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Battery energy storage is the pivotal project of renewable energy systems reform and an effective regulator of energy flow. Parallel battery packs can effectively increase the capacity of battery modules. However, the power loss caused by the uncertainty of parallel battery branch current poses severe challenge to the economy and safety of electric vehicles. Accuracy of battery branch current prediction is needed to improve the parallel connection. This paper proposes a radial basis function neural network model based on the pigeon-inspired optimization method and successfully applies the algorithm to predict the parallel branch current of the battery pack. Numerical results demonstrate the high accuracy of the proposed pigeon-inspired optimized RBF model for parallel battery branch forecasting and provide a useful tool for the prediction of parallel branch currents of battery packs.
Yanhui Zhang; Shili Lin; Haiping Ma; Yuanjun Guo; Wei Feng. A Novel Pigeon-Inspired Optimized RBF Model for Parallel Battery Branch Forecasting. Complexity 2021, 2021, 1 -7.
AMA StyleYanhui Zhang, Shili Lin, Haiping Ma, Yuanjun Guo, Wei Feng. A Novel Pigeon-Inspired Optimized RBF Model for Parallel Battery Branch Forecasting. Complexity. 2021; 2021 ():1-7.
Chicago/Turabian StyleYanhui Zhang; Shili Lin; Haiping Ma; Yuanjun Guo; Wei Feng. 2021. "A Novel Pigeon-Inspired Optimized RBF Model for Parallel Battery Branch Forecasting." Complexity 2021, no. : 1-7.
Online battery capacity estimation is a critical task for battery management system to maintain the battery performance and cycling life in electric vehicles and grid energy storage applications. Convolutional Neural Networks, which have shown great potentials in battery capacity estimation, have thousands of parameters to be optimized and demand a substantial number of battery aging data for training. However, these parameters require massive memory storage while collecting a large volume of aging data is time-consuming and costly in real-world applications. To tackle these challenges, this paper proposes a novel framework incorporating the concepts of transfer learning and network pruning to build compact Convolutional Neural Network models on a relatively small dataset with improved estimation performance. First, through the transfer learning technique, the Convolutional Neural Network model pre-trained on a large battery dataset is transferred to a small dataset of the targeted battery to improve the estimation accuracy. Then a contribution-based neuron selection method is proposed to prune the transferred model using a fast recursive algorithm, which reduces the size and computational complexity of the model while maintaining its performance. The proposed model is capable of achieving fast online capacity estimation at any time, and its effectiveness is verified on a target dataset collected from four Lithium iron phosphate battery cells, and the performance is compared with other Convolutional Neural Network models. The test results confirm that the proposed model outperforms other models in terms of accuracy and computational efficiency, achieving up to 68.34% model size reduction and 80.97% computation savings.
Yihuan Li; Kang Li; Xuan Liu; Yanxia Wang; Li Zhang. Lithium-ion battery capacity estimation — A pruned convolutional neural network approach assisted with transfer learning. Applied Energy 2021, 285, 116410 .
AMA StyleYihuan Li, Kang Li, Xuan Liu, Yanxia Wang, Li Zhang. Lithium-ion battery capacity estimation — A pruned convolutional neural network approach assisted with transfer learning. Applied Energy. 2021; 285 ():116410.
Chicago/Turabian StyleYihuan Li; Kang Li; Xuan Liu; Yanxia Wang; Li Zhang. 2021. "Lithium-ion battery capacity estimation — A pruned convolutional neural network approach assisted with transfer learning." Applied Energy 285, no. : 116410.
The full controllable electronic device based railway feeder station offers better power quality and more flexible configurations than conventional transformer based stations. This study investigates a modular multilevel converter (MMC)-based static frequency converter station with renewable energy access. Wind power generation is coupled into the station via DC link of the back to back converter. The dynamic single-phase traction load and intermittent renewable generation bring double frequency oscillation and large deviation problems to the DC link voltage. Special design considerations and control schemes are proposed for the MMC to stabilise DC link voltage by controlling the total number of total inserted modules. The proposed control scheme resolves the voltage oscillation issue caused by single-phase load and reduces the DC link voltage deviation under 10 MW step change. A series of device-based simulations validate the control scheme which realises a reliable coupling interface for connecting the renewable generation to the DC bus.
Peiliang Sun; Kang Li; Yongfei Li; Li Zhang. DC voltage control for MMC-based railway power supply integrated with renewable generation. IET Renewable Power Generation 2020, 14, 3679 -3689.
AMA StylePeiliang Sun, Kang Li, Yongfei Li, Li Zhang. DC voltage control for MMC-based railway power supply integrated with renewable generation. IET Renewable Power Generation. 2020; 14 (18):3679-3689.
Chicago/Turabian StylePeiliang Sun; Kang Li; Yongfei Li; Li Zhang. 2020. "DC voltage control for MMC-based railway power supply integrated with renewable generation." IET Renewable Power Generation 14, no. 18: 3679-3689.
In railway traction power supply, co-phase system with hybrid power quality conditioner (HPQC) is capable of tackling the power quality issues caused by single-phase traction loads. To further reduce the overall carbon emissions in railway systems, this paper considers to integrate the renewable energy with railway power supply, which however leads to a more complicated system to model, design and control. This paper first investigates its modelling aspect. To reduce the operating capacity of HPQC while addressing the power unbalance, optimal design of the compensation scheme for co-phase system is formulated as a multi-objective optimization problem which is then solved by the nondominated sorting genetic algorithm-II (NSGA-II). To eliminate the impact of errors arising from imperfect predictions of the loads and renewable power, a hybrid optimal compensation control is proposed, yielding full and optimal compensations. Comprehensive simulation studies, considering three operation modes covering variable traction loads, renewable and regenerative braking power, are conducted. The simulation results confirm the validity of the proposed optimal compensation scheme, achieving an average of more than 20% reduction in HPQC capacity compared to the full compensation scheme. Meanwhile, the power quality requirement is satisfied, even in the presence of real-time prediction errors.
Chen Xing; Kang Li; Li Zhang; Wei Li. Optimal compensation control of railway co-phase traction power supply integrated with renewable energy based on NSGA-II. IET Renewable Power Generation 2020, 14, 3668 -3678.
AMA StyleChen Xing, Kang Li, Li Zhang, Wei Li. Optimal compensation control of railway co-phase traction power supply integrated with renewable energy based on NSGA-II. IET Renewable Power Generation. 2020; 14 (18):3668-3678.
Chicago/Turabian StyleChen Xing; Kang Li; Li Zhang; Wei Li. 2020. "Optimal compensation control of railway co-phase traction power supply integrated with renewable energy based on NSGA-II." IET Renewable Power Generation 14, no. 18: 3668-3678.
Whale optimization algorithm (WOA), known as a novel nature-inspired swarm optimization algorithm, demonstrates superiority in handling global continuous optimization problems. However, its performance deteriorates when applied to large-scale complex problems due to rapidly increasing execution time required for huge computational tasks. Based on interactions within the population, WOA is naturally amenable to parallelism, prompting an effective approach to mitigate the drawbacks of sequential WOA. In this paper, field programmable gate array (FPGA) is used as an accelerator, of which the high-level synthesis utilizes open computing language (OpenCL) as a general programming paradigm for heterogeneous System-on-Chip. With above platform, a novel parallel framework of WOA named PWOA is presented. The proposed framework comprises two feasible parallel models called partial parallel and all-FPGA parallel, respectively. Experiments are conducted by performing WOA on CPU and PWOA on OpenCL-based FPGA heterogeneous platform, to solve ten well-known benchmark functions. Meanwhile, other two classic algorithms including particle swarm optimization (PSO) and competitive swarm optimizer (CSO) are adopted for comparison. Numerical results show that the proposed approach achieves a promising computational performance coupled with efficient optimization on relatively large-scale complex problems.
Qiangqiang Jiang; Yuanjun Guo; Zhile Yang; Zheng Wang; Dongsheng Yang; Xianyu Zhou. Improving the Performance of Whale Optimization Algorithm through OpenCL-Based FPGA Accelerator. Complexity 2020, 2020, 1 -15.
AMA StyleQiangqiang Jiang, Yuanjun Guo, Zhile Yang, Zheng Wang, Dongsheng Yang, Xianyu Zhou. Improving the Performance of Whale Optimization Algorithm through OpenCL-Based FPGA Accelerator. Complexity. 2020; 2020 ():1-15.
Chicago/Turabian StyleQiangqiang Jiang; Yuanjun Guo; Zhile Yang; Zheng Wang; Dongsheng Yang; Xianyu Zhou. 2020. "Improving the Performance of Whale Optimization Algorithm through OpenCL-Based FPGA Accelerator." Complexity 2020, no. : 1-15.
With the advent of sustainable and clean energy, lithium-ion batteries have been widely utilised in cleaner productions such as energy storage systems and electrical vehicles, but the management of their electrode production chain has a direct and crucial impact on the battery performance and production efficiency. To achieve a cleaner production chain of battery electrode involving strongly-coupled intermediate parameters and control parameters, a reliable approach to quantify the feature importance and select the key feature variables for predicting battery intermediate products is urgently required. In this paper, a Gaussian process regression-based machine learning framework, which incorporates powerful automatic relevance determination kernels, is proposed for directly quantifying the importance of four intermediate production feature variables and analysing their influences on the prediction of battery electrode mass load. Specifically, these features include three intermediate parameters from the mixing step and a control parameter from the coating step. After deriving four different automatic relevance determination kernels, the importance of these four feature variables based on a regression modelling is comprehensively analysed. Comparative results demonstrate that the proposed automatic relevance determination kernel-based Gaussian process regression models could not only quantify the importance weights for reliable feature selections but also help to achieve satisfactory electrode mass load prediction. Due to the data-driven nature, the proposed framework can be conveniently extended to improve the analysis and control of battery electrode production, further benefitting the manufactured battery yield, efficiencies and performance to achieve cleaner battery production.
Kailong Liu; Zhongbao Wei; Zhile Yang; Kang Li. Mass load prediction for lithium-ion battery electrode clean production: A machine learning approach. Journal of Cleaner Production 2020, 289, 125159 .
AMA StyleKailong Liu, Zhongbao Wei, Zhile Yang, Kang Li. Mass load prediction for lithium-ion battery electrode clean production: A machine learning approach. Journal of Cleaner Production. 2020; 289 ():125159.
Chicago/Turabian StyleKailong Liu; Zhongbao Wei; Zhile Yang; Kang Li. 2020. "Mass load prediction for lithium-ion battery electrode clean production: A machine learning approach." Journal of Cleaner Production 289, no. : 125159.
In the practical scenario of construction sites with extremely complicated working environment and numerous personnel, it is challenging to detect safety helmet wearing (SHW) in real time on the premise of ensuring high precision performance. In this paper, a novel SHW detection model on the basis of improved YOLOv3 (named CSYOLOv3) is presented to heighten the capability of target detection on the construction site. Firstly, the backbone network of darknet53 is improved by applying the cross stage partial network (CSPNet), which reduces the calculation cost and improves the training speed. Secondly, the spatial pyramid pooling (SPP) structure is employed in the YOLOv3 model, and the multi-scale prediction network is improved by combining the top-down and bottom-up feature fusion strategies to realize the feature enhancement. Finally, the safety helmet wearing detection dataset containing 10,000 images is established using the construction site cameras, and the manual annotation is required for the model training. Experimental data and contrastive curves demonstrate that, compared with YOLOv3, the novel method can largely ameliorate mAP by 28% and speed is improved by 6 fps.
Haikuan Wang; Zhaoyan Hu; Yuanjun Guo; Zhile Yang; Feixiang Zhou; Peng Xu. A Real-Time Safety HelmetWearing Detection Approach Based on CSYOLOv3. Applied Sciences 2020, 10, 6732 .
AMA StyleHaikuan Wang, Zhaoyan Hu, Yuanjun Guo, Zhile Yang, Feixiang Zhou, Peng Xu. A Real-Time Safety HelmetWearing Detection Approach Based on CSYOLOv3. Applied Sciences. 2020; 10 (19):6732.
Chicago/Turabian StyleHaikuan Wang; Zhaoyan Hu; Yuanjun Guo; Zhile Yang; Feixiang Zhou; Peng Xu. 2020. "A Real-Time Safety HelmetWearing Detection Approach Based on CSYOLOv3." Applied Sciences 10, no. 19: 6732.
This paper presents a partial compensation scheme for V/v transformer cophase traction power supply in high-speed railway systems. The scheme compensates variable traction load current, and controls the current phase at the secondary side of the V/v transformer for power factor correction and negative sequence current reduction. To achieve this, the grid side current phase angles are optimized while satisfying the grid code on the power factor and voltage unbalance limits. The optimized phase angles are then used to design control references under varying load conditions. The compensation control action is updated regularly based on real-time measurements of the traction load, and the required currents are controlled by a 25-level single-phase back-to-back MMC power conditioner to achieve the compensation target. Static and dynamic load compensation performances are verified based on the simulation studies.
Peiliang Sun; Kang Li; Chen Xing. A partial compensation scheme for MMC-based railway cophase power supply. Transportation Safety and Environment 2020, 2, 305 -317.
AMA StylePeiliang Sun, Kang Li, Chen Xing. A partial compensation scheme for MMC-based railway cophase power supply. Transportation Safety and Environment. 2020; 2 (4):305-317.
Chicago/Turabian StylePeiliang Sun; Kang Li; Chen Xing. 2020. "A partial compensation scheme for MMC-based railway cophase power supply." Transportation Safety and Environment 2, no. 4: 305-317.
As a large energy consumer, the railway systems in many countries have been electrified gradually for the purposes of performance improvement and emission reduction. With the widespread utilization of energy-saving technologies such as regenerative braking techniques, and in support of the full electrification of railway systems in a wide range of application conditions, energy storage systems (ESSes) have come to play an essential role. In this paper, some recent developments in railway ESSes are reviewed and a comprehensive comparison is presented for various ESS technologies. The foremost functionalities of the railway ESSes are presented together with possible solutions proposed from the academic arena and current practice in the railway industry. In addition, the challenges and future trends of ESSes in the railway industry are briefly discussed.
Xuan Liu; Kang Li. Energy storage devices in electrified railway systems: A review. Transportation Safety and Environment 2020, 2, 183 -201.
AMA StyleXuan Liu, Kang Li. Energy storage devices in electrified railway systems: A review. Transportation Safety and Environment. 2020; 2 (3):183-201.
Chicago/Turabian StyleXuan Liu; Kang Li. 2020. "Energy storage devices in electrified railway systems: A review." Transportation Safety and Environment 2, no. 3: 183-201.
The ongoing COVID-19 pandemic spread to the UK in early 2020 with the first few cases being identified in late January. A rapid increase in confirmed cases started in March, and the number of infected people is however unknown, largely due to the rather limited testing scale. A number of reports published so far reveal that the COVID-19 has long incubation period, high fatality ratio and non-specific symptoms, making this novel coronavirus far different from common seasonal influenza. In this note, we present a modified SEIR model which takes into account the time lag effect and probability distribution of model states. Based on the proposed model, it is estimated that the actual total number of infected people by 1 April in the UK might have already exceeded 610,000. Average fatality rates under different assumptions at the beginning of April 2020 are also estimated. Our model also reveals that the R0 value is between 7.5–9 which is much larger than most of the previously reported values. The proposed model has a potential to be used for assessing future epidemic situations under different intervention strategies.
Peiliang Sun; Kang Li. An SEIR Model for Assessment of Current COVID-19 Pandemic Situation in the UK. 2020, 1 .
AMA StylePeiliang Sun, Kang Li. An SEIR Model for Assessment of Current COVID-19 Pandemic Situation in the UK. . 2020; ():1.
Chicago/Turabian StylePeiliang Sun; Kang Li. 2020. "An SEIR Model for Assessment of Current COVID-19 Pandemic Situation in the UK." , no. : 1.
Shawn Li; Kang Li; Evan Xiao; Jianhua Zhang; Min Zheng. Real-time peak power prediction for zinc nickel single flow batteries. Journal of Power Sources 2020, 448, 1 .
AMA StyleShawn Li, Kang Li, Evan Xiao, Jianhua Zhang, Min Zheng. Real-time peak power prediction for zinc nickel single flow batteries. Journal of Power Sources. 2020; 448 ():1.
Chicago/Turabian StyleShawn Li; Kang Li; Evan Xiao; Jianhua Zhang; Min Zheng. 2020. "Real-time peak power prediction for zinc nickel single flow batteries." Journal of Power Sources 448, no. : 1.
The ongoing COVID-19 pandemic spread to the UK in early 2020 with the first few cases being identified in late January. A rapid increase in confirmed cases started in March, and the number of infected people is however unknown, largely due to the rather limited testing scale. A number of reports published so far reveal that the COVID-19 has long incubation period, high fatality ratio and non-specific symptoms, making this novel coronavirus far different from common seasonal influenza. In this note, we present a modified SEIR model which takes into account the latency effect and probability distribution of model states. Based on the proposed model, it was estimated in April 2020 that the actual total number of infected people by 1 April in the UK might have already exceeded 610,000. Average fatality rates under different assumptions at the beginning of April 2020 were also estimated. Our model also revealed that the \(R_0\) value was between 7.5–9 which is much larger than most of the previously reported values. The proposed model has a potential to be used for assessing future epidemic situations under different intervention strategies.
Peiliang Sun; Kang Li; Zhile Yang; Dajun Du. An SEIR Model for Assessment of COVID-19 Pandemic Situation. Communications in Computer and Information Science 2020, 498 -510.
AMA StylePeiliang Sun, Kang Li, Zhile Yang, Dajun Du. An SEIR Model for Assessment of COVID-19 Pandemic Situation. Communications in Computer and Information Science. 2020; ():498-510.
Chicago/Turabian StylePeiliang Sun; Kang Li; Zhile Yang; Dajun Du. 2020. "An SEIR Model for Assessment of COVID-19 Pandemic Situation." Communications in Computer and Information Science , no. : 498-510.
In a Fused Magnesia Smelting Process(FMSP), its electricity demand is defined as the average electric power consumption over a fixed period of time and often used to calculate the electricity cost. The power supply has to be switched off once the demand value exceeds one specific threshold for safety and economic reasons. However, it has been shown that through appropriate current control of the FMSP, the demand can be reduced hence avoiding the shut-down of the process. A key issue to adopt the control strategy to avoid switch-off of electricity is to forecast the power demand and its trend However, this is technically challenging given the complexity and unknown dynamics of the process. In this paper, a hybrid approach combining a linear model with an unknown high order function is proposed. The linear model is used to capture the priori information from the domain knowledge and historic data, while the unknown dynamics in FMSP embedded in the error of the linear model are approximated with a high order nonlinear function. The Recursive Least Square algorithm (RLS) is used for identifying the unknown parameters in the linear model. A Long-Short Term Memory (LSTM) trained by the Fast Recursive Algorithm (FRA) is proposed to fit the unknown high-order function. Finally, the output weights of LSTM is updated by the RLS again. Experimental studies reveal that compared with other hybrid models such as a linear model combined with Radial Basis Function Neural Network (RBF), the proposed model offers the better performance.
Jingwen Zhang; Kang Li; Tianyou Chai. Demand Forecasting of a Fused Magnesia Smelting Process Based on LSTM and FRA. Communications in Computer and Information Science 2020, 201 -215.
AMA StyleJingwen Zhang, Kang Li, Tianyou Chai. Demand Forecasting of a Fused Magnesia Smelting Process Based on LSTM and FRA. Communications in Computer and Information Science. 2020; ():201-215.
Chicago/Turabian StyleJingwen Zhang; Kang Li; Tianyou Chai. 2020. "Demand Forecasting of a Fused Magnesia Smelting Process Based on LSTM and FRA." Communications in Computer and Information Science , no. : 201-215.
COVID-19 has rapidly spread around the world in the past few months, researchers around the world are working around the clock to closely monitor and assess the development of this pandemic. In this paper, a time series regression model is built to assess the short-term progression of COVID-19 pandemic. The model structure and parameters are identified using COVID-19 pandemic data released by China within the time window from 22 January to 09 April 2020. The same model structure and parameters are applied to a few other countries for day ahead forecasting, showing a good fit of the model. This modeling exercise confirms that the underlying internal dynamics of this disease progression is quite similar. The differences in the impact of this pandemic on different countries are largely attributed to different eternal factors.
Xuan Liu; Kang Li; Zhile Yang; Dajun Du. A Regression Model for Short-Term COVID-19 Pandemic Assessment. Communications in Computer and Information Science 2020, 511 -518.
AMA StyleXuan Liu, Kang Li, Zhile Yang, Dajun Du. A Regression Model for Short-Term COVID-19 Pandemic Assessment. Communications in Computer and Information Science. 2020; ():511-518.
Chicago/Turabian StyleXuan Liu; Kang Li; Zhile Yang; Dajun Du. 2020. "A Regression Model for Short-Term COVID-19 Pandemic Assessment." Communications in Computer and Information Science , no. : 511-518.
Environment of construction site is becoming more complicated and risky than ever before, due to the rapidly development of society. The traditional site management system is facing the challenges of manual supervision negligence as well as the inflexible attendance system, which cannot guarantee the life safety and legitimate interests of all employees. To address the above problem and reduce the risks, many construction sites utilize intelligent approaches such as effective safety helmet detection and face recognition. This paper propose a hybrid approach combining the popular YOLOv3 and Facenet model to detect the safety helmet wearing of construction workers and help them with attendance checking through camera simultaneously. At first, this method apply YOLOv3 to implement safety helmet detection. Then, Facenet is used to achieve face recognition with face detected by MTCNN model. Finally, combined with the above two modules, helmet detection and personnel information identification can be realized in site supervision system. The experimental results show the effectiveness of the proposed combined approach. As a result, database established by video and image process results can ensure the reasonable salary payment of construction workers, further improving the safety assurance measures and the management efficiency of the construction site.
Haikuan Wang; Zhaoyan Hu; Yuanjun Guo; Yuhao Ou; Zhile Yang. A Combined Method for Face and Helmet Detection in Intelligent Construction Site Application. Communications in Computer and Information Science 2020, 401 -415.
AMA StyleHaikuan Wang, Zhaoyan Hu, Yuanjun Guo, Yuhao Ou, Zhile Yang. A Combined Method for Face and Helmet Detection in Intelligent Construction Site Application. Communications in Computer and Information Science. 2020; ():401-415.
Chicago/Turabian StyleHaikuan Wang; Zhaoyan Hu; Yuanjun Guo; Yuhao Ou; Zhile Yang. 2020. "A Combined Method for Face and Helmet Detection in Intelligent Construction Site Application." Communications in Computer and Information Science , no. : 401-415.
Zinc-nickel single flow batteries (ZNBs) have been demonstrated as a promising alternative to lithium batteries for next generation grid-tied energy storage. However, due to the dendritic growth, the monitoring of working conditions in terms of the state of charge (SoC) and battery capacity is intractable. Although longer lifespan can be achieved through the periodic reconditioning maintenance, there is no mature method to identify the moment of reconditioning. Model predictive control (MPC) is a popular optimization paradigm in the process control. By incorporating the merits of model predictive control, this work presents a novel model predictive control based observer (MPCO) for the working conditions monitoring and reconditioning identification. Strong evidence from substantial experiments and simulations manifests the convergence, robustness, effectiveness and generality of the proposed method. The competitiveness is demonstrated by analytical comparisons against other three estimators. In this regard, the relationships of the proposed observer with other estimators are summarized briefly. At last, an indicator based on the capacity changes is proposed to judge the timing of reconditioning.
Shawn Li; Kang Li; Evan Xiao; Rui Xiong; Jianhua Zhang; Peter Fischer. A novel model predictive control scheme based observer for working conditions and reconditioning monitoring of Zinc-Nickel single flow batteries. Journal of Power Sources 2019, 445, 227282 .
AMA StyleShawn Li, Kang Li, Evan Xiao, Rui Xiong, Jianhua Zhang, Peter Fischer. A novel model predictive control scheme based observer for working conditions and reconditioning monitoring of Zinc-Nickel single flow batteries. Journal of Power Sources. 2019; 445 ():227282.
Chicago/Turabian StyleShawn Li; Kang Li; Evan Xiao; Rui Xiong; Jianhua Zhang; Peter Fischer. 2019. "A novel model predictive control scheme based observer for working conditions and reconditioning monitoring of Zinc-Nickel single flow batteries." Journal of Power Sources 445, no. : 227282.
Load forecasting is one of the major challenges of power system operation and is crucial to the effective scheduling for economic dispatch at multiple time scales. Numerous load forecasting methods have been proposed for household and commercial demand, as well as for loads at various nodes in a power grid. However, compared with conventional loads, the uncoordinated charging of the large penetration of plug-in electric vehicles is different in terms of periodicity and fluctuation, which renders current load forecasting techniques ineffective. Deep learning methods, empowered by unprecedented learning ability from extensive data, provide novel approaches for solving challenging forecasting tasks. This research proposes a comparative study of deep learning approaches to forecast the super-short-term stochastic charging load of plug-in electric vehicles. Several popular and novel deep-learning based methods have been utilized in establishing the forecasting models using minute-level real-world data of a plug-in electric vehicle charging station to compare the forecasting performance. Numerical results of twelve cases on various time steps show that deep learning methods obtain high accuracy in super-short-term plug-in electric load forecasting. Among the various deep learning approaches, the long-short-term memory method performs the best by reducing over 30% forecasting error compared with the conventional artificial neural network model.
Juncheng Zhu; Zhile Yang; Monjur Mourshed; Yuanjun Guo; Yimin Zhou; Yan Chang; Yanjie Wei; Shengzhong Feng. Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches. Energies 2019, 12, 2692 .
AMA StyleJuncheng Zhu, Zhile Yang, Monjur Mourshed, Yuanjun Guo, Yimin Zhou, Yan Chang, Yanjie Wei, Shengzhong Feng. Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches. Energies. 2019; 12 (14):2692.
Chicago/Turabian StyleJuncheng Zhu; Zhile Yang; Monjur Mourshed; Yuanjun Guo; Yimin Zhou; Yan Chang; Yanjie Wei; Shengzhong Feng. 2019. "Electric Vehicle Charging Load Forecasting: A Comparative Study of Deep Learning Approaches." Energies 12, no. 14: 2692.
Decreasing initial costs, the increased availability of charging infrastructure and favorable policy measures have resulted in the recent surge in plug-in electric vehicle (PEV) ownerships. PEV adoption increases electricity consumption from the grid that could either exacerbate electricity supply shortages or smooth demand curves. The optimal coordination and commitment of power generation units while ensuring wider access of PEVs to the grid are, therefore, important to reduce the cost and environmental pollution from thermal power generation systems, and to transition to a smarter grid. However, flexible demand side management (DSM) considering the stochastic charging behavior of PEVs adds new challenges to the complex power system optimization, and makes existing mathematical approaches ineffective. In this research, a novel parallel competitive swarm optimization algorithm is developed for solving large-scale unit commitment (UC) problems with mixed-integer variables and multiple constraints – typically found in PEV integrated grids. The parallel optimization framework combines binary and real-valued competitive swarm optimizers for solving the UC problem and demand side management of PEVs simultaneously. Numerical case studies have been conducted with multiple scales of unit numbers and various demand side management strategies of plug-in electric vehicles. The results show superior performance of proposed parallel competitive swarm optimization based method in successfully solving the proposed complex optimization problem. The flexible demand side management strategies of plug-in electric vehicles have shown large potentials in bringing considerable economic benefit.
Ying Wang; Zhile Yang; Monjur Mourshed; Yuanjun Guo; Qun Niu; Xiaodong Zhu. Demand side management of plug-in electric vehicles and coordinated unit commitment: A novel parallel competitive swarm optimization method. Energy Conversion and Management 2019, 196, 935 -949.
AMA StyleYing Wang, Zhile Yang, Monjur Mourshed, Yuanjun Guo, Qun Niu, Xiaodong Zhu. Demand side management of plug-in electric vehicles and coordinated unit commitment: A novel parallel competitive swarm optimization method. Energy Conversion and Management. 2019; 196 ():935-949.
Chicago/Turabian StyleYing Wang; Zhile Yang; Monjur Mourshed; Yuanjun Guo; Qun Niu; Xiaodong Zhu. 2019. "Demand side management of plug-in electric vehicles and coordinated unit commitment: A novel parallel competitive swarm optimization method." Energy Conversion and Management 196, no. : 935-949.
The unit commitment (UC) problem is a critical task in power system operation process. The units realize reasonable start-up and shut-down scheduling and would bring considerable economic savings to the grid operators. However, unit commitment is a high-dimensional mixed-integer optimisation problem, which has long been intractable for current solvers. Competitive swarm optimizer is a recent proposed meta-heuristic algorithm specialized in solving the high-dimensional problem. In this paper, a novel binary competitive swarm optimizer (BCSO) is proposed for solving the UC problem associated with lambda iteration method. To verify the effectiveness of the proposed algorithm, comprehensive numerical studies on different sizes units ranging from 10 to 100 are proposed, and the algorithm is compared with other counterparts. Results clearly show that BCSO outperforms all the other counterparts and is therefore completely capable of solving the UC problem.
Ying Wang; Zhile Yang; Yuanjun Guo; Bowen Zhou; Xiaodong Zhu. A Novel Binary Competitive Swarm Optimizer for Power System Unit Commitment. Applied Sciences 2019, 9, 1776 .
AMA StyleYing Wang, Zhile Yang, Yuanjun Guo, Bowen Zhou, Xiaodong Zhu. A Novel Binary Competitive Swarm Optimizer for Power System Unit Commitment. Applied Sciences. 2019; 9 (9):1776.
Chicago/Turabian StyleYing Wang; Zhile Yang; Yuanjun Guo; Bowen Zhou; Xiaodong Zhu. 2019. "A Novel Binary Competitive Swarm Optimizer for Power System Unit Commitment." Applied Sciences 9, no. 9: 1776.
Short-term load forecasting is a key task to maintain the stable and effective operation of power systems, providing reasonable future load curve feeding to the unit commitment and economic load dispatch. In recent years, the boost of internal combustion engine (ICE) based vehicles leads to the fossil fuel shortage and environmental pollution, bringing significant contributions to the greenhouse gas emissions. One of the effective ways to solve problems is to use electric vehicles (EVs) to replace the ICE based vehicles. However, the mass rollout of EVs may cause severe problems to the power system due to the huge charging power and stochastic charging behaviors of the EVs drivers. The accurate model of EV charging load forecasting is, therefore, an emerging topic. In this paper, four featured deep learning approaches are employed and compared in forecasting the EVs charging load from the charging station perspective. Numerical results show that the gated recurrent units (GRU) model obtains the best performance on the hourly based historical data charging scenarios, and it, therefore, provides a useful tool of higher accuracy in terms of the hourly based short-term EVs load forecasting.
Juncheng Zhu; Zhile Yang; Yuanjun Guo; Jiankang Zhang; Huikun Yang. Short-Term Load Forecasting for Electric Vehicle Charging Stations Based on Deep Learning Approaches. Applied Sciences 2019, 9, 1723 .
AMA StyleJuncheng Zhu, Zhile Yang, Yuanjun Guo, Jiankang Zhang, Huikun Yang. Short-Term Load Forecasting for Electric Vehicle Charging Stations Based on Deep Learning Approaches. Applied Sciences. 2019; 9 (9):1723.
Chicago/Turabian StyleJuncheng Zhu; Zhile Yang; Yuanjun Guo; Jiankang Zhang; Huikun Yang. 2019. "Short-Term Load Forecasting for Electric Vehicle Charging Stations Based on Deep Learning Approaches." Applied Sciences 9, no. 9: 1723.