Projects:2014s2-82 Grid Integration of Solar PV Embedded Generation

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This project focuses on two main parts which are power system modeling and probabilistic analysis, and PV system inverter experiment and data analysis. For power system modeling and probabilistic analysis, it aims to properly build up the power system uncertainties models such as system demand, renewable generation and load flow, thus based on those models to implement probabilistic analysis to assess power system security. For PV system inverter experiment and data analysis, it aims to conducting steady-state and transient experiments on PV inverter, hence to obtain the characteristics of the inverter.

Project Information


Due to the decreasing of natural resources and greenhouse effect, renewable energy is widely used through the whole world. In South Australia rooftop solar PV installations has proliferated with total capacity up to 500MW. However, because of the variability associated with solar power, planning and operating a power system with high penetration of solar power require special consideration of their impact on power system security, and also solar PV system cannot be scheduled and dispatched similar to traditional synchronous generators because they depend on intermittent solar irradiation, so proper risk-based security assessment method needs to be implemented to ensure the security of the power system. Meanwhile transient and steady-state effect of PV system inverter to the power system needs to be concerned as well.


Traditional deterministic security assessment does not take into account the probabilistic nature of power system behaviour especially when it comes with solar PV embedded generation, the effect from uncertainty of load demand and distributed generation will become significant, so to present a probabilistic approach risk-based assessment with solar PV power generation highly embedded is important. In order to implement system security assessment, accurate system uncertainty models are needed to be built up first, and those models are based on large amount of relevant data, so proper data analysis will be implemented as well.

Understanding the characteristics of steady-state and transient behavior on solar PV inverters is crucial importance for network analysis. This is also a necessary prerequisite for the development of reliable aggregated models for steady-state power flow models.


The objective of the project are,

  • Modelling power system uncertainties
  • Implementing probabilistic analysis.
  • Demonstrating proper case study
  • conducting steady-state characterisation experiments on solar PV inverters
  • conducting transient characterisation experiments on solar PV inverters

Modeling and Probabilistic Analysis


The power system modeling includes modeling power system uncertainties which focuses on building up the models of system demand, wind power generation and solar PV generation, and modeling the power system load flow.

Power System Demand

Based on literature review the power system demand can be modelled by normal distribution. And the demand is normally varying in regions, in different dates and seasons, so for a regional demand the models are split up in a warmer and a cooler period, further, split up in weekdays and weekends. Meanwhile due to the high ambient temperature in warmer period, the demand curve is significantly different when the temperature is over 35℃, so for warmer period the demand model will be split up further by the temperature. And as an example, the 6 different types of mean value of demand curve from Metro Region are shown in Figure 1.

Figure 1: Yearly mean of demand in Metro Region

Wind Power Generation

Based on literature review, wind speed in a region can be modelled by a two-parameter Weibull distribution. As an example, the measured probability density function of wind speed in Middle North Region and corresponding formulisation of Weibull distribution are shown in Figure2.

Figure 2: Comparison of measured and calculated wind speed

From Figure 2, the calculated curve is well matching the measured one which indicates the wind speed can be properly modelled by Weibull distribution. And non-linear model is used to build up the relationship between wind speed and output power which is shown in Figure 3.

Figure 3: Non-linear model of wind speed and output power

Solar PV Generation

Step 1: Unit daily energy is modeled by Normal distribution, Figure 4.

Step 2: Formulizing the ideal solar irradiation curve, Figure 5.

Step 3: Scale the ideal irradiation curve with ratio of unit daily energy between Step 1 and Step 2.

Step 4: Using scaled irradiation curve to calculate the PV output power with considering temperature derating, Figure 6.

Figure 4: Monthly average of daily unit energy in one year of Metro region with ±2 times of the standard deviation boundary
Figure 5: Solar irradiation in a sunny day of warmer period
Figure 6: Solar PV output with and without derating of warmer period

Power System Load Flow

Because this project is based on aggregated measured data analysis, and also due to lack of system parameters, so a simplified load flow model is implemented which uses one single bus to represent a region, hence simplifies the SA power network into 6 buses system. Those 6 buses represent corresponding regions which are Eyre Peninsula (EP), Yorke Peninsular (YP), Middle North (MN), Metro (MET), River Land (RL), and South East (SE). The simplified load flow connection structure is shown in Figure 7.

Figure 7: Simplified 6 buses load flow model of SA

Probabilistic Analysis

Based on the models developed in previous sections, probabilistic analysis includes comparison of stochastic characteristics of regional renewable generation, the effect of increasing renewable generation to the power system and system security assessment in detail.

Stochastic Characteristics of Regional Renewable Generation

The capacity factor, which is the ratio between the actual output power and its installed capacity, is used to compare the stochastic characteristics of the renewable generation of each region.

The capacity factor of wind power generation is shown as, 82-f8.png

And as an example, the monthly average of daily capacity factor of wind power generation of YP region is generated and shown in Figure 8.

Figure 8: Monthly average of daily capacity factor of wind power generation in one year of YP region

The capacity factor of Solar PV generation is shown as, 82-f82.png

The mean value of daily capacity factor in one month actually is proportional to its unit daily energy, and from Figure 4, which indicates the mean value of daily capacity factor in one month can be modeled by normal distribution as well, shown in Figure 9.

Figure 9: Monthly average of daily capacity factor of PV generation in one year of YP region with ±2 times of the standard deviation boundary


When lost half of transmission lines between two regions at certain time point, double the power on the remaining transmission lines first to check whether they can deliver the doubled power without affecting their thermal capacity or not, if the value is not over the thermal capacity then this remaining lines can cover the power loss of another line, otherwise reducing or increasing the thermal generation in closest region depending on the direction of power flow to cover the losses. As an example, the lost power can’t be covered by remaining transmission lines of interconnector between South Australia and Victoria in one year is shown in Figure 10.

Figure 10: Lost power can’t be covered by remaining transmission lines of interconnector in one year

And the simulation results illustrate the power losses can be cover by only changing the thermal generation in SE and MET region which are shown in Figure11 and Figure 12.

Figure 11: Lost power covered by regions of SE in one year
Figure 12: Lost power covered by regions of MET in one year

Thermal Capacity

In this scenario, assuming the PV generation installation capacity is increasing 100MW each year, and 75% of increasing is located in MN Region, 16% in MET Region, and evenly spread in other regions. The simulation results are shown in Table 1.

Table 1: Simulation results of percentage of time over thermal capacity in one year with increasing PV generation capacity

Combing with the case study demonstrated in 'Contingency', when contingency is happened causing losing half transmission lines between SE-VIC means halving the thermal rating of SE-VIC, the simulation results are shown in Table 2.

Table 2: Simulation results of percentage of time over thermal capacity in one year with increasing PV generation capacity under contingency condition

Expected Energy Not Supplied

With certain machine failure rate and combining PV generation into demand, then the probability of wind power generation and f thermal generation can be calculated, hence to calculate the EENS which is shown in Figure13.

Figure 13: Simulation results of percentage of time over thermal capacity in one year with increasing PV generation capacity under contingency condition

Considering all the probability of wind power generation and f thermal generation, the EENS of one year is shown in Table 3.

Table 3: Results of Expected Energy Not Supplied

Referring to section 'Thermal Capacity', under current thermal capacity condition shown in Table 1 the 100MW/yr increasing of PV generation installation capacity will barely affect the transmission lines up to 20 years. If adding up those yearly increasing to the case demonstrated here, the simulation results are shown in Table 4.

Table 4: EENS with 100MW/yr increasing of PV generation installation capacity

Experiment and Data Analysis

Experimental Design

Main Experimental Equipment

1. YC500A Micro-inverter YC500A micro-inverter is the flagship product of APS America, which is a grid-tied micro-inverter with intelligent networking and monitoring systems. YC500A micro-inverter converts the DC power from the solar module to the proper AC current for power grid. Single unit connects two module and it has a pair of AC connectors. See Figure 14.

Figure 14: YC500A Micro-inverter

2. California Instruments Power Source The California Instruments CSW 5550 AC/DC Power Source combines a flexible AC/DC power source with a high performance power analyzer. It can offer a 0-156/0-312V AC or DC voltage range and 40-5000Hz output frequencies. CSW 5550 can be operated from an easy to use front panel keypad. See figures below.

Figure 15: CSW 5550
Figure 16: Front panel keypad of CSW 5550
Figure 17: Rear panel view for the CSW 5550

Experimental Setup

A grid-connected PV generation system is electricity generating soalr PV system that is connected to the utility grid. It consists of solar panels, one or several inverters, a power conditioning unit and grid connection equipment. In order to conduct steady-state and transient characterisation experiments on solar PV inverters, it is applied a DC power supply unit to simulate PV arrays, light bulbs to simulate the users' loads and California Instruments AC power source to simulate the power grid. Thus, the schematic diagram of this experiment is figure below.

Figure 18: The schematic diagram of experiment

Results Analysis

It is mainly divided two parts of this experiment, which are steady state testing and transient state testing.


  • Input and output waveform of inverter

Set the DC power source (the input of YC500A mirco-inverter): Vdc=30V, Idc=4A. Also, set California Instruments power supply as 240V, 50Hz at AC. Get the waveforms about the input/output voltage and current of the inverter as below.

Figure 19: Waveforms of input/output voltage and current of inverter

YC500A micro-inverter does the cycle operation. Every period lasts about 0.76s. There are two types of state when the inverter is working - stand by and export, which are part 1 and part 2 in the above figure respectively. When the input of the inverter is different, the periodic time is different. Their relationship is listed in the table below.

Table 5: Periodic time of inverter working
  • Working range of inverter

Change voltage or frequency respectively to get the working range of the inverter.

Voltage: 192.8V - 263.8V

Frequency: 49.25Hz - 50.44Hz

After the YC500A mirco-inverter is interrupted or stopped, it needs 80s to let it working again.The reconnection time of this inverter is 80s.

  • Power factor of inverter

Change the voltage and current of DC power supply to test the inverter output power factor as a function of DC input. Get the table below.

Table 6: Maximum power factor of inverter varying with DC input
  • Total harmonic distortion

Total harmonic distortion (THD), is a measurement of the harmonic distortion present and is defined as the ratio of the sum of the voltage or current of all harmonic components to the voltage or current of the fundamental frequency, namely 82-37.png

Use Matlab to do FFT to get the amplitude-frequency response.

a. Voltage

Figure 20: FFT for voltage harmonic

From the figure , it cannot be seen the harmonic components. It meams the harmonic components of voltage can be ignored. Thus, THDu = 0.

b. Current

Figure 21: FFT for current harmonic

According to the formula, the total harmonic distortion of current; THDi = 0.2859%.

Transient state

  • Inverter working range on the main supply disturbance

Use CSW to program the AC source which has a voltage or frequency disturbance during every testing. Programming CSW 5550 indicates to inverter that the voltage or frequency is zero during the interrupt time. Change the interrupt time to get the working range of YC500A micro-inverter under transient state.

Table 7: Inverter working range under transient state
  • Delay time

a. Voltage disturbance

A disturbance voltage which is an unwanted voltage induced in the system by CSW lasts 300ms and the voltage drops to 50V, then the AC source returns to 240V, 50Hz.

Figure 22: delay time of voltage disturbance

From Figure 22 the voltage disturbance delay is roughly four and a half cycles. It means that the voltage disturbance delay is 4.5*0.02=0.09s, namely 90ms.

b. Frequency disturbance

It is hard to see the voltage and current change due to the slight change of frequency. Thus, transfer to frequency domain to observe, making time-frequency response of voltage and current in figure 23.

Figure 23: delay time of frequency disturbance

The effect of frequency disturbance to the frequency of voltage and current can be clearly obtained from frequency domain analysis. The frequency disturbance delay is 75ms.



  • Probability based models of power system uncertainties, which include power system demand, wind power generation andsolar PV generation.
  • Probability based model of load flow
  • Case study, which include stochastic characteristics analysis, contingency,thermal effect and EENS
  • The periodic time of the inverter is increased following the more input power under stead-state. And the value of current is more significant than the voltage for impacting periodic time.
  • The higher input power will lead to higher inverter power factor. Moreover, the value of input voltage is more important than current for impacting power factor of inverter.
  • There is almost no harmonics in voltage waveform under steady-state, while it has harmonics in current waveform.
  • The shorter interrupt time will lead to the wider voltage working range. And when the interrupt time is above 150ms, the working range of inverter will keep in 193V to 263V. When the interrupt time is between 300ms to 500ms, the frequency working range of inverter is shorter than other time intervals.
  • When voltage or frequency disturbance from the main supply has happened, there is a time delay between voltage and current of the inverter output. And the current lags the voltage few millisecond.

Future work

There are many improvement and new features can be applied upon current work by future groups.

For modeling and probabilistic analysis,

  • Applying power system analysis software such as PSS/E into the future project. Because current works focus on the power system data processing and analysis, so it is hard to implement power system dynamic and transient analysis without proper power system analysis software.
  • Combining actual power system parameters such as components failure rate, installation numbers, efficiency and so on to improve the accuracy of both system models and the results obtained from case studies.
  • Introducing updated technology such as energy storage into the future project.

For experiment and data analysis,

  • Dependence on the DC side

Simulate the solar array to confirm the inverter’s capabilities to determine and operate at the maximum power point to illuminate current versus voltage (I-V) characteristics

  • Load simulation

Simulate the variability of the load to confirm the inverter’s ability to handle load changes, transients, power factors and other characteristics.

  • Develop PSS/e models to represent the measured inverter behaviour.


Group members

Hang Yin

Kai Sun


Dr Rastko Zivanovic [1]

ElectraNet [2]


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