## 3 Case Study
### Low/High Solar Penetration  
Figure 7a-d illustrates two innovative customer management and storage procedures from T = 0 to Td. Figure 7e,f compares retail and community costs when using solar energy at minimal levels. The local pool has less solar power, so the community price is more critical than in Figure 6. It will participate in the social exchange of energy and reduce the benefits of battery storage devices used by sophisticated consumers. The assumption that everyone will use the energy pool to obtain electricity is wrong. The intelligent user also incurs higher electricity costs than in the previous scenario. The proposed method can help consumers, as shown in Figure 7b. The trading behavior of the two agents is equivalent, suggesting that the algorithm is efficient. The proposed method has the potential to reduce costs for the energy community. Overall, the recommended approach is robust and comprehensive. Fuzzy Q-learning aims to stimulate the use of green energy sources. For example, when solar energy is abundant in the middle of the day, the surplus to demand ratio increases. Adding renewable energy penetration to the first scene shows that the proposed algorithm can improve the share of renewables in the energy mix. Given that it only captures 30% of solar energy, the local energy pool may have to raise prices and discourage residents from participating in electricity trading (Figure 7b,c).  
\begin{table}
\begin{tabular}{|p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}|} \hline
**Items** & _Parameters_ & _Items_ & _Parameters_ \\ \hline \multicolumn{4}{|c|}{_System technical parameters_} & \multicolumn{2}{|c|}{_PV array Specification Costs_} \\ \hline _PV related Power_ & \(1.0\) kw & _Whole Capital_ & \(1130\)\(\$\)/kw \\ \hline _Interest rate_ & 4.80\% & _Total Maintenance per year_ & \(5.001\)\(\$\)/kw \\ \hline _PV system lifetime_ & 25.0 & _Replacement_ & \(398.31\)\(\$\)/kwh \\ \hline _Rated Capacity:_\(g^{rated}_{SPV}\) (kw) & 8.02 kw & _Expected-lifetime per year_ & 21 \\ \hline _Investment cost_ (\(\delta_{PV}\)) (\(\$\)/kw) & 769.0 \(\$\)/kw & _BT array Specification Costs_ \\ \hline _PV Cell Numbers_ & _Ns_ 3; _Np_ 6 & _Whole Capital_ & \(280\)\(\$\)/kw \\ \hline \(P_{Grid,max}\) (kw) & 9725 kw & _Total Maintenance per year_ & \(14.2\)\(\$\)/kw \\ \hline _Maximum_\(G_{2}\)/_H_/_H_\({}_{2}\)G-(\(P^{HG}\), \(P^{HG}\)) & 10 kw & _Replacement_ & \(305\)\(\$\)/kwh \\ \hline _PV Efficiency_ (\(\eta\)PV) (pu) & 0.13\% & _Expected-lifetime per year_ & 11 \\ \hline _Max rated PV array power_ (kw) & 4.2 kw & _Whole Capital_ & \(1130\)\(\$\)/kw \\ \hline _BS Depth of discharge (DBS)_ (pu) & 0.6 & _BS charge Efficiency_ (\(\eta\)_BS_) (pu) & 0.97 \\ \hline \end{tabular}
\end{table}
Table 2: Input parameters.  
With minimal solar penetration, intelligent home agents exchange energy with the community energy pool (Figure 7c). There is no energy transfer between the energy pool and the intelligent user between Tc (4, 6) and Tc (9). As the use of solar energy grows slowly, costs in the community increase. Consumers then trade or store electricity. The phenomena we have described show that the algorithm works as expected. After accumulating empirical data, the agent optimizes its energy management based on the q value it has learned to evaluate. Continuous online procedures without forms are conceivable but less efficient than global optimization. The recommended method can help smart customers reduce their electricity costs and simplify their use of solar energy. An increase in the growth of renewable energy is possible with the help of the suggested fuzzy Q-learning algorithm if consumers earn more money by using renewable energy sources. (Figure 7f).