Keywords

1 Introduction

Due to multiple connotations, and cross-domain characteristics, the concept of the Energy Internet covers towns, cities, provinces and the country. Therefore, its development evaluation also involves many levels and scopes, such as eco-city, development zone, and park. Due to differences across domains, evaluation often uses indicators of different dimensions, such as economic, environmental, and social dimensions, energy supply, transmission, transaction, demand and other dimensions [1], energy quality, safety and reliability, use and service, etc.; key Technology and innovation capabilities, etc.

In addition to primary energy coal, petroleum, and natural gas, county energy resources generally include renewable energy sources such as agricultural and forestry biomass, household waste, wind resources, light resources, and geothermal resources. Except for a few resource-based counties, most counties are short of fossil energy, but renewable resources such as biomass, wind resources, and light resources are abundant. Existing research on energy system planning mainly focuses on the location and capacity of energy station equipment. Multi-energy complementary forms include electro-thermal coupling [2], electrical coupling [3], and cooling-heat-electric coupling system [4]. Literature [5] constructed a combined cooling, heating and power system including wind turbines and photovoltaics, and carried out a multi-objective optimization study on the capacity of the key equipment of the micro-energy grid.

From the perspective of sustainability and practicality of the project, this paper combines the case with the method of literature survey and expert interview to identify the factors affecting the planning of the county energy Internet, and builds the topsis collaborative planning evaluation model based on each core stakeholder. The effectiveness of the constructed model is verified through case analysis, and the results of the calculation example show that the shortcomings of incomplete risk identification and excessively idealized collaborative planning of similar projects in existing research are avoided.

2 County Energy Internet Planning Impact Index System

2.1 County Energy Internet

Focus on the local utilization of clean energy in counties rich in renewable energy. The utilization of energy resources is shown in Fig. 1. Its resource utilization methods generally include: (1) agricultural and forestry biomass: It can be used for cooking, briquette fuel, gasification, power generation and heating. (2)Domestic waste: Domestic waste can be used to generate electricity. (3) Light: Light energy can convert into electric energy, and solar collector plate can convert light energy into heat energy. (4) Wind: Wind energy can be used to generate electricity. (5) Water: Water energy can be used to generate electricity. (6) Reclaimed water/geothermal: Reclaimed Water/geothermal can be used for heating (cold).

Fig. 1.
figure 1

Schematic diagram of county energy supply system

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2.2 Index System

The terminal energy Internet focuses on flexibly interacting with users through the integration of heat, electricity, gas and other energy production, transmission, conversion, storage and other links, to enhance the coupling and complementarity between energy sources, to smooth the fluctuations caused by high-penetration renewable energy, and to improve the Renewable energy consumption capacity and users’ energy quality. The main body of energy Internet construction is power grid, gas grid, heating network, etc. This paper studies two county energy Internet solutions. Plan 1 is a single gas network, heating network, and power grid planning, and Plan 2 is a joint planning of gas, heat, and power grids. According to the four dimensions of green development, smart empowerment, safety assurance, and value creation, determine the influencing factors of the county energy Internet under different planning schemes.

3 Evaluation Model of Integrated Weighting-Topsis Method

3.1 No Quantitative Treatment of Indicators

The county-level energy Internet benefit impact index system established in this paper has the characteristics of multiple levels and multiple indicators. In order to facilitate comparative analysis, it is necessary to eliminate the difference in the unit dimensions of the evaluation indicators. Generally, the types of indicators generally have benefit type and cost type. Since the dimensions of different attributes may be different, in order to eliminate the influence of different dimensions on the decision-making results, the attribute indicators need to be dimensionless.

For benefit attributes, generally:

$$ r_{ij} = \frac{{a_{ij} - \mathop {\min }\limits_{i} a_{ij} }}{{\mathop {\max }\limits_{i} a_{ij} - \mathop {\min }\limits_{i} a_{ij} }} $$
(1)

For cost attributes, generally:

$$ r_{ij} = \frac{{\mathop {\max }\limits_{i} a_{ij} - a_{ij} }}{{\mathop {\max }\limits_{i} a_{ij} - \mathop {\min }\limits_{i} a_{ij} }} $$
(2)

The matrix \(R = \left( {r_{ij} } \right)_{m \times n}\) obtained by the above dimensionless processing, which is called the standardized decision matrix.

3.2 Differential Weighting Method

Entropy weight method is an objective weighting method, which mainly uses information entropy to calculate the entropy weight of each indicator according to the degree of variation of each indicator, and then corrects the weight of each indicator through entropy weight to obtain a more objective indicator weight.

Step 1: Calculate the bias coefficient \(\alpha ,\beta\).

According to the basic idea of moment estimation theory, for each evaluation index, the expected value of subjective weight and the expected value of objective weight are respectively.

Step 2: Solve the optimal combination weight set.

Taking into account the different weighting coefficients of different indicators, in order to calculate the feasibility, the weighting coefficients of different indicators are defined as the same, and the objective function obtained is as follows:

$$ \min H = \alpha \sum\limits_{j = 1}^{n} {\sum\limits_{s = 1}^{p} {\left( {w_{j} - w_{sj} } \right)^{2} } } + \beta \sum\limits_{j = 1}^{n} {\sum\limits_{t = 1}^{q} {\left( {w_{j} - w_{tj} } \right)^{2} } } $$
(3)

The constraint function is:

$$ \begin{gathered} \sum\limits_{j = 1}^{n} {w_{j} } = 1 \hfill \\ 0 \le w_{j} \le 1,1 \le j \le n \hfill \\ \end{gathered} $$
(4)

Step 3: Solve the trend optimal combination weight set.

The integrated weight also reflects the importance of the indicators. The weight results reflect the different importance of the indicators. Optimal objective function:

$$ \min G = \alpha \sum\limits_{j = 1,k = 1,k \ne j}^{n} {\sum\limits_{s = 1}^{p} {\left( {\frac{{w_{j} }}{{w_{k} }} - \frac{{w_{sj} }}{{w_{k} }}} \right)^{2} } } + \beta \sum\limits_{j = 1,k = 1,k \ne j}^{n} {\sum\limits_{t = 1}^{q} {\left( {\frac{{w_{j} }}{{w_{k} }} - \frac{{w_{tj} }}{{w_{k} }}} \right)^{2} } } $$
(5)

The constraint function is:

$$ s.t.\left\{ \begin{gathered} \sum\limits_{j = 1}^{n} {w_{j} } = 1 \hfill \\ \sum\limits_{k = 1}^{n} {w_{k} } = 1 \hfill \\ 0 \le w_{j} \le 1,1 \le j \le n \hfill \\ 0 \le w_{k} \le 1,1 \le k \le n \hfill \\ \end{gathered} \right. $$
(6)

Step 4: Solve the integrated weight set.

At the same time, considering the two objective functions of the smallest deviation and the best trend, the two optimization objectives are treated equally, and the final multi-objective function is obtained:

$$ \min Z = \frac{1}{2}\min H + \frac{1}{2}\min G $$
(7)

Obtain the index benchmark weight set \(W_{j}\) based on the optimal combination through the above formula. When selecting the evaluation method, it was decided to take a comprehensive evaluation based on the TOPSIS method. The specific formula is as follows shown:

$$ y_{i} = \sum\limits_{j = 1}^{m} {w_{j} (x_{ij} - x^{*} )^{2} } $$
(8)

Wherein \(y_{i}\) is the distance, \(x^{*}\) is the ideal point. The queuing indicator value is used to measure the distance from the negative ideal point. The larger the queuing indicator value, the better the queuing indicator value of this scheme:

$$ c_{i} = \frac{{y_{i}^{ - } }}{{y_{i}^{ - } + y_{i}^{ + } }} $$
(9)

4 Case Analysis

The initial matrix is standardized according to formula (1–2) to obtain a matrix. In order to avoid the subjectivity of experts’ scoring, the entropy method is used to quantitatively obtain the weight of each core stakeholder of the county energy Internet that affects the benefits of the county energy Internet, as shown in Table 2:

Table 1. Index system of county energy internet planning
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Table 2. Index weights of influencing factors in county energy internet planning
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Based on the above formula, the queuing indication value of each scheme can be calculated, as shown in Table 3:

Table 3. Item queuing indicator value
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5 Conclusions

This paper establishes an indicator system for the evaluation of county energy Internet development from four dimensions: green development, smart empowerment, safety assurance, and value creation, and uses structural entropy-factor analysis to verify the effectiveness of the indicators, and further constructs a variable weight function based on policy factors To determine the index variable weight, and use the model to evaluate the development of the energy Internet in a certain county. The evaluation result objectively reflects the development of the county energy Internet, verifies the validity of the model, and can be used for county energy Internet development evaluation.