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A Temporal Convolutional Network Based Hybrid Model for Short-term Electricity Price Forecasting

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CSEE Journal of Power and Energy Systems 2024年第3期10卷 1119-1130页

作者： Haoran Zhang weihao hu Di Cao Qi huang Zhe Chen Frede Blaabjerg School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengduChina Department of Energy Technology Aalborg UniversityAalborgDenmark IEEE

Electricity prices have complex features,such as high frequency,multiple seasonality,and nonlinearity.These factors will make the prediction of electricity prices difficult.However,accurate electricity price prediction is important for energy producers and consumers to develop bidding strategies.To improve the accuracy of prediction by using each algorithms’advantages,this paper proposes a hybrid model that uses the Empirical Mode Decomposition(EMD),Autoregressive Integrated Moving Average(ARIMA),and Temporal Convolutional Network(TCN).EMD is used to decompose the electricity prices into low and high frequency components.Low frequency components are forecasted by the ARIMA model and the high frequency series are predicted by the TCN model.Experimental results using the realistic electricity price data from Pennsylvania-New Jersey-Maryland(PJM)electricity markets show that the proposed method has a higher prediction accuracy than other single methods and hybrid methods.

关键词： Autoregressive integrated moving average model electricity price forecasting empirical mode decomposition temporal convolutional network

Critical Review on Improved Electrochemical Impedance Spectroscopy-cuckoo Search-Elman Neural Network Modeling Methods for Whole-life-cycle Health State Estimation of Lithium-ion Battery Energy Storage Systems

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Protection and Control of Modern Power Systems 2024年第2期9卷 75-100页

作者： Ran Xiong Shunli Wang Paul Takyi-Aninakwa Siyu Jin Carlos Fernandez Qi huang weihao hu Wei Zhan School of Information Engineering Southwest University of Science and TechnologyMianyangChina School of Information Engineering Southwest University of Science and TechnologyMianyang621010 China Mechanical Engineering from Kwame Nkrumah Uni-versity of Science and Technology Ghana Northeastern University ShenyangChina Robert Gordon University Scotland Southwest University of Science and Technology(SWUST) Mian-yangChina University of Electronic Science and Technology of China ChengduChina Xi’an Jiaotong University Xi’anChina SPIC Southwest Energy Research Institute ChengduChina

Efficient and accurate health state estimation is crucial for lithium-ion battery(LIB)performance monitoring and economic evaluation.Effectively estimating the health state of LIBs online is the key but is also the most difficult task for energy storage systems.With high adaptability and applicability advantages,battery health state estimation based on data-driven techniques has attracted extensive attention from researchers around the world.Artificial neural network(ANN)-based methods are often used for state estimations of LIBs.As one of the ANN methods,the Elman neural network(ENN)model has been improved to estimate the battery state more efficiently and accurately.In this paper,an improved ENN estimation method based on electrochemical impedance spectroscopy(EIS)and cuckoo search(CS)is established as the EIS-CS-ENN model to estimate the health state of LIBs.Also,the paper conducts a critical review of various ANN models against the EIS-CS-ENN model.This demonstrates that the EIS-CS-ENN model outperforms other models.The review also proves that,under the same conditions,selecting appropriate health indicators(HIs)according to the mathematical modeling ability and state requirements are the keys in estimating the health state efficiently.In the calculation process,several evaluation indicators are adopted to analyze and compare the modeling accuracy with other existing methods.Through the analysis of the evaluation results and the selection of HIs,conclusions and suggestions are put forward.Also,the robustness of the EIS-CS-ENN model for the health state estimation of LIBs is verified.

关键词： Lithium-ion battery health state esti-mation elman neural network electrochemical imped-ance spectroscopy cuckoo search health indicators

Pentacoordinate Phosphoranes as Versatile Reagents in Fluoroalkylation Reactions

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CCS Chemistry 2024年第1期6卷 165-176页

作者： huanhuan Song weihao Li Xiaoying Wang Kaiteng Wang Jingwen Li Shuai Liu Pin Gao Xin-hua Duan Jinbo hu Mingyou hu School of Chemistry Engineering Research Center of Energy Storage Materials and DevicesMinistry of EducationXi’an Key Laboratory of Sustainable Energy Material ChemistryXi’an Jiaotong UniversityXi’an 710049 Key Laboratory of Organofluorine Chemistry Center for Excellence in Molecular SynthesisShanghai Institute of Organic ChemistryUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200032

A general method for the synthesis of bench-stable bis(difluoromethyl)pentacoordinate phosphoranes has been developed.The reaction is rapid,operationally simple,and easily scalable.The pentacoordinate phosphoranes can generate both difluoromethyl radical(·CF_(2)H)and difluorocarbene(:CF_(2))intermediates.Thus,a variety of fluoroalkylation transformations havebeen achieved by·CF_(2)H,such asoxidativedifluoromethylation of electron-deficient heterocycles,nickel/photoredox dual-catalyzed difluoromethylation of aryl bromides,and photoredox difluoromethylation of alkenes,or by:CF_(2),such as gem-difluorocyclopropanation of alkenes,base-promoted difluoromethylation of heteroatom nucleophiles,Pd-catalyzed difluoromethylation of arylboronic acids,and Cu-mediated trifluoromethylation of aryl iodides(via:CF_(2) and recombined CF_(3)-).These fluoroalkylation methods have been successfully applied to late-stage fluoroalkylation of drugs and drug-like molecules.

关键词： pentacoordinate phosphorane difluoromethyl radical difluorocarbene difluoromethylation fluoroalkylation

Bifunctional noble-metal-free cocatalyst coating enabling better coupling of photocatalytic CO_(2)reduction and H_(2)O oxidation on direct Z-scheme heterojunction

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Nano Research 2024年第6期17卷 5022-5030页

作者： Wei Zhao weihao Mo Yan Zhang Lingxuan hu Yiyi Zheng Zhulei Chen Xiangyue Niu Yuling Zhao Lichun Liu Shuxian Zhong Song Bai Key Laboratory of the Ministry of Education for Advanced Catalysis Materials College of Chemistry and Materials ScienceZhejiang Normal UniversityJinhua 321004China College of Biological Chemical Sciences and Engineering and Nanotechnology Research InstituteJiaxing UniversityJiaxing 314001China College of Geography and Environmental Sciences Zhejiang Normal UniversityJinhua 321004China College of Chemical and Biological Engineering Zhejiang UniversityHangzhou 310058China School of Biological and Chemical Engineering Panzhihua UniversityPanzhihua 617000China

Selective loading of spatially separated redox cocatalysts on direct Z-scheme heterojunctions holds great promise for advancing the efficiency of artificial photosynthesis,which however is limited to the photodeposition of noble metal cocatalysts and the fabrication of hollow double-shelled semiconductor heterojunctions.Moreover,the co-exposure of discrete cocatalyst and semiconductor increases the product diversity when both the exposed sites of which participate in CO_(2)photoreduction.Herein,we present a facile and versatile protocol to overcome these limitations via surface coating of Z-scheme heterojunctions with bifunctional noble-metal-free cocatalysts.With Cu_(2)O/Fe_(2)O_(3)(CF)as a model heterojunction and layered Ni(OH)_(2)as a model cocatalyst,it is found that Ni(OH)_(2)lying on the surfaces of Cu_(2)O and Fe_(2)O_(3)separately co-catalyzes the CO_(2)reduction and H_(2)O oxidation.Thorough experimental and theoretical investigation reveals that the Ni(OH)_(2)outer layer:(i)mitigates the charge recombination in CF and balances their transfer and consumption;(ii)reduces the rate-determining barriers for CO_(2)-to-CO and H_(2)O-to-O_(2)conversion,(iii)suppresses the side proton reduction occurring on CF,and(iv)protects the CF from component detachment.As expected,the redox reactions stoichiometrically proceed,and significantly enhanced photocatalytic activity,selectivity,and stability in CO generation are achieved by the stacked Cu_(2)O/Fe_(2)O_(3)@Ni(OH)_(2)in contrast to CF.This study demonstrates the significance of the synergy between bifunctional cocatalysts and Z-scheme heterojunctions for improving the efficacy of overall redox reactions,opening a fresh avenue for the rational design of artificial photosynthetic systems.

关键词： Z-scheme heterojunction bifunctional cocatalyst CO_(2)reduction H_(2)O oxidation stoichiometric redox reaction

Multi-energy Management of Interconnected Multi-microgrid System Using Multi-agent Deep Reinforcement Learning

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Journal of Modern Power Systems and Clean Energy 2023年第5期11卷 1606-1617页

作者： Sichen Li Di Cao weihao hu Qi huang Zhe Chen Frede Blaabjerg School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengduChina Southwest University of Science and Technology MianyangChina Department of Energy Technology Aalborg UniversityDK-9220 AalborgDenmark

The multi-directional flow of energy in a multi-microgrid(MMG) system and different dispatching needs of multiple energy sources in time and location hinder the optimal operation coordination between microgrids. We propose an approach to centrally train all the agents to achieve coordinated control through an individual attention mechanism with a deep dense neural network for reinforcement learning. The attention mechanism and novel deep dense neural network allow each agent to attend to the specific information that is most relevant to its reward. When training is complete, the proposed approach can construct decisions to manage multiple energy sources within the MMG system in a fully decentralized manner. Using only local information, the proposed approach can coordinate multiple internal energy allocations within individual microgrids and external multilateral multi-energy interactions among interconnected microgrids to enhance the operational economy and voltage stability. Comparative results demonstrate that the cost achieved by the proposed approach is at most 71.1% lower than that obtained by other multi-agent deep reinforcement learning approaches.

关键词： Interconnected multi-microgrid system energy management combined heat and power demand response deep reinforcement learning

Fault Location and Classification for Distribution Systems Based on Deep Graph Learning Methods

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Journal of Modern Power Systems and Clean Energy 2023年第1期11卷 35-51页

作者： Jiaxiang hu weihao hu Jianjun Chen Di Cao Zhengyuan Zhang Zhou Liu Zhe Chen Frede Blaabjerg School of Mechanical and Electrical engineering University of Electronic Science and Technology of ChinaChengduChina Siemens Gamesa Renewable Energy A/S LyngbyDenmark Aalborg University AalborgDenmark IEEE

Accurate and timely fault diagnosis is of great significance for the safe operation and power supply reliability of distribution systems.However,traditional intelligent methods limit the use of the physical structures and data information of power networks.To this end,this study proposes a fault diagnostic model for distribution systems based on deep graph learning.This model considers the physical structure of the power network as a significant constraint during model training,which endows the model with stronger information perception to resist abnormal data input and unknown application conditions.In addition,a special spatiotemporal convolutional block is utilized to enhance the waveform feature extraction ability.This enables the proposed fault diagnostic model to be more effective in dealing with both fault waveform changes and the spatial effects of faults.In addition,a multi-task learning framework is constructed for fault location and fault type analysis,which improves the performance and generalization ability of the model.The IEEE 33-bus and IEEE 37-bus test systems are modeled to verify the effectiveness of the proposed fault diagnostic model.Finally,different fault conditions,topological changes,and interference factors are considered to evaluate the anti-interference and generalization performance of the proposed model.Experimental results demonstrate that the proposed model outperforms other state-of-the-art methods.

关键词： Fault diagnosis fault location fault type analysis distribution system deep graph learning multi-task learning

Electric Vehicle Charging Management Based on Deep Reinforcement Learning

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Journal of Modern Power Systems and Clean Energy 2022年第3期10卷 719-730页

作者： Sichen Li weihao hu Di Cao Tomislav Dragicevic Qi huang Zhe Chen Frede Blaabjerg School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengduChina Department of Electrical Engineering Center for Electric Power and Energy Smart Electric Components Technical University of DenmarkCopenhagenDenmark Department of Energy Technology Aalborg UniversityAalborgDenmark

A time-variable time-of-use electricity price can be used to reduce the charging costs for electric vehicle(EV)owners.Considering the uncertainty of price fluctuation and the randomness of EV owner’s commuting behavior,we propose a deep reinforcement learning based method for the minimization of individual EV charging cost.The charging problem is first formulated as a Markov decision process(MDP),which has unknown transition probability.A modified long short-term memory(LSTM)neural network is used as the representation layer to extract temporal features from the electricity price signal.The deep deterministic policy gradient(DDPG)algorithm,which has continuous action spaces,is used to solve the MDP.The proposed method can automatically adjust the charging strategy according to electricity price to reduce the charging cost of the EV owner.Several other methods to solve the charging problem are also implemented and quantitatively compared with the proposed method which can reduce the charging cost up to 70.2%compared with other benchmark methods.

关键词： Deep reinforcement learning data-driven control uncertainty electric vehicles(EVs)

Deep Reinforcement Learning Enabled Bi-level Robust Parameter Optimization of Hydropower-dominated Systems for Damping Ultra-low Frequency Oscillation

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Journal of Modern Power Systems and Clean Energy 2023年第6期11卷 1770-1783页

作者： Guozhou Zhang Junbo Zhao weihao hu Di Cao Nan Duan Zhe Chen Frede Blaabjerg School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengdu 610054China IEEE Department of Electrical and Computer Engineering University of ConnecticutStorrsCT06269USA Lawrence Livermore National Laboratory LivermoreUSA Department of Energy Technology Aalborg University9220 AalborgDenmark

This paper proposes a robust and computationally efficient control method for damping ultra-low frequency oscillations(ULFOs) in hydropower-dominated systems. Unlike the existing robust optimization based control formulation that can only deal with a limited number of operating conditions, the proposed method reformulates the control problem into a bi-level robust parameter optimization model. This allows us to consider a wide range of system operating conditions. To speed up the bi-level optimization process, the deep deterministic policy gradient(DDPG) based deep reinforcement learning algorithm is developed to train an intelligent agent. This agent can provide very fast lower-level decision variables for the upper-level model, significantly enhancing its computational efficiency. Simulation results demonstrate that the proposed method can achieve much better damping control performance than other alternatives with slightly degraded dynamic response performance of the governor under various types of operating conditions.

关键词： Bi-level robust parameter optimization deep reinforcement learning deep deterministic policy gradient ultralow frequency oscillation damping control stability

Selective and stable Au-Cu bimetallic catalyst for CO-PROX

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Nano Research 2023年第7期16卷 9031-9038页

作者： Feng Hong Guanjian Cheng weihao hu Shengyang Wang Qike Jiang Junhong Fu Botao Qiao Jiahui huang Dalian National Laboratory for Clean Energy Dalian Institute of Chemical PhysicsChinese Academy of SciencesDalian 116023China College of Energy Soochow Institute for Energy and Materials Innovations(SIEMIS)Soochow UniversitySuzhou 215006China CAS Key Laboratory of Science and Technology on Applied Catalysis Dalian Institute of Chemical PhysicsChinese Academy of SciencesDalian 116023China State Key Laboratory of Catalysis Dalian Institute of Chemical PhysicsChinese Academy of SciencesDalian 116023China School of Chemistry and Chemical Engineering Yulin UniversityYulin 719000China University of Chinese Academy of Sciences Beijing 100049China

Gold-based catalysts are promising in CO preferential oxidation(CO-PROX)reaction in H_(2)-rich stream on account of their high intrinsic activity for CO elimination even at ambient temperature.However,the decrease of CO conversion at elevated temperature due to the competition of H_(2)oxidation,together with the low stability of gold nanoparticles,has posed a dear challenge.Herein,we report that Au-Cu bimetallic catalyst prepared by galvanic replacement method shows a wide temperature window for CO total conversion(30-100℃)and very good catalyst stability without deactivation in a 200-h test.Detailed characterizations combined with density functional theory(DFT)calculation reveal that the synergistic effect of Au-Cu,the electron transfer from Au to Cu,leads to not only strengthened chemisorption of CO but also weakened dissociation of H_(2),both of which are helpful in inhibiting the competition of H_(2)oxidation thus widening the temperature window for CO total conversion.

关键词： gold catalysis CO preferential oxidation(CO-PROX) electronic interaction galvanic replacement