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With water resource shortages becoming a global concern, water conservation is one of the key factors in the sustainable development of watershed ecosystems. However, previous studies of water conservation have focused on annual mean water conservation, which does not reflect changes in water conservation on shorter time scales. Shorter time scales have more significant responses than longer timescales. An annual scale reflects the supply function of the water culverts, whereas a shorter time scale reflects the function of stagnant flood replenishment. Thus, we analyzed the annual, monthly, and daily changes in water conservation in the Zengjiang River Basin, an important water source area and ecological barrier in Guangzhou, from 1959 to 2018. The WEP-L distributed hydrological model and water-balance equations were used to explore the supply, regulation, and peak reduction and replenishment of the water conservation dryness functions. The results indicate that the annual mean water conservation in the Zengjiang River Basin was 685.7 mm from 1959 to 2018, with a non-significant decreasing rate (-1.30 mm/a) during this period. This decreasing trend indicates that the water supply capacity of the basin decreased, which is not conducive to the water security of the basin and downstream areas. Therefore, relevant policies should be formulated and measures should be taken to improve the water conservation capacity of the basin. The multi-year average monthly water conservation in the Zengjiang River Basin was positive in January‒September and negative in October‒December, with non-significant increases in water conservation in January, March, June, November, and December, and non-significant decreases in the other months. The trend ranged from -0.04 to -0.67 mm/a during the entire study period. Water conservation changes in the Zengjiang River Basin during pre-flood, post-flood, and dry-water periods indicate that the basin's ability to regulate water conservation runoff weakened. The maximum 1-day, 3-day, 5-day, and 7-day periods in the Zengjiang River Basin were positive, whereas the minimum 1-day, 3-day, 5-day, and 7-day periods were negative. Except for the minimum 5-day and 7-day periods, the other six daily extreme water conservation indicators exhibited nonsignificant increasing trends. The maximum water conservation values indicate that the peak reduction capacity increased, while the minimum water conservation values indicate that the capacity to replenish depletion decreased in 1- and 3-day periods and increased in 5- and 7-day periods. Spatially, the annual water conservation trend was low in the southeastern and northern parts of the basin, and high in the southwestern part of the basin. Annual, monthly, and daily water conservation levels in the Zengjiang River Basin from 1959 to 2018 embodied its supply, regulation, peak reduction, and replenishment of depletion functions, respectively. The correlation coefficients between water conservation and precipitation were significantly higher than those between evapotranspiration and surface runoff, thereby indicating that precipitation was the most important factor influencing water conservation. The findings of this study reveal multi-temporal evolutionary processes and functional differences in water conservation and provide a scientific basis for ecological protection and construction. [ABSTRACT FROM AUTHOR]

As distributed energy resources, flexible loads, and energy storage devices continue to grow, the distribution grid is gradually transforming into an active distribution network that actively participates in grid operation, regulation, and control. To address the inefficiency in utilizing energy storage devices and flexible loads, a coordinated optimization scheduling method for source, load and storage was proposed. Firstly, a multi-timescale scheduling model for active distribution networks was established. The objective functions were set to optimize the power quality and minimize the operating cost of the distribution grid, while considering constraints such as energy storage, network balance, and flexible load utilization. Finally, an active distribution network was constructed using the IEEE-33 system, and a multi-timescale scheduling strategy for day-ahead and intra-day operations was developed. A stochastic weighted particle swarm optimization algorithm was employed to solve the multi-objective optimization problem. The feasibility of the proposed method was validated. [ABSTRACT FROM AUTHOR]

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In order to solve the problem of increasing regulation pressure of conventional units in traditional power grid caused by large-scale new energy access, a multi-time scale optimal dispatching strategy considering energy storage and multi-industrial load participation was proposed. This strategy can effectively relieve the pressure of power grid regulation by coordinating electrolytic aluminum, cement, steel, unproductive load and energy storage on the load side and conventional unit calling plan on the source side. Firstly, the regulation characteristics of energy storage and various industrial loads were analyzed, and a rolling regulation framework of wind-light-load-storage was established. Secondly, aiming at the source load uncertainty, the multi-scenario stochastic programming combined with fuzzy chance constrained goal programming was adopted to establish a multi-time scale scheduling model, which aimed at the optimal system economy in the day-ahead and day-ahead stages, and took into account both security and economy in the real-time scheduling stage. Finally, through the typical days of sufficient and insufficient new energy, it can be seen that the scheduling strategy can give full play to the adjustment ability of controllable resources. The total cost on the two typical days was reduced by 17. 08% and 44. 13% respectively compared with the previous optimal scheduling, the amount of abandoned new energy was reduced by 39%, and the amount of lost load by 23%, which effectively improved the safety and economy of power grid operation. [ABSTRACT FROM AUTHOR]

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风电固有的随机性使其难以在长时间尺度上被准确预测，且预测误差随预测时间的延长而增大，导致用于抑制风电随机性的储能控制策略往往与实际需求偏差较大。为了有效应对风电的动态随机性，应用由不同响应能力及容量的储能单元组成的混合储能系统，提出一种基于多时间级分层思想与模型预测控制（model predictive control,MPC）理论相结合的混合储能分层MPC优化策略，实现对风电和储能的精准控制。在对风电进行周期预测的基础上构建储能分层动态响应优化模型，上层优化利用大功率慢速响应储能单元在长时间尺度上对风电进行能量转移，下层优化利用小功率快速响应储能单元在短时间尺度上平滑风电波动，且上层优化结果作为下层优化的状态变量。通过分层MPC，不同时间级的风电周期预测信息不断滚动更新，各层间的储能优化控制也可得到有效协调。针对微网中的风储联合发电系统，通过改变分层MPC的滚动时域及步长，得到适用于算例的最优参数。仿真结果验证了该策略可以动态响应风电变化，并有效改善风储系统输出的峰谷特性和波动性。

In view of the changes in video sampling and the different movement speed of target subject, to solve the problems of the current deep network learning with single video sequence feature and the multiple action classifiers with different classification confidence, the video action recognition method was proposed based on multi time scale two-stream CNN and confidence fusion. Two-stream network was used to learn and extract the context information features between video frames of different time span on multiple time scales, and LSTM was used to predict the action categories with various characteristics. For each scale and mode of action classifier, the confidence of category determination was established with consideration of overall difference and uniqueness of category between the sample and other categories. Each classifier was used to fuse the confidence degree and the score of the action category decision for recognizing the video action. The video action recognition experiments were performed on the dataset of UCF101. The results show that the proposed method can effectively learn the contextual information of multiple time scales of videos and improve the accuracy of video action recognition to 92. 2%. [ABSTRACT FROM AUTHOR]

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Distributed Photovoltaic (PV) is ever increasing in the power grid, as the current PV generation rapidly developed. However, it is difficult to directly coordinate the PV into the conventional power grid, due mainly to the intermittent and uncertain nature of PV power generation. As such, there is a great impact on the power flow of a system, particularly the volatility and uncertainty of the output and load demands of Distributed Generation (DG) power in grid-connected microgrids. A friendly way is widely expected that the PV can access the distribution network in the form of a microgrid for the enhanced DG absorption capacity. It is highly urgent to reduce the impact of such volatility and randomness on the energy transmission between microgrids and distribution networks. In this study, a multi-time scale coordinated optimization was performed on energy scheduling strategies using a Variational Modal Decommission-Model Predictive Control (VMD-MPC). Specifically, an MPC was a sort of optimal control with a closed-loop over a finite time domain, suitable for the nonlinear, time-varying, and uncertainty of the system. There was no differentiation scheduling on the forecast of PV power for each Controllable Micropower (CMS)in the microgrid operation because the load was directly applied in the previous multiple-time scale scheduling using MPC optimization. Consequently, some CMS (such as a Lead-Acid Battery, LAB) was run in a short time, high strength, and energy scheduling, whereas, some CMS (such as a Super Capacitor, SC) was only for a long and slow energy scheduling. Thus, the operating characteristics of CMS in different time scales should be considered in the optimization of scheduling. A VMD was utilized to acquire the different loads and subsequence in the PV series of frequency scales, thereby achieving the multiple coordinated optimization scheduling CMS models in different time scales. The scheduling model included a longtime scale of 1 hour and 15 min interval time for a short scale. Dispatching LAB, Micro gas Turbine (MT), and small Biomass Generator (BPG) were usually responded to the signals of a long-time scale. Scheduling SC, MT and BPG were responded to the signals of a short-time scale. The final scheduling was achieved for each CMS to realize the differentiated optimal processing of signals on different time scales, where the calculated values of each model were summed up. Then a feedback correction model was constructed to form a closed-loop control using the day-ahead scheduling, where the difference between the ultra-short-term forecast within the day and the day-ahead forecast was taken as the disturbance input, while the current operating state of the system was taken as the parameter, and the power increment of each CMS was taken as the control variable. The feedback correction effectively enhanced the robustness, while reduced the impact of the system that resulted from the uncertainty of load and PV output. As such, the optimal energy scheduling strategy effectively coordinated the grid-connected microgrids with multiple micro power sources and time scales. Taking a PV microgrid in North China as an example, an MATLAB software was used to simulate and verify the model, indicating optimal scheduling. Better feasibility, effectiveness, and economy of strategy were achieved from the perspectives of power scheduling and operating cost, compared with the traditional active power and MPC multi-time scale scheduling strategy. Accordingly, this finding can provide a practical and effective technical approach for high-permeability microgrids in energy trading under the environment of multiple renewable energy consumption and electricity market. [ABSTRACT FROM AUTHOR]

Water resources management in irrigation districts is important to ensure food security. The current water resources management in China focuses on underground water and surface water (i.e. blue water), and precipitation (green water). However, most studies are on large scale such as global or basin scales and few are on irrigation district scale. The aim of this paper was to quantify and evaluate the consumption of different types of water resources in the irrigation district by the concept of water footprint (WF). The maize and wheat production in the Baojixia Irrigation District of Shaanxi in China from 2008 to 2017 were selected as the case study. One hydrological station and two weather stations were included in the study area. Data of runoff, precipitation irrigation and soil-related physical properties were collected. A regional distributed hydrological model (Soil and Water Assessment Tool, SWAT) based on the hydrological cycle was established. The model was constructed by coupling with the soil water balance model and considering the water delivery losses of the irrigation canals. The blue and green water resources and WFs of crop production were quantified. The variability of the annual WFs of crops during the study period was analyzed, and the WFs of crops on daily and monthly scales were also analyzed in three typical years (dry, normal, wet). The runoff data was used to validate the SWAT model, and the MOD16A2GF Version 6 Evapotranspiration product (MOD 16) at a 500 m pixel resolution was used to verify the evapotranspiration simulation results. Then, the change of WF was analyzed. The results showed that the model could simulate well the runoff with the values of R 2 of 0.74, Nash Efficiency Coefficient (NSE) of 0.69 during the calibration period. By validation, the model was still well and both the values of R 2 and NSE were 0.51. It indicates that the model established in this study could accurately simulate the hydrological process in irrigation districts. During the period from 2008 to 2017, the blue and green water resources in Baojixia Irrigation District increased on the whole. The multi-year average blue water resources in Yangling was the highest but the multi-year average green water resources in Jingyang was the lowest. The multi-year average total WF values of maize and wheat (including the blue WF of irrigation delivery losses) were 573 and 779 m3 /t, respectively, showing an overall rising trend. The fluctuations of total WFs were mostly affected by the green WF. The green WF of crop production was higher at the jointing and filling stages and lower at the maturing stage. The WFs of crop production were fluctuated the most on the daily scale and the fluctuation decreased gradually from month scale to annual scale. The fluctuations of the green and blue WFs of wheat were the largest in the dry year. Although the irrigation water in the dry year was lower than the normal and wet years, the unit maize yield and the unit wheat yield were the highest and the total WF values of maize and wheat were the lowest. This study can provide valuable information for crop production and management in irrigation districts.. [ABSTRACT FROM AUTHOR]

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Copyright of Electric Power Automation Equipment / Dianli Zidonghua Shebei is the property of Electric Power Automation Equipment Press and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)