Optimal selection control method and system for flexible multi-state switch of DG-containing power distribution network

Abstract

The invention discloses a DG-containing power distribution network flexible multi-state switch optimization control method and system, and the method comprises the steps: constructing an evaluation index system of a flexible multi-state switch control scheme, eliminating redundant indexes in the evaluation index system through correlation analysis, and obtaining an evaluation index set of the DG-containing power distribution network flexible multi-state switch control scheme; the evaluation indexes are used for evaluating the control alternative schemes in a neutral-intelligence hesitant fuzzy form, and an initial neutral-intelligence hesitant fuzzy decision matrix is obtained; preprocessing the initial neutral-intelligence hesitant fuzzy decision matrix to obtain a normalized neutral-intelligence hesitant fuzzy decision matrix, calculating attribute weights of evaluation indexes under two conditions that the evaluation indexes are independent and the evaluation indexes have a priority relationship, and determining a weight matrix of an evaluation index set; and selecting an integrated operator, calculating a comprehensive evaluation value of the control alternative scheme, calculating a score of each alternative scheme according to the comprehensive evaluation value, and selecting the scheme with the highest score as an optimal control scheme of the DG-containing power distribution network flexible multi-state switch.

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, remove redundant indicators in the evaluation index system through correlation analysis, and obtain the evaluation index set C = {C 1 ,C 2 ,...,C n};

[0010] Step 2: Use the evaluation indicator C in the evaluation indicator set C in step 1 j For the control scheme set X = {X 1 ,X 2 ,...,X m Control alternative X in} i Perform a neutrosophic hesitant fuzzy evaluation to obtain the initial neutrosophic hesitant fuzzy decision matrix

[0011] Step 3: Initial neutrosophic hesitant fuzzy decision matrix After preprocessing, the normalized neutrosophic hesitant fuzzy decision matrix D is obtained, and the evaluation index C is calculated according to two cases: the evaluation index is independent and the evaluation index has a priority relationship. j The attribute weight w j , determine the weight matrix W of the evaluation index set C;

[0012] Step 4: Select the integration operator according to the normalized neutrosophic hesitant fuzzy decision matrix D and weight matrix W in step 3 and calculate the control alternative X i The comprehensive evaluation value n F , according to the comprehensive evaluation value n F The score of each alternative solution is calculated, and the solution with the highest score is selected as the optimal control solution for the flexible multi-state switch of the distribution network containing DG.

[0013] Preferably, in step 1, the step of eliminating redundant indicators in the evaluation indicator system through correlation analysis includes:

[0014] The correlation coefficients between the indicators in the multi-attribute evaluation indicator set are calculated and a threshold is set. When the correlation coefficients of two indicators in the same indicator layer of the evaluation indicator system exceed the set threshold, the indicator with a weaker influence on the optimization control effect of the flexible multi-state switch is eliminated based on expert experience and knowledge.

[0015] Preferably, in step 1, the evaluation index set of the flexible multi-state switch control scheme of the DG distribution network is obtained, including:

[0016] Based on the volatility of new energy, establish stability margin assessment indicators;

[0017] Establishing an evaluation index for the average power outage time of the system, wherein the average power outage time of the system refers to the average duration of power outage suffered by each user in a unit time;

[0018] Establishing an evaluation index for power supply reliability, where the power supply reliability refers to the ratio of the total number of hours without power outages to the total number of power supply hours required by users in a year;

[0019] Establishing an expected power shortage assessment index, wherein the expected power shortage refers to the ratio of power shortage caused by power outages within a year to the total number of system users;

[0020] Based on the impact of the load growth on the distribution network line, establish the line margin evaluation index;

[0021] Establish new energy consumption assessment indicators based on the penetration of distributed power sources and the phenomenon of wind and solar power abandonment;

[0022] Based on the loss in the power transmission process and energy utilization, a network loss evaluation index is established.

[0023] Preferably, step 2 comprises:

[0024] The neutrosophic hesitant fuzzy number and its score function, exact function and exact function are defined, and the comparison rules between neutrosophic hesitant fuzzy numbers are defined to obtain the neutrosophic hesitant fuzzy multi-attribute decision-making model;

[0025] 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 evaluation value of the index in the form of neutrosophic hesitant fuzzy is given. It is manually evaluated by decision makers. in They are The membership, uncertainty and non-membership of

[0026] Evaluation value by indicator As matrix elements, establish the initial neutrosophic hesitant fuzzy decision matrix

[0027] Preferably, in step 3, the initial neutrosophic hesitant fuzzy decision matrix After preprocessing, the normalized neutrosophic hesitant fuzzy decision matrix D is obtained, which includes:

[0028] The evaluation index C j Divided into benefit type and cost type, the initial neutrosophic hesitant fuzzy decision matrix is ​​calculated according to the following formula: Preprocessing:

[0029]

[0030] Where:

[0031] Evaluate the value of the indicator The complement of

[0032] The normalized index evaluation value n ij As matrix elements, we get 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 as follows:

[0034]

[0035]

[0036]

[0037] Where:

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

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

[0040] Preferably, in step 3, when there is a priority relationship between the evaluation indicators, the attribute weight is expressed by the following formula:

[0041]

[0042]

[0043] Where:

[0044] C k (X i ) represents scheme X i In the evaluation index C k The comprehensive fuzzy evaluation value is obtained by the normalized neutrosophic hesitant fuzzy decision matrix n ik The score function, precision function and exact function are weighted averaged.

[0045] Preferably, in step 4, selecting the integration 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 parameter θ, and select the comprehensive evaluation value of the NHFFWA operator, NHFFWG operator, NHFFPWA operator or NHFFPWG operator integrated alternative scheme, where parameter θ is the parameter in the Frank operation that controls the degree of influence of different input values ​​on the final result.

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

[0048]

[0049] Where:

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

[0051] t F ,i F , f F n F The membership set, uncertainty set and non-membership set of ;

[0052] #t F ,#i F , #f F Represents t F ,i F , f F The number of median values.

[0053] The second aspect of the present invention provides a DG-containing distribution network flexible multi-state switch optimization control system, and the operation of the DG-containing distribution network flexible multi-state switch optimization control method comprises:

[0054] Data collection and storage module, used to build an evaluation index system for flexible multi-state switch control schemes,

[0055] Redundant index elimination module, used to eliminate redundant indicators in the evaluation index system and generate an evaluation index set containing the flexible multi-state switch control scheme of the DG distribution network;

[0056] Neutrosophic hesitant fuzzy decision module, used to evaluate and preprocess the control alternatives in a neutrosophic hesitant fuzzy form and generate a normalized neutrosophic hesitant fuzzy decision matrix;

[0057] The decision optimization and solution module is used to determine the weight matrix of the evaluation indicator set, select the integration operator, integrate the comprehensive evaluation value of the alternative solutions, and calculate the score of each alternative solution;

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

[0059] The evaluation and feedback module is used to feedback the execution results of the optimal control solution and carry out the next step of optimization.

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

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

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

[0063] (3) The present invention enriches the multi-attribute decision-making theory and method that considers the decision maker's behavioral preferences in a neutral hesitant fuzzy environment, and provides a theoretical basis and technical support for obtaining more reasonable and objective decision results.

[0064] (4) The preferred control method and system for flexible multi-state switches in a distribution network including DG provided by the present invention realize the uniqueness of handling complex problems, the intelligent processing of data and the significant optimization performance, which brings progress to the control of flexible interconnected devices in low-voltage substations. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a flow chart of a preferred control method for a flexible multi-state switch in a distribution network including DG provided in accordance with an embodiment of the present invention. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only part of the embodiments of the present invention, not all of them. Based on the spirit of the present invention, other embodiments obtained by ordinary technicians in this field without creative work are all within the scope of protection of the present invention.

[0067] The present invention provides a flexible multi-state switch optimization control method and system for a distribution network containing DG, which utilizes the neutral intelligence hesitant fuzzy theory, removes highly correlated redundant indicators, retains the most influential key indicators, and generates a standardized neutral intelligence hesitant fuzzy decision matrix between the indicators that affect the decision, thereby achieving the optimal decision of the control strategy. The present invention collects and inputs all indicators and historical data that affect the decision in the decision-making process, calculates the attribute weights of the evaluation indicators in combination with the risk preferences of the decision makers, and obtains the core indicators that have the greatest influence on the problem decision, which significantly improves the accuracy and efficiency of the decision, has a clear structure, and is easy to operate, providing strong support for the stable and efficient operation of the distribution network.

[0068] like Figure 1 As shown, embodiment 1 of the present invention provides a method for optimal control of flexible multi-state switches in a distribution network containing a high proportion of DG, comprising the following steps:

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

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

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

[0072] Step 1.2, by calculating the correlation coefficient between indicators in the same three-level indicator layer and combining expert experience and knowledge, delete the indicator with a weaker impact on the optimization control effect of the flexible multi-state switch among the two indicators with larger correlation coefficients, thereby avoiding the problem of indicator information redundancy.

[0073] It is worth noting that the reason for combining expert experience and knowledge is that the simple correlation coefficient cannot accurately measure the information between indicators.

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

[0075] Step 1.2.1, determine the indicators in the same indicator layer that may have duplicate information.

[0076] Step 1.2.2, calculate the correlation coefficient between the indicators, which reflects the extent to which there is information duplication between the two indicators.

[0077] Let the correlation coefficient between the i-th indicator and the j-th indicator be r ij Indicates that xki represents the dimensionless value of the i-th indicator of the k-th evaluation object, x i is the mean value of the i-th indicator data, and s is 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 ij |>Q, then the one of the two indicators with the weaker influence on the optimization control effect of the flexible multi-state switch is deleted according to the expert experience knowledge. When the correlation coefficient is greater than 0.9, it is highly correlated. More preferably, Q is 0.9.

[0080] Step 1.3: Use the indicators retained in the third-level indicator layer to construct a multi-attribute evaluation indicator set C = {C 1 ,C 2 ,...,C n}.

[0081] In an exemplary but non-limiting embodiment of the present invention, taking into account the impact on the safety, reliability, environmental protection and economy of the operation of the AC and DC distribution network containing a high proportion of DG, the evaluation index set is established, wherein the evaluation indexes include: stability margin, average power outage time of the system, power supply reliability, expected power shortage, line margin, new energy consumption and network loss, and step 1.3 specifically includes:

[0082] Step 1.3.1, taking into account the volatility of new energy, establish a stability margin assessment indicator.

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

[0084] Further preferably, the ability of the distribution network to maintain stable operation of the system when the node power fluctuates greatly is evaluated by a static voltage stability index, which is expressed as follows:

[0085]

[0086] Where:

[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 They represent the active and reactive net loads of the i-th node respectively;

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

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

[0091] Step 1.3.2, establish a system average power outage time evaluation index, 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 average power outage time of the system is calculated by dividing the cumulative power outage duration experienced by users in a year by the total number of users in the year, in units of hours / (users·times), as expressed by the following formula:

[0093]

[0094] Where:

[0095] U i is the average annual outage time of load point i, in hours / year;

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

[0097] E is the load node set.

[0098] Step 1.3.3, establish a power supply reliability evaluation index. The power supply reliability refers to the ratio of the total number of hours without power outages to the total number of power supply hours required by the user in a year, which can be expressed as follows:

[0099]

[0100] Step 1.3.4, establish an expected power shortage evaluation index. The expected power shortage refers to the ratio of the power shortage caused by power outages within a year to the total number of system users, in units of (kWh) / household, expressed as the following formula:

[0101]

[0102] Where:

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

[0104] D i is the distribution transformer capacity connected to load point i, in kilovolt-amperes.

[0105] Step 1.3.5, taking into account the impact of the rapid growth of load on the load side on the distribution network line, establish the line margin evaluation index.

[0106] On the load side, the rapid growth of loads poses a threat to the operation of the distribution network, especially the growth of some industrial loads and the charging behavior of electric vehicle charging piles during peak hours, which causes the distribution network lines to be overloaded. 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 meeting the future load growth needs of emerging industrial parks, new residential buildings, etc. is also the basic function of the distribution network.

[0107]

[0108] Where:

[0109] R load Indicates line margin index;

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

[0111] I sk is the maximum operating current of the kth feeder, totaling N F Feeder line.

[0112] Step 1.3.6, taking into account the penetration of distributed power sources and the phenomenon of wind and solar power abandonment, establish new energy consumption assessment indicators.

[0113] The penetration of new energy sources such as distributed photovoltaics in distribution networks is increasing year by year, but the existing distribution networks have bottlenecks in absorbing distributed power sources such as wind power, photovoltaics, and small hydropower. The limitations of traditional control methods may lead to the abandonment 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. Whether the distribution network can reduce the abandonment of wind and solar power in typical scenarios is one of the important indicators for measuring flexibility.

[0114]

[0115] Where: R DG It represents the new energy consumption index;

[0116] P DGNi Theoretically, the active power that can be output by the renewable energy source connected to the i-th node;

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

[0118] There are n DG A node connected to new energy.

[0119] Step 1.3.7, taking into account the power transmission process loss and energy utilization rate, establish a network loss assessment index.

[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 to improving energy utilization. Different multi-state switch control strategies will produce different network losses, so 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 kth 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 in step 1 j For the multi-state switch control scheme set X = {X 1 ,X 2 ,...,X m Control alternative X in} i Perform a neutrosophic hesitant fuzzy evaluation to obtain the initial neutrosophic hesitant fuzzy decision matrix

[0126] In a preferred but non-limiting embodiment of the present invention, step 2 specifically comprises:

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

[0128]

[0129]

[0130]

[0131] Where:

[0132] s(n * ),a(n * ),c(n * ) are n *score function, exact function and exact function of ;

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

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

[0135] The comparison rule between the neutrosophic hesitant fuzzy numbers is defined as follows:

[0136] For any two neutrosophic hesitant fuzzy numbers n 1 ,n 2 , there are two relations of superiority and equivalence,

[0137] (1) If s(n 1 )>s(n 2 ), there exists n 1 Better than n 2 , that is, n 1 >n 2 ;

[0138] (2) If s(n 1 )=s(n 2 ), then when a(n 1 )>a(n 2 ), there exists n 1 Better than n 2 , that is, n1>n 2 ;

[0139] (3) If s(n 1 )=s(n 2 ) and a(n 1 )=a(n 2 ), when c(n 1 )>c(n 2 ), there exists n 1 Better than n 2 , that is, n 1 n 2 ;

[0140] (4) If and only if s(n 1 )=s(n 2 ), a(n 1 )=a(n 2 ), c(n 1 )=c(n 2 ), n 1 Equivalent to n 2 , that is, n 1 ~n 2 .

[0141] Step 2.2, taking into account the uncertainty, fuzziness and incompleteness of the decision-making problem information, according to the neutral-intelligence hesitant fuzzy multi-attribute decision-making model constructed in step 2.1, the multi-state switch optimization control problem is described in a neutral-intelligence hesitant fuzzy way, and the flexible multi-state switch optimization control problem of the distribution network with a high proportion of DG is transformed into the corresponding neutral-intelligence hesitant fuzzy multi-attribute decision-making problem, and the indicator evaluation value in the form of neutral-intelligence hesitant fuzzy is given. Manually assessed by decision makers.

[0142] Specifically, the decision maker uses the evaluation index C in the multi-attribute evaluation index set C in step 1.3 j (j=1,2,...,n) for the alternative solution X in the multi-state switch control solution set X i (i=1,2,...,m) is used for evaluation, and then the index evaluation value of the flexible multi-state switch of the distribution network with a high proportion of DG in the form of neutral intelligence hesitant fuzzy is given. in They are membership, uncertainty and non-membership.

[0143] Step 2.3, evaluate the value by indicator As matrix elements, the initial neutrosophic hesitant fuzzy decision matrix in the form of neutrosophic hesitant fuzzy information based on power grid evaluation is established.

[0144] It is understandable that the neutrosophic hesitant fuzzy sets and their extended forms can effectively characterize inconsistent, ambiguous, and incomplete information, while reflecting the hesitation of decision makers in the decision-making process, and can better describe the uncertainty of flexible multi-state switch control schemes.

[0145] Step 3: Initial neutrosophic hesitant fuzzy decision matrix After preprocessing, the normalized neutrosophic hesitant fuzzy decision matrix D is obtained, and the evaluation index C is calculated according to two cases: the evaluation index is independent and the evaluation index has a priority relationship. j The attribute weight w j , determine the weight matrix W of the evaluation indicator set C.

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

[0147] Step 3.1: Set the evaluation index C in the multi-attribute evaluation index set C j It is divided into benefit type and cost type. Before making a decision, the initial neutrosophic hesitant fuzzy decision matrix is ​​calculated according to the following formula: Preprocessing is performed to normalize the indicator evaluation value n ij As matrix elements, we get the normalized neutrosophic hesitant fuzzy decision matrix D = [n ij ]m×n .

[0148] in,

[0149] n ij = {r ij ,δ ij ,η ij} (12)

[0150]

[0151] Where:

[0152] Evaluate the value of the indicator The complement of

[0153] In an exemplary but non-limiting implementation, the stability margin, power supply reliability, line margin, new energy consumption and evaluation index in the multi-attribute evaluation index set C constructed in steps 1.3.1 to 1.3.7 are classified as benefit attributes, and the average power outage time, expected power shortage, and network loss of the system are classified as cost attributes. For benefit attributes and cost attributes, the decision matrix is ​​normalized to obtain a 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 = {w 1 ,w 2 ,...,w n}, where the matrix element of the weight matrix W, i.e. the attribute weight w j Must satisfy 0≤w j ≤1,

[0155] Specifically, the attribute weights of evaluation indicators are calculated in two cases: the evaluation indicators are independent and the weights are completely unknown, and there is a priority relationship between the evaluation indicators and the weights are completely unknown:

[0156] When the evaluation index is independent and the weight is completely unknown, the score function matrix S = [s(n ij )] m×n and neutrosophic hesitant fuzzy entropy E(C j ),

[0157]

[0158]

[0159] Where:

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

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

[0162] Then, we combine formula 16 to obtain the attribute weights when the evaluation indicators are independent:

[0163]

[0164] When there is a precedence relationship between the evaluation indicators and the weights are completely unknown, the linear sorting C 1 >C 2 >…>C n Taking the priority relationship represented as an example, for the priority relationship between evaluation indicators, the attribute weights when there is a priority relationship between evaluation indicators are obtained through Formula 17 and Formula 18:

[0165]

[0166]

[0167] Where:

[0168] C k (X i ) represents scheme X i In the evaluation index C k The comprehensive fuzzy evaluation value is obtained by the normalized neutrosophic hesitant fuzzy decision matrix n ik The score function, precision function and exact function are weighted averaged.

[0169] Step 4: Select the integration operator according to the normalized neutrosophic hesitant fuzzy decision matrix D and weight matrix W in step 3 and calculate the control alternative X i The comprehensive evaluation value n F , according to the comprehensive evaluation value n F The score of each alternative solution is calculated, and the solution with the highest score is selected as the optimal control solution for the flexible multi-state switch of the distribution network containing DG.

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

[0171] Step 4.1, obtaining the integrated operator of the neutrosophic hesitant fuzzy multi-attribute decision making, wherein the integrated operator and the theorem it satisfies specifically include:

[0172] Theorem 1: Assume is a set of neutrosophic hesitant fuzzy numbers. The comprehensive evaluation value after integration by NHFFWA operator decreases monotonically with parameter θ. Parameter θ controls the influence of different input values ​​on the final result in Frank operation. By adjusting parameter θ, Frank operation rules can be applied at different fuzziness levels to adapt to various practical situations.

[0173] Theorem 2: Assume is a set of neutrosophic hesitant fuzzy numbers, and the comprehensive evaluation value after integration using the NHFFWG operator increases monotonically with the parameter θ.

[0174] Theorem 3: Assume is a set of neutrosophic hesitant fuzzy numbers, and the comprehensive evaluation value after integration using the NHFFPWA operator decreases monotonically with the parameter θ.

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

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

[0177] In an exemplary but non-limiting embodiment, the comprehensive evaluation value of selecting the NHFFPWA operator integration alternative is expressed as follows:

[0178]

[0179] Where:

[0180] ⊕ represents weighted addition operation, which is to combine the weighted average values ​​of various fuzzy sets;

[0181] F means to perform Frank operation.

[0182] It is understandable that decision makers are influenced by emotions, cognitive levels, knowledge structures, etc., and have obvious behavioral preferences in the decision-making process, which will directly affect the quality of the decision results. When determining the value of parameter θ, the decision maker's risk preference is comprehensively considered, and the comprehensive evaluation value of each alternative scheme in the flexible multi-state switch control scheme set containing a high proportion of DG distribution network can be obtained more accurately.

[0183] Step 4.3, the integrated comprehensive evaluation value is still a neutrosophic hesitant fuzzy number, then the alternative plan X is calculated according to formula 9-11 i The score value of (i=1,2,...,m).

[0184] Step 4.4, compare alternative X according to the comparison rules in step 2.1 i The score values ​​of (i=1,2,...,m) are used to obtain the final ranking of the schemes, and then the optimal control scheme for flexible multi-state switches in the distribution network with a high proportion of DG is obtained. According to the execution results of the optimal control scheme, a new set of control schemes is generated for continuous optimization.

[0185] It can be understood that the present invention adopts neutral-intelligence hesitant fuzzy decision-making and simultaneously takes into account the behavioral preferences of decision makers for the optimal control of flexible multi-state switches in distribution networks with a high proportion of DG. Multi-attribute decision-making is performed based on neutral-intelligence hesitant fuzzy and its extended forms under different behavioral preferences. It can more accurately simulate the real decision-making environment and decision-making process, thereby more reasonably and objectively screening out the optimal control scheme for multi-state switches.

[0186] Embodiment 2 of the present invention provides a flexible multi-state switch optimization control system for a distribution network with a high proportion of DG, and the flexible multi-state switch optimization control method for a distribution network with a high proportion of DG described in Embodiment 1 is executed, including:

[0187] Data collection and storage module, used to build an evaluation index system for flexible multi-state switch control schemes,

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

[0189] Neutrosophic hesitant fuzzy decision module, used to evaluate and preprocess the control alternatives in a neutrosophic hesitant fuzzy form and generate a normalized neutrosophic hesitant fuzzy decision matrix;

[0190] The decision optimization and solution module is used to determine the weight matrix of the evaluation indicator set, select the integration operator, integrate the comprehensive evaluation value of the alternative solutions, and calculate the score of each alternative solution;

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

[0192] The evaluation and feedback module is used to feedback the execution results of the optimal control solution and carry out the next step of optimization.

[0193] It is understandable that the system collects and stores decision-related indicator data in real time through the data interface, and uses the hesitant fuzzy theory of China Intelligence to normalize the data, eliminate redundant indicators, and retain key indicators. The decision optimization and solution module and the decision implementation module use advanced optimization algorithms to solve the optimal control strategy, 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. The system significantly improves the performance of flexible multi-state switches in AC and DC distribution networks with a high proportion of DG, including power supply stability, system reliability, and reduced power loss, achieving the actual effect of stable, reliable and economical operation of the distribution network.

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

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

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

[0197] (3) The present invention enriches the multi-attribute decision-making theory and method that considers the decision maker's behavioral preferences in a neutral 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 flexible multi-state switches in a distribution network including DG provided by the present invention realize the uniqueness of handling complex problems, the intelligent processing of data and the significant optimization performance, which brings progress to the control of flexible interconnected devices in low-voltage substations.

[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 carrying 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 rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimal control of flexible multi-state switches in a distribution network containing DG, characterized in that: The following steps are involved: Step 1: construct an evaluation index system for the flexible multi-state switch control scheme, remove redundant indicators in the evaluation index system through correlation analysis, and obtain the evaluation index set C = {C1, C2, ..., C n }; Step 2: Use the evaluation indicator C in the evaluation indicator set C in step 1 j For the control scheme set X = {X1, X2, ..., X m Control alternative X in} i Perform a neutrosophic hesitant fuzzy evaluation to obtain the initial neutrosophic hesitant fuzzy decision matrix Step 3: Initial neutrosophic hesitant fuzzy decision matrix After preprocessing, the normalized neutrosophic hesitant fuzzy decision matrix D is obtained, and the evaluation index C is calculated according to two cases: the evaluation index is independent and the evaluation index has a priority relationship. j The attribute weight w j , determine the weight matrix W of the evaluation index set C; Step 4: Select the integration operator according to the normalized neutrosophic hesitant fuzzy decision matrix D and weight matrix W in step 3 and calculate the control alternative X i The comprehensive evaluation value n F , according to the comprehensive evaluation value n F The score of each alternative solution is calculated, and the solution with the highest score is selected as the optimal control solution for the flexible multi-state switch of the distribution network containing DG.

2. The method for optimal control of flexible multi-state switches in a distribution network containing DG according to claim 1 is characterized in that: In step 1, the steps of eliminating redundant indicators in the evaluation indicator system through correlation analysis include: The correlation coefficients between the indicators in the multi-attribute evaluation indicator set are calculated and a threshold is set. When the correlation coefficients of two indicators in the same indicator layer of the evaluation indicator system exceed the set threshold, the indicator with a weaker influence on the optimization control effect of the flexible multi-state switch is eliminated based on expert experience and knowledge.

3. The method for optimal control of flexible multi-state switches in a distribution network containing DG according to claim 1, characterized in that: In step 1, the evaluation index set of the flexible multi-state switch control scheme of the distribution network containing DG is obtained, including: Based on the volatility of new energy, establish stability margin assessment indicators; Establishing an evaluation index for the average power outage time of the system, wherein the average power outage time of the system refers to the average duration of power outage suffered by each user in a unit time; Establishing an evaluation index for power supply reliability, where the power supply reliability refers to the ratio of the total number of hours without power outages to the total number of power supply hours required by users in a year; Establishing an expected power shortage assessment index, wherein the expected power shortage refers to the ratio of power shortage caused by power outages within a year to the total number of system users; Based on the impact of the load growth on the distribution network line, establish the line margin evaluation index; Establish new energy consumption assessment indicators based on the penetration of distributed power sources and the phenomenon of wind and solar power abandonment; Based on the loss in the power transmission process and energy utilization, a network loss evaluation index is established.

4. The method for optimal control of flexible multi-state switches in a distribution network containing DG according to claim 1, characterized in that: Step 2 includes: The neutrosophic hesitant fuzzy number and its score function, exact function and exact function are defined, and the comparison rules between neutrosophic hesitant fuzzy numbers are defined 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 evaluation value of the index in the form of neutrosophic hesitant fuzzy is given. It is manually evaluated by decision makers. in They are The membership, uncertainty and non-membership of Evaluation value by indicator As matrix elements, establish the initial neutrosophic hesitant fuzzy decision matrix 5. The method for optimal control of flexible multi-state switches in a distribution network containing DG according to claim 4 is characterized in that: In step 3, the initial neutrosophic hesitant fuzzy decision matrix After preprocessing, the normalized neutrosophic hesitant fuzzy decision matrix D is obtained, which includes: The evaluation index C j Divided into benefit type and cost type, the initial neutrosophic hesitant fuzzy decision matrix is ​​calculated according to the following formula: Preprocessing: Where: Evaluate the value of the indicator The complement of The normalized index evaluation value n ij As matrix elements, we get the normalized neutrosophic hesitant fuzzy decision matrix D = [n ij ] m×n .

6. The method for optimal control of flexible multi-state switches in a distribution network containing DG according to claim 5 is characterized in that: In step 3, when the evaluation indicators are independent, the attribute weights are expressed as follows: Where: E(C j ) is the evaluation index C j The neutrosophic hesitant fuzzy entropy; s(n ij ) is the normalized neutrosophic hesitant fuzzy decision matrix scoring function.

7. The method for optimal control of flexible multi-state switches in a distribution network containing DG according to claim 5, characterized in that: In step 3, when there is a priority relationship between the evaluation indicators, the attribute weight is expressed as follows: Where: C k (X i ) represents scheme X i In the evaluation index C k The comprehensive fuzzy evaluation value is obtained by the normalized neutrosophic hesitant fuzzy decision matrix n ik The score function, precision function and exact function are weighted averaged.

8. The method for optimal control of flexible multi-state switches in a distribution network containing DG according to claim 1, characterized in that: In step 4, select the integration operator including: 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 parameter θ, and select the comprehensive evaluation value of the NHFFWA operator, NHFFWG operator, NHFFPWA operator or NHFFPWG operator integrated alternative scheme, where parameter θ is the parameter in the Frank operation that controls the degree of influence of different input values ​​on the final result.

9. The method for optimal control of flexible multi-state switches in a distribution network containing DG according to claim 1, characterized in that: In step 4, the score of the alternative is expressed as follows: Where: n F is the comprehensive evaluation value of the multi-state switch control alternatives, n F = {r F ,δ F ,η F } is a neutrosophic hesitant fuzzy number, where r F ,δ F ,η F Represents n F The membership, uncertainty and non-membership of t F ,i F , f F n F The membership set, uncertainty set and non-membership set of ; #t F ,#i F , #f F Represents t F ,i F , f F The number of median values.

10. A DG distribution network flexible multi-state switch optimization control system, running the DG distribution network flexible multi-state switch optimization control method according to any one of claims 1 to 9, characterized in that: include: Data collection and storage module, used to build an evaluation index system for flexible multi-state switch control schemes. Redundant index elimination module, used to eliminate redundant indicators in the evaluation index system and generate an evaluation index set containing the flexible multi-state switch control scheme of the DG distribution network; Neutrosophic hesitant fuzzy decision module, used to evaluate and preprocess the control alternatives in a neutrosophic hesitant fuzzy form and generate a normalized neutrosophic hesitant fuzzy decision matrix; The decision optimization and solution module is used to determine the weight matrix of the evaluation indicator set, select the integration operator, integrate the comprehensive evaluation value of the alternative solutions, and calculate the score of each alternative solution; The decision-making implementation module is used to select the scheme with the highest score as the optimal control scheme for the flexible multi-state switch of the distribution network containing DG and execute it; The evaluation and feedback module is used to feedback the execution results of the optimal control solution and carry out the next step of optimization.

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