## 2 EV OCP Optimization Analyses for Chinese Economic and Technological Development Zones
### EV OCP Optimization Model for Chinese Economic and Technological Development Zones  
From the perspective of grid safety and stable operation, the grid side aims to minimize the fluctuation of charging load and grid load and realize \"peak clipping and valley filling\". Furthermore, it aims to improve the quality of the power supply and reduce the loss of distribution network lines. In addition, to increase local tourism revenue and the average satisfaction of tourists, it needs to reduce charging costs as much as possible, but it also needs to fully meet the charging demands of tourists. The charging plan preference matching degree is the ratio of the cumulative sum of charging power matched by the 24 h charging plan and the 24 h preferred charging plan to the cumulative sum of charging power of the 24 h charging plan, which can quantitatively describe the charging convenience of tourists. If sufficient charging convenience is provided for tourists, time saving for tourists will be guaranteed. The specific objective function of comprehensive evaluation of charging user preference is as follows:  
\[Z=\alpha_{C}\times\frac{C_{sum}}{C_{sum,max}}+\alpha_{P}\times\left(1-D_{p} \right)+\alpha_{L}\times\frac{L_{sum}}{L_{sum,max}}+\alpha_{NL}\times\frac{NL }{NL_{max}} \tag{1}\]  
\[C_{sum}=\sum\limits_{24}^{t=1}C_{t}(t)P(t) \tag{2}\]  
\[D_{P}=\frac{\sum\limits_{24}^{t=1}\min\left(P(t),P_{P}(t)\right)}{\sum\limits _{24}^{t=1}P(t)} \tag{3}\]  
\[L_{var}=Var(L(1),\cdots,L(24)) \tag{4}\]  
\[NL=\sum\limits_{t=1}^{24}\sum\limits_{i=1}^{N}\sum\limits_{j=1}^{N}R_{ij}I_{ij} (t)^{2} \tag{5}\]  
where \(Z\) is the comprehensive evaluation, \(C_{sum}\) is the charging cost, \(C_{t}(t)\) is the charging price of the \(t\)th hour, \(P(t)\) is the charging power of the \(t\)th hour in the charging plan, \(D_{p}\) is the preference matching degree of the charging plan, \(P_{p}(t)\) is the charging power of the \(t\)th hour in the preferred charging plan, \(C_{sum,max}\) is the set upper limit of the charging fee, and \(\alpha_{C}\), \(\alpha_{p}\) are the corresponding importance weights of charging cost and charging plan preference matching degree, respectively; \(L(t)\) is the load of the power grid at the \(t\)th hour, \(L_{var}\) is the daily load curve variance, \(Var(L(1),\cdots,L(24))\) is the variance of the array \((L(1),\cdots,L(24))\), \(R_{ij}\) is the resistance between the \(i\)th and \(j\)th node in the power grid, \(I_{ij}\) is the current between the \(i\)th and \(j\)th node at \(t\)th hour in the power grid, \(L_{var,max}\) is the set upper limit of resistance, \(NL\) is the network loss, \(NL_{max}\) is the set upper limit of network loss, and \(\alpha_{L}\), \(\alpha_{NL}\) are the daily load curve variance and the corresponding importance weights of the network loss, respectively. Here, \(\alpha_{L}+\alpha_{NL}+\alpha_{C}+\alpha_{p}=1\), \(\alpha_{L}\in(0,1)\),\(\alpha_{NL}\in(0,1)\),\(\alpha_{C}\in(0,1)\),\(\alpha_{P}\in(0,1)\). Taking the actual situation into account, the EV OCP optimization needs to consider the line node voltage constraint, line node voltage constraint, vehicle battery capacity constraint and vehicle charging time constraint, which are detailed as follows:  
\[Subject\ to\ tc_{p,t}\in\{0,1\} \tag{6}\] \[U_{i,t}\in[U_{i,min},U_{i,max}]\] (7) \[TC_{p}\leq TC_{p,max}\] (8) \[S_{p,t}\in[S_{p,min},S_{p,max}] \tag{9}\]  
where \(tc_{p,t}\) is the charging state of the \(p\)th vehicle at \(t\)th hour (1 represents that the vehicle is in the charging state, otherwise, it is 0), \(U_{i,t}\) is the voltage of \(i\)th node at \(t\)th hour in the power grid, \(U_{i,min}\) and \(U_{i,max}\) are the minimum and maximum voltage of \(i\)th node in the power grid, respectively, \(TC_{p}\) is the charging time of \(p\)th vehicle, \(TC_{p,max}\) is the maximum charging time of \(p\)th vehicle, \(S_{p,t}\) is the battery capacity of \(p\) th vehicle in \(t\) th hour, and \(S_{p,min}\), \(S_{p,max}\) are the minimum and maximum battery capacity of \(p\)th vehicle, respectively.