Dr Ke Meng

Research Academic

School of Elect Engineering and Computer Science

Career Summary

Biography

I received the Ph.D. degree in electrical engineering from the University of Queensland, Australia, in 2009. From 2009-2012, I was a research associate and later research fellow in Department of Electrical Engineering at the Hong Kong Polytechnic University. I joined the Centre for Intelligent Electricity Networks (CIEN) at the University of Newcastle, Australia in March 2012. My research interest includes pattern recognition, power system stability analysis, wind power, and energy storage.

Research Expertise

Power System Stability Analysis Renewable Energy Energy Storage Pattern Recognition DCS & PLC.

Teaching Expertise
Power System Dynamics & Stability Renewable Energy.

Collaborations
The Hong Kong Polytechnic University, Hong Kong Queensland University of Technology, Australia Technical University of Denmark, Denmark East China University of Science & Technology, China Hong Kong Electric Company, Hong Kong GE Fanuc Intelligent Platforms, China.

Qualifications

  • Doctor of Philosophy, University of Queensland

Keywords

  • Energy Storage
  • Pattern Recognition
  • Power System Dynamics & Stability
  • Power System Stability Analysis
  • Renewable Energy

Fields of Research

Code Description Percentage
080109 Pattern Recognition and Data Mining 15
090608 Renewable Power and Energy Systems Engineering (excl. Solar Cells) 35
090699 Electrical and Electronic Engineering not elsewhere classified 50

Professional Experience

UON Appointment

Title Organisation / Department
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Publications

For publications that are currently unpublished or in-press, details are shown in italics.


Chapter (1 outputs)

Year Citation Altmetrics Link
2014 Yang H, Zhang D, Meng K, Dong ZY, Lai M, 'Economic Scheduling of CCHP Systems Considering the Tradable Green Certificates', Optimization and Control Methods in Industrial Engineering and Construction, Springer, Dordrecht 139-160 (2014) [B1]
DOI 10.1007/978-94-017-8044-5_9
Co-authors Joe Dong

Journal article (17 outputs)

Year Citation Altmetrics Link
2015 Qiu J, Dong ZY, Zhao JH, Meng K, Zheng Y, Hill DJ, 'Low carbon oriented expansion planning of integrated gas and power systems', IEEE Transactions on Power Systems, 30 1035-1046A (2015)

As a clean fuel source, natural gas plays an important role in achieving a low-carbon economy in the power industry. Owing to the uncertainties introduced by increasing utilizatio... [more]

As a clean fuel source, natural gas plays an important role in achieving a low-carbon economy in the power industry. Owing to the uncertainties introduced by increasing utilization of natural gas in electric power system, gas system and electricity system should be planned in an integrated manner. When considering these two systems simultaneously, there are many emerging difficulties, e.g., increased system complexity and risk, market timeline mismatch, overall system reliability evaluation, etc. In this paper, a novel expansion co-planning (ECP) framework is proposed to address the above challenges. In our approach, the planning process is modeled as a mixed integer nonlinear optimization problem. The best augmentation option is a plan with the highest cost/benefit ratio. Benefits of expansion planning considered are reductions in operation cost, carbon emission cost, and unreliability cost. By identifying several scenarios based on statistical analysis and expert knowledge, decision analysis is used to tackle market uncertainties. The operational and economic interdependency of both systems are well analyzed. Case studies on a three-bus gas and two-bus power system, plus the Victorian integrated gas and electricity system in Australia are presented to validate the performance of the proposed framework.

DOI 10.1109/TPWRS.2014.2369011
Co-authors Andy Zhao
2015 Huang J, Xue Y, Jiang C, Wen F, Xue F, Meng K, Dong ZY, 'An experimental study on emission trading behaviors of generation companies', IEEE Transactions on Power Systems, 30 1076-1083 (2015)

The overall performance of emission trading (ET), a market-based emission regulation tool, strongly relies on participants' participation and responses. In order to improve market... [more]

The overall performance of emission trading (ET), a market-based emission regulation tool, strongly relies on participants' participation and responses. In order to improve market design, it is important for policy makers to understand the participants' trading behaviors in different market environments. However, human behaviors cannot be easily modeled with conventional analytical methods due to its "bounded rationality" characteristics. In this paper, based on the complementary features between experimental and agent-based computational methods, a hybrid interactive simulation methodology is proposed to solve human behaviors related problems. Human-subjected experiment based on European Union Emissions Trading System price data in 2006 is conducted, the results show that there is no fixed emission trading interval for generation companies, and the strategic behaviors of market participants are observed. Major driving factors of emission trading are categorized into emission price, emission quantity and time related factors, which are in accordance with empirical analysis results on EU ETS 2005-2006 transaction dataset. Furthermore, more human-subjected experiments are conducted under different emission price scenarios to obtain samples for quantitative analysis. Based on thousands of samples obtained, the joint influences of driving factors on emission trading behaviors are analyzed. The quantitative analysis results obtained can reflect the trading patterns of human participants, which provide basis for constructing computer agents that can act as useful substitutes for human participants.

DOI 10.1109/TPWRS.2014.2366767
2015 Luo F, Meng K, Dong ZY, Zheng Y, Chen Y, Wong KP, 'Coordinated operational planning for wind farm with battery energy storage system', IEEE Transactions on Sustainable Energy, 6 253-262 (2015)

This paper proposes a coordinated operational dispatch scheme for a wind farm with a battery energy storage system (BESS). The main advantages of the proposed dispatch scheme are ... [more]

This paper proposes a coordinated operational dispatch scheme for a wind farm with a battery energy storage system (BESS). The main advantages of the proposed dispatch scheme are that it can reduce the impacts of wind power forecast errors while prolonging the lifetime of BESS. The scheme starts from the planning stage, where a BESS capacity determination method is proposed to compute the optimal power capacity and energy capacity of BESS based on historical wind power data; and then, at the operation stage, a flexible short-term BESS-wind farm dispatch scheme is proposed based on the forecasted wind power generation scenarios. Three case studies are provided to validate the performance of the proposed method. The results show that the proposed scheme can largely improve the wind farm dispatchability.

DOI 10.1109/TSTE.2014.2367550
Citations Scopus - 3
2015 Luo F, Dong Z, Chen G, Xu Y, Meng K, Chen Y, Wong K, 'Advanced pattern discovery-based fuzzy classification method for power system dynamic security assessment', IEEE Transactions on Industrial Informatics, 11 416-426 (2015)

Dynamic security assessment (DSA) is an important issue in modern power system security analysis. This paper proposes a novel pattern discovery (PD)-based fuzzy classification sch... [more]

Dynamic security assessment (DSA) is an important issue in modern power system security analysis. This paper proposes a novel pattern discovery (PD)-based fuzzy classification scheme for the DSA. First, the PD algorithm is improved by integrating the proposed centroid deviation analysis technique and the prior knowledge of the training data set. This improvement can enhance the performance when it is applied to extract the patterns of data from a training data set. Secondly, based on the results of the improved PD algorithm, a fuzzy logic-based classification method is developed to predict the security index of a given power system operating point. In addition, the proposed scheme is tested on the IEEE 50-machine system and is compared with other state-of-the-art classification techniques. The comparison demonstrates that the proposed model is more effective in the DSA of a power system.

DOI 10.1109/TII.2015.2399698
2014 Zheng Y, Dong ZY, Luo FJ, Meng K, Qiu J, Wong KP, 'Optimal Allocation of Energy Storage System for Risk Mitigation of DISCOs With High Renewable Penetrations', IEEE TRANSACTIONS ON POWER SYSTEMS, 29 212-220 (2014) [C1]
DOI 10.1109/TPWRS.2013.2278850
Citations Scopus - 10Web of Science - 4
Co-authors Joe Dong
2014 Zheng Y, Dong ZY, Xu Y, Meng K, Zhao JH, Qiu J, 'Electric Vehicle Battery Charging/Swap Stations in Distribution Systems: Comparison Study and Optimal Planning', IEEE TRANSACTIONS ON POWER SYSTEMS, 29 221-229 (2014) [C1]
DOI 10.1109/TPWRS.2013.2278852
Citations Scopus - 17Web of Science - 7
Co-authors Joe Dong, Andy Zhao
2014 Yao W, Zhao J, Wen F, Dong Z, Xue Y, Xu Y, Meng K, 'A multi-objective collaborative planning strategy for integrated power distribution and electric vehicle charging systems', IEEE Transactions on Power Systems, 29 1811-1821 (2014) [C1]

An elaborately designed integrated power distribution and electric vehicle (EV) charging system will not only reduce the investment and operation cost of the system concerned, but... [more]

An elaborately designed integrated power distribution and electric vehicle (EV) charging system will not only reduce the investment and operation cost of the system concerned, but also promote the popularization of environmentally friendly EVs. In this context, a multi-objective collaborative planning strategy is presented to deal with the optimal planning issue in integrated power distribution and EV charging systems. In the developed model, the overall annual cost of investment and energy losses is minimized simultaneously with the maximization of the annual traffic flow captured by fast charging stations (FCSs). Additionally, the user equilibrium based traffic assignment model (UETAM) is integrated to address the maximal traffic flow capturing problem. Subsequently, a decomposition based multi-objective evolutionary algorithm (MOEA/D) is employed to seek the non-dominated solutions, i.e., the Pareto frontier. Finally, collaborative planning results of two coupled distribution and transportation systems are presented to illustrate the performance of the proposed model and solution method. © 2014 IEEE.

DOI 10.1109/TPWRS.2013.2296615
Citations Scopus - 5Web of Science - 3
Co-authors Joe Dong, Andy Zhao
2014 Xu Y, Dong ZY, Meng K, Yao WF, Zhang R, Wong KP, 'Multi-objective dynamic VAR planning against short-term voltage instability using a decomposition-based evolutionary algorithm', IEEE Transactions on Power Systems, 29 2813-2822 (2014) [C1]

Short-term voltage stability is an increasing concern in today's power systems due to the growing penetration of induction motors. This paper proposes a systematic method for opti... [more]

Short-term voltage stability is an increasing concern in today's power systems due to the growing penetration of induction motors. This paper proposes a systematic method for optimal placement of dynamic VAR support against short-term voltage instability. The problem is formulated as a multi-objective optimization model minimizing two conflicting objectives: 1) the total investment cost and 2) the expected unacceptable short-term voltage performance subject to a set of probable contingencies. STATCOM is employed for its stronger dynamic VAR support capability. Indices for quantifying the short-term voltage stability and the related risk level are proposed for problem modeling. Candidate buses are selected based on the concept of trajectory sensitivity. Load dynamics are fully considered using a composite load model containing induction motor and other typical components. A relatively new and superior multi-objective evolutionary algorithm called MOEA/D is introduced and employed to find the Pareto optimal solutions of the model. The proposed method is verified on the New England 39-bus system using industry-grade models and simulation tool.

DOI 10.1109/TPWRS.2014.2310733
Co-authors Joe Dong
2014 Yang H, Zhang D, Meng K, Dong ZY, Lai M, 'MULTI-NETWORK COMBINED COOLING HEATING AND POWER SYSTEM SCHEDULING CONSIDERING EMISSION TRADING', PACIFIC JOURNAL OF OPTIMIZATION, 10 177-198 (2014)
Co-authors Joe Dong
2014 Yang H, Zhang D, Meng K, Dong ZY, Lai M, 'Multi-network combined cooling heating and power system scheduling considering emission trading', Pacific Journal of Optimization, 10 177-198 (2014) [C1]
Co-authors Joe Dong
2013 Li XR, Yu CW, Ren SY, Chiu CH, Meng K, 'Day-ahead electricity price forecasting based on panel cointegration and particle filter', Electric Power Systems Research, 95 66-76 (2013) [C1]
DOI 10.1016/j.epsr.2012.07.021
Citations Scopus - 8Web of Science - 7
2013 Xu Y, Dai Y, Dong ZY, Zhang R, Meng K, 'Extreme learning machine-based predictor for real-time frequency stability assessment of electric power systems', NEURAL COMPUTING & APPLICATIONS, 22 501-508 (2013) [C1]
DOI 10.1007/s00521-011-0803-3
Citations Scopus - 4Web of Science - 4
Co-authors Joe Dong
2013 Zhang R, Dong ZY, Xu Y, Meng K, Wong KP, 'Short-term load forecasting of Australian national electricity market by an ensemble model of extreme learning machine', IET Generation, Transmission and Distribution, 7 391-397 (2013) [C1]

Artificial Neural Network (ANN) has been recognized as a powerful method for short-term load forecasting (STLF) of power systems. However, traditional ANNs are mostly trained by g... [more]

Artificial Neural Network (ANN) has been recognized as a powerful method for short-term load forecasting (STLF) of power systems. However, traditional ANNs are mostly trained by gradient-based learning algorithms which usually suffer from excessive training and tuning burden as well as unsatisfactory generalization performance. Based on the ensemble learning strategy, this paper develops an ensemble model of a promising novel learning technology called extreme learning machine (ELM) for high-quality STLF of Australian National Electricity Market (NEM). The model consists of a series of single ELMs. During the training, the ensemble model generalizes the randomness of single ELMs by selecting not only random input parameters but also random hidden nodes within a pre-defined range. The forecast result is taken as the median value the single ELM outputs. Owing to the very fast training/tuning speed of ELM, the model can be efficiently updated to on-line track the variation trend of the electricity load and maintain the accuracy. The developed model is tested with the NEM historical load data and its performance is compared with some state-of-the-art learning algorithms. The results show that the training efficiency and the forecasting accuracy of the developed model are superior over the competitive algorithms.©The Institution of Engineering and Technology 2013.

DOI 10.1049/iet-gtd.2012.0541
Citations Scopus - 9Web of Science - 7
Co-authors Joe Dong
2012 Xu Y, Dong ZY, Meng K, Zhao J, Wong KP, 'A hybrid method for transient stability-constrained optimal power flow computation', IEEE Transactions on Power Systems, 27 1769-1777 (2012) [C1]
Citations Scopus - 13Web of Science - 10
Co-authors Andy Zhao, Joe Dong
2012 Dong ZY, Meng K, Xu Y, Wong KP, Ngan HW, 'Electricity price forecasting with extreme learning machine and bootstrapping', IEEE Transactions on Power Systems, 27 2055-2062 (2012) [C1]
Citations Scopus - 25Web of Science - 16
Co-authors Joe Dong
2012 Xu Y, Dong ZY, Xu Z, Meng K, Wong KP, 'An intelligent dynamic security assessment framework for power systems with wind power', IEEE Transactions on Industrial Informatics, 8 995-1003 (2012) [C1]
DOI 10.1109/TII.2012.2206396
Citations Scopus - 11Web of Science - 8
Co-authors Joe Dong
2012 Yao F, Dong ZY, Meng K, Xu Z, Iu HH-C, Wong KP, 'Quantum-inspired particle swarm optimization for power system operations considering wind power uncertainty and carbon tax in Australia', IEEE Transactions on Industrial Informatics, 8 880-888 (2012) [C1]
Citations Scopus - 22Web of Science - 19
Co-authors Joe Dong
Show 14 more journal articles

Conference (11 outputs)

Year Citation Altmetrics Link
2014 Dong ZY, Xu Y, Zhang R, Meng K, Weller SR, Burke D, Neilson G, 'Dynamic load modelling of industrial feeders and STATCOM placement for short-term voltage stability enhancement', Proc. AORC-CIGRE Technical Meeting 2014, Arcadia Ichigaya, Tokyo, Japan (2014) [E2]
Co-authors Joe Dong, Steven Weller
2014 Qiu J, Dong ZY, Zhao JH, Meng K, Tian H, Wong KP, 'Expansion Co-planning with Uncertainties in a Coupled Energy Market', 2014 IEEE PES GENERAL MEETING - CONFERENCE & EXPOSITION, National Harbor, MD (2014) [E1]
Co-authors Andy Zhao, Joe Dong
2013 Luo FJ, Dong Z, Chen YY, Meng K, Chen G, Tian H, Wong KP, 'A novel short-term dispatch scheme for wind farm with battery energy storage system', 2013 IEEE Power and Energy Society General Meeting, Vancouver, BC (2013) [E1]
DOI 10.1109/PESMG.2013.6672574
Co-authors Joe Dong
2013 Zheng Y, Dong ZY, Meng K, Luo FJ, Tian HQ, Wong KP, 'A control strategy of battery energy storage system and allocation in distribution systems', IEEE Power and Energy Society General Meeting, Vancouver, BC (2013) [E1]
DOI 10.1109/PESMG.2013.6672480
Co-authors Joe Dong
2013 Wang H, Meng K, Luo F, Dong ZY, Verbic G, Xu Z, Wong KP, 'Demand response through smart home energy management using thermal inertia', 2013 Australasian Universities Power Engineering Conference, AUPEC 2013, Hobart, Tasmania (2013) [E1]
DOI 10.1109/AUPEC.2013.6725442
Co-authors Joe Dong
2013 Qiu J, Dong ZY, Meng K, Zheng Y, Chen YY, Tian H, 'Risk sharing strategy for minimizing imbalance costs of wind power forecast errors', 2013 IEEE Power and Energy Society General Meeting, Vancouver, BC (2013) [E1]
DOI 10.1109/PESMG.2013.6672411
Co-authors Joe Dong
2012 Luo F, Dong ZY, Chen Y, Xu Y, Meng K, Wong KP, 'Hybrid cloud computing platform: The next generation IT backbone for smart grid', 2012 IEEE Power and Energy Society General Meeting, San Diego, CA (2012) [E1]
Citations Scopus - 1
Co-authors Joe Dong
2012 Luo F, Chen YY, Pozorski E, Stanic R, Qiu J, Zheng Y, et al., 'Constructing a power cloud data center to deliver multi-layer IT services for a smart grid', APSCOM 2012 Proceedings, Hong Kong (2012) [E1]
2012 Yao F, Dong ZY, Meng K, Xu Y, Iu HH-C, Wong KP, 'Unit commitment considering probabilistic wind generation', APSCOM 2012 Proceedings, Hong Kong (2012) [E1]
Co-authors Joe Dong
2012 Meng K, Dong ZY, Zheng Y, Qiu J, 'Optimal allocation of ESS in distribution systems considering wind power uncertainties', APSCOM 2012 Proceedings, Hong Kong (2012) [E1]
Co-authors Joe Dong
2011 Zhang R, Xu Y, Dong ZY, Meng K, Xu Z, 'Intelligent systems for power system dynamic security assessment: Review and classification', 2011 4th International Conference on Electric Utility Deregulation and Restructuring and Power Technologies: DRPT 2011, Weihai (2011) [E1]
Citations Scopus - 1
Co-authors Joe Dong
Show 8 more conferences
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Research Supervision

Number of supervisions

Completed0
Current3

Total current UON EFTSL

PhD1.8

Current Supervision

Commenced Level of Study Research Title / Program / Supervisor Type
2014 PhD Energy Storage System Planning in Power System with High Renewable Penetration
Electrical Engineering, Faculty of Engineering and Built Environment, The University of Newcastle
Principal Supervisor
2013 PhD Utilization of Thermal Inertia in Demand Response
Electrical Engineering, Faculty of Engineering and Built Environment, The University of Newcastle
Principal Supervisor
2013 PhD Advanced Wind Turbine Control for Grid Connections
Electrical Engineering, Faculty of Engineering and Built Environment, The University of Newcastle
Principal Supervisor
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Dr Ke Meng

Position

Research Academic
School of Elect Engineering and Computer Science
Faculty of Engineering and Built Environment

Contact Details

Email ke.meng@newcastle.edu.au
Phone (02) 40339182

Office

Room A924
Building NIER Building
Location Callaghan
University Drive
Callaghan, NSW 2308
Australia
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