## 3 Results  
### Features of the Negative and Positive Power-Energy Divergence  
Based on 5 years of data of the EG database, the magnitudes of divergences between the real PV power data and those of the day-ahead and most recent PV power forecasts and their percentage distribution were summarized in the range of 0 MW \(<\) x \(\leq\) 550 MW (Table 4, Figure 5). The negative results were given in absolute values. According to the data, it can be concluded that the most recent forecast reduced the MW size of regulation compared to the day-ahead data. For example, a maximum deviation of 50 MW occurred in 72%-76% of the day-ahead cases, whereas this figure increased to 82%-84% in the most recent forecasts. However, it should be noted that the negative power divergence can be up to about 50% of the nominal PV power. In 0.002% of the day-ahead forecast cases, 400 MW \(<\) x \(\leq\) 550 MW negative divergence occurred, which increased to 0.004% in the cases of the most recent ones. In addition, the results showed that the most recent forecast reduced the MWh regulation size of the daily positive divergence above 10 MWh compared to the day-ahead data. In the negative case, this effect occurred only above 60 MWh. In the case of the 100 \(<\) x \(\leq\) 200 MWh range, the best forecast resulted in a 6.4%-6.5% improvement in regulation compared to the day-ahead one. The examination of the negative divergence showed that there were rare cases (0.05%) where 2500 MWh \(<\) x \(\leq\) 3000 MWh deviations had to be regulated daily. The most common categories for regulation were the 0 MWh \(<\) x \(\leq\) 10 MWh and the 100 MWh \(<\) x \(\leq\) 500 MWh, because they accounted for 62% of the positive and 61% of the negative divergences, respectively (Table 5, Figure 6). \begin{table}
\begin{tabular}{c c c} \hline \hline
**The Magnitude (x) of the** & \multirow{2}{*}{**Frequency of Positive**} & **Frequency of Negative** \\
**Day-Ahead PV Power Forecast** & & **Divergence in the Case of** \\
**Divergence Compared to the** & & **Day-Ahead Forecast [\%]** & **Day-Ahead Forecast [\%]** \\
**Real PV Power Data [MW]** & & **Value [\%]** \\ \hline  
*The Magnitude (\(\mathbf{d}\)) of the Most** & \multirow{2}{*}{**Frequency of Positive**} & **Frequency of Negative** \\
**Recent (Intraday) PV Power** & & **Divergence in the Case of Most** \\
**Forecast Divergence Compared** & & **Recent Forecast [\%]** & **Recent Forecast (Absolute Value)** \\
**to the Real PV Power Data [MW]** & & **[\%]** \\ \hline  
\hline \end{tabular}
\end{table} 
Table 4: Magnitudes of the day-ahead/intraday PV power forecast divergence (15 min-based) compared to the real PV power data for the period of 30/09/2014–30/09/2019. Overall, the method showed that the accuracy of PV power and energy forecasting improved with the use of the most recent (intraday) forecast method. The best forecast reduced the MW size of regulation compared to the day-ahead data. However, in this way, it is not possible to determine the positive and negative divergence reduction value of a given storage size. That can only be determined by a 15-min based charge/discharge simulation. Figure 5: The magnitude of the day-ahead PV power forecast divergence (15 min-based) compared to the real PV power data for the period of 30/09/2014–30/09/2019. Figure 6: The magnitude of the day-ahead PV energy forecast divergence (24 h-based) compared to the real PV power data for the period of 30/09/2014–30/09/2019. \begin{table}