Using DNI Forecasts in CSP Simulation

DNICast Advances

- The simulation tool greenius has been extended to more CSP technologies.

- Spatially resolved DNI tends to deliver more accurate results than fields averages of DNI.

- The durability and safety of the plant can benefit from spatially resolved DNI when these values are used for the control system by limiting the magnitude of the temperature overshoots.

CSP plants with thermal storage are capable to deliver dispatchable solar electricity. Therefore, the plant output can be optimized to the specific boundary conditions in order to maximize revenues.

A time of delivery (ToD) similar to that which is used for some recent plants in South Africa with a nominal feed-in tariff for every kWh delivered between 5:00 and 16:29 as well as between 21:30 to 22:00 was considered. For the electricity delivered between 16:30 and 21:29 the remuneration is 2.7 times of the nominal feed-in tariff and for the remaining night hours there will be no remuneration at all. With such a tariff, plant operators will try to produce as much electricity as possible during the high tariff periods and no electricity when there is no remuneration. At the same time they will try to minimize dumping during the nominal tariff hours.


The annual yield simulation tool greenius ( was applied in order to simulate two different CSP plants under the same boundary conditions. The two plants (parabolic-trough using thermal oil and solar tower using molten salt as heat transfer fluid, 110 MWe gross) are equipped with thermal storage for five hours. Maximizing of revenues is done by applying heuristic methods instead of a full optimization scheme.

Different forecast data and historical measurements for 2 sites (PSA, Spain and Ghardaia, Algeria) have been used together with the optimized operation strategy (OS) of the plants. The ECMWF day ahead forecast is used as actual reference since this is a product already available for plant operators.


The measured data (representing the ideal forecast) is used to define the maximum revenues which can be reached by these plants and methodology.


Figure 1: Detailed results for the solar tower plant at PSA for a single day (optimized OS using Day 0 ECMWF forecast)

Figure 2: Detailed results for the solar tower plant at PSA for a single day (optimized OS using DNICast combined forecast)

Figure 3: Detailed results for the solar tower plant at PSA for a single day (optimized OS using ideal forecast)

Figures 1-3 show results for a single day with varying DNI conditions. The receiver heat output (green) follows more or less the DNI and is the same in all 3 figures. The tariff scheme is shown in orange.

Differences are in the electrical output and the dumping in these 3 figures. It is obvious that the electrical output is shifted to high tariff hours and the dumping is reduced from Fig. 1 to Fig. 3. Therefore the revenues are increased.

Figure 4: Relative revenues for the trough plant

Figure 5: Relative revenues for the tower plant

Figures 4 and 5 show how the annual revenues may be increased by using the different forecasts. The DNICast combined forecasts and the persistence forecasts were only available for 3 months in each of the years. The remaining months have been filled up with data from the ECMWF day 0 forecast.

The DNICast combined forecast gives higher revenues than the other datasets for almost all cases (except for the tower at PSA in 2015). The results show that such an optimizing makes sense and may increase the revenues. The actual benefit depends on the base line which is used as benchmark.  If the day ahead forecast is used as benchmark, DNICast nowcasting may increase the revenues by up to 4 %. According to the figures given here, this are about additional 600,000 to 900,000 Euros per year for a 100 MW CSP plant, which may be earned when an updated forecast is used to adapt the operating strategy to maximize revenues.