## 6 Case Study  
### Case Overview  
This study employed a series of typical industrial parks in southern China to validate the proposed method. The physical structure and equipment composition of the microgrid system is given in Figure 1. In regard to energy supply and demand, April to October are classified as cooling months (i.e., with large demand for cooling loads). Heating loads include the demand for drying and ventilation during the production and hot water for residential life; they have no definite supply period, but still show some seasonal characteristics. There are demands for electric loads throughout the year. Curves reflecting the demands for electric, heating, and cooling loads in the microgrid system throughout the year are shown in Figure 3. The available types of energy production/conversion equipment and economical operating parameters in the microgrid based on the above demands are listed in Table 1; the types and economical operating parameters of energy storage devices are listed in Table 2. The initial capacities of ES and HS are 30% and 50% of the rated capacity, respectively; the maximal charge-discharge power is 80% of the rated capacity [29]. The reliability parameters of various equipment units are listed in Table 3.  
Figure 3: Load demand curve. Figure 2: Optimal planning process of a multi-energy microgrid. The gas network sets the fault rate and repair time of the main gas pipeline. Various factors correlated with power-generating efficiency are discussed here. The installed capacity of the PV is 4.6 MW; the annual output curve is shown in Figure 4. The optimization of different PV configurations is not discussed in subsequent planning programs. According to the local power pricing policy, the power price peaks at 11:00-15:00 and 19:00-21:00, and then valleys at 0:00-7:00; in other periods, the price remains stable. Table 4 lists the prices for different energy types. The loss value of electric/heating/cooling loads in the microgrid are 200 \(\forall\)/kW\(\cdot\)h, 120 \(\forall\)/kW\(\cdot\)h, and 120 \(\forall\)/kW\(\cdot\)h, respectively. Self-sufficiency probability is 90%. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Devices** \\ **Types** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Initial** \\ **Capacity/kW** \\ \end{tabular} } & \begin{tabular}{c} **Operation and** \\ **Investment** \\ **Cost/(CNY-kW\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **Maintenance** \\ **Cost/(CNY-kW\({}^{-1}\))** \\ \end{tabular} & \begin{tabular}{c} **Energy** \\ **Conversion** \\ **Efficiency** \\ \end{tabular} & \
\begin{tabular}{c} **Upper** \\ **Limit/kW** \\ \end{tabular} &
\begin{tabular}{c} **Lower** \\ **Limit/kW** \\ \end{tabular} \\ \hline CCHP 1 & 1200 & 4780 & 1.558 & 1 & 1200 & 12 \\ CCHP 2 & 1500 & 4700 & 1.554 & 1 & 1500 & 15 \\ CCHP 3 & 2000 & 4590 & 1.547 & 1 & 2000 & 20 \\ CCHP 4 & 3000 & 3500 & 1.499 & 1 & 3000 & 30 \\ GHP 1 & 800 & 2430 & 0.40 & 80\% & 800 & 8 \\ GHP 2 & 1000 & 2230 & 0.40 & 80\% & 1000 & 10 \\ GHP 3 & 1500 & 1680 & 0.40 & 80\% & 1500 & 15 \\ CAC 1 & 1400 & 511 & 0.30 & 88\% & 1400 & 14 \\ CAC 2 & 2000 & 460 & 0.30 & 90\% & 2000 & 20 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Types and parameters of alternative energy production/conversion equipment. CCHP: combined cold heat and power, GHP: gas heat pump, CAC: central air conditioning. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{\begin{tabular}{c} **Devices** \\ **Types** \\ \end{tabular} } & \multirow{2}{*}{\begin{tabular}{c} **Decices Names** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **\(\lambda_{k}\) (f/a)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **\(\kappa\) (h)** \\ \end{tabular} } \\ \hline CCHP & 4 & 24 \\ GHP & 0.6 & 2 \\ CAC & 0.4 & 2 \\ PV & 0.4 & 20 \\ ES & 0.05 & 50 \\ GN & 0.9 & 20 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Different energy prices.