Flexible ion-conducting membranes with 3D continuous nanohybrid networks for high-performance solid-state metallic lithium batteries

Polyethylene oxide(PEO)-based electrolytes are considered as one of the most promising solid-state electrolytes for next-generation lithium batteries with high safety and energy density;however,the drawbacks such as insufficient ion conductance,mechanical strength and electrochemical stability hinder their applications in metallic lithium batteries.To enhance their overall properties,flexible and thin composite polymer electrolyte(CPE)membranes with 3D continuous aramid nanofiber(ANF)–Li_(1.4)Al_(0.4)Ti_(1.6)(PO_(4))_(3)(LATP)nanoparticle hybrid frameworks are facilely prepared by filling PEO–Li TFSI in the 3D nanohybrid scaffolds via a solution infusion way.The construction of the 3D continuous nanohybrid networks can effectively inhibit the PEO crystallization,facilitate the lithium salt dissociation and meanwhile increase the fast-ion transport in the continuous LATP electrolyte phase,and thus greatly improving the ionic conductivity(~3 times that of the pristine one).With the integration of the 3D continuity and flexibility of the 3D ANF networks and the thermostability of the LATP phase,the CPE membranes also show a wider electrochemical window(~5.0 V vs.4.3 V),higher tensile strength(~4–10times that of the pristine one)and thermostability,and better lithium dendrite resistance capability.Furthermore,the CPE-based Li FePO_(4)/Li cells exhibit superior cycling stability(133 m Ah/g after 100 cycles at 0.3 C)and rate performance(100 m Ah/g at 1 C)than the pristine electrolyte-based cell(79 and 29m Ah/g,respectively).This work offers an important CPE design criteria to achieve comprehensivelyupgraded solid-state electrolytes for safe and high-energy metal battery applications.

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

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.

Electricity Theft Detection Method Based on Ensemble Learning and Prototype Learning

With the development of advanced metering infrastructure(AMI),large amounts of electricity consumption data can be collected for electricity theft detection.However,the imbalance of electricity consumption data is violent,which makes the training of detection model challenging.In this case,this paper proposes an electricity theft detection method based on ensemble learning and prototype learning,which has great performance on imbalanced dataset and abnormal data with different abnormal level.In this paper,convolutional neural network(CNN)and long short-term memory(LSTM)are employed to obtain abstract feature from electricity consumption data.After calculating the means of the abstract feature,the prototype per class is obtained,which is used to predict the labels of unknown samples.In the meanwhile,through training the network by different balanced subsets of training set,the prototype is representative.Compared with some mainstream methods including CNN,random forest(RF)and so on,the proposed method has been proved to effectively deal with the electricity theft detection when abnormal data only account for 2.5%and 1.25%of normal data.The results show that the proposed method outperforms other state-of-the-art methods.

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

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.

A review of offshore wind farm layout optimization and electrical system design methods

There is more wind with less turbulence offshore compared with an onshore case,which drives the development of the offshore wind farm worldwide.Since a huge amount of money is required for constructing an offshore wind farm,many types of research have been done on the optimization of the offshore wind farm with the purpose of either minimizing the cost of energy or maximizing the total energy production.There are several factors that have an impact on the performance of the wind farm,mainly the energy production of wind farm which is highly decided bythe wind condition of construction area and micro-siting of wind turbines(WTs),as well as the initial investment which is influenced by both the placement of WTs and the electrical system design,especially the scheme of cable connection layout.In this paper,a review of the state-of-the-art researches related to the wind farm layout optimization as well as electrical system design including cable connection scheme optimization is presented.The most significant factors that should be considered in the optimization work of the offshore wind farm is highlighted after reviewing the latest works,and the future needs are specified.

Reinforcement Learning and Its Applications in Modern Power and Energy Systems: A Review

With the growing integration of distributed energy resources(DERs),flexible loads,and other emerging technologies,there are increasing complexities and uncertainties for modern power and energy systems.This brings great challenges to the operation and control.Besides,with the deployment of advanced sensor and smart meters,a large number of data are generated,which brings opportunities for novel data-driven methods to deal with complicated operation and control issues.Among them,reinforcement learning(RL)is one of the most widely promoted methods for control and optimization problems.This paper provides a comprehensive literature review of RL in terms of basic ideas,various types of algorithms,and their applications in power and energy systems.The challenges and further works are also discussed.

Deep Reinforcement Learning Based Approach for Optimal Power Flow of Distribution Networks Embedded with Renewable Energy and Storage Devices

This study proposes a deep reinforcement learning(DRL)based approach to analyze the optimal power flow(OPF)of distribution networks(DNs)embedded with renewable energy and storage devices.First,the OPF of the DN is formulated as a stochastic nonlinear programming problem.Then,the multi-period nonlinear programming decision problem is formulated as a Markov decision process(MDP),which is composed of multiple single-time-step sub-problems.Subsequently,the state-of-the-art DRL algorithm,i.e.,proximal policy optimization(PPO),is used to solve the MDP sequentially considering the impact on the future.Neural networks are used to extract operation knowledge from historical data offline and provide online decisions according to the real-time state of the DN.The proposed approach fully exploits the historical data and reduces the influence of the prediction error on the optimization results.The proposed real-time control strategy can provide more flexible decisions and achieve better performance than the pre-determined ones.Comparative results demonstrate the effectiveness of the proposed approach.

Electric Vehicle Charging Management Based on Deep Reinforcement Learning

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.

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

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.

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

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.

Solid polymer electrolytes:Ion conduction mechanisms and enhancement strategies

Solid polymer electrolytes(SPEs)possess comprehensive advantages such as high flexibility,low interfacial resistance with the electrodes,excellent film-forming ability,and low price,however,their applications in solid-state batteries are mainly hindered by the insufficient ionic conductivity especially below the melting temperatures,etc.To improve the ion conduction capability and other properties,a variety of modification strategies have been exploited.In this review article,we scrutinize the structure characteristics and the ion transfer behaviors of the SPEs(and their composites)and then disclose the ion conduction mechanisms.The ion transport involves the ion hopping and the polymer segmental motion,and the improvement in the ionic conductivity is mainly attributed to the increase of the concentration and mobility of the charge carriers and the construction of fast-ion pathways.Furthermore,the recent advances on the modification strategies of the SPEs to enhance the ion conduction from copolymer structure design to lithium salt exploitation,additive engineering,and electrolyte micromorphology adjustion are summarized.This article intends to give a comprehensive,systemic,and profound understanding of the ion conduction and enhancement mechanisms of the SPEs for their viable applications in solid-state batteries with high safety and energy density.

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

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.

Guest Editorial: Applications of Artificial Intelligence in Modern Power Systems:Challenges and Solutions

WITH the increasing integration of renewable energies,power electronic devices and flexible loads,modern power systems are becoming more sophisticated and facing higher uncertainty.Traditional model-based methods cannot fully satisfy the analysis and control requirements of modern power systems duo to its complexity and uncertainty.

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

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.

Partially Affine Policy for Multistage Robust Unit Commitment with Fast-Ramping Units

Multistage robust unit commitment(MRUC)is an important decision-making problem in power system operations.The affine policy facilitates problem-solving,but it compromises flexibility.This letter proposes a partially affine policy for MRU C problem with fast-ramping units;this policy imposes affine relations to coupling variables only and leaves the remaining variables to be optimized in the real-time dispatch.As a result,the real-time flexibility of fast-ramping units is retained.By adopting this approach,MRU C with a partially affine policy becomes a special two-stage adaptive robust optimization problem.Numerical tests verify that the proposed partially affine policy significantly reduces the conservativeness compared with affine policy,improving the dispatch economy and flexibility.