Skip to main content

Dr Ke Meng

Biography
Phone
+61-2-9385-6649

Ke Meng is power systems expert with a focus on interoperability of renewable energies with the grid. His works focuses on smoothing the intermittencies of weather dependent energy resources to ensure power supply is stable and reliable. Ke has been awarded a 2020 ARC Future Fellowship to investigate ‘Stability assessment of Australia’s future electrical grids’. Ke has been providing consulting services with a number of renewable energy and BESS grid connection studies Here he talks about electric transport interacting with the grid.

Dr Ke Meng is a Senior Lecturer in energy system at School of Electrical Engineering and Telecommunications, UNSW. He received his Ph.D. degree from the University of Queensland, followed by post-doctoral appointments at the Department of Electrical Engineering, the Hong Kong Polytechnic University. In 2012, he transferred to the Centre for Intelligent Electricity Networks at the University of Newcastle as an associate lecturer and was promoted to the research academic in late 2012. In 2015, he joined the University of Sydney as a lecturer in the School of Electrical and Information Engineering. He has been involved in renewable energy research since 2008. He has established an independent research profile in renewable energy, more specifically on easing large-scale integration of wind and solar energy into power systems. This includes direct experience in power system dispatch, power system computation, stability assessment, controller design, etc.

    Ten career-best research outputs

    • K. Meng, W. Zhang, J. Qiu, et al., “Offshore transmission network planning for wind integration considering AC and DC transmission options,” IEEE Transactions on Power Systems, vol. 34, no. 6, pp. 4258-4268, Nov. 2019.
    • K. Meng, Z.Y. Dong, Z. Xu, et al., “Coordinated dispatch of virtual energy storage systems in smart distribution networks for loading management,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 49, no. 4, pp. 776-786, Apr. 2019.
    • K. Meng and P. Li, “Software-defined web-of-nest: A fully distributed framework for managing power distribution networks,” IEEE Smart Cities Newsletter, Jul. 2018.
    • K. Meng, W. Zhang, Y. Li, et al., “Hierarchical security-constrained OPF considering wind energy integration through multi-terminal VSC-HVDC grids,” IEEE Transactions on Power Systems, vol. 32, no. 6, pp. 4211-4221, Nov. 2017.
    • K. Meng, H. Yang, Z.Y. Dong, et al., “Flexible operational planning framework considering multiple wind energy forecasting service providers,” IEEE Transactions on Sustainable Energy, vol. 7, no. 2, pp. 708-717, Apr. 2016.
    • K. Meng, Z.Y. Dong, Z. Xu, et al., “Cooperation-driven distributed model predictive control for energy storage systems,” IEEE Transactions on Smart Grid, vol. 6, no. 6, pp. 2583-2585, Nov. 2015
    • K. Meng, Z.Y. Dong, D.H. Wang, et al., “A self-adaptive RBF neural network classifier for transformer fault analysis,” IEEE Transactions on Power Systems, vol. 25, no. 3, pp. 1350-1360, Aug. 2010.
    • K. Meng, Z.Y. Dong, K.P. Wong, et al., “Speed-up the computing efficiency of power system simulator for engineering-based power system transient stability simulations,” IET Generation, Transmission & Distribution, vol. 4, no. 5, pp. 652-661, May 2010.
    • K. Meng, H.G. Wang, Z.Y. Dong, et al., “Quantum-inspired particle swarm optimization for valve-point economic load dispatch,” IEEE Transactions on Power Systems, vol. 25, no. 1, pp. 215-222, Feb. 2010.
    • K. Meng, Z.Y. Dong, and K.P. Wong, “Self-adaptive radial basis function neural network for short-term electricity price forecasting,” IET Generation, Transmission & Distribution, vol. 3, no. 4, pp. 325-335, Apr. 2009.

    Research activities

    With increasing levels of weather-dependent energy resources added to the features of Australia’s grid, i.e. long distances over an island grid, the fossil fuel-dominated Australian power system faces a number of unique challenges. Large-scale wind generation is being connected in areas with good resources, which tends to be the weaker parts of the grid, primarily designed to supply local loads. Whilst geographical and technological diversity smooths the impact of intermittency, due to the current inter-regional transmission constraints and the low power system inertia conditions, the Australian power grid is becoming more susceptible to prevalent disturbances than ever before. Secure and reliable operation of the power grid becomes now one of the basic needs for national security. To achieve a smooth grid connection, wind farm developers need to assess and comply with a range of regulatory requirements specified in the “Grid Code”. Therefore, it is important to understand the ISO’s requirements on connecting new wind power plants, grid’s ability to accommodate new wind generation, as well as its potential impacts on system stability.

    • Solving technical issues originated from interpretation of national and international standards and grid codes.
    • Providing market-leading insight, advice and technical solutions for client project.
    • Providing ongoing key-expert support during pre-contract negotiations.
    • Managing customer requests for models in PSS/E, PSCAD, DigSilent etc.
    • Managing grid stability/impact studies and load flow calculation.
    • Supporting customer’s grid consultants during GPS connection studies.
    • Managing static, dynamic and harmonic assessments of proposed project connections.
    • Liaising with manufacturer model support team and coordinate model validation.
    • Preparing grid technology related documentation for clients.
    • Contributing in stakeholder meetings to provide grid engineering expertise.
    • Liaising with consultants and client to prepare a complete connection application.

    Grid connection studies

    The Renewable Energy Integration Team (REIT) in UNSW provides a wide range of services to assess system-wide impact of increased penetration level of wind energy, allowing TNSPs and DNSPs to integrate renewables without harming network reliability. The REIT has expertise on technical and financial feasibility analysis, including wind resource assessment, wind data generation, wind turbine selection, micrositing optimization, electrical layout optimization and wind farm energy estimates for the business case. The team has years’ experience in modelling of various wind generators (DFIG, PMSG, etc) necessary for the grid connection studies. Equipped with a range of state-of-the-art simulation software, full grid integration studies and system studies are performed, in compliance with the relevant national grid codes and standards. The service covers all aspects of grid reliability when integrating renewables from pre-test simulations, GPS compliance assessment, to R2 model validation. Specifically,

    • Complete set of PSSE / PSCAD software simulation models
    • Generator Performance Standards (GPS)
    • Connection studies report(s)
    • Releasable User Guide (RUG)
    • Power System Design and Setting Data Sheet
    • PSSE Model Acceptance Test (MAT) Report
    • PSSE / PSCAD Generating System model benchmarking report

    The REIT works with and maintain relationships with consulting firms, and AEMO, NSPs, EPC contractor, and 3rd party advisors to successfully deliver project and get an Offer to Connect.

    Filed testing facilities

    Except for electrical feasibility studies, grid codes and compliance assessments, a smooth process and risk mitigation is ensured through on-site measurements and testing. In order to ensure the stable and reliable operation of the grid-connected wind farms, fault ride through capabilities are required in the national grid code. Low voltage ride through capability (LVRT) – ability of the wind turbines to withstand credible fault conditions, and support to network voltage recovery by injecting reactive current; high voltage ride through capability (HVRT) – wind turbines should have the ability to operate at high voltage condition and stay connected for a certain period.

    UNSW has developed a LVRT/HVRT testing facility. The testing equipment consists of a series-connected impedance Z1 capable of limiting the short-circuit current, a parallel-connected impedance Z2 capable of reducing the voltage level of the turbine side, and a capacitor C for raising the voltage. The impedances Z1 and Z2 consist of several coils each. By changing the ratio Z1 to Z2 the depth of the voltage dip can be configured. Depending on the respective grid code, different depths of voltage dips and rises can be simulated, ranging from 0% to 140% with a step of 1% of the rated voltage. The duration of the dip depends on the depth and ranges from 1000 milliseconds to 3000 milliseconds. Different grid faults can be simulated, including line to line (L-L), double line to ground (LL-G), and line to line to line (L-L-L). It can test generating plants up to 8 MVA in grids and up to 40 kV system.


    Selected Publications

    Journal articles

    Yuan L; Meng K; Huang J; Dong ZY, 2020, 'Investigating subsynchronous oscillations caused by interactions between PMSG-based wind farms and weak AC systems', International Journal of Electrical Power and Energy Systems, vol. 115, http://dx.doi.org/10.1016/j.ijepes.2019.105477

    Zhang G; Zhang F; Zhang X; Meng K; Dong ZY, 2020, 'Sequential Disaster Recovery Model for Distribution Systems With Co-Optimization of Maintenance and Restoration Crew Dispatch', IEEE Transactions on Smart Grid, vol. 11, pp. 4700 - 4713, http://dx.doi.org/10.1109/tsg.2020.2994111

    Zhang G; Zhang F; Zhang X; Wang Z; Meng K; Dong ZY, 2020, 'Mobile Emergency Generator Planning in Resilient Distribution Systems: A Three-Stage Stochastic Model With Nonanticipativity Constraints', IEEE Transactions on Smart Grid, vol. 11, pp. 4847 - 4859, http://dx.doi.org/10.1109/tsg.2020.3003595

    Lu S; Gu W; Zhang C; Wu Z; Meng K; Dong Z, 2020, 'Hydraulic-Thermal Cooperative Optimization of Integrated Energy Systems: A Convex Optimization Approach', IEEE Transactions on Smart Grid, http://dx.doi.org/10.1109/TSG.2020.3003399

    Shen W; Qiu J; Meng K; Chen X; Dong ZY, 2020, 'Low-Carbon Electricity Network Transition Considering Retirement of Aging Coal Generators', IEEE Transactions on Power Systems, vol. 35, pp. 4193 - 4205, http://dx.doi.org/10.1109/tpwrs.2020.2995753

    Liu B; Meng K; Dong ZY; Wong PKC; Ting T, 2020, 'Unbalance Mitigation via Phase-Switching Device and Static Var Compensator in Low-Voltage Distribution Network', IEEE Transactions on Power Systems, vol. 35, pp. 4856 - 4869, http://dx.doi.org/10.1109/TPWRS.2020.2998144

    Kong W; Jia Y; Dong ZY; Meng K; Chai S, 2020, 'Hybrid approaches based on deep whole-sky-image learning to photovoltaic generation forecasting', Applied Energy, vol. 280, http://dx.doi.org/10.1016/j.apenergy.2020.115875