Projects:2016s1-109 Development, Characterisation and Modelling of Renewable Energy-Based Microgrid

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Project Team

Li Yen Sim, Ian Wong, Chun Kit Cheung


Nesimi Ertugrul, Wai Kin Wong (ElectraNet)


Project Description:

This project features a collaboration between the University and the Industry in further developing an existing renewable microgrid experimental testbed, and initiate dynamic modelling simulation work. The experimental aspects will involve the implementation of scaled-down representations of typical generation and load elements in a typical remote area standalone renewable microgrid. The hardware development will be followed by experimental characterisation of the steady-state and transient behaviour of the microgrid. In parallel, development of dynamic software simulation models will be undertaken to experimentally validate the measured behaviour of the renewable microgrid testbed. Applications of the models in a larger “megagrid” context will also be undertaken to compare the operational reliability and security characteristics of the two types of renewable power systems.

As the uptake of renewable energy resources is increasing, the displacement of synchronous generation is posing power system stability concerns[1]. As such, finding methods to maintain the stability of the power network is crucial. As it is infeasible to perform experiments on real life microgrids due to safety concerns, measurement based validation of models is desirable and a key challenge in microgrids.


-Analyse the steady state and transient characteristics of a renewable energy based microgrid by subjecting the system to a series of voltage and frequency disturbances.

-Determine the abilities of the power system analysis tool, PowerFactory, in modelling a microgrid by comparing data to a practical small scale microgrid set up.

Project Design

Hardware experimentation:

CSW 5550 series was used to simulate the grid. A DC Power Supply simulated solar power. The CSW GUI was used to induced the system faults and an oscilloscope and sampling card was used to acquire the data. This test bed setup is shown in the figure below:

Hardware test bench.jpg

Software modelling:

DIgSILENT’s PowerFactory power network analysis tool was used to model the hardware test bench. This model is shown in the figure:

Software simluation.jpg



For the voltage disturbances, the PowerFactory model characteristics were able to replicate the shapes of the experimentation characteristic curves with the exception of the post fault behaviour. Possible reasons for the difference include the limited sampling rate of the oscilloscope used for the data acquisition and the fact that the resistive load used in the experimentation was not purely resistive and as such the model was also designed to not be purely resistive which may have greatly affected the modelling results as any small change to the model had a significant affect on the overall system due to the scaling of the software.

For the frequency disturbances, the experimentation and modelling frequency characteristics are dissimilar. This is possibly due to the difficulty of simulating these faults on PowerFactory in comparison to in the experimentation. The difference between the motor and resistive load ROCOFs for the frequency disturbances demonstrates how the presence of inertia , in the motor load, reduces the ROCOF. The overall results exhibit the complexity of modelling a real life system accurately.