## 6 Conclusions  
This work demonstrated the development of two EMSs for a residential BESS. Both strategies are designed for application on a BESS which consists of 15 lithium iron phosphate battery cells and operates without RES at the Greek ToU pricing environment. In addition, both of the strategies utilize the KiBaM model which is validated for the aforementioned BESS and a validated battery ageing model for lithium iron phosphate batteries. The EMSs are designed to take advantage of the seasonal Greek ToU tariff so as to reduce the electricity costs at the household. This is achieved by segregating the day into charging and discharging \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**DOD** & \begin{tabular}{c} **Profit during** \\ **Autumn** \\ \end{tabular} & \begin{tabular}{c} **Profit during** \\ **Winter** \\ \end{tabular} & \begin{tabular}{c} **Profit during** \\ **Spring** \\ \end{tabular} & \begin{tabular}{c} **Profit during** \\ **Summer** \\ \end{tabular} & \
\begin{tabular}{c} **Overall Profit** \\ **(1 Year)** \\ \end{tabular} &
\begin{tabular}{c} **Capacity** \\ **Loss (1 Year)** \\ \end{tabular} \\ \hline
80\% & 20.9 \(\upepsilon\) & 20.9 \(\upepsilon\) & 10.75 \(\upepsilon\) & 10.87 \(\upepsilon\) & 63.42 \(\upepsilon\) & 10.12 Ah \\ \hline
70\% & 19.25 \(\upepsilon\) & 19.25 \(\upepsilon\) & 9.38 \(\upepsilon\) & 9.53 \(\upepsilon\) & 57.41 \(\upepsilon\) & 8.84 Ah \\ \hline
60\% & 16.25 \(\upepsilon\) & 16.25 \(\upepsilon\) & 7.99 \(\upepsilon\) & 8.08 \(\upepsilon\) & 48.57 \(\upepsilon\) & 7.05 Ah \\ \hline
50\% & 13.78 \(\upepsilon\) & 13.78 \(\upepsilon\) & 6.58 \(\upepsilon\) & 6.65 \(\upepsilon\) & 40.79 \(\upepsilon\) & 5.26 Ah \\ \hline
40\% & 10.98 \(\upepsilon\) & 10.98 \(\upepsilon\) & 5.16 \(\upepsilon\) & 5.22 \(\upepsilon\) & 32.34 \(\upepsilon\) & 3.48 Ah \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Multi-season simulated scenario’s results. zone(s). During the discharging zones the BESS's operation is determined by a dynamic optimization algorithm in order to efficiently discharge the battery stack at the household. The optimization algorithm has the capability of prioritizing the load coverage at specific times of the discharging zone when there is low uncertainty for load mismatching with the forecasted load demand. Over and above, the optimizer takes into account various physical constraints and a capacity loss trajectory which can be adjusted by the BESS's end user. On the contrary, the charging is applied without considering any constraints in order to achieve to fully charge the BESS if possible, with energy when the tariff is low. This will result in a higher load coverage from the BESS when the tariff is high. The simulation results illustrate the ability of the EMSs to achieve high economic profits with respect to the constraints set. The daily economic profit for the consumer is not notably high during spring and summer days. This is due to the corresponding ToU tariff which permits only one full charge/discharge cycle per day. Contrariwise, during autumn and winter days a significantly increased economic profit is observed because of the tariff which permits the operation of two full charge/discharge cycles per day. Nonetheless, a problem to be considered during autumn/winter is that the first charging zone is remarkably short (2 h) and therefore in many cases the BESS is not fully charged at the beginning of the day's second discharging zone. This evinces the significance of distributing the energy stored efficiently by supplying energy at times with low uncertainty for load mismatching. Consequently, the EMSs performance can significantly improve when the ToU tariff comprises many fare alterations and when it takes under consideration forecast uncertainty. Furthermore, the multi-season simulated scenarios illustrate that EMSs manage to operate the BESS by following a specific ageing trajectory. Above all, is proved that it is possible to increase the economic profit by extending the lifetime expectancy of the BESS.