Comprehensive evaluation method of regional energy internet based on improved matter-element extension model

Abstract

The present invention discloses a regional energy internet comprehensive evaluation method based on an improved matter-element extension model, comprising the following steps: S1, constructing a regional energy internet evaluation index system; S2, constructing an improved matter-element extension model; S21, using the analytic hierarchy process to determine the weights of each evaluation index; S22, calculating and determining the matter-element matrix of regional energy internet development evaluation; S23, calculating and determining the classical domain matter-element matrix and the node domain matter-element matrix of regional energy internet development evaluation; S24, calculating and determining the correlation of the regional energy internet comprehensive evaluation level; S3, determining the evaluation level of the regional energy internet through the correlation of the regional energy internet comprehensive evaluation level. The present invention constructs a regional energy internet comprehensive evaluation index system from five dimensions: economic benefit, safety and reliability, green environmental protection, energy saving and high efficiency, and coordinated operation, and can comprehensively and completely evaluate the development level of the regional energy internet.

Description

Technical Field

[0001] The present invention relates to the field of regional energy, and in particular to a regional energy internet comprehensive evaluation method based on an improved matter-element extension model. Background Art

[0002] In recent years, the "third industrial revolution" has been developing rapidly around the world. The development of the industrial revolution is inseparable from the information revolution and the energy revolution. The continuous development of new energy technologies such as smart grids, distributed power generation, energy storage, and renewable energy, and the progress of information technologies such as big data and the Internet of Things have laid a solid foundation for the third industrial revolution. The energy Internet is considered to be one of the cores of the third industrial revolution and an important measure to achieve clean energy substitution and sustainable development.

[0003] Regional energy internet is the product of the deep integration of information and communication technology and energy system. It is an integrated system of energy production, supply and marketing formed by organically coordinating the generation, transmission and distribution (energy network), conversion, storage and consumption of energy. It is particularly suitable for the operation optimization of energy systems at the park and town levels. Regional energy internet can realize the interconnection, conversion and utilization, and coordinated optimization of multiple energy sources such as electricity, cold and heat. It can effectively improve energy utilization efficiency, reduce environmental pollution, and promote the transformation of energy utilization from traditional extensive to intensive and sustainable. It is one of the important means to deal with the problems of high energy consumption and high pollution in China's urbanization process.

[0004] Evaluation theory is a powerful basis for reflecting the system technology methods and engineering operation level. On the one hand, it can evaluate the operation effect and target matching degree of the new technologies, new strategies, new equipment and other technologies put into the project application of regional energy Internet; on the other hand, it can compare different technologies and projects horizontally to provide support for the large-scale promotion of demonstration projects. In recent years, the evaluation research results of energy Internet are relatively rich, which can provide a reference for the evaluation research of regional energy Internet development. Fu Xueqian et al. proposed a comprehensive evaluation model of energy Internet supply quality considering power quality, air quality and pollutant emission factors; Yang Yongbiao et al. established a hybrid multi-attribute evaluation system from the dimensions of energy network, energy use and service to evaluate the multi-source optimization operation of small energy Internet; Li Xiaopeng et al. tried to build a more concise and feasible energy Internet development evaluation index system; Hu et al. evaluated the security, economy and adaptability of energy Internet-related communication technology communication network; Wang et al. proposed a model based on critical energy function to evaluate the system stability of energy Internet.

[0005] In general, previous literature research is relatively rich. However, at present, a complete set of regional energy Internet evaluation standard system has not yet been formed at home and abroad. Related research is mostly limited to single scattered indicators, with insufficient coverage, and cannot support the development needs of regional energy Internet. The matter-element extension model comprehensively considers high-dimensional incompatible indicators. Therefore, establishing a formalized model to study the possibility and development rules of things is conducive to improving the reliability and objectivity of the evaluation results, and is suitable for the evaluation research of multi-factor complex dynamic systems. Therefore, a regional energy evaluation method can be studied for the matter-element extension model to improve the regional energy Internet evaluation standard system. Summary of the invention

[0006] The purpose of the present invention is to provide a comprehensive evaluation method for regional energy internet based on an improved matter-element extension model in order to solve the above problems.

[0007] In order to achieve the above object, the technical solution of the present invention is:

[0008] The regional energy internet comprehensive evaluation method based on the improved matter-element extension model includes the following steps:

[0009] S1. Construct a regional energy internet evaluation index system;

[0010] S2, construct an improved matter-element extension model;

[0011] S21. Use the analytic hierarchy process to determine the weight of each evaluation index;

[0012] S22. Calculate and determine the matter-element matrix for regional energy internet development evaluation;

[0013] S23. Calculate and determine the classical domain matter-element matrix and node domain matter-element matrix for regional energy internet development evaluation;

[0014] S24. Calculate and determine the correlation of the comprehensive evaluation level of the regional energy internet;

[0015] S3. Determine the evaluation level of the regional energy Internet through the correlation of the comprehensive evaluation levels of the regional energy Internet.

[0016] Furthermore, the regional energy internet evaluation index system in step S1 includes economic benefit index, safety and reliability index, green environmental protection index, energy-saving and high-efficiency index, and coordinated operation index.

[0017] Furthermore, the economic benefit indicators include internal rate of return evaluation indicators, investment payback period evaluation indicators, regional energy supply cost saving rate evaluation indicators, and energy economy level evaluation indicators; the safety and reliability indicators include user average power outage time evaluation indicators, peak-valley load ratio evaluation indicators, line average load rate evaluation indicators, and comprehensive voltage qualification rate evaluation indicators; the green environmental protection indicators include clean energy penetration rate evaluation indicators, clean energy consumption rate evaluation indicators, pollutant emission reduction rate evaluation indicators, and primary electricity consumption proportion evaluation indicators; the energy-saving and high-efficiency indicators include unit GDP energy consumption evaluation indicators, power grid comprehensive line loss rate evaluation indicators, equipment utilization rate evaluation indicators, and power generation equipment average utilization hours evaluation indicators; the coordinated operation indicators include cold, heat and electricity supply and demand balance evaluation indicators and regional energy efficiency coefficient evaluation indicators.

[0018] Furthermore, the step S21 includes the following steps:

[0019] S211, scoring the importance of the evaluation indicators and constructing a judgment matrix B;

[0020] S212, let the judgment matrix be B = [b ij ], the order is n, b ij is the element in the i-th row and j-th column of the matrix; first, calculate the product M of each row of the judgment matrix i ; Secondly, calculate M i The nth root of Get the relative weight, the specific formula is:

[0021]

[0022]

[0023]

[0024] S213, perform consistency check on the judgment matrix; calculate the maximum n-th root of the judgment matrix, and the calculation formula is:

[0025]

[0026] Then, the calculation formula of the traditional consistency index CI is defined as:

[0027]

[0028] Finally, the traditional consistency index CI and the random consistency index RI are compared to obtain the test coefficient CR, which is calculated as follows:

[0029]

[0030] When CR<0.1, the judgment matrix passes the consistency test, otherwise the judgment matrix is ​​inconsistent.

[0031] Furthermore, the calculation formula of the matter-element matrix of the regional energy internet development evaluation in step S22 is:

[0032]

[0033] Where N is the development level of regional energy internet; C is the development level characteristic; V is the development level characteristic vector.

[0034] Furthermore, the classical domain matter-element matrix R in step S23 N The calculation formula is:

[0035]

[0036] Where: N i Indicates the i evaluation levels; C1, C2, C3, …, C n is the evaluation index; (a in ,b in ) represents the evaluation index C n For the value range of the i-th evaluation level;

[0037] The node domain matter-element matrix R p The calculation formula is:

[0038]

[0039] Where: N p represents the whole composed of regional energy internet rating levels; C1, C2, C3, …, C n is the evaluation index; (a pn ,b pn ) represents the domain matter-element with respect to the evaluation index C n The value range of

[0040] For the classical domain matter-element matrix R N After normalization, the calculation formula is:

[0041]

[0042] The matter-element matrix R0 to be evaluated is normalized and its calculation formula is:

[0043]

[0044] Furthermore, the correlation calculation formula of the comprehensive evaluation level of the regional energy internet in step S24 is:

[0045]

[0046] Where: K j (V j ) represents the correlation between the i-th evaluation index and the j-th comprehensive evaluation level; ρ(V i ,V ij ) indicates point V i and the corresponding eigenvector finite interval V ij Distance; ρ(V i ,V pn ) indicates point V i and the corresponding eigenvector finite interval V pn distance;

[0047]

[0048]

[0049] Where: V i ,V pn They respectively represent the value ranges of the classic domain and node domain of the energy Internet matter-element in the region to be evaluated.

[0050] Compared with the prior art, the present invention has the following advantages and positive effects:

[0051] The present invention constructs a comprehensive evaluation index system for regional energy Internet from five dimensions: economic benefit, safety and reliability, green environmental protection, energy saving and high efficiency, and coordinated operation. It can comprehensively evaluate the development level of regional energy Internet and improve the evaluation accuracy of regional energy Internet. At the same time, based on the hierarchical analysis method, the present invention proposes an improved matter-element extension evaluation model to evaluate the development of regional energy Internet, which provides decision support for the planning and development of regional energy Internet. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0053] Figure 1 It is a schematic diagram of the correlation function value of the evaluation index;

[0054] Figure 2 Schematic diagram of the evaluation dimension correlation function value. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work, any modifications, equivalent substitutions, improvements, etc., should be included in the protection scope of the present invention.

[0056] 1. Overall architecture

[0057] The present invention provides a regional energy internet comprehensive evaluation method based on an improved matter-element extension model, which mainly includes the following two steps:

[0058] S1. Construct a regional energy internet evaluation index system;

[0059] S2. Construct an improved physical-element extension model.

[0060] 2. Construction of regional energy internet evaluation index system

[0061] 2.1 Index system

[0062] According to the definition and implementation purpose of regional energy internet, combined with the rating object and existing data statistics and collection, this paper follows the principles of systematization, hierarchy, independence, objectivity and operability to construct a regional energy internet evaluation index system. This index system selects evaluation indicators from five dimensions: economic benefit, safety and reliability, green environmental protection, energy saving and high efficiency, and coordinated operation, which can comprehensively evaluate the development level of regional energy internet. As shown in Table 1.

[0063] Table 1 Regional Energy Internet Evaluation Index System

[0064]

[0065]

[0066] 2.2 Evaluation indicators

[0067] 2.2.1 Economic benefit indicators

[0068] (1) Internal rate of return

[0069] The internal rate of return refers to the discount rate when the total present value of capital inflows is equal to the total present value of capital outflows and the net present value is equal to zero. The higher the internal rate of return, the lower the cost of investment, but the higher the income.

[0070] (2) Investment payback period

[0071] The payback period refers to the time (in years) required for the total income obtained from an investment project after it goes into production to reach the total investment in the project. The length of the payback period reflects the profitability of the project.

[0072] (3) Regional energy supply cost saving rate

[0073] The regional energy supply cost saving rate refers to the ratio of the difference between the primary energy cost consumed by cooling, heating, and power generation before multi-energy coordination and the primary energy cost consumed by cooling, heating, and power generation after multi-energy coordination, as shown in formula (1). The greater the regional energy supply cost saving rate, the higher the economic benefit of the system.

[0074]

[0075] In the formula, C1 and They respectively represent the primary energy costs consumed by cooling, heating and power generation before regional multi-energy coordination and the primary energy costs consumed by cooling, heating and power generation after multi-energy coordination.

[0076] (4) Energy economy level

[0077] The energy economy level is mainly used to reflect the ability of the regional energy Internet energy system to obtain corresponding economic benefits while reducing costs, as shown in formula (2).

[0078]

[0079] In formula (2), ρ is the economic efficiency level of the system on the energy side; D is the total economic benefit over a period of time; C ie Invest the cost for the second energy source.

[0080] 2.2.2 Safety and reliability indicators

[0081] (1) Average power outage time for users

[0082] Referring to the "Power Supply Reliability Evaluation Procedure for Power Supply System Users" (DL / T836-2012), the average power outage time of users refers to the average number of power outage hours of regional energy Internet users during the statistical period (the statistical period is usually 1 year), as shown in formula (3).

[0083]

[0084] Where AIHC refers to the average power outage time of users, T user Refers to the time of continuous power outage for users, n user Refers to users who have experienced power outages within the system. The average power outage time for users is one of the important indicators that characterize the reliability of the regional energy Internet system. This indicator can reflect the severity of the outage of the distribution system.

[0085] (2) Peak-to-valley load ratio

[0086] The peak-to-valley load ratio refers to the ratio of the peak load of users in the regional energy Internet system to their valley load, as shown in formula (4).

[0087]

[0088] In the formula, R Load Refers to the peak-to-valley load ratio of the system, L max , L min They represent the peak and minimum loads of the regional energy internet system respectively. The smaller the peak-to-valley load ratio is, the more stable the system load is, the more effective the demand response is, and the more fully the power supply and consumption equipment can be used. On the contrary, it means that the power equipment capacity is idle when the load is low, which reduces the equipment utilization efficiency.

[0089] (3) Average line load rate

[0090] The average line load rate refers to the ratio of the maximum load on the line in the area to the maximum load capacity of the line itself, as shown in formula (5).

[0091]

[0092] In the formula, R Line Refers to the average line load rate, Line max Indicates the maximum load on the line in the area. cap Indicates the maximum load capacity of the line in the area, n Line Indicates the total number of lines. Regional energy internet should adjust the grid operation mode, demand response and distributed energy access and other technical means to keep the load rate of medium-voltage distribution lines in the system within a reasonable range.

[0093] (4) Comprehensive voltage qualification rate

[0094] The comprehensive voltage qualification rate refers to the percentage of the cumulative operating time of the actual operating voltage within the allowable voltage deviation range to the corresponding total operating statistical time, as shown in formula (6).

[0095]

[0096] Where T pass It indicates the total time that the voltage at the monitoring point is within the qualified range during a certain monitoring period, and T indicates the total time of voltage monitoring. The comprehensive voltage qualified rate reflects the stability and safety of the power grid operation. my country's power supply quality standards require that the terminal voltage is allowed to fluctuate within 5% of the rated range, so the allowable range of the comprehensive voltage qualified rate is -5%-5%.

[0097] 2.2.3 Green Environmental Protection Indicators

[0098] (1) Clean energy penetration rate

[0099] Clean energy penetration rate is an indicator to measure the scale of clean energy installed capacity in a region. Its calculation method is shown in formula (7):

[0100]

[0101] In the formula, μ cp Indicates the clean energy penetration rate in the region, CP c represents the installed capacity of clean energy power generation in the region, L T Indicates the maximum load in the region. The higher the clean energy penetration rate, the higher the level of cleanliness and environmental protection in the regional energy Internet.

[0102] (2) Clean energy consumption rate

[0103] The clean energy consumption rate index reflects the proportion of clean energy when calculated by energy, as shown in formula (8).

[0104]

[0105] Where: μ Energy is the clean energy consumption rate, W Clean , W ALL They are respectively the actual power generation of the system's clean energy and the power supply to the regional grid.

[0106] (3) Pollutant emission reduction rate

[0107] Global warming is currently one of the most concerning environmental issues in the world, and the main component of greenhouse gases that cause global warming is carbon dioxide. Therefore, the pollutant emission reduction rate index η poll is defined as the ratio of the reduction in regional carbon dioxide emissions after the application of the regional energy Internet system, as shown in formula (9).

[0108]

[0109] Where EM rec In the case of regional energy internet system application, the carbon emissions generated by supplying regional loads are divided into two parts: the emissions of distributed power sources and the main grid power sources. The emissions of distributed power sources are usually generated by the consumption of natural gas, while the emissions of the main grid are calculated based on the emissions of standard coal combustion. ref In the case of application of regional energy Internet system, the carbon emissions generated by supplying regional loads are basically composed of main grid emissions.

[0110] (4) Proportion of primary electricity consumption

[0111] The proportion of primary electricity consumption is the ratio of primary electricity consumption to total energy consumption in the regional energy internet, as shown in formula (10). The smaller the proportion of primary electricity consumption, the higher the low-carbon and high-efficiency of the system.

[0112]

[0113] 2.2.4 Energy-saving and high-efficiency indicators

[0114] (1) Energy consumption per unit of GDP

[0115] Energy consumption per unit of GDP refers to the electricity supply of various primary energy sources converted into standard coal consumption and its ratio to regional GDP, as shown in formula (11).

[0116]

[0117] In the formula, EC GDP Indicates energy consumption per unit of GDP, EQ net Indicates the grid power supply, EQ dis represents distributed power generation, and GDP represents the total GDP of the region.

[0118] (2) Comprehensive line loss rate of power grid

[0119] The comprehensive line loss rate refers to the percentage of the total power loss of the unit to the total power supply. It is an important economic indicator reflecting the management level of power supply enterprises.

[0120] (3) Equipment utilization

[0121] Equipment utilization is an indicator that reflects the working status and production efficiency of the equipment in the system, and has important evaluation significance. Equipment utilization refers to the ratio of the actual working time of the equipment to the planned working time within a period of time. The value is directly related to the investment benefit. The specific expression is:

[0122]

[0123] In the formula, T0 is the unit planned working time; T n is the actual working time of the nth device in unit time; N e It is the number of energy link equipment in the regional energy system.

[0124] (4) Average utilization hours of power generation equipment

[0125] The average utilization hours of power generation equipment refers to the equivalent operating hours of power generation equipment under full load operation conditions within a given period. It is an indicator of the utilization rate of power generation equipment and is measured in hours.

[0126] 2.2.5 Collaborative operation indicators

[0127] (1) Balance between supply and demand of cooling, heating and electricity

[0128] The balance degree of supply and demand of cooling, heating and electricity is an important indicator to measure the matching degree between the supply and demand of cooling, heating and electricity in the regional energy system, as shown in formula (13).

[0129]

[0130] Where Q act elc , Q act hot , Q act cold represents the actual annual electricity generation and annual heating and cooling capacity of the regional energy internet, while Q need elc , Q need hot , Q need cold It represents the user's annual electricity consumption and the user's annual heating and cooling usage.

[0131] (2) Regional Energy Efficiency Coefficient

[0132] A1 represents the regional energy efficiency coefficient, and the calculation formula is:

[0133]

[0134] Among them, Q out The total amount of energy produced in the region, Q in It represents the total energy consumed for heating, cooling and power generation.

[0135] 3. Model construction

[0136] The present invention uses the concept of improved matter-element analysis to establish an improved matter-element model, and uses the analytic hierarchy process (AHP) to determine the weights of indicators. The main steps are as follows:

[0137] Step 1: Determine the index weight coefficient by using the AHP. The AHP is a decision-making method that combines quantitative and qualitative methods. It refers to decomposing the elements related to decision-making into goals, standards, procedures, etc. The advantage of the AHP is that it uses the research object as a system to evaluate the object or index of system analysis, which has a wide range of practicality. The brief calculation steps of AHP are as follows:

[0138] (1) Constructing a judgment matrix

[0139] According to certain rules, the importance of evaluation indicators is scored and a judgment matrix is ​​constructed. Assume that a standard factor is b1, b2, ..., b n ,So:

[0140]

[0141] Where b ij Indicates b i Relative to b j The comparison result of importance. From the judgment matrix B, we can see that b ij >0,b ij =1 / b ji , b ii =1.

[0142] (2) Hierarchical single sorting

[0143] Hierarchical single ranking refers to the degree of influence of each indicator at a certain level on the indicator at the previous level. The present invention adopts the square root method, and the judgment matrix is ​​set to B = [b ij ], the order is n, b ij is the element in the i-th row and j-th column of the matrix. First, calculate the product M of each row of the judgment matrix i ; Secondly, calculate M i The nth root of Get the relative weight, the specific formula is as follows:

[0144]

[0145]

[0146]

[0147] (3)Consistency test

[0148] First, calculate the maximum n-th root of the judgment matrix:

[0149]

[0150] The consistency index CI is defined as:

[0151]

[0152] CI = 0, there is complete consistency; CI is close to 0, there is satisfactory consistency; the larger the CI, the more serious the inconsistency. In order to measure the size of the consistency index CI, the random consistency index RI is introduced, as shown in Table 2:

[0153] Table 2 Random consistency index

[0154]

[0155] Considering that the deviation from consistency may be caused by random reasons, when checking whether the judgment matrix has satisfactory consistency, it is also necessary to compare CI and random consistency index RI to obtain the test coefficient CR, the formula is as follows:

[0156]

[0157] Generally, if CR < 0.1, the judgment matrix is ​​considered to pass the consistency test, otherwise it does not have satisfactory consistency.

[0158] Step 2: Determine the matter-element matrix for regional energy internet development evaluation. According to the definition of matter-element, the regional energy internet development level N, the development level characteristics C (secondary indicators) and the characteristic vector V (the value of each secondary indicator) together constitute the regional energy internet development matter-element R = (N, C, V). The matter-element matrix can be expressed as:

[0159]

[0160] Step 3: Determination of classical domains and node domains for regional energy internet development evaluation.

[0161] Classical domain matter-element matrix R for regional integrated energy system level evaluation N It is expressed as:

[0162]

[0163] Where: N i Indicates the i evaluation levels; C1, C2, C3, …, C n is the evaluation index; (a in ,b in ) represents the value range of the evaluation index Cn for the i-th evaluation level, that is, the classical domain.

[0164] The node domain matter-element matrix R for regional energy internet level evaluation p It is expressed as:

[0165]

[0166] Where: N p represents the whole composed of regional energy internet rating levels; C1, C2, C3, …, C n is the evaluation index; (a pn ,b pn ) represents the domain matter-element with respect to the feature C n The value range of .

[0167] However, the matter-element extension model has certain limitations and shortcomings: when the value of a certain indicator of the matter-element to be evaluated exceeds the node domain, when the value of the indicator is substituted into the correlation function calculation, the denominator will be zero. At this time, the correlation function value cannot be obtained, and thus the evaluation cannot be performed. Therefore, it is necessary to improve the matter-element extension model by normalizing the classical domain matter-element and the matter-element to be evaluated.

[0168] For the classical domain matter-element matrix R N After normalization, we can get:

[0169]

[0170] After normalizing the matter-element matrix R0, we can get:

[0171]

[0172] Step 4: Determination of the correlation of the comprehensive evaluation level of regional energy internet.

[0173] The correlation function of regional energy internet user level evaluation index is defined as:

[0174]

[0175] Where: represents the correlation between the ith indicator and the jth comprehensive evaluation level.

[0176] V ij =|b in -a in | (28)

[0177]

[0178]

[0179] Where: V i ,V pn They represent the value ranges of the classical domain and node domain of the energy Internet matter-element to be evaluated; ρ(V i ,V ij ) indicates point V i and the corresponding eigenvector finite interval V ij Distance; ρ(V i ,V pn ) indicates point V i and the corresponding eigenvector finite interval V pn distance.

[0180] The comprehensive correlation degree indicates the degree of conformity of all evaluation indicators with each evaluation level, and its calculation formula is:

[0181]

[0182] Where: K i (p) is the comprehensive relevance of the object-element N to be judged with respect to level i; w i is the weight of each evaluation index; K j (V i ) is the single index correlation degree of the matter-element to be judged with respect to level i. If Kij = max{K i (p)}, then the i-th indicator of the object to be evaluated belongs to the j-level of comprehensive evaluation. The correlation value K i The size of (p) indicates the degree to which the object to be evaluated belongs to a certain standard of the comprehensive evaluation level. i (p)>0, indicating that the evaluation index meets the requirements of the standard object range, and its value indicates the degree of compliance with the requirements; when -1<K i (p) < 0, indicating that the evaluation index does not meet the requirements of the standard object, but has the potential to be transformed into a standard object. The larger the value, the easier it is to transform. i When (p) < -1, it means that the evaluation object does not meet the requirements of the standard object and does not have the potential to be transformed into a standard object.

[0183] 4. Example Analysis

[0184] 4.1 Data and weight determination

[0185] The present invention selects the energy Internet of a large industrial park in the north as an example for verification. According to the survey results, the calculation results of each secondary indicator are shown in Table 3:

[0186] Table 3 Secondary index values

[0187]

[0188] Based on the above indicator system and the basic method of AHP, the judgment matrix of each layer of indicators is determined by expert discussion, and a consistency test is carried out to finally determine the weight of each indicator.

[0189] (1) Judgment matrix (as shown in Table 4 and Table 5)

[0190] Table 4 Judgment Matrix A

[0191]

[0192] Table 5 Judgment matrix B1

[0193]

[0194]

[0195] …

[0196] Similarly, the judgment matrix of B2-B5 was also determined by expert discussion and will not be repeated here.

[0197] (2)Consistency test

[0198] The judgment matrix of AHP should satisfy non-negativity, reflexivity, reciprocity and consistency defined by equations (19)-(21). The calculation results are as follows:

[0199] Table 6 Consistency test

[0200]

[0201] It can be seen from Table 6 that the consistency ratios of each judgment matrix are all <0.1, which meets the consistency test requirements.

[0202] (3) Calculation results

[0203] Based on the above index system and judgment matrix, the AHP method is used to calculate the weight of each index, and the results are shown in Table 7. In the actual calculation, the deviation of each index value needs to be standardized according to 0-100%.

[0204] Table 7 Determination of indicator weights

[0205]

[0206]

[0207] 4.2 Evaluation criteria for regional energy internet development

[0208] By referring to and comparing the relatively mature smart grid development evaluation standards, combined with the characteristics and actual development of regional energy Internet, the development degree of regional energy Internet is divided into four levels. According to the matter-element extension theory, the present invention determines the level standard value (i.e., classic domain and node domain) of each indicator of regional energy Internet development evaluation by referring to international, national and industry-related standards, national relevant planning goals (national "13th Five-Year Plan for Energy Development"), the background of the research object and expert opinions.

[0209]

[0210]

[0211] 4.3 Results Analysis

[0212] The correlation between the evaluation object and the comprehensive evaluation level shows that the evaluated object can be divided into four levels. According to formula (27), the correlation between the regional energy Internet evaluation index and each level is calculated. The results are shown in Table 8 and Figure 1As shown in the table. The greater the correlation, the higher the degree of integration between the indicator and the evaluation level. Taking the internal rate of return (C1) as an example, the correlation coefficients between this indicator and the four levels are -0.0314, 0.0471, -0.0177 and -0.0589 respectively. Therefore, it can be concluded that the internal rate of return (C1) indicator belongs to level II. Similarly, the correlation function values ​​of other indicators can be obtained, as shown in the table. Among them, indicators rated at level II and above account for about 70% of the total evaluation indicators, indicating that the comprehensive evaluation level of the park is good.

[0213] Table 8 Evaluation index correlation function values

[0214]

[0215] According to formula (31), the correlation between each level and economic benefit (B1), safety and reliability (B2), green environmental protection (B3), energy saving and high efficiency (B4), and coordinated operation (B5) can be calculated respectively. The results are shown in Table 9 and Figure 2 As shown. Taking economic benefit (B1) as an example, the correlation function values ​​with each level are -0.0079, 0.0057, -0.0079 and -0.0213 respectively. Therefore, the rating of the economic benefit evaluation dimension of the park is at level II. Similarly, the ratings of the park's safety and reliability, green environmental protection, energy saving and high efficiency, and coordinated operation are at level I, level III, level III and level II respectively. This shows that the park has done the best in safety and reliability, which is at level I; it is good in economic benefits and coordinated operation, which is at level II; it is qualified in green environmental protection and energy saving and high efficiency, which is at level III. Therefore, to improve the comprehensive evaluation level of the park, we should focus on improving the green environmental protection level and safety and reliability of the park.

[0216] Table 9 Correlation function values ​​of each dimension

[0217]

[0218] Finally, according to formula (31), the comprehensive evaluation results of the park and the corresponding evaluation level are calculated as shown in Table 10. It can be seen from the table that the comprehensive evaluation result of the energy Internet of the park is level II, and the overall situation is good.

[0219] Table 10 Comprehensive correlation function values

[0220]

[0221] 5. Conclusion

[0222] Aiming at the implementation purpose of the park energy Internet, the present invention establishes a complete energy Internet construction project evaluation index system from five aspects: economic benefit, safety and reliability, green environmental protection, energy saving and high efficiency, and coordinated operation, and proposes a comprehensive evaluation method based on the weighting of the analytic hierarchy process and the improvement of the object-element extension, which realizes a good combination of the objectivity of data information and the subjectivity of expert preference. An example analysis was conducted on the energy Internet construction project of a northern industrial park. The results showed that the comprehensive evaluation result of the energy Internet in the park was level II, and the overall situation was good. The ratings of the park in the five aspects of economic benefit, safety and reliability, green environmental protection, energy saving and high efficiency, and coordinated operation were level II, level I, level III, level III, and level II respectively. Therefore, to improve the development level of the energy Internet in the park, more attention should be paid to the green environmental protection and energy saving and high efficiency level of the park.

Claims

1. A comprehensive evaluation method for regional energy internet based on an improved matter-element extension model, characterized by: The following steps are involved: S1. Construct a regional energy internet evaluation index system; S2, construct an improved matter-element extension model; S21. Use the analytic hierarchy process to determine the weight of each evaluation index; S22. Calculate and determine the matter-element matrix for regional energy internet development evaluation; S23. Calculate and determine the classical domain matter-element matrix and node domain matter-element matrix for regional energy internet development evaluation; S24. Calculate and determine the correlation of the comprehensive evaluation level of the regional energy internet; S3. Determine the evaluation level of the regional energy internet through the correlation of the comprehensive evaluation level of the regional energy internet; The step S21 comprises the following steps: S211, scoring the importance of the evaluation indicators and constructing a judgment matrix B; S212, let the judgment matrix be B = [b ij ], the order is n, b ij is the element in the i-th row and j-th column of the matrix; first, calculate the product M of each row of the judgment matrix i ; Secondly, calculate M i of n square root; finally, normalization Get the relative weight, the specific formula is: S213, perform consistency check on the judgment matrix; calculate the maximum n-th root of the judgment matrix, and the calculation formula is: Then, the calculation formula of the traditional consistency index CI is defined as: Finally, the traditional consistency index CI Compare with the random consistency index RI to obtain the test coefficient CR , and its calculation formula is: when CR <0.1, the judgment matrix passes the consistency test, otherwise the judgment matrix is ​​inconsistent.

2. The regional energy internet comprehensive evaluation method based on the improved matter-element extension model as claimed in claim 1 is characterized by: The regional energy internet evaluation index system in step S1 includes economic benefit index, safety and reliability index, green environmental protection index, energy-saving and high-efficiency index, and coordinated operation index.

3. The regional energy internet comprehensive evaluation method based on the improved matter-element extension model as claimed in claim 2 is characterized by: The economic benefit indicators include internal rate of return evaluation indicators, investment recovery period evaluation indicators, regional energy supply cost saving rate evaluation indicators, and energy economy level evaluation indicators; the safety and reliability indicators include user average power outage time evaluation indicators, peak-valley load ratio evaluation indicators, line average load rate evaluation indicators, and comprehensive voltage qualification rate evaluation indicators; the green environmental protection indicators include clean energy penetration rate evaluation indicators, clean energy consumption rate evaluation indicators, pollutant emission reduction rate evaluation indicators, and primary electricity consumption proportion evaluation indicators; the energy-saving and high-efficiency indicators include unit GDP energy consumption evaluation indicators, power grid comprehensive line loss rate evaluation indicators, equipment utilization rate evaluation indicators, and power generation equipment average utilization hours evaluation indicators; the coordinated operation indicators include cold, heat and electricity supply and demand balance evaluation indicators and regional energy efficiency coefficient evaluation indicators.

4. The regional energy internet comprehensive evaluation method based on the improved matter-element extension model as claimed in claim 3 is characterized by: The calculation formula of the matter-element matrix of the regional energy internet development evaluation in step S22 is: Where N is the development level of regional energy internet; C is the characteristics of the development level; V is the characteristic vector of development level.

5. The regional energy internet comprehensive evaluation method based on the improved matter-element extension model as claimed in claim 4 is characterized by: In step S23, the classical domain matter-element matrix R N The calculation formula is: Where: N i Indicates the i evaluation levels; C1, C2, C3, …, C n is the evaluation index; (a in ,b in ) represents the evaluation index C n For the value range of the i-th evaluation level; The node domain matter-element matrix R p The calculation formula is: Where: N p represents the whole composed of regional energy internet rating levels; C1, C2, C3, …, C n is the evaluation index; (a pn ,b pn ) represents the domain matter-element with respect to the evaluation index C n The value range of For the classical domain matter-element matrix R N After normalization, the calculation formula is: The matter-element matrix R0 to be evaluated is normalized and its calculation formula is:

6. The regional energy internet comprehensive evaluation method based on the improved matter-element extension model as claimed in claim 5 is characterized by: The calculation formula for the correlation degree of the comprehensive evaluation level of the regional energy internet in step S24 is: Where: K j (V j ) represents the correlation between the i-th evaluation index and the j-th comprehensive evaluation level; ρ(V i ,V ij ) indicates point V i and the corresponding eigenvector finite interval V ij Distance; ρ(V i ,V pn ) indicates point V i and the corresponding eigenvector finite interval V pn distance; Where: V i ,V pn They respectively represent the value ranges of the classic domain and node domain of the energy Internet matter-element in the region to be evaluated.

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