This page has only limited features, please log in for full access.
This article proposes a coordinated operation strategy for a virtual power plant (VPP) with multiple DER aggregators under the wholesale energy and regulation service market. Considering the market clearing process, VPP operation profit and DER aggregators interests, a tri-layer hierarchical framework for VPP is developed to formulate the bidding plans to the superordinate market operator and the dynamic price incentive curves for the subordinate DER owners cooperatively. By using the discrete price quota curves, big-M method, KKT optimal conditions and strong duality theorem, the tri-layer problem is transformed into an equivalent and tractable mixed integer linear programming problem. Case study with the 141-bus system is conducted to validate the effectiveness of the proposed approach. The advantages of lower cost with operation security guarantee and higher flexibility are demonstrated by comparing with other models.
Zhongkai Yi; Yinliang Xu; Huaizhi Wang; Linwei Sang. Coordinated Operation Strategy for a Virtual Power Plant with Multiple DER Aggregators. IEEE Transactions on Sustainable Energy 2021, PP, 1 -1.
AMA StyleZhongkai Yi, Yinliang Xu, Huaizhi Wang, Linwei Sang. Coordinated Operation Strategy for a Virtual Power Plant with Multiple DER Aggregators. IEEE Transactions on Sustainable Energy. 2021; PP (99):1-1.
Chicago/Turabian StyleZhongkai Yi; Yinliang Xu; Huaizhi Wang; Linwei Sang. 2021. "Coordinated Operation Strategy for a Virtual Power Plant with Multiple DER Aggregators." IEEE Transactions on Sustainable Energy PP, no. 99: 1-1.
The sodium aluminate solution of evaporation process is provided for the digestion process to recycle useful resources, decrease alkali concentration in waste solution, and reduce the environmental pollution. However, the concentration of recycled sodium aluminate solution as an indispensable indicator for manipulating the evaporation process of the industrial alumina production, is acquired off-line, leading to delayed feedback information. To ensure stable production of subsequent process, reduce energy and resource consumption, this paper focuses on developing a hybrid prediction model for recycled sodium aluminate solution concentration in evaporation process. First, data reconciliation is utilized to improve the quality of material flow information, and the process mechanism model is established through mechanism research and equilibrium principle to obtain the concentration prediction result. Moreover, an industrial production condition analysis as well as fuzzy expert rules, are introduced for modifying prediction results from mechanism model. Furthermore, the errors are predicted by the kernel extreme learning machine, and a concentration prediction model integrated error compensation results and modified predictions is established. The experimental simulations and industrial application show that the accuracy of the prediction error obtained by the proposed model reaches to 90% within ±2%, and the advantages of the hybrid model are particularly prominent under different conditions, which is beneficial for efficient and clean production.
Sen Xie; Huaizhi Wang; Jianchun Peng. A Hybrid Prediction Model of Recycled Sodium Aluminate Solution Concentration in Evaporation Process. IEEE Transactions on Instrumentation and Measurement 2021, 70, 1 -13.
AMA StyleSen Xie, Huaizhi Wang, Jianchun Peng. A Hybrid Prediction Model of Recycled Sodium Aluminate Solution Concentration in Evaporation Process. IEEE Transactions on Instrumentation and Measurement. 2021; 70 (99):1-13.
Chicago/Turabian StyleSen Xie; Huaizhi Wang; Jianchun Peng. 2021. "A Hybrid Prediction Model of Recycled Sodium Aluminate Solution Concentration in Evaporation Process." IEEE Transactions on Instrumentation and Measurement 70, no. 99: 1-13.
The energy management and energy efficiency optimization are of particularly significant for promoting the sustainable development of industrial processes. However, industrial raw data with uncertain, relevant and inaccuracy characteristics influence the reliability and accuracy of energy efficiency analysis and optimization modeling. Therefore, an energy efficiency analysis and optimization method based on a novel data reconciliation (DR) integrating Gaussian mixture model (GMM) and mutual information (MI) is put forward. First, the material flow information with multiple data characteristics corresponding operation modes is divided through the GMM. Moreover, the novel data reconciliation model integrated with critical variable and mutual information is established considering time-scale redundancy information in different modes, then the comprehensive data reconciliation result is evaluated by the hypothesis testing. Furthermore, the reconciled data is applied to analyze the exergy balance and built the energy efficiency optimization model with multi-objective for a case study of industrial evaporation process. Finally, simulation case and industrial application case are used to analyze and discuss, and the results show that the validity and applicability of the proposed approach are illustrated in energy saving potential which is about 14.81%.
Sen Xie; Huaizhi Wang; Jianchun Peng. Energy efficiency analysis and optimization of industrial processes based on a novel data reconciliation. IEEE Access 2021, PP, 1 -1.
AMA StyleSen Xie, Huaizhi Wang, Jianchun Peng. Energy efficiency analysis and optimization of industrial processes based on a novel data reconciliation. IEEE Access. 2021; PP (99):1-1.
Chicago/Turabian StyleSen Xie; Huaizhi Wang; Jianchun Peng. 2021. "Energy efficiency analysis and optimization of industrial processes based on a novel data reconciliation." IEEE Access PP, no. 99: 1-1.
The rapid development of distributed generators and demand response management programs are transforming the traditional consumers to emerging prosumers. While, it is difficult to manage these prosumers because different types of energy are locally generated and consumed with the autonomous operations. For this purpose, this paper proposes a multi-energy forecasting framework based on deep learning methodology to simultaneously predict the electrical, thermal and gas net load of integrated local energy systems. First, the inherent multi-energy load and generation features of heterogeneous prosumers are qualitatively analyzed, and a hierarchical clustering framework is formulated to classify these prosumers into various aggregations to facilitate the multi-energy forecasting model. Then, a deep belief network based forecasting method is developed to extract the hidden features in multi-energy time series, thereby achieving the net-load prediction of numerous prosumers. Finally, the proposed multi-energy net load forecasting methodology is extensively and comprehensively validated using the real data from household-scale prosumers. The comparative results demonstrate the superiority and high forecast accuracy of the proposed methodology, and confirm its capability to cope with the multi-prosumer prediction problem with multi-energy carriers.
Bin Zhou; Yunfan Meng; Wentao Huang; Huaizhi Wang; Lijun Deng; Sheng Huang; Juan Wei. Multi-energy net load forecasting for integrated local energy systems with heterogeneous prosumers. International Journal of Electrical Power & Energy Systems 2020, 126, 106542 .
AMA StyleBin Zhou, Yunfan Meng, Wentao Huang, Huaizhi Wang, Lijun Deng, Sheng Huang, Juan Wei. Multi-energy net load forecasting for integrated local energy systems with heterogeneous prosumers. International Journal of Electrical Power & Energy Systems. 2020; 126 ():106542.
Chicago/Turabian StyleBin Zhou; Yunfan Meng; Wentao Huang; Huaizhi Wang; Lijun Deng; Sheng Huang; Juan Wei. 2020. "Multi-energy net load forecasting for integrated local energy systems with heterogeneous prosumers." International Journal of Electrical Power & Energy Systems 126, no. : 106542.
Short-term predictions of wind power and its ramp events play a critical role in economic operation and risk management of smart grid. This paper proposes a hybrid forecasting model based on semi-supervised generative adversarial network (GAN) to solve the short-term wind power outputs and ramp event forecasting problems. In the proposed model, the original time series of wind energy data can be decomposed into several sub-series characterized by intrinsic mode functions (IMFs) with different frequencies, and the semi-supervised regression with label learning is employed for data augmentation to extract non-linear and dynamic behaviors from each IMF. Then, the GAN generative model is used to obtain unlabeled virtual samples for capturing data distribution characteristics of wind power outputs, while the discriminative model is redesigned with a semi-supervised regression layer to perform the point prediction of wind power. These two GAN models form a min-max game so as to improve the sample generation quality and reduce forecasting errors. Moreover, a self-tuning forecasting strategy with multi-label classifier is proposed to facilitate the forecasting of wind power ramp events. Finally, the real data of a wind farm from Belgium is collected in the case study to demonstrate the superior performance of the proposed approach compared with other forecasting algorithms.
Bin Zhou; Haoran Duan; Qiuwei Wu; Huaizhi Wang; Siu Wing Or; Ka Wing Chan; Yunfan Meng. Short-term prediction of wind power and its ramp events based on semi-supervised generative adversarial network. International Journal of Electrical Power & Energy Systems 2020, 125, 106411 .
AMA StyleBin Zhou, Haoran Duan, Qiuwei Wu, Huaizhi Wang, Siu Wing Or, Ka Wing Chan, Yunfan Meng. Short-term prediction of wind power and its ramp events based on semi-supervised generative adversarial network. International Journal of Electrical Power & Energy Systems. 2020; 125 ():106411.
Chicago/Turabian StyleBin Zhou; Haoran Duan; Qiuwei Wu; Huaizhi Wang; Siu Wing Or; Ka Wing Chan; Yunfan Meng. 2020. "Short-term prediction of wind power and its ramp events based on semi-supervised generative adversarial network." International Journal of Electrical Power & Energy Systems 125, no. : 106411.
This paper proposes a bilevel multi-house energy management (MHEM) framework to coordinate the residential demand response (DR) of heterogeneous households based on many-criteria optimality. In the upper level, the loss of life (LOL) cost of transformers is formulated into the DR cost model, and a stochastic scheduling is implemented to determine the optimum amount of transformer load deferment and curtailment. The lower level aims to optimally allocate the transformer load from the aggregator to individual households, and a many-criteria DR optimality model is proposed to maximize the multi-house benefits from DR while achieving coordination of the DR participation. Furthermore, a hypercube spatial transformation based classification and sorting scheme is developed to form an evolutionary many-objective (EMO) algorithm in order to solve the many-criteria decision making (MCDM) problem of coordinated DR with numerous participants. The performance of the proposed method was benchmarked and validated on different scaled neighborhood systems over a 24-h scheduling horizon, and comparative results demonstrated its superiority and optimality in solving many-house DR problems.
Bin Zhou; Yingping Cao; Canbing Li; Qiuwei Wu; Nian Liu; Sheng Huang; Huaizhi Wang. Many-criteria optimality of coordinated demand response with heterogeneous households. Energy 2020, 207, 118267 .
AMA StyleBin Zhou, Yingping Cao, Canbing Li, Qiuwei Wu, Nian Liu, Sheng Huang, Huaizhi Wang. Many-criteria optimality of coordinated demand response with heterogeneous households. Energy. 2020; 207 ():118267.
Chicago/Turabian StyleBin Zhou; Yingping Cao; Canbing Li; Qiuwei Wu; Nian Liu; Sheng Huang; Huaizhi Wang. 2020. "Many-criteria optimality of coordinated demand response with heterogeneous households." Energy 207, no. : 118267.
With the increasing demand for energy conversation and high efficiency, data quality is of great important to the operation management and monitoring in industrial applications. Data reconciliation, as a data processing technology, provides great potential to improve quality of process data, and is widely used to reduce measurement error and estimate unmeasured parameters. However, there are reactors connected in series in the long-running industrial processes so that liquid material information is difficult to mark and trace, and the liquid material has different residence times in each reactor due to the differences in the internal structure and operation mode. The time-delay in different reactors may be various and time-varying. In this paper, to solve these problems, a multiple time-delay interval estimation based hierarchical data reconciliation method is put forward. First, the multiple time-delay interval estimation is developed according to the process mechanism analysis and modeling. Then, an improved discrete state transition solution approach is presented to solve the data time-matching with multiple time-delay interval estimation for different reactors. Finally, a hierarchical data reconciliation frame is built by data characteristics. The feasible of the proposed data reconciliation method is verified utilizing the industrial application results.
Sen Xie; Huaizhi Wang; Jianchun Peng; Xiaoli Liu; Xiaofeng Yuan. A hierarchical data reconciliation based on multiple time-delay interval estimation for industrial processes. ISA Transactions 2020, 105, 1 .
AMA StyleSen Xie, Huaizhi Wang, Jianchun Peng, Xiaoli Liu, Xiaofeng Yuan. A hierarchical data reconciliation based on multiple time-delay interval estimation for industrial processes. ISA Transactions. 2020; 105 ():1.
Chicago/Turabian StyleSen Xie; Huaizhi Wang; Jianchun Peng; Xiaoli Liu; Xiaofeng Yuan. 2020. "A hierarchical data reconciliation based on multiple time-delay interval estimation for industrial processes." ISA Transactions 105, no. : 1.
Information and communication technologies (ICTs) introduce many Internet-based entry points that pose potential risks to cyber physical energy system (CPES). Therefore, it is significant to enhance the cybersecurity of CPES from cyber-attacks. In this paper, a new dynamic attack model that takes into account the dynamic characteristics of energy systems is developed based on traditional false data injection attack. The proposed attack model can be used to describe the attack behaviors of a malicious attacker over time. Then, we propose a new generalized interval state estimator to quantify the normal fluctuations of all CPES state variables. In this state estimator, the Unscented Kalman Filter (UKF) is used to predict the real-time operating level of the state variables. Copula theory is introduced to model the prediction uncertainty of sustainable energy and load as a set of conditional quantiles. We then model the normal fluctuation range of each CPES state as a bilevel nonlinear programming problem based on the worst case analysis. Consequently, an anomaly detection method is developed to detect whether there is an attack or not in the CPES. In this method, any state variable that falls outside its estimated interval is considered an abnormal point. Finally, the feasibility of the dynamic attack model and the effectiveness of the anomaly detection method have been extensively validated on test systems in power and energy society of the Institute of Electrical and Electronics Engineers (IEEE).
Huaizhi Wang; Anjian Meng; Yitao Liu; XueQian Fu; Guangzhong Cao. Unscented Kalman Filter based interval state estimation of cyber physical energy system for detection of dynamic attack. Energy 2019, 188, 116036 .
AMA StyleHuaizhi Wang, Anjian Meng, Yitao Liu, XueQian Fu, Guangzhong Cao. Unscented Kalman Filter based interval state estimation of cyber physical energy system for detection of dynamic attack. Energy. 2019; 188 ():116036.
Chicago/Turabian StyleHuaizhi Wang; Anjian Meng; Yitao Liu; XueQian Fu; Guangzhong Cao. 2019. "Unscented Kalman Filter based interval state estimation of cyber physical energy system for detection of dynamic attack." Energy 188, no. : 116036.
The intermittency of solar energy resources has brought a big challenge for the optimization and planning of a future smart grid. To reduce the intermittency, an accurate prediction of photovoltaic (PV) power generation is very important. Therefore, this paper proposes a new forecasting method based on the recurrent neural network (RNN). At first, the entire solar power time series data is divided into inter-day data and intra-day data. Then, we apply RNN to discover the nonlinear features and invariant structures exhibited in the adjacent days and intra-day data. After that, a new point prediction model is proposed, only by taking the previous PV power data as input without weather information. The forecasting horizons are set from 15 to 90 minutes. The proposed forecasting method is tested by using real solar power in Flanders, Belgium. The classical persistence method (Persistence), back propagation neural network (BPNN), radial basis function (RBF) neural network and support vector machine (SVM), and long short-term memory (LSTM) networks are adopted as benchmarks. Extensive results show that the proposed forecasting method exhibits a good forecasting quality on very short-term forecasting, which demonstrates the feasibility and effectiveness of the proposed forecasting model.
Gangqiang Li; Huaizhi Wang; Shengli Zhang; Jiantao Xin; HuiChuan Liu. Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach. Energies 2019, 12, 2538 .
AMA StyleGangqiang Li, Huaizhi Wang, Shengli Zhang, Jiantao Xin, HuiChuan Liu. Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach. Energies. 2019; 12 (13):2538.
Chicago/Turabian StyleGangqiang Li; Huaizhi Wang; Shengli Zhang; Jiantao Xin; HuiChuan Liu. 2019. "Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach." Energies 12, no. 13: 2538.
With the development of advanced information and communication technologies, the electric power grid has been moving forward into an energy internet for improving operational efficiency and reliability. However, energy internet also introduces many internet based entry points, which bring in additional vulnerabilities from malicious cyber-attacks, threatening the economic health of the nations. Therefore, this paper proposes a new defense mechanism based on interval state predictor to effectively detect the malicious attacks. In this mechanism, the variation bounds of each state variable are formulated as a bilevel dual optimization problem. Any resultant state that falls outside the estimated bounds can be recognized as an anomaly, indicating a high possibility of data manipulating. In addition, a typical deep learning algorithm, termed as deep belief network (DBN), is applied for electric load forecasting. DBN has a strong capability for nonlinear feature extraction, which will greatly improve the forecasting accuracy and thus narrow down the variation bounds of state variables, increasing the detection accuracy of the proposed defense mechanism. Finally, the feasibility and effectiveness of the proposed defense mechanism have been validated on IEEE 14- and 118-bus systems.
Huaizhi Wang; Jiaqi Ruan; Zhengwei Ma; Bin Zhou; XueQian Fu; Guangzhong Cao. Deep learning aided interval state prediction for improving cyber security in energy internet. Energy 2019, 174, 1292 -1304.
AMA StyleHuaizhi Wang, Jiaqi Ruan, Zhengwei Ma, Bin Zhou, XueQian Fu, Guangzhong Cao. Deep learning aided interval state prediction for improving cyber security in energy internet. Energy. 2019; 174 ():1292-1304.
Chicago/Turabian StyleHuaizhi Wang; Jiaqi Ruan; Zhengwei Ma; Bin Zhou; XueQian Fu; Guangzhong Cao. 2019. "Deep learning aided interval state prediction for improving cyber security in energy internet." Energy 174, no. : 1292-1304.
This paper proposes a novel hybrid recursive method for distribution system reliability evaluation to deal with the computational limit and low-efficiency problem which exist in previously developed techniques as the system becomes larger. This method includes a bottom-up process and a top-down process, which are developed on the basis of a recursive principle, and the synthesis of both processes yield the reliability performance of each bus of the system. The bottom-up process considers the effects of downstream failures on upstream customers, and the top-down process considers the effects of upstream failures on downstream customers. In addition, a novel switch zone concept is defined and introduced into the bottom-up recursive process to save the computation cost. Besides, section technique (ST) and shortest path method (SPM) are employed to effectively simplify the recursive path and thus, the computation efficiency can be improved. The most significant feature of the proposed method over ST, SPM, failure mode and effect analysis (FMEA) is that it provides a more generalized equivalent approach to maximally simplify the network for reliable evaluation irrespective of the network topology. The effectiveness of the proposed method has been validated through comprehensive tests on Roy Billinton test system (RBTS) bus 6 and a practical-sized distribution system in China.
Huaizhi Wang; Xian Zhang; Qing Li; Guibin Wang; Hui Jiang; Jianchun Peng. Recursive Method for Distribution System Reliability Evaluation. Energies 2018, 11, 2681 .
AMA StyleHuaizhi Wang, Xian Zhang, Qing Li, Guibin Wang, Hui Jiang, Jianchun Peng. Recursive Method for Distribution System Reliability Evaluation. Energies. 2018; 11 (10):2681.
Chicago/Turabian StyleHuaizhi Wang; Xian Zhang; Qing Li; Guibin Wang; Hui Jiang; Jianchun Peng. 2018. "Recursive Method for Distribution System Reliability Evaluation." Energies 11, no. 10: 2681.
A power factor correction (PFC) converter with interleaved multi-channel topology is gaining increasing attention due to its ability in reducing input and output current ripples, but an Electromagnetic Interference (EMI) noise filter is still required for suppressing the large high-frequency switching noise that could potentially degrade the input power quality of the supplying grid and cause malfunctions to other grid-connected systems. In this paper, a magnetic modeling of an interleaved PFC converter with an input differential mode (DM) EMI filter has been successfully implemented, which considers the nonlinear behavior of the inductive component in the EMI filter. The Jiles-Atherton (J-A) model is applied to describe the filtering inductor whose core displays saturation and hysteresis. The simulation results are verified with the experimental test.
Yitao Liu; Shan Yin; Xuewei Pan; Huaizhi Wang; Guibin Wang; Jianchun Peng. Effects of Nonlinearity in Input Filter on the Dynamic Behavior of an Interleaved Boost PFC Converter. Energies 2017, 10, 1530 .
AMA StyleYitao Liu, Shan Yin, Xuewei Pan, Huaizhi Wang, Guibin Wang, Jianchun Peng. Effects of Nonlinearity in Input Filter on the Dynamic Behavior of an Interleaved Boost PFC Converter. Energies. 2017; 10 (10):1530.
Chicago/Turabian StyleYitao Liu; Shan Yin; Xuewei Pan; Huaizhi Wang; Guibin Wang; Jianchun Peng. 2017. "Effects of Nonlinearity in Input Filter on the Dynamic Behavior of an Interleaved Boost PFC Converter." Energies 10, no. 10: 1530.