## 5 Case Study
### Comparison with the Related Work  
To clarify the benefits of proposed model and algorithm, our approach is compared with several related papers concerning multiple MGs, with regard to RESs, the power loss, BESS, the number of MGs, the operation mode of MGs, exchanged information, the solution algorithm and the iteration number. Table 8 shows the comparative results. The results show that the proposed method features advantages in several aspects, as compared to the related studies. In this paper, PV, WT and power loss are incorporated into the model, which makes the optimal dispatching model more realistic with respect to the practical applications. In regards to power loss, most related literature ignores power loss, whereas this paper takes consideration of the optimization of power loss to make the results as optimal as possible for actual operation and reduced power loss. BESS is an important component for improving the utilization of RESs. In most literature, BESS is not considered in the optimization. In this paper, BESS is integrated into the model, which improves the consumption of RESs. As for operation mode, the MGs in [18; 42] are both operated in island mode, while the energy sharing framework of multiple MGs in this paper is constructed at the distribution level. In terms of exchanged information, [42] belongs to centralized optimization, in which all of the data including load and generation should be transmitted to the control center. Thus, this would result in the leakage of the privacy of MGs and more communication requirements to meet the huge amount of data. As for [18; 38], the Lagrange multipliers (i.e., price) and the expected purchasing energy quantities should be shared with each other among MGs. However, to some extent, Lagrange multipliers are equal to power generation information within MGs, thus the privacy protection for MGs is limited. In this paper, the shared information among MGs is limited to expected exchange power, which protects the local information of MGs. With regard to iteration number, [42] belongs to centralized optimization, whereas others are classified as distributed optimization. The work in [18] proposes a subgradient-based cost minimization algorithm. A cooperative power dispatching algorithm of interactions among microgrids is proposed in [38] for power sharing within the grid. In this paper, a distributed optimal algorithm based on ADMM is proposed. In terms of the algorithm performance, the iteration number of these papers is compared. As the basic data and scenarios in these papers are different, their simulation results are not comparable. However, it can be roughly concluded from the table that the proposed algorithm in this paper has a relatively superior solution among these papers. In short, the results show that the proposed method features advantages in the model and algorithm. From the perspective of the model, the PV, WT, CHP, BESS and power loss are taken into \begin{table}
\begin{tabular}{p{113.8 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Properties** & **Ref. [42]** & **Ref. [18]** & **Ref. [38]** & **This Paper** \\ \hline RESs & - & - & PV & PV WT \\ \hline Optimize power loss? & No & No & No & Yes \\ \hline Optimize BESS? & No & No & No & Yes \\ \hline Operation mode of MGs & Island mode & Island mode & Grid-connected & Grid-connected \\ \hline Exchanged information & All data of sources and load transmitted to control center & Price and expected purchasing energy quantities & Price and expected purchasing energy quantities & Expected exchange power \\ \hline Solution algorithm & Centralized optimization & Based on subgradient & statistical cooperative power dispatching (SCPD) algorithm & Based on alternating direction method of multipliers (ADMM). \\ \hline Iteration number & - & About 100 & 93 & Average 33.33 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison with several related papers.