Optimal Control Method and System for Flexible Multi-State Switch in Distribution Network with DG

Abstract

Optimal control method and system for flexible multi-state switches in distribution networks with DG. The method includes: constructing an evaluation index system for the control scheme of flexible multi-state switches, eliminating redundant indexes in the evaluation index system through correlation analysis to obtain an evaluation index set for the control scheme of flexible multi-state switches in distribution networks with DG; using the evaluation indexes to evaluate the control alternative schemes in the form of neutrosophic hesitant fuzzy, obtaining an initial neutrosophic hesitant fuzzy decision matrix; preprocessing the initial neutrosophic hesitant fuzzy decision matrix to obtain a normalized neutrosophic hesitant fuzzy decision matrix, and calculating the attribute weights of the evaluation indexes in two cases where the evaluation indexes are independent and there is a priority relationship between the evaluation indexes, determining the weight matrix of the evaluation index set; selecting an aggregation operator, calculating the comprehensive evaluation value of the control alternative schemes, calculating the scores of each alternative scheme according to the comprehensive evaluation value, and selecting the scheme with the highest score as the optimal control scheme for flexible multi-state switches in distribution networks with DG.

Description

Technical Field

[0001] The present invention belongs to the technical field of analysis of intelligent distribution networks, and more specifically, relates to a method and system for optimal control of flexible multi-state switches in a distribution network containing DG. Background Art

[0002] In the operation and management of AC and DC distribution networks with a high proportion of DG (Distributed Generation) access, there are multiple indicators that affect decision-making. When faced with these intertwined and interrelated multiple indicators, it is usually necessary to conduct quantitative evaluation and make decisions. For problems with too many indicators and complex correlations, how to accurately and effectively evaluate and make decisions is particularly complex and difficult.

[0003] At the same time, the operating environment of the distribution network is complex and changeable, and the various indicators have strong mutual influence and correlation, which makes the evaluation of indicators and the optimization decision of multi-state switch control schemes more difficult. For example, if the decision of multi-state switch scheme is made based on individual indicators only, the influence of other related indicators may be ignored, resulting in wrong decision-making. On the other hand, if all indicators are treated equally, it may increase the complexity of decision-making and affect the efficiency of decision-making.

[0004] In decision-making practice, even for the same decision problem, decision makers with different risk attitudes may make completely opposite decisions. The behavior of decision makers essentially reflects the subjective preferences of actors with thinking and emotions, and this preference should not be ignored from a completely rational perspective or a limited rational perspective. At the same time, the weights of various decision indicators and the complex relationships between indicators not only reflect the uncertainty of the decision environment, but also directly affect the decision results.

[0005] However, the existing optimization control methods for flexible multi-state switches in distribution networks containing DG rarely conduct comprehensive investigations on multiple indicators and consider the correlation and influence between various indicators, making it difficult to provide effective support for the control decision-making problems of complex multi-state switches. Summary of the invention

[0006] In order to solve the deficiencies in the prior art, the present invention provides a flexible multi-state switch optimization control method and system for a DG distribution network, models the flexibility indicators such as economy, technology and environmental protection of a high-proportion DG AC / DC distribution network, and proposes a practical multi-attribute decision-making method.

[0007] The present invention adopts the following technical solution.

[0008] A first aspect of the present invention provides a method for optimal control of a flexible multi-state switch in a distribution network including a DG, comprising the following steps:

[0009] Step 1, construct an evaluation index system for the flexible multi-state switch control scheme, and eliminate redundant indicators in the evaluation index system through correlation analysis to obtain the evaluation index set C = {C1, C2,..., C n} for the flexible multi-state switch control scheme of the DG-integrated distribution network;

[0010] Step 2, use the evaluation index C in the evaluation index set C obtained in Step 1 j to evaluate the control alternative X in the control scheme set X = {X1, X2,..., X m} in the form of neutrosophic hesitant fuzzy, and obtain the initial neutrosophic hesitant fuzzy decision matrix i ;

[0011] Step 3, preprocess the initial neutrosophic hesitant fuzzy decision matrix to obtain the normalized neutrosophic hesitant fuzzy decision matrix D, and calculate the attribute weight w j of the evaluation index C j in two cases: independent evaluation indicators and there is a priority relationship between evaluation indicators, and determine the weight matrix W of the evaluation index set C;

[0012] Step 4, select an aggregation operator according to the normalized neutrosophic hesitant fuzzy decision matrix D and the weight matrix W obtained in Step 3, calculate the comprehensive evaluation value n i of the control alternative X F , calculate the scores of each alternative according to the comprehensive evaluation value n F , and select the alternative with the highest score as the optimal control scheme for the flexible multi-state switch of the DG-integrated distribution network.

[0013] Preferably, in Step 1, the steps of eliminating redundant indicators in the evaluation index system through correlation analysis include:

[0014] Calculate the correlation coefficients between the indicators in the multi-attribute evaluation index set and set a threshold. When the correlation coefficient between two indicators in the same indicator layer of the evaluation index system exceeds the set threshold, eliminate the indicator with a weaker influence on the optimization control effect of the flexible multi-state switch according to expert experience knowledge.

[0015] Preferably, in Step 1, the evaluation index set obtained for the flexible multi-state switch control scheme of the DG-integrated distribution network includes:

[0016] Based on the volatility of new energy, establish a stability margin evaluation index;

[0017] Establish a system average interruption duration index, where the system average interruption duration refers to the average interruption duration suffered by each user per unit time;

[0018] Establish an evaluation index for power supply reliability rate, where the power supply reliability rate refers to the ratio of the total number of hours that users do not experience power outages in a year to the total power supply hours required by users;

[0019] Establish an evaluation index for expected power supply shortage, where the expected power supply shortage refers to the ratio of the power supply shortage caused by power outages in a year to the total number of users in the system;

[0020] Based on the impact of the increasing load on the load side on the distribution network lines, establish an evaluation index for line margin;

[0021] Based on the penetration of distributed power sources and the phenomenon of curtailment of wind and solar power, establish an evaluation index for new energy consumption;

[0022] Based on the losses in the power transmission process and the energy utilization rate, establish an evaluation index for network losses.

[0023] Preferably, step 2 includes:

[0024] Define neutrosophic hesitant fuzzy numbers and their score functions, accuracy functions, and certainty functions, and define the comparison rules between neutrosophic hesitant fuzzy numbers to obtain a neutrosophic hesitant fuzzy multi-attribute decision-making model;

[0025] According to the neutrosophic hesitant fuzzy multi-attribute decision-making model, conduct a neutrosophic hesitant fuzzy description of the optimal control problem of multi-state switches, and give the index evaluation values in the form of neutrosophic hesitation fuzzy Obtained by manual evaluation by the decision maker, where are respectively the membership degree, uncertainty degree, and non-membership degree of;

[0026] Taking the index evaluation value as matrix elements, establish an initial neutrosophic hesitant fuzzy decision matrix

[0027] Preferably, in step 3, preprocess the initial neutrosophic hesitant fuzzy decision matrix to obtain a normalized neutrosophic hesitant fuzzy decision matrix D, including:

[0028] Divide the evaluation index C j into benefit type and cost type, and preprocess the initial neutrosophic hesitant fuzzy decision matrix according to the following formula :

[0029]

[0030] In the formula:

[0031] is the complement of the index evaluation value ;

[0032] Using the normalized index evaluation value n ij as matrix elements, the normalized neutrosophic hesitant fuzzy decision matrix D = [n ij m×n .

[0033] Preferably, in step 3, when the evaluation indicators are independent, the attribute weights are expressed by the following formula

[0034]

[0035]

[0036]

[0037] wherein:

[0038] E(C j ) is the neutrosophic hesitant fuzzy entropy of the evaluation indicator C j ;

[0039] s(n ij ) is the score function of the normalized neutrosophic hesitant fuzzy decision matrix

[0040] Preferably, in step 3, when there is a precedence relationship between the evaluation indicators, the attribute weights are expressed by the following formula

[0041]

[0042]

[0043] wherein:

[0044] C k (X i ) represents the comprehensive fuzzy evaluation value of the alternative X i under the evaluation indicator C k , which is obtained by the weighted average of the score function, accuracy function and certainty function of n ik in the normalized neutrosophic hesitant fuzzy decision matrix

[0045] Preferably, in step 4, the selected aggregation operator includes:

[0046] Collect the decision maker's risk preference information in the normalized neutrosophic hesitant fuzzy decision matrix D and the weight matrix W, determine the value of the parameter θ, and select the NHFFWA operator, NHFFWG operator, NHFFPWA operator or NHFFPWG operator to aggregate the comprehensive evaluation values of the alternative solutions, where the parameter θ is the parameter that controls the influence degree of different input values on the final result in the Frank operation

[0047] Preferably, in step 4, the score of the alternative solution is expressed by the following formula ​

[0048]

[0049] In the formula:

[0050] n F is the comprehensive evaluation value of the multi-state switch control alternative, and n F ={r F ,δ F ,η F} is a neutrosophic hesitant fuzzy number, where r F ,δ F ,η F represent the membership degree, uncertainty degree and non-membership degree of n F respectively;

[0051] t F , i F , f F are the membership degree set, uncertainty degree set and non-membership degree set of n F respectively;

[0052] #t F , #i F , #f F represent the number of values in t F , i F , f F respectively.

[0053] The second aspect of the present invention provides a flexible multi-state switch optimal control system for a DG-integrated distribution network, which operates the above-mentioned flexible multi-state switch optimal control method for a DG-integrated distribution network, including:

[0054] A data collection and storage module, which is used to construct an evaluation index system for the flexible multi-state switch control scheme,

[0055] A redundant index elimination module, which is used to eliminate redundant indexes in the evaluation index system and generate an evaluation index set for the flexible multi-state switch control scheme of the DG-integrated distribution network;

[0056] A neutrosophic hesitant fuzzy decision-making module, which is used to evaluate and preprocess the control alternative in the form of neutrosophic hesitant fuzzy, and generate a normalized neutrosophic hesitant fuzzy decision matrix;

[0057] A decision optimization and solution module, which is used to determine the weight matrix of the evaluation index set, select an integration operator, integrate the comprehensive evaluation values of the alternative solutions, and calculate the scores of each alternative solution;

[0058] A decision implementation module, which is used to select the solution with the highest score as the optimal control scheme for the flexible multi-state switch of the DG-integrated distribution network and execute it;

[0059] An evaluation and feedback module is used to feedback the execution results of the optimal control scheme and perform further optimization.

[0060] Compared with the prior art, the beneficial effects of the present invention at least include:

[0061] (1) The present invention can provide decision support for the safe, reliable and economic operation of the distribution network, and has reliability, effectiveness and practicability.

[0062] (2) The present invention takes into account the hesitancy and behavioral preferences of the decision maker in the decision-making process. The neutrosophic hesitant fuzzy multi-attribute decision-making model based on different behavioral preferences is more accurate than a single neutrosophic hesitant fuzzy decision-making model.

[0063] (3) The present invention enriches the multi-attribute decision-making theory and methods considering the behavioral preferences of decision makers in the neutrosophic hesitant fuzzy environment, and provides a theoretical basis and technical support for obtaining more reasonable and objective decision results.

[0064] (4) The optimal control method and system for flexible multi-state switches in a DG-integrated distribution network provided by the present invention achieve uniqueness in dealing with complex problems, intelligence in processing data, and significance in optimization performance, bringing progress to the control of flexible interconnection devices in low-voltage areas. Description of the Drawings

[0065] Figure 1 is a flow chart of the optimal control method for flexible multi-state switches in a DG-integrated distribution network provided according to an embodiment of the present invention. Detailed Embodiments

[0066] To make the objectives, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. The embodiments described in this application are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the spirit of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0067] The present invention provides an optimal control method and system for flexible multi-state switches in a DG-integrated distribution network. By using the neutrosophic hesitant fuzzy theory, redundant indicators with high correlation are eliminated, and the most influential key indicators are retained to generate a normalized neutrosophic hesitant fuzzy decision matrix among the indicators affecting the decision, thereby realizing the optimal decision-making of the control strategy. In the decision-making process of the present invention, all indicators and historical data affecting the decision are collected and input, the attribute weights of the evaluation indicators are calculated in combination with the risk preferences of the decision maker, and the core indicators with the greatest influence on the problem decision are obtained, significantly improving the accuracy and efficiency of the decision-making. The structure is clear and the operation is simple, providing strong support for the stable and efficient operation of the distribution network.

[0068] As Figure 1 shown, Embodiment 1 of the present invention provides a method for optimally controlling a flexible multi-state switch in a distribution network with a high proportion of DG, including the following steps:

[0069] Step 1, construct an evaluation index system for the control scheme of the flexible multi-state switch, and eliminate highly correlated redundant indicators in the evaluation index system through correlation analysis to obtain a multi-attribute evaluation index set C for the control scheme of the flexible multi-state switch in the distribution network with a high proportion of DG.

[0070] In a preferred but non-limiting embodiment of the present invention, Step 1 specifically includes:

[0071] Step 1.1, construct an evaluation index system for the economy, environmental protection, and technology of the control scheme of the flexible multi-state switch. Further preferably, the evaluation index system includes three index levels.

[0072] Step 1.2, by calculating the correlation coefficients between the indicators within the same third-level index level and combining expert experience and knowledge, delete the indicator with a weaker influence on the optimal control effect of the flexible multi-state switch among the two indicators with larger correlation coefficients, so as to avoid the problem of redundant indicator information.

[0073] It should be noted that the reason for combining expert experience and knowledge is that a simple correlation coefficient cannot accurately measure the information situation between indicators.

[0074] In a further preferred but non-limiting embodiment of the present invention, Step 1.2 specifically includes:

[0075] Step 1.2.1, determine the indicators that may have duplicate information among the indicators within the same index level.

[0076] Step 1.2.2, calculate the correlation coefficients between the indicators, and reflect to what extent the two indicators have duplicate information through the correlation coefficients.

[0077] Let the correlation coefficient between the i-th indicator and the j-th indicator be represented by r ij denote, x ki represent the dimensionless value of the i-th indicator of the k-th evaluation object, x i be the mean of the data of the i-th indicator, and s be the number of evaluation indicators. The correlation coefficient calculation formula is as follows:

[0078]

[0079] Step 1.2.3, set a threshold Q between 0 and 1. If |r ijIf the correlation coefficient is greater than Q, one of the two indicators with a weaker impact on the optimal control effect of the flexible multi-state switch is deleted according to expert experience and knowledge. When the correlation coefficient is greater than 0.9, it belongs to a high degree of correlation. More preferably, Q is taken as 0.9.

[0080] Step 1.3, construct a multi-attribute evaluation index set C = {C1, C2,..., C n} for the flexible multi-state switch control scheme of the distribution network with a high proportion of DG by using the remaining indicators within the three-level index layer.

[0081] In an exemplary but non-limiting embodiment of the present invention, considering the impacts on the safety, reliability, environmental protection, and economy of the operation of the AC-DC distribution network with a high proportion of DG, the evaluation index set is established, where the evaluation indicators include: stability margin, system average power outage time, power supply reliability rate, expected power supply shortage, line margin, new energy consumption, and network loss. Step 1.3 specifically includes:

[0082] Step 1.3.1, considering the volatility of new energy, establish an evaluation index for the stability margin.

[0083] Due to the natural volatility of new energy such as wind energy and photovoltaic energy, there may be a large peak-valley difference on the load side, and the distribution network needs to maintain the power balance between the source and load sides to maintain the stable operation of the system.

[0084] Further preferably, the ability of the distribution network to maintain the stable operation of the system under large fluctuations in node power is evaluated through the static voltage stability index, which is expressed by the following formula:

[0085]

[0086] In the formula:

[0087] L ij is the static voltage stability of the feeder between the i-th node and the j-th node;

[0088] P i and Q i respectively represent the active and reactive net loads of the i-th node;

[0089] R ij and X ij respectively represent the line impedance between the i-th node and the j-th node;

[0090] The static voltage stability index L of the distribution network v is taken as the maximum value of the static voltage stability L of all feeders ij .

[0091] Step 1.3.2, establish the evaluation index of the system average power outage time, where the system average power outage time refers to the average power outage duration suffered by each user per unit time.

[0092] Further preferably, the system average power outage time is calculated by dividing the cumulative power outage duration experienced by users in a year by the total number of users in that year, with the unit of hour / (user·time), and is expressed by the following formula:

[0093]

[0094] In the formula:

[0095] U i is the average annual outage time of load point i, with the unit of hour / year;

[0096] N i represents the number of users connected to load point i;

[0097] E is the set of load nodes.

[0098] Step 1.3.3, establish the evaluation index of the power supply reliability rate, where the power supply reliability rate refers to the ratio of the total number of hours without power outage of users in a year to the total power supply hours required by users, and is expressed by the following formula:

[0099]

[0100] Step 1.3.4, establish the evaluation index of the expected unsupplied energy, where the expected unsupplied energy refers to the ratio of the unsupplied energy caused by power outages in a year to the total number of users in the system, with the unit of (kW·h) / user, and is expressed by the following formula:

[0101]

[0102] In the formula:

[0103] α i represents the distribution transformer load rate of load point i;

[0104] D i is the distribution transformer capacity connected to load point i, with the unit of kVA.

[0105] Step 1.3.5, consider the impact of the high-growth load on the distribution network lines on the load side and establish the line margin evaluation index.

[0106] On the load side, the rapidly growing load poses a threat to the operation of the distribution network. In particular, the growth of some industrial loads and the charging behavior of electric vehicle charging piles during peak periods result in heavy and overloaded conditions on the distribution network lines. Therefore, the load-side margin is also one of the important indicators for evaluating the multi-state switch control strategy. Sufficient line margin is a necessary condition for the distribution network to adjust its own operation control mode, and it is also the basic function of the distribution network to meet the load growth requirements of future emerging industrial parks, newly built residential buildings, etc.

[0107]

[0108] In the formula:

[0109] R load represents the line margin index;

[0110] I k is the actual current on the k-th feeder;

[0111] I sk is the maximum operating current of the k-th feeder, with a total of N F feeders.

[0112] Step 1.3.6, considering the penetration of distributed power sources and the phenomenon of curtailment of wind and solar power, establish an evaluation index for new energy consumption.

[0113] The penetration of new energy such as distributed photovoltaic in the distribution network increases year by year, while the existing distribution network has bottlenecks in the consumption of distributed power sources such as wind power, photovoltaic power, and small hydropower. The limitations of traditional control means may lead to the phenomenon of curtailment of wind and solar power. Therefore, it is necessary to evaluate whether the multi-state switch control strategy can adapt to the penetration of new energy, and whether the distribution network can reduce the phenomenon of curtailment of wind and solar power in typical scenarios, which is one of the important indicators for measuring flexibility.

[0114]

[0115] In the formula: R DG represents the new energy consumption index;

[0116] P DGNi The active power that the new energy connected to the i-th node can theoretically output;

[0117] P DGi is the active power that the new energy connected to the i-th node can actually be consumed by the distribution network;

[0118] There are a total of n DG nodes connected to new energy.

[0119] Step 1.3.7, considering the losses in the process of electric energy transmission and the energy utilization rate, establish an evaluation index for network losses.

[0120] The network loss of the distribution network accounts for the main part of the loss in the entire power transmission process. Reducing the line loss of the distribution network is of great significance for improving energy utilization efficiency. Different multi-state switch control strategies will result in different network losses. Therefore, the network loss index of the distribution network is introduced as follows:

[0121]

[0122] Where:

[0123] P loss·k is the active power loss of the k-th line;

[0124] P loss is the overall active power loss of the distribution network.

[0125] Step 2: Use the evaluation index C in the multi-attribute evaluation index set C obtained in Step 1 j to evaluate the control alternative X m in the multi-state switch control scheme set X = {X1, X2,..., X i} in the form of neutrosophic hesitant fuzzy, and obtain the initial neutrosophic hesitant fuzzy decision matrix

[0126] In the preferred but non-limiting embodiment of the present invention, Step 2 specifically includes:

[0127] Step 2.1: Define the neutrosophic hesitant fuzzy number and its score value related function, and define the comparison rule between neutrosophic hesitant fuzzy numbers based on the score value to obtain the neutrosophic hesitant fuzzy multi-attribute decision model. Specifically, let n * = {r, δ, η} be any neutrosophic hesitant fuzzy number, where r, δ, η are the membership degree, uncertainty degree, and non-membership degree of n * respectively. Then the score value related function is expressed by the following formula,

[0128]

[0129]

[0130]

[0131] Where:

[0132] s(n * ), a(n * ), c(n * ) are the score function, accuracy function, and certainty function of n * respectively;

[0133] t, i, f are the membership degree set, uncertainty degree set, and non-membership degree set respectively;

[0134] #t, #i, and #f represent the number of values in t, i, and f respectively.

[0135] The comparison rules between the neutrosophic hesitant fuzzy numbers are defined as follows:

[0136] For any two neutrosophic hesitant fuzzy numbers n1 and n2, there are two relationships: superior and equivalent.

[0137] (1) If s(n1) > s(n2), then n1 is superior to n2, denoted as n1 > n2.

[0138] (2) If s(n1) = s(n2), then when a(n1) > a(n2), n1 is superior to n2, denoted as n1 > n2.

[0139] (3) If s(n1) = s(n2) and a(n1) = a(n2), then when c(n1) > c(n2), n1 is superior to n2, denoted as n1 > n2.

[0140] (4) n1 is equivalent to n2, denoted as n1 ~ n2, if and only if s(n1) = s(n2), a(n1) = a(n2), and c(n1) = c(n2).

[0141] Step 2.2: Considering the uncertainty, fuzziness, and incompleteness of the decision-making problem information, according to the neutrosophic hesitant fuzzy multi-attribute decision-making model constructed in Step 2.1, the optimal control problem of the multi-state switch is described in a neutrosophic hesitant fuzzy manner, and the optimal control problem of the flexible multi-state switch in the distribution network with a high proportion of DG is transformed into a corresponding neutrosophic hesitant fuzzy multi-attribute decision-making problem, and the index evaluation values in the form of neutrosophic hesitant fuzzy are given. Obtained by manual evaluation by the decision maker.

[0142] Specifically, the decision maker evaluates the alternative solutions Xi (i = 1, 2,..., m) in the multi-state switch control solution set X according to the evaluation index Cj (j = 1, 2,..., n) in the multi-attribute evaluation index set C in Step 1.3, and then gives the index evaluation values of the flexible multi-state switch in the distribution network with a high proportion of DG in the form of neutrosophic hesitant fuzzy. j (j = 1, 2,..., n) for the alternative solutions X i (i = 1, 2,..., m) in the multi-state switch control solution set X, and then gives the index evaluation values of the flexible multi-state switch in the distribution network with a high proportion of DG in the form of neutrosophic hesitant fuzzy. where are respectively the membership degree, uncertainty degree, and non-membership degree.

[0143] Step 2.3: Using the index evaluation value as the matrix elements, an initial neutrosophic hesitant fuzzy decision matrix in the form of neutrosophic hesitant fuzzy information based on power grid evaluation is established.

[0144] It can be understood that the neutrosophic hesitant fuzzy set and its extended forms can effectively represent inconsistent, unclear, and incomplete information, reflect the hesitancy of decision-makers in the decision-making process, and better describe the uncertainty of the flexible multi-state switch control scheme.

[0145] Step 3: Preprocess the initial neutrosophic hesitant fuzzy decision matrix to obtain the normalized neutrosophic hesitant fuzzy decision matrix D, and calculate the attribute weights w j of the evaluation index C j in two cases: independent evaluation indexes and priority relationships between evaluation indexes, and determine the weight matrix W of the evaluation index set C.

[0146] In a preferred but non-limiting embodiment of the present invention, Step 3 specifically includes:

[0147] Step 3.1: Divide the evaluation indexes C j in the multi-attribute evaluation index set C into benefit type and cost type. Before making a decision, preprocess the initial neutrosophic hesitant fuzzy decision matrix according to the following formula to normalize the index evaluation value n ij as the matrix element to obtain the normalized neutrosophic hesitant fuzzy decision matrix D = [n ij m×n .

[0148] Where

[0149] n ij = {r ij , δ ij , η ij} (12)

[0150]

[0151] In the formula:

[0152] is the complement of the index evaluation value ,

[0153] In an exemplary but non-limiting embodiment, the stability margin, power supply reliability rate, line margin, new energy consumption, and evaluation indexes in the multi-attribute evaluation index set C constructed in Steps 1.3.1 - 1.3.7 are divided into benefit type attributes, and the system average power outage time, expected unsupplied electricity, and network loss are divided into cost type attributes. For benefit type attributes and cost type attributes, through the normalization process of the decision matrix, the normalized neutrosophic hesitant fuzzy decision matrix D = [n ij 5×7 .

[0154] ​​Step 3.2, determine the weight of each evaluation index in the multi-attribute evaluation index set C of the flexible multi-state switch control scheme for the distribution network with a high proportion of DG, and obtain the weight matrix W = {w1, w2,..., w n}, where the matrix elements of the weight matrix W, that is, the attribute weights w j need to satisfy 0 ≤ w j ≤ 1,

[0155] Specifically, it is divided into two cases to calculate the attribute weights of the evaluation indexes: when the evaluation indexes are independent and the weights are completely unknown, and when there is a priority relationship between the evaluation indexes and the weights are completely unknown:

[0156] When the evaluation indexes are independent and the weights are completely unknown, use Equation 11 and Equation 15 to obtain the score function matrix S = [s(n ij )] m×n and the neutrosophic hesitant fuzzy entropy E(C j ) of the neutrosophic hesitant fuzzy set,

[0157]

[0158]

[0159] In the formula:

[0160] E(C j ) is the neutrosophic hesitant fuzzy entropy of the evaluation index C j ;

[0161] s(n ij ) is the score function of the normalized neutrosophic hesitant fuzzy decision matrix.

[0162] Furthermore, combine Equation 16 to obtain the attribute weights when the evaluation indexes are independent:

[0163]

[0164] When there is a priority relationship between the evaluation indexes and the weights are completely unknown, taking the priority relationship expressed by the linear order C1 > C2 > … > C n as an example, for the priority relationship between the evaluation indexes, obtain the attribute weights when there is a priority relationship between the evaluation indexes through Equation 17 and Equation 18:

[0165]

[0166]

[0167] In the formula:

[0168] C k (X i)Indicates Solution X i In evaluation index C k The comprehensive fuzzy evaluation value, which is obtained by the weighted average of the score function, accuracy function, and certainty function of n ik in the normalized neutrosophic hesitant fuzzy decision matrix.

[0169] Step 4: According to the normalized neutrosophic hesitant fuzzy decision matrix D and weight matrix W in Step 3, select an aggregation operator to calculate the comprehensive evaluation value n i of the control alternative X F According to the comprehensive evaluation value n F calculate the scores of each alternative, and select the alternative with the highest score as the optimal control solution for the flexible multi-state switch of the DG-integrated distribution network.

[0170] In a preferred but non-limiting embodiment of the present invention, Step 4 specifically includes:

[0171] Step 4.1: Obtain the aggregation operators for neutrosophic hesitant fuzzy multi-attribute decision-making. The specific aggregation operators and the theorems they satisfy include:

[0172] Theorem 1: Let be a set of neutrosophic hesitant fuzzy numbers. Then, the comprehensive evaluation value integrated by the NHFFWA operator decreases monotonically with the parameter θ, where the parameter θ controls the influence degree of different input values on the final result in the Frank operation. By adjusting the parameter θ, the Frank operation rule can be applied at different levels of fuzziness to adapt to various actual situations.

[0173] Theorem 2: Let be a set of neutrosophic hesitant fuzzy numbers. Then, the comprehensive evaluation value integrated by the NHFFWG operator increases monotonically with the parameter θ.

[0174] Theorem 3: Let be a set of neutrosophic hesitant fuzzy numbers. Then, the comprehensive evaluation value integrated by the NHFFPWA operator decreases monotonically with the parameter θ.

[0175] Theorem 4: Let be a set of neutrosophic hesitant fuzzy numbers. Then, the comprehensive evaluation value obtained by the NHFFPWG operator decreases monotonically with the parameter θ.

[0176] Step 4.2: Collect the decision maker's risk preference information in the normalized neutrosophic hesitant fuzzy decision matrix D and weight matrix W, and refer to Theorem 2 and Theorem 3 to determine the value of the parameter θ. Then, according to different risk preference situations, select the NHFFWA operator, NHFFWG operator, NHFFPWA operator, or NHFFPWG operator to aggregate the comprehensive evaluation value of the alternative X i (i = 1, 2,..., m).

[0177] In an exemplary but non-limiting embodiment, the comprehensive evaluation value for selecting the integrated alternative of the NHFFPWA operator is expressed by the following formula:

[0178]

[0179] In the formula:

[0180] ⊕ represents weighted addition operation, that is, the weighted average values of each fuzzy set are integrated;

[0181] F represents performing Frank operation.

[0182] It can be understood that due to the influence of emotions, cognitive level, knowledge structure, etc. on the decision maker, there are obvious behavioral preferences in the decision-making process, which will directly affect the quality of the decision-making result. When determining the value of parameter θ, the risk preference of the decision maker is comprehensively considered, and the comprehensive evaluation value of each alternative in the flexible multi-state switch control scheme set of the distribution network with a high proportion of DG can be obtained more accurately.

[0183] Step 4.3, if the integrated comprehensive evaluation value is still a neutrosophic hesitant fuzzy number, then calculate the score value of alternative X i (i = 1, 2,..., m).

[0184] Step 4.4, according to the comparison rule in Step 2.1, compare the score values of alternatives X i (i = 1, 2,..., m), obtain the final ranking of the schemes, and then obtain the optimal control scheme of the flexible multi-state switch of the distribution network with a high proportion of DG. Generate a new control scheme set according to the execution result of the optimal control scheme and continuously optimize it.

[0185] It can be understood that the present invention adopts neutrosophic hesitant fuzzy decision-making and simultaneously takes into account the behavioral preferences of the decision maker for the optimal control of the flexible multi-state switch of the distribution network with a high proportion of DG. Based on neutrosophic hesitant fuzzy and its extended forms under different behavioral preferences for multi-attribute decision-making, it can more accurately simulate the real decision-making environment and process, and thus more reasonably and objectively screen out the optimal control scheme of the multi-state switch.

[0186] Embodiment 2 of the present invention provides an optimal control system for a flexible multi-state switch of a distribution network with a high proportion of DG, which runs the optimal control method for a flexible multi-state switch of a high-proportion DG distribution network as described in Embodiment 1, including:

[0187] A data collection and storage module, used to construct an evaluation index system for the flexible multi-state switch control scheme,

[0188] A redundancy index elimination module, which is used to eliminate redundant indices in the evaluation index system and generate an evaluation index set containing the control scheme of the flexible multi-state switch of the DG distribution network;

[0189] A neutrosophic hesitant fuzzy decision-making module, which is used to evaluate and preprocess the control alternative schemes in the form of neutrosophic hesitant fuzzy, and generate a normalized neutrosophic hesitant fuzzy decision matrix;

[0190] A decision optimization and solution module, which is used to determine the weight matrix of the evaluation index set, select an aggregation operator, aggregate the comprehensive evaluation values of the alternative schemes, and calculate the scores of each alternative scheme;

[0191] A decision implementation module, which is used to select the scheme with the highest score as the optimal control scheme of the flexible multi-state switch of the DG distribution network and execute it;

[0192] An evaluation and feedback module, which is used to feedback the execution results of the optimal control scheme and conduct further optimization.

[0193] It can be understood that the system collects and stores decision-related index data in real time through a data interface, and uses the neutrosophic hesitant fuzzy theory to normalize the data, eliminate redundant indices, and retain key indices. The decision optimization and solution module and the decision implementation module solve the optimal control strategy through advanced optimization algorithms, generate control instructions and implement them. At the same time, the evaluation and feedback module monitors the system operation data, evaluates the decision effect, and adjusts and optimizes the strategy according to the evaluation results to ensure continuous and efficient operation. This system significantly improves the performance of the flexible multi-state switch of the AC-DC distribution network with a high proportion of DG, including the stability of the power supply, the reliability of the system, and the reduction of power loss, and realizes the actual effect of stable, reliable, and economic operation of the distribution network.

[0194] Compared with the prior art, the beneficial effects of the present invention at least include:

[0195] (1) The present invention can provide decision support for the safe, reliable, and economic operation of the distribution network, and has reliability, effectiveness, and practicability.

[0196] (2) The present invention takes into account the hesitancy and behavioral preferences in the decision-making process of the decision maker. The neutrosophic hesitant fuzzy multi-attribute decision-making model based on different behavioral preferences is more accurate than a single neutrosophic hesitant fuzzy decision-making model.

[0197] (3) The present invention enriches the multi-attribute decision-making theory and methods considering the behavioral preferences of decision makers in the neutrosophic hesitant fuzzy environment, and provides a theoretical basis and technical support for obtaining more reasonable and objective decision results.

[0198] (4) The preferred control method and system for the flexible multi-state switch of the DG-integrated distribution network provided by the present invention achieve uniqueness in dealing with complex problems, intelligence in processing data, and significance in optimizing performance, bringing progress to the control of flexible interconnection devices in low-voltage distribution areas.

[0199] The present disclosure may be a system, a method, and / or a computer program product. The computer program product may include a computer-readable storage medium having thereon computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0200] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications or equivalent replacements can still be made to the specific implementation manners of the present invention, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the protection scope of the claims of the present invention.

Claims

1. A preferred control method for a flexible multi-state switch in a DG distribution network, characterized in that, It includes the following steps: Step 1: Construct an evaluation index system for the flexible multi-state switch control scheme, and eliminate redundant indicators in the evaluation index system through correlation analysis to obtain the evaluation index set C = {C1, C2,..., C n} for the flexible multi-state switch control scheme of the distribution network with DG; Step 2, use the evaluation metric C in the evaluation metric set C of Step 1 j to evaluate the control alternative X in the control solution set X = {X1, X2,..., X m} in the form of neutrosophic hesitant fuzzy, and obtain the initial neutrosophic hesitant fuzzy decision matrix i ​ Step 3: Preprocess the initial neutrosophic hesitant fuzzy decision matrix to obtain the normalized neutrosophic hesitant fuzzy decision matrix D, and calculate the attribute weights w j of the evaluation index C j , and determine the weight matrix W of the evaluation index set C; Step 4: Select an integration operator according to the normalized Zhongzhi hesitant fuzzy decision matrix D and the weight matrix W in Step 3, and calculate the comprehensive evaluation value n of the control alternative X i ; and calculate the scores of each alternative according to the comprehensive evaluation value n F F F and select the alternative with the highest score as the optimal control scheme for the flexible multi-state switch of the DG-integrated distribution network.

2. The optimal control method for a flexible multi-state switch in a DG-integrated distribution network according to claim 1, characterized in that: In step 1, the steps of removing redundant indicators from the evaluation index system through correlation analysis include: Calculating the correlation coefficients between various indicators in the multi-attribute evaluation index set and setting a threshold. When the correlation coefficient between two indicators in the same indicator layer of the evaluation index system exceeds the set threshold, the indicator with a weaker impact on the optimal control effect of the flexible multi-state switch is removed according to expert experience and knowledge.

3. The optimal control method for a flexible multi-state switch in a DG-integrated distribution network according to claim 1, characterized in that: In step 1, the evaluation index set for obtaining the control scheme of the flexible multi-state switch in the DG-integrated distribution network includes: Based on the volatility of new energy, a stability margin evaluation index is established; An evaluation index for the system average interruption duration is established, where the system average interruption duration refers to the average interruption duration suffered by each user per unit time; An evaluation index for the power supply reliability rate is established, where the power supply reliability rate refers to the ratio of the total number of hours without power interruption of users in a year to the total number of power supply hours required by users; An evaluation index for the expected unsupplied energy is established, where the expected unsupplied energy refers to the ratio of the unsupplied energy caused by power outages in a year to the total number of users in the system; Based on the impact of the increasing load on the distribution network lines on the load side, a line margin evaluation index is established; Based on the distributed power penetration and the phenomena of curtailment of wind and solar power, a new energy consumption evaluation index is established; Based on the power transmission process loss and energy utilization rate, a network loss evaluation index is established.

4. The optimal control method for a flexible multi-state switch in a DG-integrated distribution network according to claim 1, characterized in that: Step 2 includes: Defining the neutrosophic hesitant fuzzy number and its score function, accuracy function and certainty function, and defining the comparison rules between neutrosophic hesitant fuzzy numbers to obtain the neutrosophic hesitant fuzzy multi-attribute decision-making model; According to the neutrosophic hesitant fuzzy multi-attribute decision-making model, the optimal control problem of multi-state switches is described in a neutrosophic hesitant fuzzy manner, and the index evaluation values in the form of neutrosophic hesitant fuzzy are given. Obtained by manual evaluation of the decision maker. Where Are respectively The membership degree, uncertainty degree and non-membership degree of. Taking the index evaluation value as matrix elements, an initial neutrosophic hesitant fuzzy decision matrix is established 5. The optimal control method for a flexible multi-state switch in a DG-integrated distribution network according to claim 4, characterized in that: In step 3, the initial neutrosophic hesitant fuzzy decision matrix is preprocessed to obtain the normalized neutrosophic hesitant fuzzy decision matrix D, including: Evaluate the index C j Divide it into benefit type and cost type, and preprocess the initial neutrosophic hesitant fuzzy decision matrix according to the following formula: In the formula: is the index evaluation value is the complement set of Using the evaluation value n of the normalization index ij as matrix elements, the normalized neutrosophic hesitant fuzzy decision matrix D = [n ij m×n .​ 6. The optimal control method for a flexible multi-state switch in a DG-integrated distribution network according to claim 5, characterized in that: In step 3, when the evaluation indicators are independent, the attribute weights are expressed by the following formula, In the formula: E(C j ) is the neutrosophic hesitant fuzzy entropy of evaluation index C j ; s(n ij ) is the scoring function of the normalized neutrosophic hesitant fuzzy decision matrix.

7. The optimal control method for a flexible multi-state switch in a DG-integrated distribution network according to claim 5, characterized in that: In step 3, when there is a priority relationship between the evaluation indicators, the attribute weights are expressed by the following formula, In the formula: C k (X i ) represents Solution X i In the evaluation index C k The comprehensive fuzzy evaluation value, which is obtained by the weighted average of the score function, accuracy function, and certainty function of n ik in the normalized neutrosophic hesitant fuzzy decision matrix.

8. The optimal control method for a flexible multi-state switch in a DG-integrated distribution network according to claim 1, characterized in that: In step 4, the selection of the integration operator includes: Collecting the decision maker's risk preference information in the normalized neutrosophic hesitant fuzzy decision matrix D and the weight matrix W, determining the value of the parameter θ, and selecting the NHFFWA operator, NHFFWG operator, NHFFPWA operator or NHFFPWG operator to integrate the comprehensive evaluation values of the alternative solutions, where the parameter θ is a parameter in the Frank operation that controls the influence degree of different input values on the final result.

9. The preferred control method for the flexible multi-state switch of the DG-integrated distribution network according to claim 1 is characterized in that: In step 4, the score of the alternative solution is expressed by the following formula. In the formula: n F is the comprehensive evaluation value of the multi-state switch control alternative, n F ={r F , δ F , η F} is a neutrosophic hesitant fuzzy number, where r F , δ F , η F represent the membership degree, uncertainty degree and non-membership degree of n F respectively; t F ,i F ,f F are the membership degree set, uncertainty degree set, and non-membership degree set of n F respectively; #t F ,#i F ,#f F respectively represent the number of values in t F ,i F ,f F in the number of 10. A preferred control system for a flexible multi-state switch in a DG-integrated distribution network, operating according to the preferred control method for a flexible multi-state switch in a DG-integrated distribution network as described in any one of claims 1-9, characterized in that, It includes: A data collection and storage module, which is used to construct an evaluation index system for the flexible multi-state switch control solution. A redundant index elimination module, which is used to eliminate redundant indexes in the evaluation index system and generate an evaluation index set for the flexible multi-state switch control solution of the DG-integrated distribution network. A neutrosophic hesitant fuzzy decision-making module, which is used to evaluate and preprocess the control alternative solutions in the form of neutrosophic hesitant fuzziness and generate a normalized neutrosophic hesitant fuzzy decision matrix. A decision optimization and solution module, which is used to determine the weight matrix of the evaluation index set, select an aggregation operator, aggregate the comprehensive evaluation values of the alternative solutions, and calculate the score of each alternative solution. A decision implementation module, which is used to select the solution with the highest score as the optimal control solution for the flexible multi-state switch of the DG-integrated distribution network and execute it. An evaluation and feedback module, which is used to feedback the execution result of the optimal control solution and conduct further optimization.

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