Light Intensity Affects the Coloration and Structure of Chimeric Leaves of Ananas comosus var.bracteatus

Ananas comosus var.bracteatus is an important ornamental plant because of its green/white chimeric leaves.The accumulation of anthocyanin makes the leaf turn to red especially in the marginal part.However,the red fades away in summer and winter.Light intensity is one of the most important factors affecting leaf color along the seasons.In order to understand the effects of light intensity on the growth and coloration of the chimeric leaves,Ananas comosus var.bracteatus was grown under full sunlight,50%shade and 75%shade for 75 days to evaluate the concentration of pigments,the color parameters(values L^(*),a^(*),b^(*))and the morpho-anatomical variations of chimeric leaves.The results showed that a high irradiance was beneficial to keep the chimeric leaves red.However,prolonged exposure to high irradiance caused a damage,some of the leaves wrinkled and even burned.Shading instead decreased the concentration of anthocyanin and increased the concentration of chlorophyll,especially in the white marginal part of the leaves.Numerous chloroplasts were observed in the mesophyll cells of the white marginal part of the chimeric leaves under shading for 75 days.The increase in chlorophyll concentration resulted in a better growth of plants.In order to balance the growth and coloration of the leaves,approximately 50%shade is suggested to be the optimum light irradiance condition for Ananas comosus var.bracteatus in summer.

Optimal Allocation of Fault Current Limiter in MMC-HVDC Grid Based on Transient Energy Flow

Economical and reliable protection of DC-side shortcircuit faults has become a key technology for promoting the development of module multilevel converters based on the high voltage direct current grid(MMC-HVDC-Grid).The fault current limiter(FCL)can effectively suppress the rapid development of the fault current and reduce the current breaking capacity of the circuit breaker.In this paper,a method based on transient energy flow(TEF)analysis is proposed to optimize the allocation of a resistive and inductive FCL in the MMC-HVDC-Grid.In the proposed method,the electromagnetic TEF is measured first,and then,the TEF suppression rate and suppression efficiency are defined as optimization objectives,and the installation location of the FCL and its impedance parameters as optimization variables.To test the proposed method,two-terminal and four-terminal bipolar MMC-HVDC-Grids with single-pole-to-ground DC faults are modeled in the PSCAD/EMTDC so that the TEF data can be acquired.The optimal FCLs’location and parameter values are determined through investigating the evolution paradigm of TEF along with changes of the FCL position and parameters.The results prove that the selected parameters can effectively slow down the DC fault current rising rate,thus reducing the requirements on tripping current of the DC breakers.

Intelligent Voltage Control Method in Active Distribution Networks Based on Averaged Weighted Double Deep Q-network Algorithm

High penetration of distributed renewable energy sources and electric vehicles(EVs)makes future active distribution network(ADN)highly variable.These characteristics put great challenges to traditional voltage control methods.Voltage control based on the deep Q-network(DQN)algorithm offers a potential solution to this problem because it possesses humanlevel control performance.However,the traditional DQN methods may produce overestimation of action reward values,resulting in degradation of obtained solutions.In this paper,an intelligent voltage control method based on averaged weighted double deep Q-network(AWDDQN)algorithm is proposed to overcome the shortcomings of overestimation of action reward values in DQN algorithm and underestimation of action reward values in double deep Q-network(DDQN)algorithm.Using the proposed method,the voltage control objective is incorporated into the designed action reward values and normalized to form a Markov decision process(MDP)model which is solved by the AWDDQN algorithm.The designed AWDDQN-based intelligent voltage control agent is trained offline and used as online intelligent dynamic voltage regulator for the ADN.The proposed voltage control method is validated using the IEEE 33-bus and 123-bus systems containing renewable energy sources and EVs,and compared with the DQN and DDQN algorithms based methods,and traditional mixed-integer nonlinear program based methods.The simulation results show that the proposed method has better convergence and less voltage volatility than the other ones.

Schedulable capacity forecasting for electric vehicles based on big data analysis

Fast and accurate forecasting of schedulable capacity of electric vehicles(EVs)plays an important role in enabling the integration of EVs into future smart grids as distributed energy storage systems.Traditional methods are insufficient to deal with large-scale actual schedulable capacity data.This paper proposes forecasting models for schedulable capacity of EVs through the parallel gradient boosting decision tree algorithm and big data analysis for multi-time scales.The time scale of these data analysis comprises the real time of one minute,ultra-short-term of one hour and one-day-ahead scale of 24 hours.The predicted results for different time scales can be used for various ancillary services.The proposed algorithm is validated using operation data of 521 EVs in the field.The results show that compared with other machine learning methods such as the parallel random forest algorithm and parallel k-nearest neighbor algorithm,the proposed algorithm requires less training time with better forecasting accuracy and analytical processing ability in big data environment.