two decades, therefore, the increase in snowy days in most areas of the TP is caused by more frequent snow events in the fairly constant snowy season.

Comparison Of Winter Maintenance Requirements (Estonia, Latvia, Lithuania)

Three Baltic states are located next to each other on the coast of the Baltic Sea. Similarly, to Finland, density of population in these countries is low. All three countries have large amount of state roads with low traffic intensity.Climatic differences are minimal. Milder climate is in coastal area but inland the weather conditions are more severe. The average air temperature in January in Estonia is from -2 to -7 degrees Celsius but in Lithuania from -1 to -5 degrees Celsius. The number of snowy days fluctuates from 50 to 120.All three countries have common contracts for summer and winter maintenance where the responsibility for maintenance activities lays on the contractors. Despite common history the methods of the assignment of road maintenance contracts are different in every country. Estonia awards contracts in open tenders and has performance-based contracts. Latvia has the contract awarded to the State Joint Stock Company «Latvijas autoceļu uzturētājs» (“Latvian Road Maintainer”) by law and the contract is unit price based. Lithuania has the contract awarded to the state-owned company « Kelių priežiūra» the contract is performance-based.The requirements for road conditions are quite similar in all three countries. They have three levels of maintenance.High service level means snow and ice-free surface of the road pavement during winter in constant weather conditions and quick response in case of worsening road conditions.Medium service level means that snow and ice is allowed on the surface, but activities must be undertaken to improve skid resistance. The lowest service level means that snow and ice is allowed on the surface and activities to improve skid resistance (mostly only snow cleaning) may be undertaken in some spots only.In Estonia the expenses for winter maintenance are lower than in the neighbouring countries but it does not have impact on traffic safety during winter.

The effect of geo-climatic determinants on the distribution of cutaneous leishmaniasis in a recently emerging focus in eastern Iran

BackgroundCutaneous leishmaniasis (CL) has been reported in recent years in South Khorasan Province, a desert region of eastern Iran, where the main species is Leishmania tropica. Little is known of the influence of geography and climate on its distribution, and so this study was conducted to determine geo-climatic factors by using geographic information system.MethodsThe home addresses of patients with CL patients who were diagnosed and notified from 2009 to 2017 were retrieved from the provincial health center and registered on the village/town/city point layer. The effects of mean annual rainfall (MAR) and mean annual humidity (MAH), mean annual temperature (MAT), maximum annual temperature (MaxMAT), minimum annual temperature (MinMAT), mean annual number of high-velocity wind days (MAWD), mean annual frosty days (MAFD) and snowy days (MASD), elevation, soil type and land cover on CL distribution were examined. The geographical analysis was done using ArcMap software, and univariate and multivariate binary logistic regression were applied to determine the factors associated with CL.ResultsA total of 332 CL patients were identified: 197 (59.3%) male and 135 (40.7%) female. Their mean age was 29.3 ± 2.1 years, with age ranging from 10 months to 98 years. CL patients came from a total of 86 villages/towns/cities. By multivariate analysis, the independent factors associated with increased CL were urban setting (OR = 52.102), agricultural land cover (OR = 3.048), and MAWD (OR = 1.004). Elevation was a protective factor only in the univariate analysis (OR = 0.999). Soil type, MAH, MAT, MinMAT, MaxMAT, and MAFD did not influence CL distribution in eastern Iran.ConclusionsThe major risk zones for CL in eastern Iran were urban and agricultural areas with a higher number of windy days at lower altitudes. Control strategies to reduce human vector contact should be focused in these settings.Graphical abstract

Characteristics and Outcomes of Patients With Acute Coronary Syndrome Who Received Percutaneous Coronary Intervention During Snowy Days.

Acute coronary syndrome (ACS) is affected by several weather conditions. Studies from different geographical locations have yielded mixed results regarding the outcomes of patients presenting with ACS during snowy days, and we aim to report the Cleveland Clinic experience. Patients who presented with an ACS and underwent percutaneous coronary intervention (PCI) from July 1, 2009 to September 30, 2017 were divided into ST-segment elevation myocardial infarction (STEMI), and non-ST segment elevation ACS (NSTE-ACS). According to snowy day arrival, we compared in-hospital mortality, culprit lesion anatomy, and door-to-balloon (DTB) time (in STEMI patients). Findings were confirmed in propensity-score matched cohorts. A total of 6878 patients were included: 1608 patients with STEMI (139 snowy-day vs 1469 non-snowy day PCIs) and 5270 NSTE-ACS (419 snowy-day vs 4851 non-snowy day PCIs). Right coronary artery territories accounted for most of the stented culprit lesions in all STEMI and NSTE-ACS snowy-day PCIs. While left anterior descending artery lesions were predominant in NSTE-ACS non-snowy day PCIs. There was no difference in in-hospital mortality between the snowy-day vs non-snowy day groups (4.3% vs 4.5% in the STEMI group [P=.92] and 1.2% vs 1.7% in the NSTE-ACS group [P=41]). In STEMI patients, mean DTB times were similar (43 ± 55.1 minutes vs 46.7 ± 59.6 minutes; P=.61), which remained true after hours, during weekends and holidays. Outcomes were similar in propensity-score matched cohorts. At our institution, snowy days do not seem to affect in-patient mortality. In STEMI patients, DTB times were similar in those who underwent PCI regardless of the snowfall.

Comparing industrial grain and seed for sowing of corn in the conditions of Uzbekistan

It is necessary to harvest the matured crop of corn at low cost and on time to produce qualitative seed. Therefore, research was conducted about the determination of the maturing period of the corn in the period of waxy maturity of the corncob and physical-mechanical properties of the corn were studied too, for comparing industrial grain and corn for seeding. It was defined that during the experiment the corn may be harvested in its waxy matured period, namely 10-16 days earlier than maturing completely for producing industrial grain in Uzbekistan. However, this harvesting method can not be used to produce corn for seeding. If the corn-crop is harvested earlier for producing industrial grain, there might be an opportunity to do next agronomic and mechanized operations such as ploughing or providing the field with organic fertilizers before snowy days. However, for producing corn-seed, of course, the completely ripening period of the corn-grain should be waited. Studying the physical-mechanical properties of the corn is important to determine the parameters of the corn-harvester and thresher machines for harvesting seed-corn.

Riding to Learn, Learning to Ride: Early School-busing in Connecticut, 1900–1945

Riding to Learn, Learning to Ride: Early School-busing in Connecticut, 1900–1945

EVALUATING HOW THE TEMPERATURE CHANGES IN A SUNKEN SOLAR GREENHOUSE

An increasing amount of sunlight is available to sunken greenhouses located in northern China, but it remains unclear how the indoor temperature changes at different heights and at the north and south ends of the structure. To study how the temperature in a single indoor section, at multiple points, varies in a sunken solar greenhouse, 10 measurement lines were arranged in a section 30 m from the west wall. Each line was equipped with a corresponding WT-59 wireless sensor. Temperature and humidity data were collected from 8 March 2019 to 26 December 2019. The following results were obtained. (1) The indoor temperature variation in a day could be divided into four stages: from 0:00 to 7:30 h, the temperature at all measurement points decreased linearly; from 7:30 to 14:30 h, the temperature at all measurement points increased curvilinearly; from 14:30 to 18:00, the temperature at all measurement points decreased curvilinearly; and from 18:30 to 24:00, the temperature at all measurements points decreased linearly. (2) The maximum temperature on sunny days in the greenhouse was higher than that on cloudy days and snowy days, but the maximum temperature on cloudy days and snowy days in the greenhouse occurred 3 h earlier than on sunny days. (3) The lowest average temperature of a sunken solar greenhouse over 3–6 months was 7.5ଌ, without heating. The research results provide theoretical and reference data on how the temperature changes in the interior of a solar greenhouse and inform about the three-dimensional planting height and ventilation times.

Study on short-term optimal operation of cascade hydro-photovoltaic hybrid systems

Formulating an optimal operation strategy is a real challenge in the actual operation stage of a hybrid system. This study presented a research framework for determining the short-term optimal operation strategy for cascade hydro-photovoltaic hybrid system. First, a method was presented for describing the cascade hydro-photovoltaic hybrid system net load utilizing the daily average net load and fluctuation coefficient. Second, a model was established for the short-term optimal operation of the cascade hydro-photovoltaic hybrid system. Then, a synchronous peak-shaving strategy of cascade hydropower stations was proposed based on the fluctuation coefficient. Finally, the framework was applied to a case study in the Qinghai Province, China. The results showed an obvious competitive relationship between the two objectives. In the hybrid system, the peak-shaving task is mainly carried out by a daily regulation hydropower station, while its base-load task is jointly carried out by runoff and daily regulation hydropower stations. When the average daily runoff is less than 30% of the hydropower rated flow, the cascade hydro-photovoltaic hybrid system should bear single-peak load on sunny, cloudy, and dusty days, otherwise, it should bear double-peak load. It should bear double-peak load under different daily average runoffs on rainy and snowy days. After the cascade hydropower relative position was interchanged, the cascade hydropower overall peak-shaving capability decreased. Thus, the above results prove the feasibility of the research framework, and it provides a reference for the short-term operation of cascade hydro-photovoltaic hybrid system.