We know that solar radiation is not constant due to changes in sun-earth geometry, interactions with the atmosphere components, and impacts from the installation environment. These variability in solar resource is very important to know for several points of views. Hello. In this sequence, we will see that the temporal and the spatial scales of variability do matter for plant project developers, photovoltaic producers, the electrical grid operator, self-consumers, and electricity aggregators. Variability scales go from seconds to years and from meters to hundreds of kilometers. Let's say I'm a PV developer. My interest will be in feasibility and bankability of the project and have reliable data to convince investors. For this, the resource estimation is crucial. The question would be, how many kilowatt hour per square meter of solar radiation will dependents receive per year? That might be a tricky question. Take the example of Palazzo in France. As a mean value, the annual irradiation is 1,180 kilowatt hour per square meter. But see this sequence of 13 years. The year-to-year variability is quite remarkable. In 2006, the annual irradiation was 3.5 percent lower. In 2018, 7.2 percent higher. The coefficient of variation, the COV calculated as the ratio of the standard deviation of the annual values to the mean is the way to quantify the inter annual variability. In Palazzo, the COV is around three percent, but in desert areas can be about one percent, and it can be as high as 8-10 percent depending on the climate zone. Let's have a look at the same data, but with mean daily energy per month. The line shows the mean monthly values, and all the dots, the monthly observed data for all the years. Notice that for a given month, let's take June month 6, there might be differences as large as plus, minus 30 percent from one year to another. How many years of data do we need for this analysis? Well, this will depend on the climate characteristics of the site, but not less than 10 years in any case. Knowing the spatial variability can help a PV developer to judge if available measurements from a nearby location can be representative of the site of interests. If this is the case, it might save the need for additional measurements. See as example, the large difference in resource in Madagascar when moving from the East to the West Coast. Especially variability characterizes the micro climatic features and regional resource gradients. Now, if I'm a big phototype producer, I could have several interests. First, I would be interested to check if the variability that they observe in the output production of the PV plan is related to the variability in solar resource or if it is not. That is to check if my P plan performs as it should. In this example, the plan under performance in the morning due to snow cover. However, in the afternoon, the plan work perfectly, and the variability observed is due to the solar resource as the two lines follow each other perfectly. Second, if the feed-in tariffs, that is the price at which I sell the electricity depends on the market, I would be interested to have the forecast of the solar variability from 30 minutes to one day ahead, so to know the amount of energy that I can put in the market and avoid penalties. Third, for daytime maintenance scheduling, I would also be interested to have few days ahead forecast of solar resource, so to pick a great cloudy day to do maintenance so to minimize the economic losses. Let's take now the case of a grid operator or a transmission system operator, PSO. With the constant need to ensure the balance between production and demand, and thus, variability in the production causes an extra challenge. Frequency of electricity needs to be kept constant at 50 Hertz in Europe, and instabilities can come from solar variability in the scale of seconds. Also, PSOs need to know the solar variability through forecasts at the scale of hours to days, so to schedule the energy production mix and activate the reserve and flexibility means. Let's say now that I'm a self-consumer, in this case, the interest will be in adapting as much as possible my consumption with the production. As a simple example, I may want to wait until tomorrow to do my laundry. If today is cloudy today, but sunny tomorrow. An extreme case of self consumption is when there is no utility grid available. That is an off-grid conditions. In this case, it is very usual to use batteries or and diesel generators. In this case, greatest stability becomes critical, and having a forecast of the variability in the scale, especially to one or from 1-10 minutes in advance, can help in saving a lot of money and a lot of CO_2 related to the emissions due to the reduction of the need or for using extra diesel generation as a backup. If I am an electricity aggregator, I am interested in both temporal and spatial variability. Aggregators sum up the power from several photovoltaic plants. This might have a smoothing effect of the variability and reduce it significantly, as shown in the figure. Which might make me more competitive in the market. But in any case, I would be still needing to use solar forecast, typically from horizones of 30 minutes to 24 hours, so to decide how much energy to sell and avoid penalties. In order to fill the needs of PV plan project developers, photovoltaic producers, the electric grid operator, self consumers, and electricity aggregators, irradiance measurements, estimations, and forecasting are absolutely needed. This will be addressed in future sequences. Thank you.
