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

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Project Information

Modeling and Probabilistic Analysis

Modeling

The power system uncertainty modeling includes models of power system demand, wind power generation and solar PV generation.

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







Experiment and Data Analysis

Experimental Design

Main Experimental Equipment

1. YC500A Micro-inverter



2. California Instruments Power Source



Experimental Setup

Results Analysis

1. Steady-state

  • Input and output waveform of inverter


  • Working range of inverter


  • Power factor of inverter varying with DC input


  • Total harmonic distortion



2. Transient state

  • Inverter working range on the main supply disturbance



  • Delay time
 Voltage disturbance




 Frequency disturbance


Conclusion

Outcomes

Future work

Team

Group members

Hang Yin

Kai Sun

Supervisor

Dr Rastko Zivanovic [1]

ElectraNet [2]



References

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[25] AMETEK, “California Instruments CSW Series”, AMETEK Programmable Power, 2010

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