## 6 Case Study
### Results of Stochastic Scheduling  
Results of a stochastic scheduling of 3 MGs are shown in Figure 6. Within the coalition, each MG has an equivalent position regardless of its own load demand and DERs' output. The exporting and importing power are exchanged via DC-links.  
In Figure 6, the scheduling results can be divided into three time periods. Firstly, in the early morning, the RES is insufficient, the MCO has to purchase electricity from the DNO at a higher price  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Microgrid** & **Connected Node** & **Load (kW)** & **Microturbine (kW)** & **PV (kW)** & **WT (kW)** \\ \hline MG1 & 15 & 60 & — & 0–60 & — \\ MG2 & 31 & 210 & 100 & 0–60 & 0–60 \\ MG3 & 28 & 120 & 100 & 0–70 & — \\ \hline \hline \end{tabular}
\end{table}
Table 1: Capacity of distributed energy resources (DERs) in each Microgrids (MG).  
Figure 6: Results of stochastic scheduling of 3 MGs.  
and maximize the output of microturbines to meet its load demand. There is an industrial shock load in \(MG2\) which causes the fluctuating electricity purchasing from the DNO from 2:00 to 10:00. Secondly, from 11:00 to 19:00, with the increasing solar and wind energy, the MCO sells its extra electricity to DNO and even turns off the microturbines to minimize the operation cost. Finally, from 20:00 to 24:00, the amount of purchasing electricity from the DNO has been gradually increased, and the microturbines have been operated at the rated power, due to the reduction of RES and increased load demand.  
Exchanging powers via different DC-links are shown in Figure 7. \(P_{T(2\text{-}1)}\) and \(P_{T(3\text{-}2)}\) are respectively denoted the exchanging power between \(MG2\) and \(MG1\) (positive means energy transmitted from \(MG2\) to \(MG1\)) and exchanging power between \(MG3\) and \(MG2\) (positive means energy transmitted from \(MG3\) to \(MG2\)). In Figure 7, the results show that the DC-links have acted as efficient bridges for the inner energy sharing between the MGs. In the first two hours, energy is transferred from \(MG2\) to \(MG1\) due to the heavy load in \(MG1\). Furthermore, from 3:00 to 10:00, both \(MG1\) and \(MG3\) have transferred energy to \(MG2\) to satisfy the fluctuating heavy load. After that, with surplus solar and wind energy and light load in \(MG2\), the power flow directions of DC-links have been changed at 10:00 and 15:00 to meet the growing load demand in \(MG1\) and \(MG3\). Finally, after 21:00, the growing load demand in \(MG2\) results in decreasing the exchanging power transferred from \(MG2\).  
In order to test the effectiveness by using the stochastic programming, the scheduling costs under stochastic and deterministic programming are shown in Table 2. The scheduling cost reduction reaches to 11.82% by using stochastic programming, as taken the uncertainties of RES and loads into consideration.  
Furthermore, the added value of co-operation between different MGs should be analyzed. From the viewpoint of MG owners, the network tariff can be ignored and the energy purchased from the adjacent MGs could be cheaper than the one from the distribution network, thus it is more economic for the MGs to work as a coalition via DC-links. The comparison between the separated mode and co-operation mode of MGs is shown in Table 3. In the separated mode, we assume that the  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Deterministic Programming (CNY/h)** & **Stochastic Programming (CNY/h)** & **Reduction Rate (\%)** \\ \hline
220.60 & 194.53 & 11.82 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Mean scheduling cost using stochastic and deterministic programming.  
Figure 7: Exchanging power on DC-links.  
DC-links are opened, and each MG trades energy with the distribution network directly when there is plenty or lack of renewable energy. The results show that the scheduling cost of separated MGs is 6.45% higher than the co-operated MGs.