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

Modeling and Probabilistic Analysis

Modeling

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,

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,

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

Contingency

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

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 X.

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 figure X.

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.

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

Team

Group members

Hang Yin

Kai Sun

Supervisor

Dr Rastko Zivanovic [1]

Sponsor

ElectraNet [2]

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