## 5 Conclusions  
The reliability of PS networks with enough operating reserves is critical to the stability of world economies. PSs are susceptible to problems like load shedding and total blackouts when these reserves are inadequate. A large proportion of RES makes maintaining grid stability even more difficult. However, there is a lot of promise for RES as AS sources when they are connected by power electronic devices.  
AS provisions from RES can be further enhanced via MGs based on renewable energy, which can aggregate resources from several RES and offer significant potential for load control. The issue of how to distribute ancillary service signals to all resources as efficiently as possible is brought up by the diversity of AS resources.  
This paper presents a comprehensive approach to optimizing AS provisions in renewable energy-based MGs. The proposed model integrates a detailed MG model, thus including voltage profiles and power losses and highlighting their critical role in accurate AS allocation. Through a detailed case study using the CIGRE MV MG benchmark, the paper illustrates how these factors influence the operational efficiency of realistic MGs. The paper underscores that simple MG models using a single-bus representation fail to capture the true behavior of MG operations, leading to suboptimal resource utilization.  
The case study incorporates wind and solar generation units, as well as CHP units, battery storage, and industrial load control as AS assets. Two sub-cases are analyzed: (1) One with grid modeling that accounts for voltage profiles and power losses, using the CIGRE MV benchmark model; (2) One without grid modeling, treating the MG as a single-bus system. The findings reveal that ignoring grid modeling leads to a misrepresentation of the MG's capabilities. Specifically, the fitness value, which initially seemed comparable between the two sub-cases, diverged significantly after accounting for actual power losses and voltage profiles. The results indicate that detailed modeling of microgrids can impact the results by 11%. Even though not explicitly quantified in this paper, considering that the objective function is a financial indicator of MG operation, the results consequently suggest that a well-modeled MG also maximizes financial returns from AS participation, particularly in markets with variable electricity prices.  
Figure 11: The procedure of comparative analysis between sub-cases (**a**) and (**b**).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Node** & **Node** & **R\({}^{\prime}\)** & **X\({}^{\prime}\)** & **C\({}^{\prime}\)** & **L** \\  & **From** & **To** & **[**\(\Omega\)/km**]** & **[**\(\Omega\)/km**]** & **[**nF/km**]** & **[**km**]** \\ \hline \multirow{8}{*}{Sub-network (SN 1)} & 0 & 1 & — & — & — & — \\ \cline{2-6}  & 1 & 2 & 0.579 & 0.367 & 158.88 & 2.82 \\ \cline{2-6}  & 2 & 3 & 0.164 & 0.113 & 6608 & 4.42 \\ \cline{2-6}  & 3 & 4 & 0.262 & 0.121 & 6480 & 0.61 \\ \cline{2-6}  & 4 & 5 & 0.354 & 0.129 & 4560 & 0.56 \\ \cline{2-6}  & 5 & 6 & 0.336 & 0.126 & 5488 & 1.54 \\ \cline{2-6}  & 6 & 7 & 0.256 & 0.13 & 3760 & 0.24 \\ \cline{2-6}  & 7 & 8 & 0.294 & 0.123 & 5600 & 0.67 \\ \cline{2-6}  & 8 & 9 & 0.339 & 0.13 & 4368 & 0.32 \\ \cline{2-6}  & 9 & 10 & 0.399 & 0.133 & 4832 & 0.77 \\ \cline{2-6}  & 10 & 11 & 0.367 & 0.133 & 4560 & 0.33 \\ \cline{2-6}  & 11 & 4 & 0.423 & 0.134 & 4960 & 0.49 \\ \cline{2-6}  & 3 & 8 & 0.172 & 0.115 & 6576 & 1.3 \\ \hline \multirow{4}{*}{SN 2} & 0 & 12 & — & — & — & — \\ \cline{2-6}  & 12 & 13 & 0.337 & 0.358 & 162.88 & 4.89 \\ \cline{2-6}  & 13 & 14 & 0.202 & 0.122 & 4784 & 2.99 \\ \hline \end{tabular}
\end{table}
Table 3: CIGRE MV benchmark model parameters [22].