An intelligent distribution network green high-reliability evaluation method

Abstract

The present invention discloses an intelligent distribution network green high-reliability evaluation method, comprising the following steps: S1: constructing a distribution network performance evaluation index system, the distribution network performance evaluation index system including a plurality of evaluation indexes; S2: calculating the weights of the respective evaluation indexes in the distribution network performance evaluation index system by using an improved entropy weight method; S3: evaluating the performance of the intelligent distribution network by introducing a fuzzy VIKOR method according to the weights of the respective evaluation indexes obtained by calculation. In this embodiment, the green development and high reliability of the sustainable development network are fully considered in the evaluation index system, and then, based on the weighted method of the improved entropy method and the fuzzy VIKOR method, a hybrid model based on a fuzzy method is proposed, which has high practicability.

Description

Technical Field

[0001] The present invention relates to the field of power grid reliability evaluation, and more specifically, to an intelligent distribution network green high-reliability evaluation method. Background Art

[0002] With the construction of a new power system, a large number of distributed power sources, electric vehicles, energy storage devices and more equipment are connected to the distribution network. The development of intelligent distribution technologies such as microgrids, active distribution networks, and energy Internet is rapid. Due to the construction of China's new power system, it is necessary to accelerate the construction of intelligent distribution networks (SDNs) to adapt to the wide access of distributed energy and demand-side resources. The comprehensive benefit evaluation of the distribution network can solve the quantitative calculation of the operation benefits of the distribution network, control the development target management, and realize the comparison of different projects. At the same time, the comprehensive benefit evaluation of the sustainable development network can clarify the expected benefits in multiple aspects and measure its development level. Reliable and effective comprehensive benefit evaluation results can help decision-makers formulate development plans and clarify the construction priorities.

[0003] Many studies on the comprehensive performance evaluation of intelligent distribution network construction have been published. Liang F et al. [1] proposed a life cycle assessment scheme for the evaluation of investment projects in intelligent distribution network construction. The evaluation process is divided into three steps: pre-evaluation, mid-evaluation, and post-evaluation of investment projects. A fuzzy evaluation method is used to evaluate the comprehensive benefits and feasibility of intelligent distribution network projects. Shen Y et al. [2] proposed four quantitative indicators to evaluate the self-healing of intelligent distribution networks, including self-healing reliability, self-healing rate, self-healing speed, and self-healing efficiency. To solve the problems of insufficient samples and uncertainty in self-healing evaluation, uncertainty theory is used to quantitatively describe the uncertainty of self-healing. Kong X et al. [3] proposed a multi-objective power supply capacity evaluation method for active distribution networks. Considering the uncertainty of distributed generation and demand response resources, an active distribution network multi-objective optimization deterministic system is established to evaluate its performance. Its multi-objective optimization function is to maximize the regional power supply capacity and minimize the active control cost. Yang X et al. [4] established an energy efficiency index system for distribution networks. The index system includes distribution network planning, equipment parameters and operating status, and the improvement of the energy efficiency level of distribution networks. Ma L et al. [5] considered the economy and safety of distribution network equipment and proposed a comprehensive efficiency evaluation model of distribution network equipment based on the annual load duration curve.

[0004] The prior art discloses an intelligent distribution network power supply comprehensive performance evaluation method and system based on a knowledge graph. The method includes the steps of: obtaining multi-source data in the physical space of the intelligent distribution network to be evaluated and establishing a digital model of the intelligent distribution network power supply system; analyzing the characteristics of the digital model of the intelligent distribution network power supply system, and based on the knowledge graph, establishing an intelligent distribution network power supply comprehensive performance evaluation index system from four aspects: cleanliness, economy, flexibility, and reliability; using the analytic hierarchy process and the entropy weight method to determine the index weights of the intelligent distribution network power supply comprehensive performance evaluation indexes, and using the fuzzy evaluation method to evaluate the intelligent distribution network power supply comprehensive performance according to the set index levels and their corresponding index weights to obtain an evaluation result. The evaluation template for the comprehensive performance of the intelligent distribution network needs to be updated to pay more attention to green development and high reliability. Summary of the Invention

[0005] The present invention provides an intelligent distribution network green and high-reliability evaluation method, and establishes a comprehensive evaluation index system for sustainable development network evaluation to reflect the coordination between green development and safety.

[0006] To solve the above technical problems, the technical solution of the present invention is as follows:

[0007] An intelligent distribution network green and high-reliability evaluation method includes the following steps:

[0008] S1: Construct a distribution network performance evaluation index system, and the distribution network performance evaluation index system includes a number of evaluation indexes;

[0009] S2: Use the improved entropy weight method to calculate the weights of each evaluation index in the distribution network performance evaluation index system;

[0010] S3: According to the calculated weights of each evaluation index, introduce the fuzzy VIKOR method to evaluate the performance of the intelligent distribution network.

[0011] Preferably, the distribution network performance evaluation index system in step S1 includes five aspects: green development, reliability, economy, technology, and power quality, specifically:

[0012] The green development aspect includes the distributed energy penetration rate C11, the ratio of electricity consumption to total energy consumption C12, the clean energy generation share C13, and the standard coal amount saved due to power substitution C14;

[0013] The reliability aspect includes the power supply reliability C21, the line fault self-healing rate C22, and the grid equipment fault frequency C23;

[0014] The economy aspect includes the electricity sales volume per unit of grid assets C31, the investment payback period C32, and the net present value C33;

[0015] The technical aspects include the comprehensive line loss rate C41, the percentage of intelligent meters installed C42, and the average number of distribution line segments C43;

[0016] The power quality aspects include the integral voltage passing rate C51 and the grid frequency passing rate C52.

[0017] Preferably, in the step S2, the improved entropy weight method is used to calculate the weights of the evaluation indexes in the distribution network performance evaluation index system, specifically:

[0018] Assume that there are m schemes and n indexes in the evaluation system, and the evaluation index value of index i in scheme j is designated as

[0019] Standardize the evaluation index values to obtain a standardized matrix;

[0020] Calculate the frequency f of indicator j ij , and calculate the information entropy value d of the index according to f ij ; j ;

[0021] Determine the weight value of each index:

[0022]

[0023] Preferably, the standardization of the evaluation index values to obtain a standardized matrix is specifically:

[0024] For welfare type indexes, the standardization formula is:

[0025]

[0026] For cost type indexes, the standardization formula is:

[0027]

[0028] The standardized matrix D is established as:

[0029]

[0030] Preferably, the calculation of the frequency f of the indicator ij , and the calculation of the information entropy value d of the index according to f ij , is specifically: j :

[0031] Calculate the frequency f of indicator j ij :

[0032]

[0033] According to f ij Calculate the information entropy value d of the indexj :

[0034]

[0035] Preferably, step S3 is specifically as follows:

[0036] S3.1: Determine the fuzzy positive ideal solution and the negative ideal solution

[0037] S3.2: Calculate the normalized distance between each index and the fuzzy positive ideal solution;

[0038] S3.3: Calculate the collective interest value S i of the intelligent distribution network scheme to be evaluated, the individual regret value R i and the compromise value Q i ;

[0039] S3.4: Rank according to S i , R i and Q i to evaluate and obtain the optimal intelligent distribution network scheme.

[0040] Preferably, in S3.1, determining the fuzzy positive ideal solution and the negative ideal solution is specifically as follows:

[0041]

[0042]

[0043] where J 1 is the set of benefit-oriented indicators, and J 2 is the set of cost-oriented indicators. Then

[0044] Preferably, in step S32., calculating the normalized distance between each index and the fuzzy positive ideal solution is specifically as follows:

[0045]

[0046] where is the distance between the j-criterion value from the i-th object and the fuzzy positive ideal solution of the j-criterion, is the Euclidean distance between the positive ideal solution and the negative ideal solution, and:

[0047]

[0048]

[0049] Preferably, in S3.3, calculate the group interest value S of the smart distribution network plan to be evaluated i , individual regret value R i and compromise value Q i , specifically:

[0050]

[0051]

[0052]

[0053] In the formula, w i refers to the standard weight value, η represents the decision factor, and η ∈ [0, 1].

[0054] Preferably, in S3.4, rank according to S i , R i and Q i to evaluate and obtain the optimal smart distribution network plan, specifically:

[0055] S j , R j and Q j values are sorted in ascending order respectively. The smaller these values are, the better the performance of the plan; when the following two criteria are met, the object with the Q j value is the best:

[0056] Condition 1: Acceptable advantage value, that is, the value of Q j should be:

[0057]

[0058] where B (1) , B (2) are the first and second objects after sorting the Q j values in ascending order;

[0059] Condition 2: Acceptable decision stability;

[0060] That is, the group interest value of object B (2) should be greater than the group interest value of object B (1) , or the individual regret value of object B (2) should be greater than the regret value of object B (1) ;

[0061] If the above two conditions cannot be met simultaneously, a compromise solution should be selected according to the following principle;

[0062] (1) If Condition 2 is not met, the two solutions B (1) , B(2) The conclusion is optimal;

[0063] (2) If condition 1 is not satisfied, then each of the solutions B (1) , B (2) , ……, B (M) is optimal;

[0064] The value of M is the maximum value that satisfies the following formula, as shown below,

[0065]

[0066] Compared with the prior art, the beneficial effects of the technical solution of the present invention are:

[0067] The present invention is an intelligent distribution network green and high-reliability evaluation method, which fully considers the green development and high reliability of the sustainable development network in the evaluation index system. The evolution index system consists of 15 indicators. Then, based on the weighted method of the improved entropy method and the fuzzy VIKOR method, a hybrid model based on the fuzzy method is proposed. Finally, the practicability of the hybrid evaluation framework is verified through empirical analysis. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 is a schematic flow chart of the method of the present invention.

[0069] Figure 2 is a schematic diagram of the comprehensive performance evaluation index system of the intelligent distribution network. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0070] The drawings are only for illustrative purposes and should not be construed as a limitation of this patent;

[0071] For better illustration of this embodiment, some components in the drawings will be omitted, enlarged or reduced, and do not represent the dimensions of the actual product;

[0072] For those skilled in the art, it is understandable that some well-known structures and their descriptions in the drawings may be omitted.

[0073] The technical solution of the present invention will be further described below with reference to the drawings and embodiments.

[0074] Embodiment 1

[0075] An intelligent distribution network green and high-reliability evaluation method, as Figure 1 shown, includes the following steps:

[0076] S1: Construct a distribution network performance evaluation index system, and the distribution network performance evaluation index system includes a number of evaluation indicators;

[0077] S2: Calculate the weights of each evaluation index in the performance evaluation index system of the distribution network by using the improved entropy weight method;

[0078] S3: According to the weights of each evaluation index calculated, introduce the fuzzy VIKOR method to evaluate the performance of the intelligent distribution network.

[0079] Example 2

[0080] Based on Example 1, this example further discloses the following content:

[0081] In step S1, the performance evaluation index system of the distribution network is as Figure 2 shown, including five aspects: green development, reliability, economy, technology, and power quality. Specifically:

[0082] In terms of green development, it includes distributed energy penetration rate C11, ratio of electricity consumption to total energy consumption C12, clean energy generation share C13, and standard coal saved due to power substitution C14;

[0083] In terms of reliability, it includes power supply reliability C21, line fault self-healing rate C22, and grid equipment fault frequency C23;

[0084] In terms of economy, it includes electricity sales per unit of grid assets C31, investment payback period C32, and net present value C33;

[0085] In terms of technology, it includes comprehensive line loss rate C41, percentage of installed smart meters C42, and average number of distribution line segments C43;

[0086] In terms of power quality, it includes integral voltage passing rate C51 and grid frequency passing rate C52.

[0087] The comprehensive performance of the intelligent distribution network should not only include economy, but also environmental protection, reliability, and practicability. According to the principles of scientificity, comprehensiveness, typicality, practicability, and independence, a performance evaluation index system of the distribution network is constructed from five aspects: green development, reliability, economy, technology, and power quality. 15 standards are selected from a large number of literatures and integrated into the index system.

[0088] Example 3

[0089] Based on Example 1 and Example 2, this example further discloses the following content:

[0090] In step S2, calculate the weights of each evaluation index in the performance evaluation index system of the distribution network by using the improved entropy weight method, specifically:

[0091] Assume that there are m schemes and n indexes in the evaluation system, and the evaluation index value of index i in scheme j is designated as

[0092] Standardize the evaluation index values to obtain a standardized matrix;

[0093] Calculate the frequency f of indicator j ij , and based on f ij Calculate the information entropy value d of the indicator j ;

[0094] Determine the weight value of each indicator:

[0095]

[0096] The standardization of the evaluation index values to obtain a standardized matrix is specifically as follows:

[0097] For the welfare type index, the standardization formula is:

[0098]

[0099] For the cost type index, the standardization formula is:

[0100]

[0101] The standardized matrix D is established as:

[0102]

[0103] The calculation of the frequency f of the indicator ij , and based on f ij Calculate the information entropy value d of the indicator j , specifically as follows:

[0104] Calculate the frequency f of indicator j ij :

[0105]

[0106] Based on f ij Calculate the information entropy value d of the indicator j :

[0107]

[0108] The entropy method is a traditional objective entropy weight method. It reflects the importance of indicators by allocating corresponding weights according to the degree of change of different indicators. This method uses entropy to determine the degree of dispersion between indicators. The smaller the entropy, the greater the discreteness of the indicator and the greater the impact on the evaluation result. In this embodiment, the traditional entropy method is improved by using the coefficient of variation method to reduce the impact of entropy changes on weights.

[0109] Example 4

[0110] On the basis of Embodiments 1 to 3, the following content is further disclosed in this embodiment:

[0111] Step S3 is specifically as follows:

[0112] S3.1: Determine the fuzzy positive ideal solution and the negative ideal solution

[0113] S3.2: Calculate the normalized distance between each index and the fuzzy positive ideal solution;

[0114] S3.3: Calculate the group benefit value S i of the intelligent distribution network scheme to be evaluated, the individual regret value R i and the compromise value Q i ;

[0115] S3.4: Rank according to S i , R i and Q i to evaluate and obtain the optimal intelligent distribution network scheme.

[0116] In S3.1, the determination of the fuzzy positive ideal solution and the negative ideal solution is specifically as follows:

[0117]

[0118]

[0119] In the formula, J 1 is the set of benefit-oriented indicators, and J 2 is the set of cost-oriented indicators. Then

[0120] In step S32., the calculation of the normalized distance between each index and the fuzzy positive ideal solution is specifically as follows:

[0121]

[0122] Among them, is the distance between the j-criterion value of the i-th object and the fuzzy positive ideal solution of the j-criterion, is the Euclidean distance between the positive ideal solution and the negative ideal solution, and:

[0123]

[0124]

[0125] In S3.3, the calculation of the group benefit value S i of the intelligent distribution network scheme to be evaluated, the individual regret value Ri and the compromise value Q i , specifically:

[0126]

[0127]

[0128]

[0129] In the formula, w i refers to the standard weight value, η represents the decision-making factor, and η ∈ [0, 1].

[0130] In the above S3.4, ranking is performed according to S i , R i and Q i to evaluate and obtain the optimal intelligent distribution network plan, specifically:

[0131] S j , R j and Q j values are sorted in ascending order respectively. The smaller these values are, the better the performance of the plan; when the following two criteria are met, the object with the Q j value is the optimal one:

[0132] Condition 1: The acceptable advantage value, that is, the value of Q j should be:

[0133]

[0134] where, B (1) , B (2) are the first and second objects after sorting the Q j values in ascending order;

[0135] Condition 2: The acceptable decision-making stability;

[0136] That is, the group interest value of the object B (2) should be greater than the group interest value of the object B (1) , or the individual regret value of the object B (2) should be greater than the regret value of the object B (1) ;

[0137] If the above two conditions cannot be met simultaneously, the compromise plan should be selected according to the following principle;

[0138] (1) If Condition 2 is not met, the conclusion of the two plans B (1) , B (2) is the optimal;

[0139] (2) If condition 1 is not satisfied, then each solution B (1) , B (2) , ……, B (M) is optimal;

[0140] The value of M is the maximum value that satisfies the following formula, as shown below,

[0141]

[0142] The VIKOR method is a multi-attribute decision-making method proposed by Professor Opricovic S in 1998. Its core idea is to rank different solutions by distinguishing their advantages and disadvantages according to the weighted distance between the proposed solutions and the ideal solution. Triangular fuzzy numbers are introduced to solve the multi-attribute problem of fuzzy index attribute values in the decision-making process. Therefore, an evaluation model for user energy storage configuration solutions suitable for uncertain conditions is established.

[0143] In a specific embodiment, four typical distribution systems (U1, U2, U3, and U4) are selected to verify the effectiveness of the model. First, the original index data of the four alternative solutions are collected. The weights of the criteria are calculated by the improved entropy weight method, as shown in Table 1:

[0144] Table 1 Criteria weights based on the improved entropy weight method

[0145]

[0146] Then, the fuzzy VIKOR evaluation method is used to evaluate the comprehensive performance of the four solutions. The normalized distance between each index and the fuzzy positive ideal can be determined. Obtain S i , R i and Q i The evaluation process and calculation results are shown in Table 2.

[0147] Table 2 Evaluation process and results of all alternative solutions

[0148]

[0149]

[0150] S i , R i and Q i Sorted from small to large, their priority order is shown in Table 3. Based on the two conditions of VIKOR, the U3 alternative is always the best. According to the Q i ranking, the comprehensive performance of the U1 alternative ranks second, followed by U4 and U2.

[0151] Table 3 S i , R i and Qi ranking order

[0152]

[0153] Like reference numerals correspond to like or similar components;

[0154] The terms used in the drawings to describe the positional relationships are for illustrative purposes only and should not be construed as limiting the present patent;

[0155] Obviously, the above embodiments of the present invention are merely examples given for clearly illustrating the present invention and are not intended to limit the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all the implementation manners here. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the claims of the present invention.

Claims

1. An intelligent distribution network green high - reliability evaluation method, characterized in that, it includes the following steps: S1: Construct a distribution network performance evaluation index system, and the distribution network performance evaluation index system includes several evaluation indexes; S2: Use the improved entropy weight method to calculate the weights of each evaluation index in the distribution network performance evaluation index system; S3: According to the weights of each evaluation index obtained by calculation, introduce the fuzzy VIKOR method to evaluate the performance of the intelligent distribution network; In step S1, the distribution network performance evaluation index system includes five aspects: green development, reliability, economy, technology, and power quality; In step S2, when using the improved entropy weight method to calculate the weights of each evaluation index in the distribution network performance evaluation index system, specifically: Suppose there are m alternatives and n criteria in an evaluation system, and the evaluation index value of criterion i in alternative j is specified as Standardize the evaluation index values to obtain a standardized matrix; Calculate the frequency f of the indicator j ij , and calculate the information entropy value d of the indicator according to f ij ; j ; Determine the weight value of each index: The specific method of standardizing the evaluation index values to obtain a standardized matrix is as follows: For welfare - type indicators, the standardization formula is: For cost - type indicators, the standardization formula is: The standardized matrix D is established as: Calculate the frequency f of the calculation indicator ij , and based on f ij calculate the information entropy value d of the indicator j , specifically: Calculate the frequency f of the indicator j ij : According to f ij Calculate the information entropy value d of the index j :

2. The intelligent distribution network green high - reliability evaluation method according to claim 1, characterized in that, in step S1, the distribution network performance evaluation index system includes five aspects: green development, reliability, economy, technology, and power quality, specifically: The green development aspect includes the distributed energy penetration rate C11, the ratio of electricity consumption to total energy consumption C12, the clean energy power generation share C13, and the standard coal savings due to power substitution C14; The reliability aspect includes the power supply reliability C21, the line fault self - healing rate C22, and the grid equipment fault frequency C23; The economy aspect includes the electricity sales per unit of grid assets C31, the investment payback period C32, and the net present value C33; The technology aspect includes the comprehensive line loss rate C41, the percentage of installed smart meters C42, and the average number of distribution line segments C43; The power quality aspect includes the integral voltage passing rate C51 and the grid frequency passing rate C52.

3. The intelligent distribution network green high - reliability evaluation method according to claim 2, characterized in that, step S3 is specifically: S3.1: Determine the fuzzy positive ideal solution and the negative ideal solution S3.2: Calculate the normalized distance between each index and the fuzzy positive ideal solution; S3.3: Calculate the group benefit value S of the smart distribution network scheme to be evaluated i , the individual regret value R i and the compromise value Q i ; S3.4: Rank according to S i , R i and Q i to obtain the optimal intelligent distribution network plan through evaluation.

4. The intelligent distribution network green high - reliability evaluation method according to claim 3, characterized in that, Determining the fuzzy positive ideal solution in S3.1 and the negative ideal solution Specifically: where, J 1 is the set of benefit-oriented indicators, and J 2 is the set of cost-oriented indicators, and then 5. The intelligent distribution network green high - reliability evaluation method according to claim 4, characterized in that, in step S3.2, the specific method of calculating the normalized distance between each index and the fuzzy positive ideal solution is: wherein, is the distance between the j-criterion value of the i-th object and the fuzzy positive ideal solution of the j-criterion, is the Euclidean distance between the positive ideal solution and the negative ideal solution, and:

6. The intelligent distribution network green high - reliability evaluation method according to claim 5, characterized in that, Calculate the group interest value S of the smart distribution network scheme to be evaluated in S3.3 i , the individual regret value R i and the compromise value Q i , specifically as follows: where w i denotes the standard weight value, η represents the decision-making factor, and η ∈ [0, 1].

7. The intelligent distribution network green high - reliability evaluation method according to claim 6, characterized in that, In step S3.4, ranking is performed according to S i , R i and Q i to evaluate and obtain the optimal intelligent distribution network solution, specifically: S j , R j and Q j The values of S, R, and Q are sorted in ascending order respectively. The smaller these values are, the better the performance of the solution. When the following two criteria are met, the object with the Q j value is the optimal one: Condition 1: Acceptable advantage value, that is, the value of Q j should be: Among them, B (1) , B (2) is the first and second objects after sorting the Q j values from smallest to largest; Condition 2: Acceptable decision - making stability; That is, object B (2) The group interest value of should be greater than that of object B (1) , or the individual regret value of object B (2) should be greater than that of object B (1) ; If the above two conditions cannot be met simultaneously, the compromise solution should be selected according to the following principle; (1) If condition 2 is not satisfied, then the two solutions B (1) , B (2) The conclusion is the optimal; (2) If condition 1 is not satisfied, then each of the solutions B (1) , B (2) , ……, B (M) is optimal; The value of M is the maximum value that meets the following formula, as follows,

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