## 2 Methods and Details of the Technical Assessment
### The Aspects of the Analysis
#### 2.2.1 The Circumstances of the Modeling  
For the analyses, the Elia Group's (EG) 15-min-based PV power data (measured data, day-ahead, and intraday forecasts) were used for the period of 30/09/2014-30/09/2019 [82]. The EG has been developing PV power forecasting systems since 2012 to maximize the grid integration of PV technology, primarily to the day-ahead and intraday market. The size of the monitored system was 2.9 GW in 2012 and had reached the value of 3.9 GW by September 2019 [82]. The frequency of the database measurement periods is continuous with 15-min intervals. The day-ahead PV data are a snapshot of day + 1 forecast, which is updated at 11:35 based on 11:00 meteorological forecast input information. The most recent forecast provides an opportunity to update the PV electricity production forecasts in the intraday market; this is updated four times a day, at 06:00, 11:00, 16:00, and 21:00. About a 1 to 2-h delay is to be applied for the application of those updates into the grid model and intraday grid calculations from Elia [82,83]. In this article, the analyses were converted to 1-GW PV system. The EG has been developing a wind power forecasting system also, but this research focuses only on PV technology.  
In this manuscript, a 0% divergence limit was analyzed with 15-min frequency measurement periods. This model approach fits the \"Transmission Congestion Relief\" [76] utility services category. For example, today in Hungary, PV system operators need to create 15-min-based PV day-ahead electricity production forecasts for every single day for the Hungarian TSO [84]. Intraday PV forecasts are optional. However, it should be noted that PV system owners need to pay a kWh-based surcharge for any intervals of 15 min if the divergence is more than \(\pm 50\%\) between measurements and forecasts from 1 July 2018. For this reason, creating intraday PV forecasts is highly recommended, because it can reduce the rate of the surcharge [84,85,86,8]. According to the new regulatory model, the \(\pm 50\%\) divergence limit will be 0% within a few years due to the rapid growth of PV systems [11,16,87,88]. This is because when the real energy output of the PV system in the 15-min time interval does not reach the day-ahead or intraday forecast, then officially the TSO needs to regulate in a positive direction [85]. From the perspective of the PV system operators, this means that the positive TSO regulation equals a negative divergence because of the difference between real power and day-ahead or the most recent power data, which is caused by the official calculation method. This logic (Figure 4) was illustrated by real data of 12/07/2019. This European market example shows that tightening the legal framework for PV forecasting accuracy in the day-ahead and intraday market can make investors economically interested in using energy storage systems to achieve more accurate daily energy generation by PV.  
Today, it has become a relevant question how effectively battery storage systems can be used to maintain day-ahead and intraday PV electricity production forecasts, because the current PV-battery management technology is capable of programmed energy generation [89,90,91,92]. Table 3 shows the efficiency features associated with battery technologies.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Battery Technology** & **Li-ion** & **VRFB** \\ \hline Maximum battery charging/discharging & & \\ efficiency.[\%] & 98/98 & 94/88 \\ \hline Hybrid inverter maximum efficiency [\%] & & 95 \\ \hline Other system losses [\%] & & 2 \\ \hline Charge/discharge time [h] & & 2/2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Main technical–economic parameters for simulations using different battery technologies [86,90,91,92,93,94,95,96,97,98].