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RSS2024 Vol. 11, No. 1
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2024, 11(1): 1-1.
Abstract:
2024, 11(1): 1-9.
doi: 10.16516/j.ceec.2024.1.01
Abstract:
Introduction This work aims to select the optimal wind-measurement instrument to satisfy observational requirements of Airborne Wind Energy System (AWES). Method Observation campaign between wind lidar and wind profiler radar was carried out on an AWES demonstration project location. Data acquisition rate, vertical profile characteristics and temporal variation characteristics of both instruments were compared and analyzed. Result The results show that the data acquisition rate of wind lidar decreases to less than 0.4 with altitude rising to 3 km, while the wind profiler radar can maintain above 0.98, revealing better observational adaptability. The vertical profiles of wind speed and direction, as well as the day-by-day and multi-day fluctuation characteristics are consistent in both instruments and can be verified by the reanalysis data and the contemporaneous radiosonde data of high-altitude meteorological stations. Statistical indicators like median, extreme deviation and standard deviation of the wind lidar observations are closer to and better correlated with the reanalysis data, while extreme deviation and standard deviation of the wind profiler radar observations are larger overall. Therefore, the wind-measurement accuracy of wind profiler radar is not as good as that of wind lidar. Conclusion This work suggests that wind-measurement instrument should be reasonably selected and wind measurement schemes should be scientifically set up at different stages of AWES power plant project according to the climatic conditions of the project location.
2024, 11(1): 10-18.
doi: 10.16516/j.ceec.2024.1.02
Abstract:
Introduction To achieve "carbon peaking and carbon neutrality" goals, the total installed capacity of wind and photovoltaic power generation in China will have to be increased by more than twice the current level by 2030. The international community has reached a consensus on developing solar energy resources and other kinds of renewable and clean energy vigorously. In this article, taking Fang County in Hubei Province as an example, a refined evaluation of solar energy resources is conducted and the technical exploitable capacity is calculated by combining reanalysis data and terrain data to provide data support for the appropriate development and utilization of solar energy resources. Method The reanalysis data of nearby ERA5 were corrected and a radiation grid product with an accuracy of 0.1°×0.1° was produced based on the radiation observation data obtained from 2012 to 2020 in one radiation observation station (Station A) located in the county. A geographic information-based radiation model was established by using GIS technology and considering factors such as slope, aspect, altitude, and their impact on radiation. Based on Landsat TM/ETM/OLI remote sensing image data and incorporating land use type data, the limitations of photovoltaic development and the availability of different land types were discussed. Result The built model can satisfactorily reflect the actual radiation situation. The model simulation results are compared with the actual observation data in two other radiation observation stations (Station B and C) in the region and the results are that, considering the overall situation throughout the year, the annual average relative errors of Station B and Sstation C is 5.7% and 3.4%, respectively, and from the perspective of the monthly radiation, the relative error of Station B is between 2% (January) and 14.1% (August), and that of Station C is between 0.1% (February) and 15.4% (June). Conclusion Based on the simulation results, it is clear how solar energy resources are distributed in Fangxian County. Spatially, there are more resources in the windy region of the central valley and fewer resources in the high-altitude mountainous areas in the south and north, with an annual total radiation of 4.00~4.13 GJ/m2. It is calculated that the exploitable area for photovoltaic power generation in Fangxian County is 60.7 km2, and the technological development capacity is 4.501 GW.
2024, 11(1): 54-63.
doi: 10.16516/j.ceec.2024.1.06
Abstract:
Introduction In order to improve the power generation efficiency of photovoltaic brackets, the research and design focus is on a photovoltaic tracker based on Fourier fitting algorithm for apparent solar motion trajectory. Method The tracking accuracy of traditional solar motion trajectory algorithms was analyzed using MATLAB. Furthermore and an 8-order Fourier fitting solar motion trajectory tracking algorithm with better accuracy was proposed. The real-time solar motion trajectory was obtained combined with GNSS positioning technology. The system design employed the STM32 microcontroller as the microprocessor and adopted 6-axis acceleration sensor. The real-time tilt of the photovoltaic tracking bracket was determined by the projection of the gravity vector on its axis. Based on this, a three-dimensional operation model of the tracking bracket was established. By analyzing the cosine effect of sunlight on the bracket, the action angle required for the motor to operate can be obtained. At the same time, to solve the problem of shadow shielding between photovoltaic modules at dawn and dusk, the system added an inverse tracking algorithm. Considering the application of large-scale units, a master-slave motor synchronous control strategy was proposed. Result The Fourier fitting algorithm has higher tracking accuracy, reaching an accuracy of 10-2 orders of magnitude, one order of magnitude higher than traditional algorithms. At the same time, reverse tracking technology can save 24.3% of the photovoltaic array land area, significantly improving land use efficiency. Conclusion This study adopts a more accurate apparent solar motion trajectory tracking model, which effectively solves the cosine effect of solar radiation utilization, improves the power generation efficiency of power stations, realizes the construction of a safe and efficient green energy system, and promotes the promotion and achievement of China's goals of carbon peak and carbon neutrality.
2024, 11(1): 19-32.
doi: 10.16516/j.ceec.2024.1.03
Abstract:
Introduction In order to promote the sustainable and healthy development of wind energy resources and provide a scientific basis for the rational development and use of wind energy resources in the inland areas, the paper analyzes the characteristics of the main wind energy parameters and their differences between the mountainous, hilly, plain and lake areas. Method We used the observation data of 11 wind masts with heights of 90~150 m distributed in five different terrains of mountainous, hilly, plain and lake areas in Hubei Province for a whole year, analyzed the characteristics of the main wind energy parameters and their differences between the mountainous, hilly, plain and lake areas. Result The analysis results show that: (1) the daily variation of wind speed at the upper level of each mast is in the range of 1.0~2.3 m/s and is significantly larger in the mountainous and hilly terrain than in the plain and lake areas, and the variation tends to be consistent at various levels in that it is small during the day and large at night, while in the plain and lake areas, the daily variation at the lower level has opposite characteristics to the upper level and is large during the day and small at night; (2) the composite wind shear index of each mast ranges from 0.055~0.328, which is greater in the mountainous and hilly terrain than in the plain and lake areas, the daily variation of wind shear index is from 0.063~0.378, which is small during the day and large at night and significantly larger in the plain and lake areas than in the mountainous and hilly areas, and wind shear under high wind conditions is more discrete in the mountainous and hilly terrain and more concentrated in the plain and lake terrain, basically stable between 0.1~0.2; (3) the annual mean turbulence intensity in the effective wind speed section at the upper level of each mast is 0.13~0.18, which is greater in the mountainous terrain than in the plain and lake areas, and the daily variation of turbulence intensity in each mast is from 0.07~0.15, characterized in that the turbulence intensity is large during the day and small at night and the daily variation is significantly larger in the mountainous and hilly terrain than in the plain and lake areas. Conclusion It can be seen that the characteristics of wind energy resources under different terrains show obvious spatial and temporal variations under the action of power and heat, the results of the analyses can provide guidance for the development and utilization of wind energy resources.
2024, 11(1): 33-41.
doi: 10.16516/j.ceec.2024.1.04
Abstract:
Introduction Wind power density is an important parameter for wind resource assessment, and the accurate calculation of wind power density relies on the accuracy of fitting the wind frequency with the Weibull distribution. It is helpful that reasonable analysis the wind power density in that decreasing the risks and improving the decision-making of wind farm investment. Considering the lack of research on the accuracy of Weibull distribution fitting in wind resource assessment, the paper aims to improve the accuracy of wind resource assessment by comparing and studying which method provides a higher accuracy in Weibull distribution fitting. Method Five commonly used methods for simulating wind frequency distribution based on the Weibull model were studied. The coefficient of determination was introduced to determine the accuracy of Weibull simulation. The absolute error and relative error between the wind power density calculated by the Weibull function and the wind power density calculated from measured data were compared. Result The results show that the energy pattern factor (EPF) method and the maximum likelihood estimation (MLE) method obtained higher coefficients of determination for Weibull fitting compared to other methods, including empirical methods (EPJ and EPL) and the least squares (LLSA) method. The wind power density calculate using these two methods, based on the obtained parameters, has smaller absolute errors and relative errors compared to the other three methods when compared to the wind power density calculate from measured data. Conclusion The research results can provide a reference for selecting the appropriate Weibull method to calculate wind power density in wind resource assessment and the true features of wind farm can be revealed, then, the accuracy of wind resource assessment can be improved.
2024, 11(1): 42-53.
doi: 10.16516/j.ceec.2024.1.05
Abstract:
Introduction The wake effect of wind turbines is an important cause of energy loss in wind farms. The wake study of wind turbines is beneficial to optimization of the turbine arrangement and improvement of the economic efficiency of wind farms. Method This paper presented a comparative study of the wake wind speed decay and turbulence intensity prediction of eight common wake models, respectively. To ensure the rationality of the evaluation, the quantitative analysis was carried out based on wind farm measurements and wind tunnel experimental data, and the range of comparison data was limited to 3 to 10 times the diameter downstream the turbine. Result The analysis results show that, for the prediction of wake wind speeds, the two-dimensional model fits the actual wake wind speed distribution structure better than the one-dimensional model, in which Jensen-Guass has better wake width prediction ability, while the 2D-k-Jensen wake center wind speed prediction has higher accuracy and adaptability to multiple conditions, with the maximum mean deviation and standard deviation of 8.7% and 5.5%, respectively, which are both applicable to the prediction of turbine wake wind speed. Jensen model has the best prediction ability of wake center wind speed in one-dimensional model, and the mean deviation of some conditions is less than 10%. While Park model is better in predicting the horizontal distribution of wake wind speed. In Case 2, the prediction performance is comparable to that of two-dimensional model, and the mean deviation of each condition is less than 10%, so it is more suitable for wake speed prediction than the former. For the prediction of turbulence intensity, the Ishihara model shows a clear advantage in the prediction of turbulent structure, with mean deviations below 10%, but the prediction of turbulence intensity at the center of the wake is poor, which is not conducive to the prediction of the turbulence intensity at locations downstream the turbine. Among the remaining models, Frandsen and Jensen-Guass models are relatively good at prediction of low ambient turbulence intensities. However, there is an opposite trend between the two for high ambient turbulence intensities: the Frandsen model has higher prediction accuracy and is suitable for turbine turbulence intensity prediction, whereas the prediction result of Jensen-Guass is much larger than the experimental value, and is unstable. The prediction accuracy of wake wind speed for all models is greatly improved at high upstream wind speeds, and the increase in ambient turbulence intensity contributes to the prediction accuracy of the wake wind speed and turbulence intensity for all models, except for the Jensen-Guass model. Conclusion The predicted values of wake wind speed for the Jensen-Guass model and the 2D-k-Jensen model coincides better with the measured data, while the prediction performance of the Frandsen model turbulence intensity is better, so they can be used as the reference wake models for the optimization of wind turbine placement and wake control analysis for offshore wind farms.
2024, 11(1): 64-72.
doi: 10.16516/j.ceec.2024.1.07
Abstract:
Introduction Ports are facing significant electricity demand and carbon reduction pressure. The abundant wind energy resources in port areas make wind power highly promising for port applications. The site selection and design of wind turbines directly impact the economic benefits of power plants and production safety in the port area, thus, it has important research significance in the engineering design of wind energy systems. Method Taking Chuanshan port area of Ningbo-Zhoushan Port as an example, this study investigated the engineering design principles and limiting factors for large-scale wind turbines in port areas and analyzed the wind resources and the potential for wind energy utilization in the port and the special weather affecting the port area based on statistic history data; based on the actual conditions of the port, the site selection and design of wind turbines were analyzed considering the limiting factors of the port area. The study also examined the selection and design methods of wind turbines and analyzed the selection and design from multiple dimensions, taking four mainstream models on the market as examples. Result Ultimately, the WTG2 wind turbine that meets at least IEC Class I standards and has been specially designed to withstand typhoons is selected. The annual electricity generation can reach 24.53 GWh, resulting in cost savings of 0.233 hundred million and a reduction in CO2 emissions of approximately 1.425 1 ten thousand tons. Conclusion The proposed engineering design method for large-scale wind turbines in port areas, based on actual conditions, has been verified as feasible and can achieve significant economic and ecological benefits. It can contribute to carbon peaking and energy self-sufficiency in ports, providing valuable references for the engineering design of wind farms in port areas.
2024, 11(1): 73-84.
doi: 10.16516/j.ceec.2024.1.08
Abstract:
Introduction To test the reliability of the CMA-WSP wind speed product in short-term wind resource forecasting, the CMA-WSP 3 d wind speed forecasting product with a wind speed of 100 m is tested and analyzed. Method This research was based on the measured data of 100 m wind speed in three wind farms in Zaoyang Zhoulou, Macheng Caijiazhai and Central Dajin. Result The results are as follows: (1) CMA-WSP has a good overall forecasting performance on the wind speed in Zaoyang wind farm within three days. The forecasted results are consistent with the measured wind speed change trend. The correlation between the forecasted and the measured wind speed on the first day are 0.728, 0.74 and 0.86 for 15 min intervals, hourly averages, and daily averages, respectively. (2) The relative error between the CMA-WSP forecast and the measured wind speed shows a strong regularity. The relative errors for the forecasted wind speed on the first day are 68 %, 70 % and 92 % for 15 min intervals, hourly averages, and daily averages, respectively. The forecasted wind speed is higher than the measured wind speed. The hourly average wind speed and relative error are characterized by low wind speed during the day and high wind speed at night. The change of monthly average wind speed is opposite to the change of MRE value, and it is the lowest from January to June and from October to December, and the highest from July to September. (3) When considering regional differences, CMA-WSP has the best forecasting effect on the wind speed in Zaoyang wind farm. The correlation between the forecasted and the measured wind speed on the first day can reach 0.728, and the correlation on the second to third days is also more than 0.6. The correlation between the forecasted wind speed and the measured wind speed in Caijiazhai and Central Dajin by CMA-WSP is less than 0.6. Conclusion CMA-WSP forecasting performance is favorable as a whole, and the relative error has strong regularity. It is beneficial to revise the product and reduce the error level in the next step.
2024, 11(1): 85-95.
doi: 10.16516/j.ceec.2024.1.09
Abstract:
Introduction With the extensive construction of wind farms, the combination of researches on different machine learning algorithms and meteorological forecasting modes has received widespread attention. Method This paper was based on the spatial distribution characteristics of wind energy resources in Hubei Province, and utilized representative stations in combination with experimental data analysis to conduct in-depth discussions on the results. Result The wind farms in operation and under construction in Hubei Province are all located in the "Three Zones and One Area", including the north-south wind zone from Jingmen to Jingzhou in the central part of Hubei Province, the east-west wind zone from Zaoyang to Yingshan in the north of Hubei Province, certain lake islands and zones along the lake, as well as some high mountainous areas in the southwest and southeast of Hubei Province. This research uses four different numerical forecasting products, namely CMA-WSP, CMA-GD, WHMM, and EC, to compare with the measured wind speeds and investigated the applicable range of these four numerical modes. Conclusion By analyzing the performance of five ensemble forecasting methods based on machine learning and the mean method, we identified suitable algorithm and forecasting model combinations, providing references for improving the accuracy of ensemble forecasting.
2024, 11(1): 96-104.
doi: 10.16516/j.ceec.2024.1.10
Abstract:
Introduction Combining mesoscale numerical model and large eddy simulation (LES) model, numerical sumulation of sub-kilometer-scale project unit placement is carried out, which takes into account atmospheric boundary layer changes. It provides offshore wind turbine projects with high-efficiency power generation placement schemes. Method This study converted the mesoscale numerical weather simulation results into boundary conditions for the input of the LES model and introduced model parameters reflecting the operation of an actual wind farm into the LES simulation. The numerical sumulation experiments of the ambient wind field in the wind farm region was carried out under the consideration of the change of the actual atmospheric boundary layer, and the results of the refined simulation scheme of this wind field were evaluated based on the observation data collected from the wind farm. Result The simulation results indicate that by converting the results of the mesoscale weather model into the dynamic drive which is read by the LES model and simulating the wind field where the wind farm is located based on the model, the simulation results are able to replicate the changes in the external wind field after passing through the wind farm and the wake generated within the wind turbine fleet, as well as its impact on the internal wind field of the wind farm. The root mean square error of wind speed simulation at the hub of wind turbines is 1.54 m/s. Conclusion The refined wind field simulation scheme, which takes into account the variation of mesoscale meteorological elements and the impact of wind farms on the ambient wind field, can provide guidance for the design phase of actual projects.
2024, 11(1): 105-111.
doi: 10.16516/j.ceec.2024.1.11
Abstract:
Introduction In order to analyze the influence of atmospheric stability on the output of wind turbines, and to provide technical reference for improving the simulation accuracy of CFD wind energy resources. Method Based on the observation data of different heights of two wind towers in flat and complex mountainous terrain, including wind speed, air temperature, air pressure and other data of multi-layer height, the atmospheric stability of the two wind towers in the region was calculated by using the Moning-Obkhov length method, and the stability calculation results were classified according to Irwin atmospheric stability classification standard. Result The results show that: in the near surface layer, the vertical mixing effect caused by the atmospheric thermal effect in complex mountain area is more obvious, and the atmospheric instability is stronger, but the vertical mixing effect is not sufficient; The impact of atmospheric stability on wind turbine output in complex mountainous area is greater than that in flat terrain, and its uncertainty is higher. The reason largely depends on the complexity of atmospheric stability, so it is more necessary to consider the impact of atmospheric stability in wind energy resource assessment. Conclusion It is necessary to consider the influence of atmospheric stability in CFD wind farm fluid modeling, especially under the condition of ultra-low wind speed complex mountain site, the evaluation of atmospheric stability is particularly important for fan selection and power generation simulation accuracy.
2024, 11(1): 112-121.
doi: 10.16516/j.ceec.2024.1.12
Abstract:
Introduction The volatility and intermittency of wind energy pose significant challenges for large-scale wind power integration. An effective approach to address this issue is to provide accurate wind power forecasting. Method In response to this challenge, this study proposed a wind power forecasting model for deep learning neural networks based on an improved LSTM (Long Short-Term Memory) architecture. The model incorporated a wind power forecasting approach that included independently developed data anomaly detection and processing, wind speed feature extraction and hyperparameter tuning. To enhance the neural network model's ability to accurately learn the impact of wind speed features on wind power, a feature engineering method combining feature screening and feature augmentation was also defined. Result The simulation results demonstrate that the proposed data cleaning and data augmentation algorithm can enhance the accuracy of various machine learning algorithms by approximately 5%. Furthermore, the proposed improved LSTM neural network model, after data cleaning, outperforms traditional algorithms and state-of-the-art neural network algorithms in the industry, achieving a 2.5% increase in accuracy. Conclusion The improved approach not only exhibits robust capability in cleaning noisy data but also consistently outperforms other algorithms in terms of forecasting accuracy across all experiments. This model provides valuable guidance for practical applications in the field of wind power forecasting.
2024, 11(1): 122-132.
doi: 10.16516/j.ceec.2024.1.13
Abstract:
Introduction The rapid development of clean energy sources, such as wind and solar power, has led to significant changes in the energy structure of the power system, which consequently has increased uncertainty in safe grid operation and imposed new challenges in accurately forecasting electricity consumption. Among the numerous influencing factors on grid electricity consumption, meteorological factors exert a significant impact. Therefore, it is imperative to analyze the influence of meteorological factors on the refined forecast of grid electricity consumption. Method The influence of meteorological factors on electricity consumption was investigated, based on the daily electricity consumption data and meteorological elements in 2017, including the maximum temperature, average temperature, minimum temperature, atmospheric pressure, relative humidity and wind speed, and using the combined method of ensemble empirical mode decomposition (EEMD) and back-propagation (BP) neural networks. Result This study reveals a significant correlation between the average temperature, maximum temperature, minimum temperature, atmospheric pressure, and relative humidity with the low-frequency component of the electricity consumption series processed by EEMD, and an insignificant correlation with the high-frequency component and periodic component. Conclusion The electricity consumption forecast using the BP regression model exhibits considerable deviations when compared to the actual status. The electricity consumption forecast by the EEMD-BP regression model shows a significant improvement in accuracy, attributed to the incorporation of meteorological factors, indicating that the combined forecast method of EEMD-BP based on meteorological factors effectively enhances the accuracy of electricity consumption forecast. Consequently, it can serve as an effective technical support for improving short-term electricity consumption forecast methods.
2024, 11(1): 133-142.
doi: 10.16516/j.ceec.2024.1.14
Abstract:
Introduction This study focuses on the prediction of summer daily maximum power load in Wuhan, Huangshi, and Yichang of the Hubei section in the Yangtze River Economic Belt based on climatic forecast model temperature data. Method By analyzing the daily maximum power load data from 2008 to 2019, along with meteorological elements such as average temperature, maximum temperature, minimum temperature, and regional climate model (RegCM4) forecast data, the characteristics of meteorologically sensitive power load in the three regions were analyzed. Regression analysis and a group-particle optimized back-propagation (BP) neural network algorithm were used to quantitatively predict the future (from 2020 to 2096) daily maximum power load. Result The results indicate that there is a significant correlation between summer average temperature and meteorologically sensitive load. The predicted values of the summer daily maximum power load in Wuhan and Yichang show a steady increase similar to the past decade, with the growth rate of regression prediction slightly higher than that of neural network prediction. The growth rate in Yichang is higher than that in Wuhan, exceeding 40% at its peak. The expected values of the daily maximum power load in Huangshi show distinctly different prediction results compared to the other two locations. Conclusion Predicting the variation patterns of summer daily maximum power load in medium-to-large cities along the Yangtze River Economic Belt is beneficial for planning the required additional grid capacity in the future.
2024, 11(1): 143-156.
doi: 10.16516/j.ceec.2024.1.15
Abstract:
Introduction Accurate load forecasting underpins the operation optimization of the electricity systems and is an indispensable aspect of energy management within such systems. Given the low accuracy and high computational complexity inherent in traditional methodologies that combine data decomposition and machine learning models, this study proposes a novel Empirical Mode Decomposition and Multi-Layer Perceptron (EMD-MLP) model for predicting day-ahead electrical load. Method Initially, the EMD method decomposed the original load time series into multiple Intrinsic Mode Function (IMF). These IMFs were then reconstructed into high-frequency and low-frequency components using extreme point partitioning, simplifying the prediction target. Subsequently, each reconstructed components was modeled separately for prediction, and the results were cumulatively used to provide the forecasted electrical load value. Result The proposed model is tested using real-world electrical load data of 2018 and 2019 from the Australian electricity market. Conclusion Comparing the extrapolative capabilities of our EMD-MLP model with persistence model, standalone MLP model and traditional EMD ensemble model confirms the effectiveness of our model in enhancing prediction accuracy. Moreover, while ensuring accuracy, the proposed EMD-MLP model simplifies the complexity and improves the efficiency of the forecasting process, thereby providing a practical solution for both day-ahead and real-time electrical load forecasting.
2024, 11(1): 157-165.
doi: 10.16516/j.ceec.2024.1.16
Abstract:
Introduction To ensure the safety, reliability and economy of power supply, the paper evaluates the power consumption meteorological risk of Yinchuan City from 2014 to 2022. Method A power consumption meteorological risk assessment model was established by risk index, sensitivity index and vulnerability index, and the correlation analysis, percentile method, normalization and other methods based on meteorological data from automatic stations and electricity load data. Result The results show that the peak of electricity load appeared in heating period and transition period (January, March, October to December) and Summer (June to July), and the lower values in April and September. The results of correlation analysis show that the higher temperature, the greater the electrical load. High temperature intensity, high temperature frequency, daily electricity consumption, power supply load, population density and community power supply facilities are selected as indicators of power consumption by the correlation analysis and the expert questionnaire survey, and the weights are 0.5, 0.5, 0.98, <0.01, 0.99, <0.01. The results of the power consumption meteorological risk model show that the high and higher risk areas of power consumption meteorology in Yinchuan City are mainly located in Jinfeng District, Xingqing District is mainly located in medium risk areas, and Xixia District is mainly characterized by lower and lower risks. Conclusion The research results can provide reasonable power dispatch and supply strategies for regional power supply based on the meteorological risk assessment model for power supply and the actual needs of the power supply department.
2024, 11(1): 166-175.
doi: 10.16516/j.ceec.2024.1.17
Abstract:
Introduction Accurate and efficient short-term electricity load forecasting is a prerequisite for ensuring the safe and reliable operation of power system, and it is also the basis for the rational arrangement of power generation plans in the power grid. Therefore, studying the relationship between meteorology and electricity load is of great significance for load forecasting. Method Based on the electricity load data at 15 min intervals during the period between January 1 of 2013 and December 31 of 2021 provided by the State Grid Hebei Electric Power Co., Ltd. as well as the corresponding meteorological observation data of Shijiazhuang station, this paper analyzed the temporal variation characteristics of daily peak electricity load in Shijiazhuang, and in particular, the meteorological conditions corresponding to the samples with a daily peak electricity load that was 10% higher than that of the previous day were analyzed. The Spearman's rank correlation method was used to analyze the correlation between daily peak electricity load in Shijiazhuang and the meteorological factors of the previous day, and significantly correlated meteorological factors were identified. The response curves of the significantly correlated meteorological factors to the next day's peak electricity load were drawn using the smooth curve fitting method, and the analysis revealed the changing trend of daily peak electricity load with the variations of meteorological factors, as well as the response thresholds. For different threshold ranges, the relative risk of meteorological factors to the changes of the daily peak electricity load was calculated based on the Poisson distribution. On this basis, the variation magnitudes of daily peak electricity load caused by per unit change in each meteorological factor within different threshold ranges in Shijiazhuang were calculated, that is, the quantitative impacts of the changes in different meteorological factors on the variation of daily peak electricity load were revealed. Result Taking temperature as an example, when the daily average, maximum and minimum temperatures are higher (lower) than the thresholds, the relative risk to the next day′s peak electricity load increases (decreases) by 2.25% (0.62%), 1.92% (0.57%) and 2.07% (0.60%) respectively for every 1 °C increase in temperature. Conclusion Based on the relative risk of different meteorological factors to daily peak electricity load in Shijiazhuang, a method for predicting the next day′s peak electricity load is proposed. The test performed using the daily electricity load and meteorological data of Shijiazhuang in 2022 reveals that the prediction effect can meet the needs of daily electricity meteorological service.
2024, 11(1): 176-184.
doi: 10.16516/j.ceec.2024.1.18
Abstract:
Introduction In order to solve the safety problem of towing transportation of floating OWT（ offshore wind turbine ), the towing characteristics of fully submersible floating OWT are studied. Method The model of the fully submersible floating OWT towing system was established through Moses software. For the towing process, five groups of sea conditions were set up for simulation, the numerical model calculations were carried out, the influence of wind speed and wind wave direction on towing response of fully submersible floating OWT was studied. According to the analysis results, some suggestions were put forward to help the towing safety of the fully submersible floating OWT. Result The results show that, compared with rolling, the pitch and heave of the fully submersible floating OWT are more obviously affected by the wind speed, and the greater the wind speed, the greater the motion amplitude; when the wind wave direction is 90°, the rolling of OWT is greatly influenced by the wind wave direction, and when the wind wave direction is 0°, the pitch and heave of the OWT is greatly influenced by the wind wave direction. Conclusion On the premise of ensuring that the towing process is within the towing window period, it is suggested that the fully submersible floating OWT should be towed against the wind in practical engineering to reduce the risk of vibration and resonance of OWT. At the same time, we should try our best to avoid the situation that the wind direction is perpendicular to the towing direction, which will easily lead to a large degree of rolling and make the towing process more dangerous. When towing against the wind, the motion response and towing force of the fully submersible floating OWT can still meet the requirements of safe towing at a large wind speed of 30.9 m/s exceeding the average sea condition.
2024, 11(1): 185-195.
doi: 10.16516/j.ceec.2024.1.19
Abstract:
Introduction With the increasing frequency of extreme weather in China's coastal areas, typhoon disasters pose a great threat to coastal power grids, in order to improve the lines wind protection ability, the design of coastal strong wind area lines usually take to improve the design of the meteorological return period, with the promulgation of the current standards of overhead transmission lines (DL/T 5551－2018), due to the differences in wind load calculation between the new and old codes, the compatibility of the previous wind reinforcement measures with the current code has become an urgent issue to be studied. Method This paper took the 500 kV important lines in the coastal strong wind area of the southern power grid as an example, and based on the grading system of wind protection reliability of lines, by formulating different wind protection design schemes, and then evaluating the level of wind resistance and reliability under different schemes, and comparing the technology and economy under different defense schemes, finally, combined with the engineering examples, it recommended the wind protection design scheme for 500 kV important lines in coastal strong wind areas. Result Under the premise of implementing the current standards, designing according to the 50-year return period wind speed and considering 1.1 times the importance coefficient (the third option) and the previous design based on GB 50545－2010 according to the 100-year return period, both in the wind resistance and engineering investment can be a good fit. Conclusion The third option is the choice of the best combination of technical and economic indicators among the fortification options and the wind-resistant capacity can reach the requirement of the medium limit of 16-stage typhoon, which is of great significance for saving resources and reducing carbon emissions.
2024, 11(1): 196-204.
doi: 10.16516/j.ceec.2024.1.20
Abstract:
Introduction The distribution characteristics of mountain fires by using the historical disaster data of mountain fires related to transmission lines in Hubei province has been analyzed. Method Based on the mountain fire disaster data of transmission lines in Hubei Province from 2016 to 2021, the methods of mathematical statistics, climate statistics and GIS spatial superposition were used to analyze the spatiotemporal distribution of transmission line mountain fires in Hubei Province as well as the characteristics of the relationship between mountain fires and meteorological, underlying surface, and social humanities factors. Result In terms of time, 2019 is the year in which mountain fires occurred most frequently on transmission lines in Hubei in recent years. Autumn and winter are peak seasons for mountain fires, with February and September being the months with a higher risk of occurrence. In terms of space, the eastern part of Hubei Province is the region with frequent mountain fires, while the Yichang and Jingmen regions are the second highest risk areas. The relationship between the mountain fire and the meteorology shows that the precipitation in the month before the occurrence of most mountain fires is 30% to 100% less than the normal level, the temperature is 0.5 ℃ to 3 ℃ higher than the normal level, the humidity is 3.8% to 23% lower than the normal level, and the wind speed is 0.1 to 1.9 m/s higher than the normal level. The relationship between mountain fire and underlying surface shows that mountain fire mainly occurs in cultivated land, accounting for 50.32% of the total. The mountain fires are mainly concentrated in the areas with an altitude below 150 meters and a slope below 6°, and 76.90% of the mountain fires occur on the sunny slope. The relationship between mountain fire and social humanities shows that mountain fire mainly occurs in the towns around the urban circle with a population density of 100~600 people/square kilometer, as well as the buffer zone about 1.6 kilometers away from the road. Conclusion Mastering the distribution characteristics of mountain fires related to Hubei transmission lines can help select indicators and determine thresholds for mountain fire risk warning models.
