This page has only limited features, please log in for full access.

Unclaimed
Zhejing Bao
Hangzhou, Zhejiang Province, China, 310027

Basic Info

Basic Info is private.

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 15 June 2021 in IEEE Transactions on Industry Applications
Reads 0
Downloads 0

Hydrogen energy storage system (HESS) has attracted tremendous interest due to its low emissions and high storage efficiency. In this paper, the HESS is considered as an essential tool in hydrogen-integrated transportation and power systems to alleviate EV charging demand forecast error in a fast-charging station (FCS) and to solve voltage deviation problem due to the huge uptake of fast chargers on the utility grid. First, the wavelet transform (WT) method and long short-term memory (LSTM) neural network are combined to precisely predict the non-stationary traffic flow (TF). Then, a queuing theory-based model is developed to convert the predicted TF to the expected EV charging demand in FCS by considering charging service limitations and driver behaviors. Third, the charging demand prediction error is used to schedule the components in a HESS by considering their inherent properties and operational limits. As a result, the HESS configuration can be determined by analyzing the tradeoff between the investment cost and the monetary penalty due to charging demand forecast error and voltage deviation. The proposed solution is validated through a case study with mathematical justifications and simulation results.

ACS Style

Ting Wu; Xinyu Ji; Guibin Wang; Yun Liu; Qiang Yang; Zhejing Bao; Jianchun Peng. Hydrogen Energy Storage System for Demand Forecast Error Mitigation and Voltage Stabilization in a Fast-Charging Station. IEEE Transactions on Industry Applications 2021, PP, 1 -1.

AMA Style

Ting Wu, Xinyu Ji, Guibin Wang, Yun Liu, Qiang Yang, Zhejing Bao, Jianchun Peng. Hydrogen Energy Storage System for Demand Forecast Error Mitigation and Voltage Stabilization in a Fast-Charging Station. IEEE Transactions on Industry Applications. 2021; PP (99):1-1.

Chicago/Turabian Style

Ting Wu; Xinyu Ji; Guibin Wang; Yun Liu; Qiang Yang; Zhejing Bao; Jianchun Peng. 2021. "Hydrogen Energy Storage System for Demand Forecast Error Mitigation and Voltage Stabilization in a Fast-Charging Station." IEEE Transactions on Industry Applications PP, no. 99: 1-1.

Article
Published: 02 March 2019 in Journal of Modern Power Systems and Clean Energy
Reads 0
Downloads 0

This paper discusses a security-constrained integrated coordination scheduling framework for an integrated electricity-natural gas system (IEGS), in which both tight interdependence between electricity and natural gas transmission networks and their distinct dynamic characteristics at different timescales are fully considered. The proposed framework includes two linear programming models. The first one focuses on hour-based steady-state coordinated economic scheduling on power outputs of electricity generators and mass flow rates of natural gas sources while considering electricity transmission N − 1 contingencies. Using the steady-state mass flow rate solutions of gas sources as the initial value, the second one studies second-based slow gas dynamics and optimizes pressures of gas sources to ensure that inlet gas pressure of gas-fired generator is within the required pressure range at any time between two consecutive steady-state scheduling. The proposed framework is validated via an IEGS consisting of an IEEE 24-bus electricity network and a 15-node 14-pipeline natural gas network coupled by gas-fired generators. Numerical results illustrate the effectiveness of the proposed framework in coordinating electricity and natural gas systems as well as achieving economic and reliable operation of IEGS.

ACS Style

Dawei Chen; Zhejing Bao; Lei Wu. Integrated coordination scheduling framework of electricity-natural gas systems considering electricity transmission N − 1 contingencies and gas dynamics. Journal of Modern Power Systems and Clean Energy 2019, 7, 1422 -1433.

AMA Style

Dawei Chen, Zhejing Bao, Lei Wu. Integrated coordination scheduling framework of electricity-natural gas systems considering electricity transmission N − 1 contingencies and gas dynamics. Journal of Modern Power Systems and Clean Energy. 2019; 7 (6):1422-1433.

Chicago/Turabian Style

Dawei Chen; Zhejing Bao; Lei Wu. 2019. "Integrated coordination scheduling framework of electricity-natural gas systems considering electricity transmission N − 1 contingencies and gas dynamics." Journal of Modern Power Systems and Clean Energy 7, no. 6: 1422-1433.

Journal article
Published: 21 January 2019 in Applied Sciences
Reads 0
Downloads 0

Recently, the increasing integration of electric vehicles (EVs) has drawn great interest due to its flexible utilization; moreover, environmental concerns have caused an increase in the application of combined heat and power (CHP) units in multi-energy systems (MES). This paper develops an approach to coordinated scheduling of MES considering CHPs, uncertain EVs and battery degradation based on model predictive control (MPC), aimed at achieving the most economic energy scheduling. After exploiting the pattern of the drivers’ commuting behavior, the stochastic characteristics of available charging/discharging electric power of aggregated EVs in office or residential buildings are analyzed and represented by the scenarios with the help of scenario generation and reduction techniques. At each step of MPC optimization, the solution of a finite-horizon optimal control is achieved in which a suitable number of available EVs scenarios is considered, while the economic objective and operational constraints are included. The simulation results obtained are encouraging and indicate both the feasibility and the effectiveness of the proposed approach.

ACS Style

Xiaogang Guo; Zhejing Bao; Wenjun Yan. Stochastic Model Predictive Control Based Scheduling Optimization of Multi-Energy System Considering Hybrid CHPs and EVs. Applied Sciences 2019, 9, 356 .

AMA Style

Xiaogang Guo, Zhejing Bao, Wenjun Yan. Stochastic Model Predictive Control Based Scheduling Optimization of Multi-Energy System Considering Hybrid CHPs and EVs. Applied Sciences. 2019; 9 (2):356.

Chicago/Turabian Style

Xiaogang Guo; Zhejing Bao; Wenjun Yan. 2019. "Stochastic Model Predictive Control Based Scheduling Optimization of Multi-Energy System Considering Hybrid CHPs and EVs." Applied Sciences 9, no. 2: 356.

Journal article
Published: 07 January 2019 in Energy
Reads 0
Downloads 0

In an integrated energy system (IES), such as electricity-heating-gas system, remarkable difference in response time among multiple energy subsystems make the overall energy scheduling a challenging issue. Meanwhile, storage capabilities, such as inherent storage capability of gas pipelines, provide a potential way to improve system scheduling flexibility. In this paper, an optimal scheduling approach for IES operated in an islanded mode is developed, while covering inter- and intra-hour timescales simultaneously. Specifically, in inter-hour timescale, steady-state models of individual energy subsystems are used, and the heuristic particle swarm optimization (PSO) is integrated into the decomposition-based sequential multi-energy flow (MEF) calculation to derive optimal scheduling of CHPs and flow rates of gas sources with respect to forecasts of renewable energy sources (RESs); While in intra-hour timescale, with the dynamic model of gas flows, the optimal range of pressure of gas source node is scheduled to ensure robustness against RES uncertainties while leveraging storage capabilities of gas pipelines. An integrated energy test system is studied to demonstrate effects of integrated inter-hour and intra-hour schedules in handling different dynamic response time and effects of storages capabilities of gas linepack in achieving robust operation against uncertainties.

ACS Style

Zhejing Bao; Dawei Chen; Lei Wu; Xiaogang Guo. Optimal inter- and intra-hour scheduling of islanded integrated-energy system considering linepack of gas pipelines. Energy 2019, 171, 326 -340.

AMA Style

Zhejing Bao, Dawei Chen, Lei Wu, Xiaogang Guo. Optimal inter- and intra-hour scheduling of islanded integrated-energy system considering linepack of gas pipelines. Energy. 2019; 171 ():326-340.

Chicago/Turabian Style

Zhejing Bao; Dawei Chen; Lei Wu; Xiaogang Guo. 2019. "Optimal inter- and intra-hour scheduling of islanded integrated-energy system considering linepack of gas pipelines." Energy 171, no. : 326-340.

Journal article
Published: 03 May 2018 in Energies
Reads 0
Downloads 0

Short-term load forecasting is an important task for the planning and reliable operation of power grids. High-accuracy forecasting for individual customers helps to make arrangements for generation and reduce electricity costs. Artificial intelligent methods have been applied to short-term load forecasting in past research, but most did not consider electricity use characteristics, efficiency, and more influential factors. In this paper, a method for short-term load forecasting with multi-source data using gated recurrent unit neural networks is proposed. The load data of customers are preprocessed by clustering to reduce the interference of electricity use characteristics. The environmental factors including date, weather and temperature are quantified to extend the input of the whole network so that multi-source information is considered. Gated recurrent unit neural networks are used for extracting temporal features with simpler architecture and less convergence time in the hidden layers. The detailed results of the real-world experiments are shown by the forecasting curve and mean absolute percentage error to prove the availability and superiority of the proposed method compared to the current forecasting methods.

ACS Style

Yixing Wang; Meiqin Liu; Zhejing Bao; Senlin Zhang. Short-Term Load Forecasting with Multi-Source Data Using Gated Recurrent Unit Neural Networks. Energies 2018, 11, 1138 .

AMA Style

Yixing Wang, Meiqin Liu, Zhejing Bao, Senlin Zhang. Short-Term Load Forecasting with Multi-Source Data Using Gated Recurrent Unit Neural Networks. Energies. 2018; 11 (5):1138.

Chicago/Turabian Style

Yixing Wang; Meiqin Liu; Zhejing Bao; Senlin Zhang. 2018. "Short-Term Load Forecasting with Multi-Source Data Using Gated Recurrent Unit Neural Networks." Energies 11, no. 5: 1138.

Journal article
Published: 19 January 2018 in Energies
Reads 0
Downloads 0

In this paper, an adaptively constrained stochastic model predictive control (MPC) is proposed to achieve less-conservative coordination between energy storage units and uncertain renewable energy sources (RESs) in a microgrid (MG). Besides the economic objective of MG operation, the limits of state-of-charge (SOC) and discharging/charging power of the energy storage unit are formulated as chance constraints when accommodating uncertainties of RESs, considering mild violations of these constraints are allowed during long-term operation, and a closed-loop online update strategy is performed to adaptively tighten or relax constraints according to the actual deviation probability of violation level from the desired one as well as the current change rate of deviation probability. Numerical studies show that the proposed adaptively constrained stochastic MPC for MG optimal operation is much less conservative compared with the scenario optimization based robust MPC, and also presents a better convergence performance to the desired constraint violation level than other online update strategies.

ACS Style

Xiaogang Guo; Zhejing Bao; Zhijie Li; Wenjun Yan. Adaptively Constrained Stochastic Model Predictive Control for the Optimal Dispatch of Microgrid. Energies 2018, 11, 243 .

AMA Style

Xiaogang Guo, Zhejing Bao, Zhijie Li, Wenjun Yan. Adaptively Constrained Stochastic Model Predictive Control for the Optimal Dispatch of Microgrid. Energies. 2018; 11 (1):243.

Chicago/Turabian Style

Xiaogang Guo; Zhejing Bao; Zhijie Li; Wenjun Yan. 2018. "Adaptively Constrained Stochastic Model Predictive Control for the Optimal Dispatch of Microgrid." Energies 11, no. 1: 243.

Journal article
Published: 01 January 2013 in Advanced Materials Research
Reads 0
Downloads 0

Ice storage air-conditioning system can bring benefits to power supplier and consumers for its advantage of shifting power consumption at peak hours during day to the off-peak hours at night. In this paper, we adopted an improved particle swarm optimization algorithm to develop an optimal control strategy for ice storage air-conditioning system with the aim of minimizing operation cost subject to various operational constrains. Comparing with the usual chiller-priority and ice-storage-priority control strategy, the proposed control scheme can not only meet the building cooling load but also achieve the minimum operation cost.

ACS Style

Ting Wu; Gang Wu; Zhe Jing Bao; Wen Jun Yan. Optimization Control of Ice Storage Air-Conditioning System. Advanced Materials Research 2013, 655-657, 1492 -1495.

AMA Style

Ting Wu, Gang Wu, Zhe Jing Bao, Wen Jun Yan. Optimization Control of Ice Storage Air-Conditioning System. Advanced Materials Research. 2013; 655-657 ():1492-1495.

Chicago/Turabian Style

Ting Wu; Gang Wu; Zhe Jing Bao; Wen Jun Yan. 2013. "Optimization Control of Ice Storage Air-Conditioning System." Advanced Materials Research 655-657, no. : 1492-1495.

Research article
Published: 07 August 2012 in Transactions of the Institute of Measurement and Control
Reads 0
Downloads 0

In this paper, an optimization model is developed for co-ordinately scheduling electricity and heat production within a microgrid. The model achieves the minimization of total operating cost including electricity and fuel consumption with various operational constraints considered. Comparative research between the mathematical method of mixed-integer programming (MIP) and meta-heuristic technique of improved particle swarm optimization (IPSO) for solving this model is implemented. Simulation results for the scheduling problem with different sizes and different operational constraints show that the solution precision achieved by IPSO and MIP is very similar, IPSO is much less time-consuming than MIP for the large-scale scheduling problem when the non-linear constraints of power flow within the microgrid are considered and the situation is the opposite when power flow constraints are not considered.

ACS Style

Li-Zhong Xu; Jin-Jiang Zhang; Zhe-Jing Bao; Yi-Jia Cao. A comparative study between IPSO and MIP for co-ordinated scheduling of electricity and heat within a microgrid. Transactions of the Institute of Measurement and Control 2012, 35, 444 -456.

AMA Style

Li-Zhong Xu, Jin-Jiang Zhang, Zhe-Jing Bao, Yi-Jia Cao. A comparative study between IPSO and MIP for co-ordinated scheduling of electricity and heat within a microgrid. Transactions of the Institute of Measurement and Control. 2012; 35 (4):444-456.

Chicago/Turabian Style

Li-Zhong Xu; Jin-Jiang Zhang; Zhe-Jing Bao; Yi-Jia Cao. 2012. "A comparative study between IPSO and MIP for co-ordinated scheduling of electricity and heat within a microgrid." Transactions of the Institute of Measurement and Control 35, no. 4: 444-456.

Journal article
Published: 05 October 2011 in Journal of Zhejiang University SCIENCE C
Reads 0
Downloads 0
ACS Style

Zhe-Jing Bao; Gang Wu; Wen-Jun Yan. Control of cascading failures in coupled map lattices based on adaptive predictive pinning control. Journal of Zhejiang University SCIENCE C 2011, 12, 828 -835.

AMA Style

Zhe-Jing Bao, Gang Wu, Wen-Jun Yan. Control of cascading failures in coupled map lattices based on adaptive predictive pinning control. Journal of Zhejiang University SCIENCE C. 2011; 12 (10):828-835.

Chicago/Turabian Style

Zhe-Jing Bao; Gang Wu; Wen-Jun Yan. 2011. "Control of cascading failures in coupled map lattices based on adaptive predictive pinning control." Journal of Zhejiang University SCIENCE C 12, no. 10: 828-835.

Research article
Published: 21 September 2011 in Transactions of the Institute of Measurement and Control
Reads 0
Downloads 0

This paper develops a co-ordinated electricity and heat dispatching model for a Microgrid under a day-ahead environment. In addition to operational constraints, network loss and physical limits are addressed in this model, which were always ignored in previous work. As an important component of the Microgrid, a detailed combined heat and power (CHP) model is developed. The part load performance of CHP is modelled by a curve fitting method. Furthermore, an electric heater is introduced into the model to improve the economy of the Microgrid operation and enhance the flexibility of the Microgrid by electricity–heat conversion. Particle swarm optimization is employed to solve this model for the operation schedule to minimize the total operational cost of the Microgrid by co-ordinating the CHP, electric heater, boiler and heat storage. The efficacy of the model and methodology is verified with different operation scenarios.

ACS Style

Li Zhong Xu; Guang Ya Yang; Zhao Xu; Zhe Jing Bao; Quan Yuan Jiang; Yi Jia Cao; Jacob Østergaard. A co-ordinated dispatch model for electricity and heat in a Microgrid via particle swarm optimization. Transactions of the Institute of Measurement and Control 2011, 35, 44 -55.

AMA Style

Li Zhong Xu, Guang Ya Yang, Zhao Xu, Zhe Jing Bao, Quan Yuan Jiang, Yi Jia Cao, Jacob Østergaard. A co-ordinated dispatch model for electricity and heat in a Microgrid via particle swarm optimization. Transactions of the Institute of Measurement and Control. 2011; 35 (1):44-55.

Chicago/Turabian Style

Li Zhong Xu; Guang Ya Yang; Zhao Xu; Zhe Jing Bao; Quan Yuan Jiang; Yi Jia Cao; Jacob Østergaard. 2011. "A co-ordinated dispatch model for electricity and heat in a Microgrid via particle swarm optimization." Transactions of the Institute of Measurement and Control 35, no. 1: 44-55.