## Appendix A  
* Comprehensive evaluation method of power quality based on data envelope analysis
* DEA is a comprehensive evaluation method for evaluating the relative efficiency of the same type of multi-index inputs and multi-index outputs [35]. In this method, the weights of the input and output variables are determined from the actual data of each decision unit, and the evaluation is performed from the perspective of the most favorable for the decision unit, which can avoid the influence of subjective factors. Additionally, it is not necessary to determine the display expressions of input and output, which simplifies the internalized relationship between input indicators and output indicators. In the case where the relationship between inputindicators and output indicators is complex and difficult to determine, it can reflect its obvious advantages. The data envelopment analysis method was used to evaluate the power quality of each access point, and the access plan was evaluated for power quality at the planning stage, and finally, an ETSHE access plan with a good power quality level was obtained. The basic model and simplified transformation of DEA are as follows:  
(1) \(C^{2}R\) Model  
The \(C^{2}R\) model is the most basic DEA model. Suppose there are \(n\) evaluation objects, each Decision Making Units (DMU) has \(m\) types of input indicators and \(q\) types of output indicators. Construct the following \(C^{2}R\) model [35]:  
\[w_{j}=\frac{\sum\limits_{i=1}^{q}v_{ij}y_{ij}}{\sum\limits_{r=1}^{m}u_{r}x_{rj}} \tag{1}\]  
\[\left\{ \begin{array}{l}\sum\limits_{i=1}^{q}v_{ij}y_{ij}/\sum\limits_{r=1}^{ m}u_{r}x_{rj}\ \leq\ 1\\ u_{r}\ \geq\ 0\ \ \ i=1,2,\ldots,n;v_{i}\ \geq\ 0\ r=1,2,\ldots,m\end{array} \right. \tag{2}\]  
where \(w_{j}\) is the relative production efficiency; \(u_{r}\) is the weight of the \(r\)-th power quality indicator (\(r=1,2,\ \ldots,\ m\)); \(v_{i}\) indicates the weight of the \(i\)-th quality level (\(i=1,2,\ \ldots,\ q\)); \(x_{rj}\) indicates the \(r\)-th power quality input indicator in the \(j\)-th DMU; and \(y_{ij}\) indicates the \(i\)-th quality level output in the \(j\)-th DMU indicator (\(j=1,2,\ \ldots,\ n\)). Choose a reasonable input weight vector \(v_{i}\) and output weight vector \(u_{r}\) so that \(w_{j}\ \leq\ 1\). When \(w_{j}=1\), the \(j\)-th DMU is considered to be the most effective. (2) Charnes-Cooper transformation  
Due to the difficulty of solving the \(C^{2}R\) model, the fractional programming is transformed into an equivalent linear programming problem using the Charnes-Cooper transformation, and the dual model is obtained using dual theory. Carry out the Charnes-Cooper transformation on Equation (2), and obtain a model in the form of equivalent linear programming as in Equation (2):  
\[\left\{ \begin{array}{l}\min[\theta-\varepsilon\left(\delta^{T}s^{+}+\delta ^{T}s^{+}\right)]\\ \sum\limits_{j=1}^{m}\lambda_{j}x_{j}+s^{-}=\theta x_{0j},\ \sum\limits_{j=1}^{n}\lambda_{j}y_{j}+s^{+}=y_{0}\\ \lambda_{j}\geq 0,j=1,2,\ldots,m;s^{-}\geq 0,s^{+}\geq 0\end{array} \right. \tag{3}\]  
where \(x_{j}\) and \(y_{j}\) represent the \"input\" and \"output\" of the \(j\)-th DMU, respectively. In order to solve in a larger feasible region, \(s^{-}=(s_{1}^{-},s_{2}^{-},\ldots s_{m}^{-})^{T}\) and \(s^{+}=(s_{1}^{+},s_{2}^{+},\ldots s_{q}^{+})^{T}\) are the relaxation variables. \(\lambda_{j}\) represents the weight of \(D\)ML\(j_{0}\). \(\theta\) is the efficiency value of the \(j\)-th DMU. Generally take \(\varepsilon=10^{-7}\) in practice. \(\delta^{T}=\) (1,1, \(\ldots,1\)) \(\in R_{s}\) is used to adjust the relaxation variable. * Power quality evaluation of the ETSHE planning scheme  
The power quality evaluation is carried out for the planning scheme of the ETSHE to be connected to the distribution network. First, the classification of grades should be carried out. There are many ways of classification, among which domestic literatures are mainly divided into grades 3, 5, 7, 9, and 10. This paper divided the power quality indicators into five grades.