# A Real Model of a Micro-Grid to Improve Network Stability
## 5 Results of the Tested Algorithm  
The hardware configuration outlined in Figure 1 was, together with the microgrid stability control structure, tested within one of the scenarios possible. The experiment is based on a plan which actively uses the demand-response algorithm. This algorithm then depends on several factors, including the total energy in the grid, the connected local storage reservoirs, and the weather forecast. The designed procedure, referred to as \"consume all you produce\", triggers when we know from the forecast that, during the days to come, enough energy will be provided by the power stations and that the grid will not be able to absorb the entire volumes supplied. Exploiting the given scenario, we measured and displayed the patterns of the voltage and energy flow in the microgrid during a period of 75 min. Figure 10 below represents the power arrangement in the grid. The green pattern characterizes the power supplied to the microgrid; in the described case, the monitoring was performed on a sunny day, enabling the system to draw power especially from the photovoltaic panels. The red pattern then illustrates the consumption of the appliances being operated at the moment (including, for example, regular home appliances). As the electrical energy consumption rate is significantly smaller than the rate of energy supplied to the grid, and considering that all the storage reservoirs were full, we triggered the demand-response algorithm, which embodies \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Appliance**} & \multirow{2}{*}{**Power (\&W)**} & \multicolumn{6}{c}{**Hour of the Day**} & \multirow{2}{*}{**Run Time (min)**} & \multirow{2}{*}{**Frequency Per Day**} \\ \cline{3-3} \cline{5-8}  & & & **0** & & & **1** & & **21** & **22** & **23** \\ \hline Washing & 3 & 0 & 0 & & 0 & 0 & 0 & 150 & 1 \\ machine & 1.5 & 0 & 0 & & 0 & 0 & 72 & 0 \\ Fridge & 0.5 & 1 & 1 & 1 & 1 & 1 & 10.2 & 24 \\ Freezer & 0.5 & 1 & 0 & 0 & 1 & 0 & 7.5 & 12 \\ Over & 2.5 & 0 & 0 & & 0 & 0 & 120 & 0 \\ Iron & 0.7 & 0 & 0 & & 0 & 0 & 120 & 0 \\ TV1 & 0.2 & 0 & 0 & & 1 & 1 & 1 & 60 & 4 \\ TV2 & 0.15 & 0 & 0 & & 0 & 0 & 60 & 5 \\ Radio & 0.02 & 0 & 0 & & 0 & 0 & 90 & 2 \\ PC & 0.25 & 0 & 0 & & 1 & 1 & 1 & 60 & 14 \\ Notebook & 0.07 & 0 & 0 & 1 & 1 & 0 & 60 & 4 \\ Light 1 & 0.04 & 0 & 0 & 1 & 2 & 1 & 5 & 7 \\ Light 2 & 0.06 & 0 & 0 & 1 & 1 & 0 & 60 & 3 \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The estimated energy consumption schedule of a typical small consumer (a summertime Monday). a part of the control strategy (Figure 8) executed through LabVIEW. The applied procedure then allowed us to activate the load (including but not limited to water heaters, freezers, air conditioning, thermoregulatory, and powerbank or other chargers); this step led to an increased energy off-take in the grid. The corresponding pattern is plotted in violet (Figure 10). Figures 10 and 11 show that, at almost all the given instants of time, more energy than produced was taken off the grid. As regards the voltage stability (Figure 12), however, the energy in the microgrid can be neglected because the appliances are supplied from selected storage spots in the grid and the implemented weather forecast informed the control algorithm that at least the following three days would be sunny, making it possible for the photovoltaic panels to supply the maximum energy. This energy would subsequently be stored back into the relevant storage reservoirs. Without the demand-response algorithm, the surplus energy, represented in black (Figure 11), could not be utilized. Integrating the pattern showed that the energy lost during the 75 min cycle amounted to approximately 7.9 kJ; conversely, the proposed algorithm facilitates reaching the pursued goal of \"consume all you produce\".