Dr Andy Zhao

Senior Lecturer

School of Elect Engineering and Computer Science (Electrical and Computer Engineering)

Career Summary

Biography

Dr. Junhua(Andy) Zhao obtained his Ph.D. in Electrical Engineering from the University of Queensland in 2007. Since then, he has been working as a Post-doctoral Research Fellow at the University of Queensland (2007 - 2008), a Graduate Analyst at Suncorp Corporation (2008), and a Research Fellow at the University of Queensland (2009 - 2011). He joined the University of Newcastle at 2011 as the Principal Research Scientist at the Centre for Intelligent Electricity Networks (CIEN). Since Jun 2012, he has been appointed as a Senior Lecturer at School of Electrical Engineering and Computer Science.

Research Expertise
Smart Grid and Cyber-Physical Systems, Power System Planning, Power System Control and Stability, Renewable and Distributed Generation Technologies, Energy Economics and Electricity Markets, Computational Intelligence and its application, Data mining and statistical methods

Teaching Expertise
Electricity Market Power system analysis Curriculum development for electricity market and power systems courses Information systems

Collaborations
I have established research collaboration with a number of top research institutes around the world, which include: - Electric Power Research Institute (EPRI), USA - The University of Sydney, Australia - The University of Western Australia, Australia - The University of Michigan, USA - Hong Kong Polytechnic University, Hong Kong - Technical University of Denmark, Denmark - State Grid Electric Power Research Institute (SGEPRI), China - Zhejiang University, China - Hunan University, China


Qualifications

  • PhD (Electrical Engineering), University of Queensland

Keywords

  • computational intelligence
  • cyber-physical systems
  • data mining
  • electricity market
  • energy economics and electricity markets
  • information systems
  • power system control and stability
  • power system planning
  • power systems analysis
  • renewable and distributed generation technologies
  • smart grid
  • statistical methods

Languages

  • English (Fluent)
  • Cantonese (Fluent)
  • Mandarin (Fluent)

Fields of Research

CodeDescriptionPercentage
080109Pattern Recognition and Data Mining30
090607Power and Energy Systems Engineering (excl. Renewable Power)50
090699Electrical and Electronic Engineering not elsewhere classified20

Professional Experience

UON Appointment

DatesTitleOrganisation / Department
21/10/2014 - 15/08/2017Senior LecturerUniversity of Newcastle
School of Elect Engineering and Computer Science
Australia

Awards

Honours

YearAward
2012PVC's Award for Excellence in Research Performance
Unknown
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Publications

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


Journal article (28 outputs)

YearCitationAltmetricsLink
2015Qiu 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 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.

DOI10.1109/TPWRS.2014.2369011
Co-authorsKe Meng
2015Wu M, Dong J, Zhao A, Tang WC, Sher W, Chen GW, et al., 'A Cooling Vest for Construction Workers', Advanced Materials Research, 1061-1062 728-732 (2015) [C1]
DOI10.4028/www.scientific.net/AMR.1061-1062.728
Co-authorsJoe Dong, Willy Sher, Patrick Tang
2015Xu Y, Dong ZY, Zhao J, Xue Y, Hill DJ, 'Trajectory sensitivity analysis on the equivalent one-machine-infinite-bus of multi-machine systems for preventive transient stability control', IET GENERATION TRANSMISSION & DISTRIBUTION, 9 276-286 (2015)
DOI10.1049/iet-gtd.2014.0263Author URL
2015Wang G, Zhao J, Wen F, Xue Y, Ledwich G, 'Dispatch Strategy of PHEVs to Mitigate Selected Patterns of Seasonally Varying Outputs From Renewable Generation', IEEE TRANSACTIONS ON SMART GRID, 6 627-639 (2015)
DOI10.1109/TSG.2014.2364235Author URL
2014Dong Z, Zhao J, Wen F, Xue Y, 'From smart grid to energy internet: Basic concept and research framework', Dianli Xitong Zidonghua/Automation of Electric Power Systems, 38 1-11 (2014) [C1]

The traditional way of economic and social development, characterized by centralized utilization of fossil fuel energy, is gradually changing. On the other hand, the third industrial revolution is now rising. As the core technology of the third industrial revolution, the Energy Internet aims at facilitating large-scale utilization and sharing of renewable energy by integrating renewable energy and internet technologies. It will enhance the merging of electricity, transportation and natural gas networks, change the way of energy utilization, and finally achieve the goal of promoting sustainable economic and social development. Given this background, an overview of the Energy Internet is first provided, and a basic research framework developed. A definition of the Energy Internet is then suggested, followed by its basic architecture and main components. Several main research challenges to the Energy Internet, such as the wide-area coordination and control of distributed devices, the integration of the power system with the transportation system and natural gas network, as well as the cyber physical modeling and security, are next discussed in more details.

DOI10.7500/AEPS20140613007
CitationsScopus - 4
Co-authorsJoe Dong
2014Zhao J, Xu Y, Luo F, Dong Z, Peng Y, 'Power system fault diagnosis based on history driven differential evolution and stochastic time domain simulation', Information Sciences, (2014) [C1]

Fault diagnosis is an important task in power system analysis. In this paper, a hybrid method is proposed to perform online fault diagnosis of transmission lines. Stochastic time domain simulation (STDS) is firstly introduced to generate simulated fault and system data so as to improve the computational speed of fault diagnosis and handle the possible malfunction of protective relays and circuit breakers. The fault diagnosis problem is then formulated as an optimization problem, which can take into account the possible malfunction of protection devices and post-fault system trajectories. We propose a novel optimization algorithm, namely history driven differential evolution (HDDE) to solve the formulated optimization problem. The proposed methodology is finally tested using comprehensive case studies to demonstrate its effectiveness. © 2014.

DOI10.1016/j.ins.2014.02.039
Co-authorsJoe Dong
2014Zheng 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]
DOI10.1109/TPWRS.2013.2278852Author URL
CitationsScopus - 15Web of Science - 6
Co-authorsJoe Dong, Ke Meng
2014Yao 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 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.

DOI10.1109/TPWRS.2013.2296615
CitationsScopus - 5Web of Science - 2
Co-authorsKe Meng, Joe Dong
2014Lai M, Yang H, Yang S, Zhao J, Xu Y, 'CYBER-PHYSICAL LOGISTICS SYSTEM-BASED VEHICLE ROUTING OPTIMIZATION', JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION, 10 701-715 (2014) [C1]
DOI10.3934/jimo.2014.10.701Author URL
2014Yang H, Yi D, Zhao J, Luo F, Dong Z, 'Distributed optimal dispatch of virtual power plant based on elm transformation', Journal of Industrial and Management Optimization, 10 1297-1318 (2014) [C1]

To implement the optimal dispatch of distributed energy resources (DER) in the virtual power plant (VPP), a distributed optimal dispatch method based on ELM (Extreme Learning Machine) transformation is proposed. The joint distribution of maximum available outputs of multiple wind turbines in the VPP is firstly modeled with the Gumbel-Copula function. A VPP optimal dispatch model is then formulated to achieve maximum utilization of renewable energy generation, which can take into account the constraints of electric power network and DERs. Based on the Gumbel-Copula joint distribution, the nonlinear functional relationship between the wind power cost and wind turbine output is approximated using ELM. The approximated functional relationship is then transformed into a set of equality constraints, which can be easily integrated with the optimal dispatch model. To solve the optimal dis-patch problem, a distributed primal-dual sub-gradient algorithm is proposed to determine the operational strategies of DERs via local decision making and limited communication between neighbors. Finally, case studies based on the 15-node and the 118-node virtual power plant prove that the proposed method is effective and can achieve identical performance as the centralized dispatch approach.

DOI10.3934/jimo.2014.10.1297
CitationsScopus - 4
Co-authorsJoe Dong
2014Luo F, Zhao J, Qiu J, Foster J, Peng Y, Dong Z, 'Assessing the transmission expansion cost with distributed generation: An australian case study', IEEE Transactions on Smart Grid, 5 1892-1904 (2014) [C1]

Distributed generation (DG) is rapidly increasing its penetration level worldwide and is expected to play a more important role in providing power. An important benefit of DG is its ability to defer transmission investments. In this paper, a simulation model is implemented to conduct quantitative analysis on the effect of DG on transmission investment deferral. The transmission expansion model is formulated as a multi-objective optimization problem with comprehensive technical constraints, such as AC power flow and system reliability and/or security. The case study that was selected is the Queensland electricity market in Australia. Simulation results show that DG can reduce transmission investments significantly. This ability however is greatly influenced by a number of factors, such as the location of DG, the network topology, and power system technical constraints. © 2014 IEEE.

DOI10.1109/TSG.2014.2314451
Co-authorsJoe Dong
2014Yang H, Yang S, Zhao J, Dong Z, 'Complex dynamics and chaos control of electricity markets with heterogeneous expectations', INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS, 24 1047-1064 (2014) [C1]
DOI10.1002/etep.1773Author URL
Co-authorsJoe Dong
2013YE X, ZHAO J, WEN F, 'Quantitative Vulnerability Assessment for Power Information System Based on Adjacency Matrix', Automation of Electric Power Systems, 37 41-46 (2013)
DOI10.7500/AEPS20130324004
2013Xie YY, Xue YS, Wen FS, Dong ZY, Zhao JH, 'Space-time Evaluation for Impact of Ice Disaster on Transmission Line Fault Probability', Automation of Electric Power Systems, 37 32-41 (2013)
2013Zhang LY, Zhao JH, Wen FS, Xue YS, Wang J, 'Networked Robust Control of Electric Vehicles Based on Linear Matrix Inequalitie', Automation of Electric Power Systems, 37 54-62 (2013)
DOI10.7500/AEPS201207097
2013Yao W, Zhao J, Wen F, Xue Y, Ledwich G, 'A Hierarchical Decomposition Approach for Coordinated Dispatch of Plug-in Electric Vehicles', IEEE TRANSACTIONS ON POWER SYSTEMS, 28 2768-2778 (2013) [C1]
DOI10.1109/TPWRS.2013.2256937Author URL
CitationsScopus - 15Web of Science - 6
2013Yang H, Chung CY, Zhao J, 'Application of plug-in electric vehicles to frequency regulation based on distributed signal acquisition via limited communication', IEEE Transactions on Power Systems, 28 1017-1026 (2013) [C1]
DOI10.1109/TPWRS.2012.2209902
CitationsScopus - 13Web of Science - 11
2013Yang H, Yi D, Zhao J, Dong Z, 'Distributed optimal dispatch of virtual power plant via limited communication', IEEE Transactions on Power Systems, 28 3511-3512 (2013) [C1]
DOI10.1109/tpwrs.2013.2242702Author URL
CitationsScopus - 6Web of Science - 4
Co-authorsJoe Dong
2013Yang H, Chung CY, Zhao J, Dong Z, 'A probability model of ice storm damages to transmission facilities', IEEE Transactions on Power Delivery, 28 557-565 (2013) [C1]
DOI10.1109/tpwrd.2012.2212216Author URL
CitationsScopus - 1
Co-authorsJoe Dong
2013Yang H, Yi J, Zhao J, Dong Z, 'Extreme learning machine based genetic algorithm and its application in power system economic dispatch', Neurocomputing, 102 154-162 (2013) [C1]
DOI10.1016/j.neucom.2011.12.054Author URL
CitationsScopus - 8Web of Science - 7
Co-authorsJoe Dong
2013WANG H, WANG G, ZHAO J, WEN F, LI J, 'Optimal Planning for Electric Vehicle Charging Stations Considering Traffic Network Flows', Automation of Electric Power Systems, 37 63-69 (2013) [C2]
DOI10.7500/AEPS201211031
2013Xie Y, Xue Y, Wen F, Dong Z, Zhao J, 'Space-time evaluation for impact of ice disaster on transmission line fault probability', Dianli Xitong Zidonghua/Automation of Electric Power Systems, 37 (2013) [C2]

The evolution process of blackout caused by ice disaster can be divided into four stages: ice and snow leading to freezing rain, freezing rain leading to transmission line icing, transmission line icing leading to power grid fault, and cascading failure leading to blackout. The way freezing rain affects transmission line fault probability involves the meteorological law of sleet forming, the thermodynamics law of transmission line icing, and the mechanical law of transmission line broken and tower collapse. Therefore, the information needed for online evaluation of blackout risk due to freezing rain is summed up first. Then, the ice thickness covered on the transmission line is calculated using weather forecast and micromorphology information. And the online transmission line fault probability, which can be used to update the envisioned fault table of the security stability analysis software, is evaluated using real-time line monitoring information. An early-warning model for blackout due to ice disaster is established finally based on the electrical law of flow transfer and dynamic law of system crash. © 2013 State Grid Electric Power Research Institute Press.

DOI10.7500/AEPS20130606017
CitationsScopus - 4
2012Xu 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]
CitationsScopus - 13Web of Science - 9
Co-authorsKe Meng, Joe Dong
2012Xu Y, Dong ZY, Zhao J, Zhang P, Wong KP, 'A reliable intelligent system for real-time dynamic security assessment of power systems', IEEE Transactions on Power Systems, 27 1253-1263 (2012) [C1]
CitationsScopus - 16Web of Science - 9
Co-authorsJoe Dong
2012Dong ZY, Zhao J, Hill DJ, 'Numerical simulation for stochastic transient stability assessment', IEEE Transactions on Power Systems, 27 1741-1749 (2012) [C1]
CitationsScopus - 16Web of Science - 10
Co-authorsJoe Dong
2012Zhao J, Wen F, Dong ZY, Xue Y, Wong KP, 'Optimal dispatch of electric vehicles and wind power using enhanced particle swarm optimization', IEEE Transactions on Industrial Informatics, 8 889-899 (2012) [C1]
CitationsScopus - 31Web of Science - 21
Co-authorsJoe Dong
2011Yang H, Xu W, Zhao J, Wang D, Dong ZY, 'Predicting the probability of ice storm damages to electricity transmission facilities based on ELM and Copula function', Neurocomputing, 74 2573-2581 (2011) [C1]
DOI10.1016/j.neucom.2010.12.039
CitationsScopus - 5Web of Science - 4
Co-authorsJoe Dong
2011Zhao J, Wen F, Xue Y, Dong ZY, 'Modeling analysis and control research framework of cyber physical power systems', Dianli Xitong Zidonghua - Automation of Electric Power Systems, 35 1-8 (2011) [C2]
CitationsScopus - 11
Co-authorsJoe Dong
Show 25 more journal articles

Conference (2 outputs)

YearCitationAltmetricsLink
2014Qiu 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]
Author URL
Co-authorsKe Meng, Joe Dong
2012Chen G, Zhao J, Dong ZY, Weller SR, 'Complex network theory based power grid vulnerability assessment from past to future', APSCOM 2012 Proceedings, Hong Kong (2012) [E1]
CitationsScopus - 1
Co-authorsSteven Weller, Joe Dong
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Grants and Funding

Summary

Number of grants6
Total funding$425,635

Click on a grant title below to expand the full details for that specific grant.


20142 grants / $23,135

Developing an intelligent system for real-time assessment of power system short-term voltage stability in a smart grid environment $13,135

Funding body: University of Newcastle - Faculty of Engineering & Built Environment

Funding bodyUniversity of Newcastle - Faculty of Engineering & Built Environment
Project TeamDoctor Daniel Xu, Miss Rui Zhang, Doctor Andy Zhao
SchemePilot Grant
RoleInvestigator
Funding Start2014
Funding Finish2014
GNoG1400979
Type Of FundingInternal
CategoryINTE
UONY

Load modelling for electricity networks in a smart grid environment$10,000

Funding body: University of Newcastle

Funding bodyUniversity of Newcastle
Project TeamConjoint Professor Joe Dong, Doctor Andy Zhao
SchemeNear Miss Grant
RoleInvestigator
Funding Start2014
Funding Finish2014
GNoG1301376
Type Of FundingInternal
CategoryINTE
UONY

20131 grants / $5,000

New Staff Grant 2013$5,000

Funding body: University of Newcastle

Funding bodyUniversity of Newcastle
Project TeamDoctor Andy Zhao
SchemeNew Staff Grant
RoleLead
Funding Start2013
Funding Finish2013
GNoG1300803
Type Of FundingInternal
CategoryINTE
UONY

20123 grants / $397,500

Part II STATCOM Study$175,000

Funding body: Department of Resources Energy and Tourism

Funding bodyDepartment of Resources Energy and Tourism
Project TeamConjoint Professor Joe Dong, Doctor Andy Zhao
SchemeSmart Grid, Smart City Project
RoleInvestigator
Funding Start2012
Funding Finish2012
GNoG1201066
Type Of FundingOther Public Sector - State
Category2OPS
UONY

Network vulnerability assessment and risk management strategy for a smart grid$145,000

Funding body: Department of Defence

Funding bodyDepartment of Defence
Project TeamConjoint Professor Joe Dong, Doctor Andy Zhao
SchemeResearch Support for National Security
RoleInvestigator
Funding Start2012
Funding Finish2012
GNoG1101177
Type Of FundingAust Competitive - Commonwealth
Category1CS
UONY

ERM Grid Battery Research$77,500

Funding body: Department of Resources Energy and Tourism

Funding bodyDepartment of Resources Energy and Tourism
Project TeamConjoint Professor Joe Dong, Doctor Andy Zhao
SchemeSmart Grid, Smart City Project
RoleInvestigator
Funding Start2012
Funding Finish2012
GNoG1201065
Type Of FundingOther Public Sector - State
Category2OPS
UONY
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Research Supervision

Current Supervision

CommencedResearch Title / Program / Supervisor Type
2014Energy Storage System Planning in Power System with High Renewable Penetration
Electrical Engineering, Faculty of Engineering and Built Environment
Co-Supervisor
2014A Cyber Physical System Based Networked Control for Electric Vehicles
Electrical Engineering, Faculty of Engineering and Built Environment
Principal Supervisor
2013Power System Security Assessment and Enhancement Considering Energy Storage Resources and Renewable Energy Generation
Electrical Engineering, Faculty of Engineering and Built Environment
Principal Supervisor
2012Intelligent Electricity Networks
Electrical Engineering, Faculty of Engineering and Built Environment
Co-Supervisor

Past Supervision

YearResearch Title / Program / Supervisor Type
2015Optimal Allocation and Operation of Distributed Generation and Energy Storage in Distribution Systems
Electrical Engineering, Faculty of Engineering and Built Environment
Principal Supervisor
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Dr Andy Zhao

Position

Senior Lecturer
Centre for Intelligent Electricity Networks
School of Elect Engineering and Computer Science
Faculty of Engineering and Built Environment

Focus area

Electrical and Computer Engineering

Contact Details

Emailandy.zhao@newcastle.edu.au
Phone(02) 4033 9187

Office

RoomEAG15
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