# A Comparison of Households' Energy Balance in Residential Smart Grid Pilots in the Netherlands
## 2 Pilot Descriptions, Data and Processing Methods
### Analysis Indicators and Equations  
Each performance indicator will be briefly introduced, and the formulas applied in the analysis will be shown in the section on analysis methods. For monthly values of energy in kWh, we separately took the sum of the consumption values and production values during the month in question, as shown in Equations (1)-(4):  
\[E_{\text{C\_}m}^{i}=\sum_{m\_0}^{m\_end}E_{\text{C}}^{i}(t) \tag{1}\]  
\[E_{\text{C\_}m}=\ \frac{\sum_{i}^{n}E_{\text{C\_}m}^{i}}{n} \tag{2}\]  
\[E_{R\_}^{i}=\sum_{m\_0}^{m\_end}E_{R}^{i}(t) \tag{3}\] \begin{table}
\begin{tabular}{l l c c c c c c c} \hline \hline
**Residential Smart** & \multirow{2}{*}{**Location**} & \multirow{2}{*}{**No.HH \({}^{*}\)**} & \multicolumn{2}{c}{**Time Resolution of Data**} & \multicolumn{2}{c}{**PV Systems**} & \multicolumn{2}{c}{**Energy**} & \multicolumn{2}{c}{**Heat**} & \multicolumn{2}{c}{**Smart**} \\  & & & & **of Data** & & **App** & **Pumps** & **m-CHP** & **Appliances** \\ \hline Jourw energy moment & Breda Easy street & 91 & 15 min & & & & & \\  & Breda Meulenspie & 26 & 15 min & & & & & \\  & Groiningen & 21 & 5 min \({}^{*}\) & & & & & \\  & & Amersfoort & 79 & 10 s \({}^{**}\) & & & & & \\  & Zonnedael & Anonymized & 70 & 15 min & & & & & \\ \hline \hline \multicolumn{10}{l}{\({}^{*}\)Only households with \(>\)90\% of the monitoring fraction have been included to the analysis (at 15 min). ** In order to perform the comparison, the time resolution has been reduced to 15 min by using the mean values, to enable the comparison.} \\ \end{tabular}
\end{table} 
Table 1: Summary of the main features of the smart grid pilots, their data, and the reference dataset. \[E_{R\_m}=\ \frac{\sum_{i}^{n}E_{R\_m}^{i}}{n} \tag{4}\]  
where \(t\) is the discrete time at 15 min resolution, \(n\) is the number of households of the pilot (as cited in Table 1), \(E_{\text{C}}^{i}\) is the consumption of the household \(i\) (kWh), \(E_{\text{C\_m}}^{i}\) is the total consumption of the household \(i\) for the month (kWh), \(E_{\text{C\_m}}\) is the average household consumption value of the pilot for the month in question (kWh), and \(m\_0\) and \(m\_end\) are the beginning and the end of the month, respectively. At same time step interval, \(E_{R}^{i}\) is the production of the household \(i\) (kWh), \(E_{R\_m}^{i}\) is the total production of the household \(i\), and \(E_{R\_m}\) is the average household production value of the pilot for the month in question. For grid operators, besides monthly values, the load duration curve based on residual load, the peak imported electricity, and the PV feed-in and simultaneity are also important parameters. These define the capacity that is needed for household connections, cables, and/or transformer substations. The renewable generated electricity subtracted from the electricity consumption provides the hourly residual load, as shown in Equation (5). This gives an indication of the imported and exported electricity to and from the grid and helps to analyze the impacts of new technologies on the grid. \[P_{residual}=P_{C}-P_{RE} \tag{5}\]  
where \(P_{residual}\) is the residual load, \(P_{c}\) is the momentary total electricity consumption in kW, and \(P_{RE}\) is the momentary total renewable electricity production in kW. The residual load quantifies the import electricity that the pilot community needs from the grid or the export of renewable generated electricity that the pilot community feeds into the grid. To visualize the residual load throughout the year, a load duration curve is commonly used. This is a curve showing the sorted values of electricity totals, with electricity expressed in power on the y-axis, and its occurrence percentage sorted by power on the x-axis.