Projects:2018s1-182 Inertia Characterisation and Modelling in a Renewable Energy and Battery Based Microgrid

Introduction

Synchronous inertia, is basically the amount of stored energy in a power system that can be utilised during supply demand imbalances. Recently, due to high penetration of wind and solar power in a power system, thus form of inertia has slowly been decreasing thus causing instability in the power system as the frequency fluctuates more.

A possible solution for this is to provide more stored energy in the system using batteries. This is called synthetic inertia. Although not instantaneous like synchronous inertia, with fast frequency processing, synthetic inertia could be a viable way of minimising supply demand imbalances at all times and therefore stabilising frequency.

Project Team

• Maxwell Weppner
• Pei Ying Lim

Project Supervisors

• Assoc Prof. Nesimi Ertugrul
• Dr Wai-Kin Wong (Electranet)

Motivation

Power Systems are changing rapidly. In the South Australian case, on average, approximately 50% of the electricity is produced by asynchronous generators. However, the asynchronous supply at any given time can reach 100% and regularly does. This is in stark contrast with historical power systems where 100% of the electricity was sourced by synchronous generators all of the time. This has consequently had a number of effects on the modern power system, one being the ever decreasing synchronous inertia that historical systems inherently had.

Low system inertia is a problem when it comes to system stability as was seen in the 2016 Blackout in South Australia. Thus, it is desired to somehow replace this stability. Synthesizing inertia is one such method and has been explored in this project.

Objectives

The objective of this project is to model a renewable energy and battery based microgrid focusing on inertia characterisation. The project objectives are listed below :

a) Develop microgrid with +/- 0.15Hz of frequency regulation

b) Improve the rate of change of frequency measurement (RoCoF) scheme

c) Demonstrate the role of battery energy storage solution (BESS) for frequency regulation and frequency contingency

d) Software modelling and validation

Project Structure

This project is divided into multiple stages.

Stage 0 - Literature Research

Stage 1 - Validation of Previous Result

Stage 2 - New System Improvements

Stage 3 - Software Modelling and Validation

Project Stages

Stage 1 - Validation of Previous Results.

In this stage, we reproduced the setup from the previous year and aimed to validate previous results.

We repeated all the experimental test beds that were performed in 2017. However, the exact results were not recreated because of the petrol generator itself. There were a number of problems with the generator, these included a fluctuating output voltage frequency and a distorted voltage sine wave. Under the load step changes that were imposed on the generator in these test beds, these problems were amplified. After reproducing the previous results to an acceptable limit, a deeper understanding of this system and its problems was realised allowing a platform for the remainder of the project to commence from.

Stage 2 - The New System

Due to the problems with the petrol generator it was replaced with a different system incorporating a DC Motor as the prime mover of a three phase synchronous generator. Simple resistive loads were applied to the system under operation to act as the "base load", electronic load banks were attached to each phase and were controlled by LabVIEW software. The same test beds were applied to this system to investigate the difference, the results showing that the new setup was superior. Following this, the LabVIEW software was significantly adjusted such that it could measure a declining frequency and turn the E-Loads off accordingly (FFR). It is important to note that this fast frequency response action was implemented such that it was controlled by the frequency and was basically blind as too what the electronic loads were doing. Once this was implemented, various scenarios were tested and the characteristics of this FFR and this system as a whole were recorded.

Stage 3 - Modelling the System With a viable system and useful results the system needed to be modelled such that this FFR solution could be scaled and investigated further to prove its use in frequency stability in power systems. The system was modelled initially in Power Factory which is a power systems modelling tool. However, it was difficult to accurately model this system in Power Factory. The difficulty was due to the nature of the inherent frequency control of this system. In this case the DC Motor prime mover's output power is controlled by the frequency and it therefore acts like a governor with a substantial droop. Firstly all the known settings and parameters were modelled but modelling the DC Motor Prime Mover as a Governor was basically trial and error. The system

Project Results

Stage 1 - Validation of Previous Results.

Stage 2 - The New System

We implemented the microgrid in the machines lab using a DC Machine to drive the synchronous machine as a generator. The testbed from previous year is repeated in the three phase system. We have +-0.02Hz of frequency variation during regulation, which is one of our objectives.

We then implemented the Fast Frequency Response System in LabVIEW and the electronic loads will turn on at 2 seconds and turn off when the frequency is detected to be 49.9Hz. The delay of the system is 160ms. After testing the frequency processing system, the delay in LabVIEW is 21ms, all the other delays are from the communication between the electronic loads, LabVIEW software and the hardware data acquisition. Hence, we decided not to change the frequency processing software as this would require a whole new system setup.

The load injected to the system ranges from 1500W to 3300W. As the power injected increases, the frequency dip increases, rate of change of frequency increases as well, provided with the same amount of inertia. The starting frequency and the settling frequency of the system is equal to 50Hz which indicates that the system is stable.

Stage 3 - Modelling the System

We have implemented our system model in Matlab. From the graph shown below, The frequency dip as well as the overshoot are all within 0.05Hz of error.

References

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