Performance Analysis of Large Scale Solar PV Plants: Study of MW to MWh

India is the third largest economy in the world and needs energy to fuel its growth. The way India chooses to meet its energy requirement has a large impact globally. However, India is among the few countries which is on the path to meet its NDC targets. Until 2030 the nation aims to have 300,000 MW of solar power capacity installed. So far more than 40,000 MW have been installed. To analyse whether the installed MW capacities are delivering the foreseen and required amount of electricity (megawatt hours), GIZ India carried out an analysis of large solar PV systems installed in the country. 

To analyse the performance, the publicly available data of 44 solar generating stations from Central Electricity Authority (CEA) RE generation reports for the period of January to October 2020 were used. The data was segregated into two categories, government owned stations and privately owned stations. The spread of the generating stations across 15 different states is shown in figure 1. 

3 states have been shortlisted. It was observed that the performance of government owned stations seemed to be fairly below the reference level with average monthly CUF between 13.4–19.87% and losing 3.1 MU of electricity every month in comparison to the designed capacity. Privately owned power stations seem to have been performing above this level with average CUF ranging 21.28–28.5% and gaining 105.3 MU of electricity every month as shown in figure 2.

One reason for such a variation in the generation of solar PV plants in government owned stations vs private stations was found to be the oversizing of DC solar capacity of plants. The oversizing recommended is generally 10% to achieve maximum power output from the installed system. It was observed that the government owned stations with the cumulative capacity of 594.92 MW were designed with a DC:AC ratio of around 1.1 which means the systems were oversized by 10% in DC capacity in comparison to rated AC capacity. However, the privately owned stations with the cumulative capacity of 2480 MW seemed to significantly perform better due to the high over-loading ratio i.e. 1.2 in Madhya Pradesh, 1.4-1.5 in Rajasthan and 1.1 in Gujarat. The performance of the government, as well as private owned stations in Gujarat, were comparatively lowest across the three states. The lower performance for the government owned stations may be also caused by poor maintenance, plant outages or design issues.

The results indicate that private developers are oversizing the system DC capacity to generate additional units of electricity and are able to provide lower tariffs in competitive biddings. In the year 2020, the range of tariff that was discovered in the solar PV tender for the Rajasthan location was 2.00-2.01 INR/kWh, whereas for Gujarat it was 2.73-2.78 INR/kWh. The difference in tariff may be related to the overloading being practiced by developers at these locations, wherein the developers are able to generate more energy from the plant at a lower cost. As per the latest CERC tariff regulation for renewable energy, the minimum CUF stated for solar PV plants is 21%. If the analysed government owned stations would add around 77 MW on the DC side, operation at 21% CUF could be achieved. Certainly, this would mean that the recommended oversizing factor needs to be increased in agreement with the competent authorities. As a result, by maintaining the AC infrastructure of the solar PV plants, the project owner will get the additional benefit from increased power generation. The true efficiency of a plant can certainly only be measured if the output power is related to the installed DC capacity. This data is mostly not available. Another approach to determine the efficiency of a solar plant and which does not require the information on the total DC capacity installed, is to relate the electricity generation (megawatt hours) with the square meter land covered by the entire plant or by the solar panels only. This could be easily done via satellite image analysis.

For more information please contact Mr. Abhinav Jain, abhinav.jain(at)