*2.1. The Individuality Indexes System of Evaluation Model of DSER*

Based on the above analysis, indexes that can represent the characteristics of various types of DSER are selected as individuality indexes. Among them, WG and PV resources share a set of individuality indexes, EV and ES resources share a set of individuality indexes, and flexible load resources use a set of individuality indexes. Output characteristics and reliability are selected as first-level indexes of WG and PV, reliability, peak shaving capacity and discharge performance are selected as first-level indexes of EV and ES, response capacity is selected as first-level indexes of flexible loads. The framework is shown in Figure 3.

#### *2.2. Selection of Individuality Indexes*

### 2.2.1. The Individuality Indexes of WG and PV

(1) Output characteristics

The output characteristics of WG and PV resources are of great importance for the consumption of WG and PV. The day with the maximum load is selected as the typical day. The output characteristics of WG and PV resources are quantified and evaluated in terms of three aspects: daily load rate, peak valley difference, and daily load fluctuation rate [22]. The definitions of the indexes are shown in Table 3.




**Figure 3.** The framework of the individuality indexes.

### (2) Reliability

The maximum daily average load of WG or PV is recorded as *P*, and 0.8*P* is defined as the threshold. The reliabilities of WG and PV in a certain region are quantified and evaluated in terms of two aspects: the proportion of days meet the threshold and number of system failures, correspondingly, the definitions of the indexes are shown in Table 4.

**Table 4.** Definition of reliability of WG and PV.



#### 2.2.2. The Individuality Indexes of EV and ES

#### (1) Reliability

The reliabilities of EV and ES in a certain region are quantified and evaluated in terms of two aspects: power outage time and number of system failures. Such two indexes are represented by the power outage time in one year in a region and the number of failures in the year.

(2) Peak shaving capacity

Realizing peak shaving and valley filling is one of the important goals for developing EV and ES. Therefore, Peak shaving capacity is an important index for evaluating EV and ES, and it is quantified and evaluated in terms of two aspects: battery capacity and charging and discharging speed, correspondingly, the definitions of the indexes are shown in Table 5.

**Table 5.** Definition of peak shaving capability.



#### (3) Discharge performance

A cycle means the ES system undergoes a charge–discharge process. Discharge performances of EV and ES in a certain region are quantified and evaluated in terms of two aspects: cycle life and charge and discharge efficiency, correspondingly, the definitions of the indexes are shown in Table 6.

**Table 6.** Definition of discharge performance.



#### 2.2.3. The Individuality Indexes of Flexible Loads

The response capacity of flexible loads is quantified and evaluated in terms of four aspects: users' willingness, responsive device capacity, response cost, and response success rate. Taking the users' willingness as an example, users' willingness in a certain region are quantified from low to high as 1–5, according to different types of users (public, commercial, industrial, and residents). Then, the quantized value upon the user's willingness of each type of user is multiplied by the proportion of each type of user in the region, and the comprehensive quantized value upon the user's willingness in the region is obtained. Specific quantization rule is shown in Table 7.


**Table 7.** The rule of quantifying users' response capacity.

#### **3. Determination of Commonality Indexes Weights and Calculation of Comprehensive Score**

#### *3.1. Standardization of Evaluation Indexes*

The dimensionlessness in the evaluation index system is the prerequisite for the integration of indexes. If the nondimensionalized value of the index is called the index evaluation value, then the dimensionlessness process is the process of converting the actual value of the index into the evaluation value of the index, and the dimensionlessness method is to eliminate the influence of the primitive variable (index) dimension by the mathematical transformation. When the indexes are nondimensionalized, it is necessary to note that the positive and negative indexes have different effects on the overall target. For example, indexes such as economic benefits, policy support, and response capacity are positive indexes. The higher the index value, the better; the indexes such as power outage time, number of failures, and response cost are negative indexes, the lower the index value, the better.

In summary, the threshold method in the linear nondimensionalization method is used to nondimensionalize the index; threshold method is a dimensionless method to get the index evaluation value through the ratio of the actual value to the threshold of the index. The corresponding formulas are as follows.

Assume that there are *m* regions, *n* evaluation indexes, and *xij* represents the index value of the *i*th region under the *j*th index [23].

Positive indexes

$$\mathbf{x}'\_{ij} = \frac{\mathbf{x}\_{ij} - \min\{\mathbf{x}\_{1j}, \dots, \mathbf{x}\_{mj}\}}{\max\{\mathbf{x}\_{1j}, \dots, \mathbf{x}\_{mj}\} - \min\{\mathbf{x}\_{1j}, \dots, \mathbf{x}\_{mj}\}} \tag{2}$$

Negative indexes

$$\mathbf{x}'\_{ij} = \frac{\max\{\mathbf{x}\_{1j}, \dots, \mathbf{x}\_{mj}\} - \mathbf{x}\_{ij}}{\max\{\mathbf{x}\_{1j}, \dots, \mathbf{x}\_{mj}\} - \min\{\mathbf{x}\_{1j}, \dots, \mathbf{x}\_{mj}\}} \tag{3}$$

#### *3.2. The Calculation of Weights for Commonality Indexes*

Since the commonality indexes such as benefits, resource development, and development potential are indexes shared by the five DSER, and the preference of the decision makers for these indexes is more obvious, the Analytic Hierarchy Process (AHP) is used to evaluate the commonality indexes.

On one hand, the AHP takes the subjective experience judgement of expert scoring into account. On the other hand, the expert judgement is transformed into a mathematical model for quantitative calculation, so that the proportion of each index in the company evaluation index can be calculated. The combination of analysis and calculation is extremely useful for highlighting corporate evaluation in different periods.

The basic idea of the AHP is to build the problem hierarchically based on the decision goal (as is shown in Figure 4). The highest level is the target level, several intermediate levels are the criterion level, and the bottom level is the various options selected for solving the problem, which is called the plan level [24,25].

**Figure 4.** Hierarchical structure.

The weights of indexes are determined by AHP as follows:

(1) Compare pairs of indexes in the same level, and refer to the number 1–9 and its reciprocal as a scale to define the judgment matrix *A*, as shown in Table 8.



(2) Calculate consistency ratios and test consistency

$$CR = \frac{CI}{RI} \tag{4}$$

$$CI = \frac{\lambda\_{\text{max}} - n}{n - 1} \tag{5}$$

where *CI—*Consistency indicator; *RI*—Random consistency indicator; *CR*—Test coefficient; *λ*max—The maximum eigenvalue of the judgment matrix *A; n*—The order of the judgment matrix *A.*

The value of *RI* is shown in Table 9

**Table 9.** Average random consistency.


When CR < 0.10, the consistency of the judgment matrix is considered acceptable, otherwise the judgment matrix should be properly modified.

#### (3) The calculation of weight vector *W*

The weight vector *W* in AHP is calculated by the eigenvector method, and the weight vector *W* is multiplied by the judgement matrix *A*

$$AW = \lambda\_{\text{max}} \mathcal{W} \tag{6}$$

#### *3.3. The Calculation of the Comprehensive Score upon Commonality Indexes*

The comprehensive score upon the commonality indexes of all kinds of DSER can be obtained by weighted overlaying the quantized values of each index and their corresponding weights in a certain region, as shown in formula (7). Suppose there are *n* commonality indexes,

$$f\_A = \sum \mathcal{W}\_{A\circ} \cdot \mathbf{x}\_{A\circ} \tag{7}$$

Taking WG as an example, *WAj* is the weight of the *j*th index in the general index of WG, *xAj* is the quantified value of WG in *j*th index, and *fA* is the comprehensive score of the commonality index of WG.

#### **4. Determination of Individuality Indexes Weights and Calculation of Comprehensive Score**

#### *4.1. Standardization of Evaluation Indexes*

The evaluation indexes are nondimensionalized, same as Section 3.1.

#### *4.2. The Calculation of Weights for Individuality Indexes*

Since the individuality indexes upon various DSER are different, and the relative importance of each index is difficult to divide artificially, therefore, the entropy weight method is used to evaluate the individuality index.

Entropy, one of the parameters that characterize the state of matter in thermodynamics, is a measure of the degree of chaos in the system. Entropy method is to use the degree of variation of information entropy to calculate the weight of each index, to evaluate the importance of each index [26].

Assuming that there are *m* regions, *n* evaluation indexes, the weights of indexes are determined as follows:

(1) Calculate *Pij*, the weight of the index values of each region under various indexes

$$P\_{ij} = \frac{\mathbf{x}\_{ij}}{\sum\_{i=1}^{m} \mathbf{x}\_{ij}} \tag{8}$$

where *i* represents the *i*th region, *j* represents the *j*th index, and *xij* represents the index value of the *i*th region under the *j*th index.

(2) Calculate *ej*, the entropy of each index

$$c\_j = -\frac{1}{\ln(m)} \sum\_{i=1}^{m} p\_{ij} \ln p\_{ij} \tag{9}$$

(3) Calculate *gj*, the difference coefficient of each index

$$\mathbf{g}\_{\circ} = \mathbf{1} - \mathbf{e}\_{\circ} \tag{10}$$

(4) Calculate *Wj*, the weight of each index

$$\mathcal{W}\_{B\circ} = \frac{\mathcal{S}\_{\bar{j}}}{\sum\_{j=1}^{n} \mathcal{S}\_{\bar{j}}} \tag{11}$$

#### *4.3. The Calculation of the Comprehensive Score upon Individuality Indexes*

The comprehensive score upon the individuality indexes of all kinds of DSER can be obtained by weighted overlaying the quantized values of each index as well as their corresponding weights in a certain region, as shown in formula (12). Suppose there are n individuality indexes,

$$f\_B = \sum W\_{Bj} \cdot x\_{Bj} \tag{12}$$

Taking WG as an example, *WBj* is the weight of the *j*th index in the general index of WG, *xBj* is the quantified value of WG in this index, and *fB* is the comprehensive score upon the individuality index of WG.

#### **5. Case Analysis**

Twenty regions are selected as evaluation objects for example analysis. The schematic diagram is shown in Figure 5.

**Figure 5.** The schematic diagram of 20 regions.

#### *5.1. Commonality Indexes*

#### 5.1.1. Standardization of Indexes

According to Equations (2) and (3), each index value can be processed to obtain the relative value of each index. Taking the commonality indexes of WG as an example, the processed index values are shown in Figure 6.

**Figure 6.** Value of commonality indexes of wind power generation (WG) in 20 regions.

As can be seen from Figure 6, among the 20 regions, WG in region 2 has the highest economic benefits, however, whose social benefits, resource development and policy support are almost behind those in other regions. The social benefits and funding sources of WG in region 18 are in a leading position, however, the environmental benefits are the lowest, moreover, policy support is not satisfactory.

#### 5.1.2. The Calculation of Weights of Commonality Indexes

According to Equation (6), AHP is used to calculate the weights of benefits, resource development and development potential of the commonality indexes, so are the weights of their respective subindexes. The calculation results are shown in Table 10.


**Table 10.** Weight of commonality indexes.

As can be seen from Table 10, the weight of benefits in the commonality indexes is the largest, followed by the weight of development potential, and the weight of resource development is the least. For the subindexes, under the benefit index, the economic benefit accounts for the largest proportion, and under the development potential index, the sources of funds account for the largest proportion.

#### 5.1.3. The Calculation of Comprehensive Score

After calculating the weights of the subindexes under the commonality indexes, the comprehensive scores of the commonality indexes of various DSER in various regions are calculated according to Equation (7). Analyzed results are as follows:

As shown in Figure 7, the scores of commonality indexes of the PV and EV in region 1 are relatively high, but that of WG, flexible load, and ES are in the middle level; the score of flexible loads in region 2 is relatively high, and the score of PV is relatively low. As is shown in Figure 8, the scores of commonality indexes of WG in regions 15 and 17 are relatively high, the scores of WG in regions 5, 7, and 19 are relatively low; the scores of commonality indexes of EV in regions 1, 3, 8, and 15 are relatively high, the scores of EV in regions 6, 12, 16, and 18 are relatively low.

**Figure 7.** The comprehensive scores of commonality indexes of various DSER in a certain region. (**a**) The score of various DSER in region 1; (**b**) The scores of various DSER in region 2.

**Figure 8.** The comprehensive scores of commonality indexes in various regions. (**a**) The scores of WG in various regions; (**b**) The scores of electric vehicle (EV) in various regions.

#### *5.2. Individuality Indexes*

#### 5.2.1. Standardization of Indexes

According to Equations (2) and (3), each index value can be processed to obtain the relative value of each index. Taking the individuality indexes of WG as an example, the processed index values are shown in Figure 9.

**Figure 9.** Value of individuality indexes of WG in 20 regions.

It can be seen from Figure 9 that among the 20 regions, the number of system failures of WG in region 5 is the lowest, but the remaining indexes are in a backward position in the ranking. The daily peak rate and daily load fluctuation rate of WG in region 9 are relatively good, but the system has more failures.

#### 5.2.2. The Calculation of Weights of Individuality Indexes

The entropy weight method is used to calculate the weights of the individuality indexes upon various DSER according to Equations (8) to (11), as shown in Figure 10.

As can be seen in Figure 10, among the individual indexes of WG and PV, the weight of the daily load fluctuation rate is the largest, and the proportion of the number of days meet the threshold accounts for the smallest part. Among the individual indexes of EV and ES, the weight of the power outage time, number of system failures and the battery capacity index is relatively large, and the weight of charge and discharge speed is relatively small. Among the individual indexes of flexible loads, the weight of the response success rate index is the largest, and the response cost index has the smallest weight value.

#### 5.2.3. The Calculation of Comprehensive Score

After calculating the weights of the subindexes under the individuality indexes, the comprehensive scores of the individuality indexes upon various DSER in various regions are calculated according to Equation (12). Analyzed results are as follows:

As shown in Figure 11, the score of individuality indexes of the PV in region 1 is relatively low, whereas that of the other DSER are relatively high. The scores of WG, PV, ES, and flexible loads in region 2 are higher than the score of EV. As is shown in Figure 12, the scores of individuality indexes of WG in regions 2 and 4 are relatively high, the scores of WG in regions 5 and 14 are relatively low; the scores of individuality indexes of EV in regions 4, 6, and 13 are relatively high, the scores of EV in regions 16 and 17 are relatively low.

**Figure 10.** The weight of individuality indexes of various DSER. (**a**) The weight of indexes of WG; (**b**) The weight of indexes of photovoltaic (PV); (**c**) The weight of indexes of EV; (**d**) The weight of indexes of energy storage (ES); (**e**) The weight of indexes of flexible loads.

**Figure 11.** The comprehensive scores of individuality indexes of various DSER in a certain region. (**a**) The score of various DSER in region 1; (**b**) The scores of various DSER in region 2.

V

**Figure 12.** The comprehensive scores of individuality indexes in various regions. (**a**) The scores of WG in various regions; (**b**) The scores of EV in various regions.

#### V **6. Conclusions**

To facilitate the decision-making of optimal planning and optimally aggregate utilization of DSER, this paper proposes an evaluation model of DSER based on geographic information, that is, various DSER in a region are evaluated based on geographic information. Firstly, for five kinds of demand-side energy resources of WG, PV, EV, ES, and flexible load, select the evaluation indexes of all kinds of resources and divide all indexes into commonality indexes and individuality indexes. Then, AHP is used to determine the weight of each subindex under the commonality indexes, and the entropy weight method is used to determine the weight of each subindex under the individuality indexes. Finally, weighted overlay is acquired according to the weights and quantized values of each index, and a comprehensive score is obtained for the commonality indexes and individuality indexes of various DSER in a region. The following conclusions are obtained through the cases analysis.


**Author Contributions:** The author F.C. carried out the main research tasks and wrote the full manuscript, S.G. participated in the revision of the manuscript, and Y.G. proposed the original idea, analysed and double-checked the results and the whole manuscript. W.Y. contributed to data processing and to writing and summarizing the proposed ideas, Y.Y. and Z.Z. provided technical and financial support throughout, while A.E. modified the English grammar.

**Funding:** The Nature Science Foundation of China (51607068); The Fundamental Research Funds for the Central Universities (2018MS082); The Fundamental Research Funds for the Central Universities (2017MS090).

**Acknowledgments:** This work is supported in part by the Nature Science Foundation of China (51607068), the Fundamental Research Funds for the Central Universities (2018MS082) and the Fundamental Research Funds for the Central Universities (2017MS090).

**Conflicts of Interest:** The authors declare no conflict of interest.
