Energize RE 4: Renewable Energy Supplement - March 2016 – Page 25

Management/Planning

Solar photovoltaic (PV) energy variation is due to a number of factors. The primary source of PV energy is the sun, therefore any variation that impacts on the path between earth and the sun results in PV energy variation.

Design influence: Fixed for the life of the plant

The output of a PV panel is dependent on its geographic location, which is fixed relative to the surface of the earth at the time of construction, and therefore site-specific clear sky solar irradiance can be accurately determined [5, 6]. Fig. 1 shows the expected global horizontal irradiation on the world map.

The angle at which the PV panels are installed also determines the energy output [6]. The angle of installation is calculated for optimal PV energy conversion. Variable tilt PV plants are in operation and are used to optimise the available solar irradiance by constantly positioning the PV panel to maximise the quantity of incident solar irradiance.

Solar influence: Constant periodic variation

The change in the distance between the surface of the earth and the sun due to the orbit and tilt of the earth results in seasonal variation of solar irradiation, which is periodic and can be forecasted using forecasting

Solar photovoltaic energy output forecasting for the South African power system by A Pillay, R Stephen, and H Geldenhuys, Eskom, and J van Coller, University of Witwatersrand

South Africa is in the process of including more renewable energy technologies into the existing energy mix by means of the REI4P. Hydro, wind, solar photovoltaic, wave/tidal, concentrated solar and biomass technologies are the most suitable renewable energy (RE) technologies. Solar energy sources are particularly favourable as South Africa experiences some of the highest level of solar radiation in the world.

models [7]. The rotation of the earth along its axis results in a constant change in the position of the sun relative to the location of the PV panel, which causes a change in the distance between the sun and the PV panel and results in variation in the solar irradiation [7].

Environmental conditions

A number of environmental conditions impact on solar irradiance and hence the PV energy output. These include dust, aerosols, water vapour, and cloud cover, the impact of which is variable and cannot be accurately forecasted.

The effect of a dust storm on PV output has shown to be significant. The forecasted solar photovoltaic energy output without taking into account dust is 50 – 120% higher than the actual measured output. The effects of sand storms are not currently included in weather prediction models [7]. Forecasting models for PV energy output take into account the effect of aerosols, water vapour and ozone [9].

The most significant contributor to the variation in solar irradiation from environmental conditions is cloud cover. The effect of PV energy output on the electrical power system due to cloud cover is a current area of research. Weather prediction models and PV energy output forecasting models have been developed to determine effects of cloud cover [9].

Solar PV efficiency

The efficiency of the PV panel will vary over the operating life. A decline in PV energy output due to ageing occurs over its operating life [10]. The ambient temperature of the PV panel also impacts the efficiency and hence the energy output. Research has shown that an increase in the ambient temperature results in decreased solar photovoltaic energy output [10, 11]. Poor maintenance such as the presence of surface contamination on the PV panel and vegetation resulting in shading over the PV panel will also impact on energy output [12].

Solar PV energy data

Mulilo Renewable Energy Solar PV was a successful bidder for the construction and operation of a 19,9 MW solar photovoltaic energy park in Prieska, Northern Cape. The Mulilo solar PV farm is connected via an 11 kV overhead line to the Cuprum Substation. Fig. 2 shows the active power output from Mulilo solar photovoltaic energy measured at the Cuprum substation for the month of November 2014.

Fig. 2 shows the variation in active power output from day to day of the Mulilo PV farm, which is illustrative of the variable nature of power output from PV farms. The nearest weather office to the energy park is at De Aar. The observed cloud cover obtained for the same time period is shown in Fig. 3.

Fig. 2 shows that the lowest maximum power output occurred on 1 November 2014, which in Fig. 3 corresponds with the observed cloud cover for that day (7/8ths). Fig. 3 also shows that the ambient temperature was average. Fig. 2 also shows that the highest maximum power output occurred on 16 November 2014, which in Fig. 3 corresponds to a clear day and a lower than average ambient temperature. The observations made from Fig. 2 and Fig. 3 show the dependency of power output of a solar PV park to the prevailing cloud cover and ambient temperature. Fig. 1: World map of global horizontal irradiation [5].

Energize RE 4: Renewable Energy Supplement - March 2016 – Page 26

Management/Planning

Methods to accommodate solar PV energy variation

A number of researchers have presented solutions to overcome the effect of solar PV energy variation. Energy storage systems have been used to store energy during periods of excess energy and release energy during periods of insufficient generating capacity [15 – 17]. However the use of energy storage systems increases the cost of the installation [18 – 20].

Energy storage systems used for capacity storage are not well-suited for fast response to mitigate the effects of rapid power variation, therefore different energy storage technologies are required for capacity storage and for transient energy variation [21]. Ultra-capacitors have been proposed to accommodate transient solar PV active power variation [22]. For capacity storage, temporary use of the battery from an electric vehicle was proposed [23, 24]. Energy storage was also effectively used together with coordinated control of distribution network transformer tap settings to control the line voltage [25].

Spatial aggregation of solar PV parks reduces the correlation of cloud transients over a wide area. The overall result is that a large amount of solar photovoltaic energy does not fal l away suddenly [11, 23, 26 – 28]. The drawback of this approach is that spatial aggregation is still dependent on uncontrollable environmental conditions, and can result in a situation where energy output from multiple PV farms falls away suddenly resulting in a drop in power delivered to the power system.

Solar PV energy output forecasting

PV energy output forecasting is another method that has been used to accommodate the variable nature of PV.

This has gained importance with increasing amounts of solar PV integration. Two directions in terms of solar photovoltaic forecasting techniques exist. The first method makes use of specific models of the individual processes in the PV energy conversion process and the other uses models to represent the solar photovoltaic energy conversion process in its entirety.

Clear sky solar irradiance

The ower output of a solar PV farm is dependent on total global horizontal irradiance and ambient temperature as shown in (1).

PPV = ƒ(GHI,Tamb) (1)

Where:

PPV is the active power output of the solar photovoltaic energy park,

GHI is the total global horizontal irradiance

Tamb the ambient temperature.

The most significant variation in total global horizontal radiance is due to cloud cover over the solar PV farm. Therefore, the clear sky index was developed by quantifying the cloud cover over the solar PV park [38].

The theoretical clear sky solar irradiation (the amount of sun power per square meter (W/m²) reaching the earth’s surface) for a specific site can be determined using solar irradiance models [9, 35, 36]. The theoretical value is used to normalise forecasted solar irradiation and is used as an input to solar PV forecasting models [12]. The clear sky index for a specific day can be obtained from the weather services and is dependent on the cloud cover. The clear sky index is defined as shown in (2)

where

Kp is the clear sky index,

GHIc is the total clear sky global horizontal irradiance,

and GHI is the total global irradiance for specific cloud cover.

Table 1 shows the relationship between the cloud cover and the clear sky index.

Solar PV energy forecasting horizons

There are three distinct solar PV output forecasting horizons [23]. The first is the short term hourly forecasting which is used for forecasting solar PV power ramps. The main input to short-term hourly forecasting models is terrestrial solar irradiation sensors [37]. Medium-term daily forecasting is used for generation scheduling which uses weather satellite imagery [35, 37]. Long-term solar PV forecasting is used for planning purposes and uses numerical weather pattern models as modell ing inputs [35, 37]

Forecasting using physical models

Solar photovoltaic power output can be forecasted using process models, which are mathematical representations of the process being mo delled. In the case of solar PV output forecasting, the process models required are a model to determine the incident solar irradiation and a model representing the conversion of solar PV energy to electrical energy. The method of using individual process models was successfully implemented for forecasting regional solar photovoltaic active power output in Germany [37].

Forecasted cloud cover Clear Sky Index (Kp)

Clear 0,9

Partly-cloudy 0,7

Cloudy, foggy 0,3

Rainy, snowy, overcast 0,1

Table 1: Quantification of cloud cover [37].

Fig. 2: Measured active power output of Mulilo solar PV farm at the Cuprum substation [13].

Kp = GHI (2)

GHIc

Energize RE 4: Renewable Energy Supplement - March 2016 – Page 27

Management/Planning

Forecasting using artificial neural networks

Artificial neural networks (ANN) do not require

the transfer functions of the process being

modelled. ANNs are used in complex systems

and learn the relationship between their inputs and output using historical input and output

data to train the neural network. Different types of ANN models have been implemented to improve performance by reducing training time, accuracy and complexity. Solar PV output forecasting using ANN was implemented using the clear sky index, ambient temperature and active power solar photovoltaic farm as inputs [39].

Persistence forecasting has also been

used to forecast PV output. The basis of

persistence forecasting is that the prevailing

conditions will remain the same, ho wever

the per formance of the pers is tence

forecasting method decreases in the

presence of variable cloud cover [35]. At

present solar PV forecasting models do not

accurately forecast the output. A number

of researchers have reported the error

between the forecasted solar PV forecasting

methods and the measured energy output

[35, 38, 40].

Acknowledgement

This paper was presented at the Cigre/IEC

international symposium on “The development

of electricity infrastructure in sub-saharan

Africa” Cape Town October 2015, and is

republished with permission.

References

For a full list of references please visit

http://wp.me/a5dDng-Bjt.

Contact Rob Stephen, Eskom,

Tel 031 204-5693, stephenr@eskom.co.za

Fig. 3: Graph of cloud cover and ambient temperature over De Aar [14].

Est 1976