## 4 Case Study  
### Basic Data  
In this paper, the case system in Reference [27] is appropriately modified. The typical winter day was selected for analysis to verify the correctness of the configuration method. The operating parameters of the equipment in the RIES can be found in Reference [27], as shown in Table 1. The decision vector parameters of the energy storage systems can be found in Reference [25] and [27], as shown in Table 2. Other parameters for the case study are shown in Table 3.  
The time of use (TOU) power price [16] and nature gas price [10] are shown in Figure 2a. The wind and photovoltaic forecasted power [27] are shown in Figure 2b, and the wind turbine has anti-peak characteristics. As shown in Figure 1, the heat demand for users was met by the supply side and the load side (electric heating equipment). The total air conditioning heat pump power for users was 100 kW and the total water heater power was 50 kW. The load forecasting curve [27] is shown in Figure 3. It can  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & **Power Lower** & **Power Upper** & **Lower** & **Upper** & **Unit** \\  & **Limit/(kW)** & **Limit/(kW)** & **Climbing** & **Climbing** & **Maintenance** \\  & **Limit/(kW)** & **Rate/(kW-h)** & **Rate/(kW-h)** & **Cost/(yuan/kW)** \\ \hline MT & 40 & 180 & 5 & 15 & 0.025 \\ EB & 0 & 120 & 12 & 12 & 0.016 \\ GB & 0 & 120 & 11 & 11 & 0.012 \\ P2G & 0 & 120 & - & - & 0.021 \\ WT & 0 & 400 & - & - & 0.0196 \\ PV & 0 & 250 & - & - & 0.0235 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The operating parameters of the equipment in the RIES.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Type**} & **Efficiency** & **Self-Release** & **Unit Capacity** & **Unit Power** & **Unit** \\  & **Rate** & **Cost/(yuan/kW-h)** & **Cost/(yuan/kW)** & **Maintenance** & **Life/(year)** \\ \hline EES & 0.9 & 0.001 & 1000 & 200 & 0.0018 & 10 \\ TES & 0.88 & 0.01 & 150 & 30 & 0.0017 & 10 \\ GES & 0.9 & 0.1 & 130 & 300 & 0.0015 & 20 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The decision vector parameters.  
be seen from Figure 3 that the peak of the aggregated demand (after-diversity maximum demand) was much lower than the sum of individual load peaks.  
The Gurobi Optimizer [32] is a state-of-the-art solver for mathematical programming. The solvers in the Gurobi Optimizer were designed from the ground up to exploit modern architectures and multi-core processors, using the most advanced implementations of the latest algorithms. It includes the following solvers: Linear programming solver (LP); mixed-integer linear programming solver (MILP); mixed-integer quadratic programming solver (MIQP); etc. The configuration optimization model proposed in this paper belongs to the MILP problem. In the Matlab software, the Yalmip toolbox was used to model the model, and the Gurobi solver was called to solve the model. The test platform solved by this model was: Intel Core i7-4710 HQ 2.5 GHz CPU (Intel, Santa Clara, CA, USA); 16 GB memory (DDR3, Kingston, CA, USA); software version: Matlab R2016b (The MathWorks, Inc, Natick, MA, USA); Yalmip R20180926; Gurobi 8.1 (Gurobi Optimization, Beaverton, OR, USA).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameter** & **Value** & **Remarks** & **Parameter** & **Value** & **Remarks** \\ \hline \(\eta_{\text{MT}}\) & 0.35 & [27] & \(\eta_{\text{P2G}}\) & 0.7 & [10] \\ \(\eta_{\text{L}}\) & 15\% & - & C\({}_{\text{st,EB}}\) & 2.7 (yuan/kW) & [27] \\ \(\eta_{\text{r}}\) & 0.9 & - & C\({}_{\text{st,MT}}\) & 1.94 (yuan/kW) & - \\ \(C_{\text{O}}\) & 1.2 & [21] & C\({}_{\text{st,GB}}\) & 2.1 (yuan/kW) & - \\ \(\eta_{\text{GB}}\) & 0.9 & [10] & \(\eta_{EB}\) & 0.95 & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: The parameters for the case study.  
Figure 2: (**a**) Power price and gas price; (**b**) Wind and photovoltaic forecasted power.