Power grid operation state evaluation method

Abstract

The invention discloses a power grid operation state evaluation method, which is characterized in that on the basis of fully considering the importance of each decision matrix, the order of the decision matrixes is rearranged, weight vectors related to positions are set, the important influence of position weights on decisions is highlighted, and the accurate and safe evaluation of power grid operation is realized. The method comprises the following steps: firstly, constructing a hierarchical and multi-dimensional power grid index evaluation system according to power grid operation characteristics; secondly, an existing analytic hierarchy process is improved, and the analytic hierarchy process based on the improved q-order orthogonal fuzzy number is provided; secondly, constructing a four-level membership cloud model, and displaying the process that the operation state of the evaluation index changes along with time; and finally, comprehensively evaluating the index weight and the evaluation index membership degree by using a fuzzy operator and a fuzzy comprehensive evaluation principle, obtaining an evaluation result of the power grid operation state, and completing multi-level and multi-dimensional evaluation of the power grid operation state.

Description

Technical Field

[0001] The invention relates to the technical field of power grid operation status evaluation, and in particular to a power grid operation status evaluation method based on improved q-order orthogonal fuzzy numbers. Background Art

[0002] In the context of new power systems and new energy systems, with the accelerated advancement of smart grid construction, large-scale grid connection of distributed power sources, represented by photovoltaic power generation, new energy storage equipment and electric vehicles, has brought huge challenges to the safe and stable operation of the power system due to its inherent output volatility and randomness. Building a power grid evaluation index system is an important means to cope with the transformation of the power market and achieve safe and sustainable economic development. Through a scientific and reasonable evaluation system, it can not only ensure the stability and safety of power supply, but also promote the effective use of renewable energy and further achieve low-carbon environmental protection goals. Therefore, it is urgent to build and improve the power grid evaluation system. The operation of the power grid involves many factors, and there is a lack of multi-dimensional and comprehensive power grid planning evaluation indicators and standard systems. Therefore, it is necessary to propose a new evaluation system and evaluation method, which fully considers the multi-dimensional operation status of the power grid, improves the operation efficiency and service life of the power grid, and is crucial for the safe and stable operation of the power grid. Summary of the invention

[0003] The present invention aims to overcome the deficiencies of the above-mentioned prior art and provides a method for evaluating the operation status of a power grid, which aims to more finely describe and process the uncertain information in the power grid by introducing q-order orthogonal fuzzy numbers, and use fuzzy operators and fuzzy comprehensive evaluation principles to comprehensively evaluate the weights of evaluation indicators and the membership of evaluation indicators, so as to achieve accurate and comprehensive evaluation of the operation of the power grid. The method is specifically implemented by the following technical solutions:

[0004] The power grid operation status assessment method comprises:

[0005] Step 1) obtaining grid operation parameter information, including equipment failure information, equipment cost information, node voltage, load data information, line information, and installed capacity and output information of generator sets, new energy generator sets, and energy storage equipment;

[0006] Step 2) Building a power grid indicator evaluation system;

[0007] Step 3) q-order orthogonal fuzzy numbers are introduced to simulate the uncertainty in the decision-making process, and the subjective weights of the evaluation indicators are calculated by improving the q-ROFEOWA operator;

[0008] Step 4) construct a four-level membership cloud model to show the process of the evaluation indicator operation status changing over time;

[0009] Step 5) Use fuzzy operators and fuzzy comprehensive evaluation principles to comprehensively evaluate the index weights and the index memberships to obtain the evaluation results of the power grid operation status.

[0010] The power grid operation status evaluation method is further designed in that the construction of the power grid index evaluation system includes:

[0011] Target layer, evaluates the operation status of the power grid;

[0012] The criterion layer includes indicators in five dimensions: economy, reliability, technology, environmental protection, and safety; the indicator layer includes secondary indicators that are subordinate to each dimension of the criterion layer and are independent of each other.

[0013] The further design of the power grid operation status assessment method is that the secondary indicators include: operating cost indicators, energy storage equipment investment cost indicators, and other equipment investment cost indicators belonging to the economic dimension; energy supply reliability indicators, energy supply shortage rate indicators, energy equipment failure rate indicators, and controllable load proportion indicators belonging to the reliability dimension; voltage regulation rate indicators, frequency deviation rate indicators, and output fluctuation rate indicators belonging to the technical dimension; carbon dioxide emissions indicators, clean energy consumption rate indicators, and renewable energy power generation proportion indicators belonging to the environmental protection dimension; and power grid power supply margin indicators, frequency safety margin indicators, line overload rate indicators, and main transformer load rate indicators belonging to the safety dimension.

[0014] The further design of the power grid operation status evaluation method is that the calculation process of each secondary indicator value specifically includes:

[0015] The operating cost is calculated according to formula (1), wherein the operating cost includes: power generation cost, load shedding cost and wind and solar power abandonment cost;

[0016]

[0017] In formula (1), C OPE represents the total operating cost, c fuel Represents the unit power generation cost of the generator set, P i g represents the active output of conventional unit i, and They represent the unit load shedding cost and the unit wind and solar curtailment cost respectively. and They represent the load shedding and wind / solar curtailment at node i respectively.

[0018] Calculate the investment cost of energy storage equipment according to formula (2);

[0019]

[0020] In formula (2), C e is the investment cost of energy storage equipment, T is the dispatch period, I is the total number of energy storage equipment in the system, i is the annual equal investment conversion coefficient for different energy storage equipment, c i is the equipment cost per unit installed capacity, s i Installed capacity for different equipment;

[0021] Calculate the investment cost of other equipment according to formula (3):

[0022]

[0023] In formula (3), C d is the investment cost of other equipment, T is the scheduling period, J is the total number of other equipment in the system, X j is the conversion coefficient of annual equal investment of different equipment, c j is the equipment cost per unit installed capacity, s j Installed capacity for different equipment;

[0024] The energy supply reliability index represents the ratio of the total energy supply to the expected total energy supply. When the energy supply reliability is greater than 1, it indicates that the system has a high complementary reliability; when the energy supply reliability is less than 1, it indicates that the system has a low complementary reliability. The energy supply reliability index is calculated according to formula (4):

[0025]

[0026] In formula (4), S p For energy supply reliability, A g is the total energy supply, A eg is the expected total energy supply; the energy supply shortage rate index is calculated according to formula (5):

[0027]

[0028] In formula (5), r us is the energy supply shortage rate, T n is the average power outage time per unit time, T o is the length of unit time, m is the number of components that can cause power outage, λ i is the failure rate of the ith component, and the formula is: i =n i / t i , where n i is the number of failures of the i-th component, t i is the running time; γ i is the mean fault repair time of the i-th component;

[0029] According to formula (6), the energy equipment failure rate index is calculated:

[0030]

[0031] In formula (6), r f is the energy equipment failure rate, T f is the failure duration of energy equipment, T use The duration of use of energy equipment;

[0032] According to formula (7), the controllable load ratio index is calculated:

[0033]

[0034] In formula (7), r cl is the controllable load proportion, N cl N is the number of loads that can be turned on and off. all is the total load quantity;

[0035] According to formula (8), the voltage regulation rate index is calculated:

[0036]

[0037] In formula (8), r V is the voltage regulation rate, T s-V T is the total time of voltage detection within the qualified range, V is the total duration of voltage detection;

[0038] According to formula (9), the frequency deviation rate index is calculated:

[0039]

[0040] In formula (9), r p is the frequency deviation rate, T s-p T is the total duration of the frequency within the qualified range of detection, p is the total duration of the detection frequency,

[0041] The output fluctuation index is calculated according to formula (10);

[0042]

[0043] In formula (10), r vol is the output fluctuation rate, D w is the variance of the sampled energy output, k is the number of energy types, W i is the output value of the i-th energy source;

[0044] According to formula (11), the carbon dioxide emission index is calculated:

[0045]

[0046] In formula (11), is the carbon dioxide emissions, is the original carbon dioxide emissions, The carbon dioxide emissions after reasonable configuration;

[0047] According to formula (12), the clean energy consumption rate index is calculated:

[0048]

[0049] In formula (12), θ clb is the clean energy consumption rate, A cl-a is the actual power generation of clean energy, A cl-max Maximum power generation for clean energy;

[0050] According to formula (13), the proportion of renewable energy generation is calculated:

[0051]

[0052] In formula (13), r clm is the proportion of electricity generated by renewable energy, A l is the power supply of different renewable energy sources, A e is the electricity production of different renewable energy sources;

[0053] According to formula (14), the power supply margin index of the power grid is calculated:

[0054]

[0055] In formula (14), ψ represents the power supply margin of the power grid, P sup Indicates the power supply capacity of the partition. Indicates the maximum load of the partition;

[0056] According to formula (15), the general frequency is expressed based on the power system inertia constant, generator turbine parameters and governor regulation constant,

[0057]

[0058] In formula (15), Δf max represents the maximum frequency disturbance, ΔP represents the power disturbance, D represents the load damping constant, R represents the speed regulator adjustment constant, T R represents the time constant, H represents the system inertia constant, F H represents the turbine parameter, ω n , α, ω r , t nadir They are all intermediate variables and have no clear physical meaning;

[0059] Given the lowest frequency point, obtain the maximum power disturbance at this time. According to formula (16), the power is defined as the frequency

[0060] Safety margin:

[0061]

[0062] According to formula (17), the line overload rate index is calculated:

[0063] The line overload rate describes the line's carrying capacity for transmission power, and is specifically defined as follows:

[0064] δ=n br / N br (17)

[0065] In formula (17), δ represents the line overload rate, n br Indicates the number of lines with transmission power exceeding 80% of capacity, N br

[0066] Indicates the total number of lines;

[0067] According to formula (18), the main transformer load factor index is calculated:

[0068] β=P i sub / P i max (18)

[0069] In formula (18), β represents the load factor of the main transformer, P i sub Represents the power transmitted through the main transformer i, P i max Represents the transmission capacity of main transformer i.

[0070] The further design of the power grid operation status evaluation method is that in step 3), according to formula (19)

[0071] Construct q-order orthogonal fuzzy numbers, set X as the domain, and define the q-order orthogonal fuzzy sets on X according to equation (19):

[0072]

[0073] In formula (19), u A (x) represents the membership of x to the domain X, v A (x) indicates the non-membership degree of x and satisfies:

[0074]

[0075] Assume α = (u, v) is a q-order orthogonal fuzzy number. The score function of any q-order orthogonal fuzzy number α is defined as S(α) = u q -v q , the exact function is defined as H(α)=u q +v q .

[0076] The further design of the power grid operation status evaluation method is to construct a group decision model for power grid operation status evaluation to determine the expert weights in order to make the expert's judgment on the evaluation index weights more comprehensive and integrated. The group decision model is specifically as follows:

[0077] Set A = {A j |j=1,2,…,n} is the evaluation index set, e={e t |t=1,2,…,s} is the expert set, is the corresponding expert weight and satisfies ω t =[0,1], then expert e t The decision matrix can be expressed as in Indicates expert t For the evaluation index A j The judgment of weight is described by q-order orthogonal fuzzy numbers. The steps to solve the expert weight are as follows:

[0078] Step 3-1) Establish expert t The decision matrix in is a q-order orthogonal fuzzy number;

[0079] Step 3-2) Build a standardized decision matrix The evaluation indicators are divided into benefit type b and cost type λ c In order to facilitate the unified processing of evaluation indicators of different natures, the cost-based indicators are transformed into benefit-based indicators according to formula (20).

[0080]

[0081] Step 3-3) Calculate expert e t The indicator score matrix set under decision making in

[0082] Step 3-4) Calculate the indicator average score matrix according to the indicator score matrix set S Among them, indicator A j The average score is Step 3-5) Calculate the expert e according to formula (21) t The score matrix St (t=1,2,…,s) and the average score matrix The relationship between The calculation formula is:

[0083]

[0084] Step 3-6) Determine the expert weights Get the expert weight matrix W e ={ω t |t=1,2,…,s}.

[0085] The further design of the power grid operation status assessment method is that in step 3), the mathematical definition of the improved q-ROFEOWA operator is:

[0086] Setting α i =(u i ,v i )(i=1,2,…,m) is a set of q-order orthogonal fuzzy numbers, parameter q≥1, and the q-ROFEOWA operator is calculated according to equation (22),

[0087]

[0088] In formula (22), ξ represents the improved q-ROFEOWA operator, ω = (ω 1 ,ω 2 ,…,ω m ) T is α 1 ,α 2 ,…,α m exponentially weighted vector, and satisfying ω j ∈[0,1], The specific steps for calculating the subjective weight of the evaluation index are as follows:

[0089] Combining expert weights and improved q-ROFEOWA operator, the expert decision matrix B t (t=1,2,…,s) is integrated into a comprehensive decision matrix Β=(b j ) 1×n ;

[0090] According to formula (23), the support degree Sup(k l ,k p ), and the support matrix (Sup) is obtained n×n ,

[0091]

[0092] In formula (23), u l 、v l It is indicator Al The membership and non-membership values ​​of u p 、v p It is indicator A p The membership value and non-membership value of .

[0093] Through the support matrix (Sup) n×n According to formula (24), the T matrix is ​​calculated.

[0094]

[0095] In formula (24), T(k l ) is the indicator A l T value;

[0096] Calculate the subjective weight value of the evaluation index by T matrix The corresponding subjective weight matrix of the evaluation index is W s ={ω sj |j=1,2,…,n}.

[0097] The further design of the power grid operation status assessment method is that in step 4), the four-level membership cloud model is:

[0098] For each indicator, data sampling is performed n times. When the indicator is a quantitative numerical indicator and its numerical boundary is known, the indicator approximation method is used according to formula (25) to solve the digital characteristics (E x ,E n ,H e ),

[0099]

[0100]

[0101] In formula (25), E x is the expected value, E n is entropy, H e is the super entropy, q is a constant;

[0102] The steps of the generation algorithm of the membership cloud model are:

[0103] Generate E n For expectation, H e 2 E' is the random number with standard deviation n ; Generate E x is the expectation, E′ n 2 is a random number c with standard deviation; calculate Then (c, μ) is a cloud droplet; repeat the above steps until a cloud droplet that meets the requirements is generated.

[0104] The further design of the power grid operation status evaluation method is that in step 5), the fuzzy comprehensive evaluation principle is: adopting the weighted average principle to perform fuzzy comprehensive evaluation according to formula (26),

[0105]

[0106] In formula (26), b i A represents the membership of the i-th indicator to the evaluation vocabulary; j The standard vectors representing different levels are usually determined manually. The level standard vector is taken as the evaluation vocabulary V = {V 1 ,V 2 ,V 3 ,V 4}, k is the description for minb j To secb j Wait for small b j Ignoring the degree, when k = 1, it means that all b j Neither can be ignored.

[0107] The present invention also provides an electronic device, comprising a memory, a processor and a computer program, wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the power grid operation status assessment method.

[0108] The advantages of the present invention are as follows:

[0109] The power grid operation status evaluation method of the present invention constructs a hierarchical and multi-dimensional power grid operation status evaluation index system, and further introduces q-order orthogonal fuzzy numbers to more finely describe and process uncertain information in the power grid, and uses fuzzy operators and fuzzy comprehensive evaluation principles to comprehensively evaluate the index weights and evaluation index membership, which can provide a reference for the future operation and planning of the power grid. BRIEF DESCRIPTION OF THE DRAWINGS

[0110] Figure 1 It is a flow chart of the method of the present invention.

[0111] Figure 2 It is a schematic diagram of the power grid operation status evaluation index system. DETAILED DESCRIPTION

[0112] The technical solution of the present invention is described in detail below.

[0113] Embodiment 1

[0114] like Figure 1 As shown, the present invention proposes a power grid operation status evaluation method based on improved q-order orthogonal fuzzy numbers, the method comprising the following steps:

[0115] Step 1) Obtaining grid operation parameter information, including equipment failure information, equipment cost information, node voltage, load data information, line information, and installed capacity and output information of generator sets, new energy generator sets, and energy storage equipment.

[0116] Step 2) Build a hierarchical and multi-dimensional power grid indicator evaluation system.

[0117] Step 3) q-order orthogonal fuzzy numbers are introduced to simulate the uncertainty in the decision-making process, and the subjective weights of the evaluation indicators are calculated by improving the q-ROFEOWA operator.

[0118] Step 4) construct a four-level membership cloud model to show the process of the evaluation indicator operation status changing over time;

[0119] Step 5) Use fuzzy operators and fuzzy comprehensive evaluation principles to comprehensively evaluate the index weights and the index memberships to obtain the evaluation results of the power grid operation status.

[0120] Furthermore, in step 2), a hierarchical and multi-dimensional grid indicator evaluation system is developed:

[0121] The first layer is the target layer, that is, the starting point and end point of constructing the evaluation index system is to evaluate the operating status of the power grid.

[0122] The second layer is the criteria layer, which is divided into five parts according to different dimensions, namely economy, reliability, technology, environmental protection and safety.

[0123] The third layer is the indicator layer. Each criterion layer has its own corresponding indicator. These indicators are combined to form the indicator layer. Each indicator is independent and non-repetitive, and can comprehensively evaluate the operation status of the power grid. The indicators of different dimensions are as follows:

[0124] 1) Economic indicators

[0125] 1-a) Operating costs

[0126] The operating cost takes into account the cost of power generation, load shedding and wind and solar power abandonment. Among them, the cost of power generation mainly comes from the cost of fuel, and renewable energy power generation does not require fuel consumption, so only the fuel cost of conventional thermal power units is considered.

[0127]

[0128] In the formula, C OPE represents the total operating cost, c fuel Represents the unit power generation cost of the generator set, P i g represents the active output of conventional unit i, and They represent the unit load shedding cost and the unit wind and solar curtailment cost respectively. and They represent the load shedding and wind / solar curtailment at node i respectively.

[0129] 1-b) Investment cost of energy storage equipment

[0130] The total cost of all energy storage equipment, such as lithium-ion batteries, composite phase change thermal storage systems, thermal storage tanks, compressed air systems, etc., is defined as follows:

[0131]

[0132] In the formula, C e is the investment cost of energy storage equipment, T is the dispatch period, I is the total number of energy storage equipment in the system, i is the annual equal investment conversion coefficient for different energy storage equipment, c i is the equipment cost per unit installed capacity (yuan), s i Installed capacity (MWh) for different equipment.

[0133] 1-c) Other equipment investment costs

[0134] Including the cost of transportation equipment, such as liquefied natural gas tankers, mobile power trucks, etc., the calculation formula is as follows:

[0135]

[0136] In the formula, C d is the investment cost of other equipment, T is the scheduling period, J is the total number of other equipment in the system, χ j is the conversion coefficient of annual equal investment of different equipment, c j is the equipment cost per unit installed capacity (yuan), s j Installed capacity (MWh) for different equipment.

[0137] 2) Reliability indicators

[0138] 2-a) Energy supply reliability

[0139] Energy supply reliability is the ratio of the total energy supply to the expected total energy supply in the energy system. When the energy supply reliability is greater than 1, it means that the system has a high complementary reliability; when the energy supply reliability is less than 1, it means that the system has a low complementary reliability. The formula is:

[0140]

[0141] In the formula, S p For energy supply reliability, A g is the total energy supply (kWh), A egis the expected total energy supply (degrees).

[0142] 2-b) Energy supply shortage rate

[0143] The disadvantage of wind and solar power output is its uncertainty, and the output of energy storage equipment can reduce the impact of this uncertainty. In order to reflect the system's ability to meet load demand, an energy supply shortage rate indicator is established. The smaller the value, the more stable the system. The formula is as follows:

[0144]

[0145] In the formula, r us is the energy supply shortage rate, T n is the average power outage time per unit time, T o is the length of the unit time (hours), m is the number of components that can cause power outage, λ i is the failure rate of the ith component, and the formula is: i =n i / t i , where n i is the number of failures of the i-th component, t i is the running time (hours); i is the average fault repair time of the ith component (hours).

[0146] 2-c) Energy equipment failure rate

[0147] In the integrated energy system, it represents the ratio of the failure time of energy equipment to the usage time. Generally speaking, the lower the failure rate of energy equipment, the better the scheduling of the integrated energy system. The formula is as follows:

[0148]

[0149] In the formula, r f is the energy equipment failure rate, T f is the duration of energy equipment failure (hours), T use The usage time (hours) of energy equipment.

[0150] 2-d) Controllable load ratio

[0151] Controllable load refers to the load whose energy consumption can be restricted for a period of time by the power supply department according to the contract. The larger the value, the larger the number of controllable loads in the system, that is, the more stable the system is. The formula is as follows:

[0152]

[0153] Where r cl is the controllable load proportion, N cl N is the number of loads that can be turned on and off.all is the total number of loads.

[0154] 3) Technical indicators

[0155] 3-a) Voltage regulation rate

[0156] The voltage regulation rate indicates the system's ability to withstand impact response. The calculation formula for the voltage regulation rate is as follows:

[0157]

[0158] Where r V is the voltage regulation rate, T s-V T is the total time of voltage detection within the qualified range, V is the total duration of voltage detection.

[0159] 3-b) Frequency deviation rate

[0160] The frequency deviation rate can reflect the voltage system stability, frequency stability, and the ability of the power system to recover after experiencing fluctuations. The calculation formula is as follows:

[0161]

[0162] Where r p is the frequency deviation rate, T s-p T is the total duration of the frequency within the qualified range of detection, p is the total duration of the detection frequency.

[0163] 3-c) Output fluctuation rate

[0164] Output fluctuation rate represents the output of different energy sources. The smaller the value, the better the energy complementarity of the system. The calculation formula is as follows:

[0165]

[0166] Where r vol is the output fluctuation rate, D w is the variance of the sampled energy output, k is the number of energy types, W i is the output value of the i-th energy source.

[0167] 4) Environmental performance indicators

[0168] Environmental protection index is a comprehensive index for evaluating energy efficiency, environmental pressure and self-sufficiency. It can be used to measure the ability of the power system to maintain a good environment for future generations, and also represents the sustainable development capacity of the multi-energy system.

[0169] 4-a) Carbon dioxide emissions

[0170] The calculation formula for carbon dioxide emissions is as follows:

[0171]

[0172] In the formula, is the carbon dioxide emissions, is the original carbon dioxide emissions, It is the carbon dioxide emission after reasonable configuration.

[0173] 4-b) Clean energy consumption rate

[0174] Clean energy consumption rate refers to the ratio of actual clean energy power generation in the power system to the maximum clean energy power generation. The calculation formula is as follows:

[0175]

[0176] Where θ clb is the clean energy consumption rate, A cl-a is the actual power generation of clean energy, A cl-max Maximum power generation from clean energy.

[0177] 4-c) Proportion of electricity generated by renewable energy

[0178] The proportion of renewable energy power generation refers to the ratio of the power supply of renewable energy in the system to the power generation. The calculation formula is as follows:

[0179]

[0180] Where r clm is the proportion of electricity generated by renewable energy, A l is the power supply of different renewable energy sources, A e is the electricity production of different renewable energy sources.

[0181] 5) Safety indicators

[0182] 5-a) Grid power supply margin

[0183]

[0184] Where: ψ represents the power supply margin of the power grid, P sup Indicates the power supply capacity of the partition. Indicates the maximum load of the partition.

[0185] 5-b) Frequency safety margin

[0186] A general frequency expression based on the power system inertia constant, generator turbine parameters and governor regulation constant is used:

[0187]

[0188]

[0189] Where Δf max represents the maximum frequency disturbance, ΔP represents the power disturbance, D represents the load damping constant, R represents the speed regulator adjustment constant, T R represents the time constant, H represents the system inertia constant, F H represents the turbine parameter, ω n , α, ω r , t nadir They are all intermediate variables and have no clear physical meaning.

[0190] Given the lowest frequency point, the maximum power disturbance at this time can be obtained, and this power is defined as the frequency safety margin:

[0191]

[0192] 5-c) Line overload rate

[0193] The line overload rate describes the line's carrying capacity for transmission power, and is specifically defined as follows:

[0194] δ=n br / N br (twenty two)

[0195] In the formula, δ represents the line overload rate, n br Indicates the number of lines with transmission power exceeding 80% of capacity, N br

[0196] Indicates the total number of lines.

[0197] 5-d) Main transformer load factor

[0198] β=P i sub / P i max (twenty three)

[0199] In the formula, β represents the load factor of the main transformer, P i sub Represents the power transmitted through the main transformer i, P i max Represents the transmission capacity of main transformer i.

[0200] Furthermore, in step 3, the mathematical expression of the q-order orthogonal fuzzy number is as follows:

[0201] Let X be the domain, then the q-order orthogonal fuzzy set defined on X can be expressed as:

[0202] A={ <x,u A (x),v A(x)>|x∈X} (24)

[0203] In the formula, u A (x) represents the membership of x to the domain X, v A (x) indicates the non-membership degree of x and satisfies:

[0204]

[0205] For convenience, let α = (u, v) be a q-order orthogonal fuzzy number. The score function of any q-order orthogonal fuzzy number α is defined as S(α) = u q -v q , the exact function is defined as H(α)=u q +v q .

[0206] In order to make the experts' judgment on the weights of evaluation indicators more comprehensive, a group decision-making model for power grid operation status evaluation is constructed, which is described as follows:

[0207] Assume A={A j |j=1,2,…,n} is the evaluation index set, e={e t |t=1,2,…,s} is the expert set, ω t (t=1,2,…,s) is the corresponding expert weight and satisfies ω t =[0,1], then expert e t The decision matrix can be expressed as in Indicates expert t For the evaluation index A j The judgment of weight is described by q-order orthogonal fuzzy numbers. The steps to solve the expert weight are as follows:

[0208] (1) Establish expert e t The decision matrix in is a q-order orthogonal fuzzy number;

[0209] (2) Constructing a standardized decision matrix

[0210] The evaluation indicators are divided into benefit type b and cost type λ c In order to facilitate the unified processing of evaluation indicators of different natures, cost-based indicators are converted into benefit-based indicators. The specific conversion method is as follows:

[0211]

[0212] (3) Computing Expert tThe indicator score matrix set S under decision making is S = {S t |t=1,2,…,s}where

[0213] (4) Based on the indicator score matrix set S, calculate the indicator average score matrix Among them, indicator A j The average score is

[0214] (5) Computing Expert t The score matrix St(t=1,2,…,s) and the average score matrix The relationship between The calculation formula is:

[0215]

[0216] (6) Determine expert weights Get the expert weight matrix W e ={ω t |t=1,2,…,s}.

[0217] Furthermore, in step 3, the mathematical definition of the improved q-ROFEOWA operator is:

[0218] Let α i =(u i ,v i )(i=1,2,…,m) is a set of q-order orthogonal fuzzy numbers with parameter q≥1. The improved q-ROFEOWA operator is a mapping Ω m →Ω, the calculation formula is:

[0219]

[0220] Where ξ represents the improved q-ROFEOWA operator, ω=(ω 1 ,ω 2 ,…,ω m ) T is α 1 ,α 2 ,…,α m exponentially weighted vector, and satisfying ω j ∈[0,1],

[0221] Therefore, the steps to solve the subjective weight of the evaluation index are as follows:

[0222] (1) Combining expert weights and improved q-ROFEOWA operator, the expert decision matrix B t (t=1,2,…,s) is integrated into a comprehensive decision matrix Β=(b j )1×n ;

[0223] (2) Calculate the support Sup(k l ,k p ), and the support matrix (Sup) is obtained n×n , the formula is as follows:

[0224]

[0225] In the formula, u l 、v l It is indicator A l The membership and non-membership values ​​of u p 、v p It is indicator A p The membership value and non-membership value of .

[0226] (3) According to the support matrix (Sup) n×n Calculate and form the T matrix,

[0227]

[0228] In the formula, T(k l ) is the indicator A l T value.

[0229] (4) Calculate the subjective weight value of the evaluation index using the T matrix Therefore, the subjective weight matrix of the evaluation index is W s ={ω sj |j=1,2,…,n}.

[0230] Furthermore, in step 4, the four-level membership cloud model is:

[0231] For each indicator, data sampling is performed n times. When the indicator is a quantitative numerical indicator and its numerical boundary is known, the “indicator approximation method” can be used to solve the digital characteristics of the membership cloud model (E x ,E n ,H e ), the specific calculation formula is as follows:

[0232]

[0233] q is a constant (33)

[0234] Where: E x is the expected value, E n is entropy, H e It is super entropy.

[0235] The steps of the generation algorithm of the membership cloud model are as follows:

[0236] (1) Generate E n For expectation, H e 2 E' is the random number with standard deviation n ;

[0237] (2) Generate E x is the expectation, E′ n 2 is a random number c with standard deviation;

[0238] (3) Calculation Then (c, μ) is a cloud droplet;

[0239] (4) Repeat the above steps until cloud droplets that meet the requirements are generated.

[0240] Furthermore, in step 5, the fuzzy comprehensive evaluation principle is:

[0241]

[0242] Where b i A represents the membership of the i-th indicator to the evaluation vocabulary; j The standard vectors representing different levels are usually determined manually. The level standard vector is taken as the evaluation vocabulary V = {V 1 ,V 2 ,V 3 ,V 4}. k is the description for minb j To secb j Wait for small b j Ignoring the degree, when k = 1, it means that all b j Neither can be ignored.

[0243] Embodiment 2

[0244] This embodiment provides an electronic device, including a memory, a processor, and a computer program, wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the power grid operation status assessment method.

[0245] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for evaluating the operation status of a power grid, characterized in that include: Step 1) obtaining grid operation parameter information, including equipment failure information, equipment cost information, node voltage, load data information, line information, and installed capacity and output information of generator sets, new energy generator sets, and energy storage equipment; Step 2) Building a power grid indicator evaluation system; Step 3) q-order orthogonal fuzzy numbers are introduced to simulate the uncertainty in the decision-making process, and the subjective weights of the evaluation indicators are calculated by improving the q-ROFEOWA operator; Step 4) construct a four-level membership cloud model to show the process of the evaluation indicator operation status changing over time; Step 5) Use fuzzy operators and fuzzy comprehensive evaluation principles to comprehensively evaluate the index weights and the evaluation index membership to obtain the evaluation results of the power grid operation status.

2. The method for evaluating the operation status of a power grid according to claim 1, characterized in that The construction of the power grid index evaluation system includes: Target layer, evaluates the operation status of the power grid; The criteria layer includes indicators in five dimensions: economy, reliability, technology, environmental protection, and safety; The indicator layer includes the secondary indicators that belong to each dimension of the criterion layer and are independent of each other.

3. The method for evaluating the operation status of a power grid according to claim 2, characterized in that The secondary indicators include: operating cost indicators, energy storage equipment investment cost indicators, and other equipment investment cost indicators belonging to the economic dimension; energy supply reliability indicators, energy supply shortage rate indicators, energy equipment failure rate indicators, and controllable load proportion indicators belonging to the reliability dimension; voltage regulation rate indicators, frequency deviation rate indicators, and output fluctuation rate indicators belonging to the technical dimension; carbon dioxide emissions indicators, clean energy consumption rate indicators, and renewable energy power generation proportion indicators belonging to the environmental dimension; and power supply margin indicators, frequency safety margin indicators, line overload rate indicators, and main transformer load rate indicators belonging to the safety dimension.

4. The method for evaluating the operation status of a power grid according to claim 3, characterized in that The calculation process of each secondary indicator value specifically includes: The operating cost is calculated according to formula (1), wherein the operating cost includes: power generation cost, load shedding cost and wind and solar power abandonment cost; In formula (1), C OPE represents the total operating cost, c fuel Represents the unit power generation cost of the generator set, P i g represents the active output of conventional unit i, and They represent the unit load shedding cost and the unit wind and solar curtailment cost respectively. and They represent the load shedding and wind and solar power abandonment at node i respectively; Calculate the investment cost of energy storage equipment according to formula (2); In formula (2), C e is the investment cost of energy storage equipment, T is the dispatch period, I is the total number of energy storage equipment in the system, χ i is the annual equal investment conversion coefficient for different energy storage equipment, c i is the equipment cost per unit installed capacity, s i Installed capacity for different equipment; Calculate the investment cost of other equipment according to formula (3): In formula (3), C d is the investment cost of other equipment, T is the scheduling period, J is the total number of other equipment in the system, χ j is the conversion coefficient of annual equal investment of different equipment, c j is the equipment cost per unit installed capacity, s j Installed capacity for different equipment; The energy supply reliability index represents the ratio of the total energy supply to the expected total energy supply. When the energy supply reliability is greater than 1, it indicates that the system has a high complementary reliability; when the energy supply reliability is less than 1, it indicates that the system has a low complementary reliability. The energy supply reliability index is calculated according to formula (4): In formula (4), S p For energy supply reliability, A g is the total energy supply, A eg is the expected total energy supply; the energy supply shortage rate index is calculated according to formula (5): In formula (5), r us is the energy supply shortage rate, T n is the average power outage time per unit time, T o is the length of unit time, m is the number of components that can cause power outage, λ i is the failure rate of the ith component, and the formula is: i =n i / t i , where n i is the number of failures of the i-th component, t i is the running time; γ i is the mean fault repair time of the i-th component; According to formula (6), the energy equipment failure rate index is calculated: In the formula, r f is the energy equipment failure rate, T f is the failure duration of energy equipment, T use is the usage time of energy equipment; the controllable load proportion index is calculated according to formula (7), In formula (7), r cl is the controllable load proportion, N cl N is the number of loads that can be turned on and off. all is the total load quantity; According to formula (8), the voltage regulation rate index is calculated: In formula (8), r V is the voltage regulation rate, T s-V T is the total time of voltage detection within the qualified range, V is the total duration of voltage detection; According to formula (9), the frequency deviation rate index is calculated: In formula (9), r p is the frequency deviation rate, T s-p T is the total duration of the frequency within the qualified range of detection, p is the total duration of the detection frequency, Calculate the output fluctuation index according to formula (10); In formula (10), r vol is the output fluctuation rate, D w is the variance of the sampled energy output, k is the number of energy types, W i is the output value of the i-th energy source; According to formula (11), the carbon dioxide emission index is calculated: In formula (11), is the carbon dioxide emissions, is the original carbon dioxide emissions, is the carbon dioxide emissions after reasonable configuration; According to formula (12), the clean energy consumption rate index is calculated: In formula (12), θ clb is the clean energy consumption rate, A cl-a is the actual power generation of clean energy, A cl-max Maximum power generation for clean energy; According to formula (13), the proportion of renewable energy generation is calculated: In formula (13), r clm is the proportion of electricity generated by renewable energy, A l is the power supply of different renewable energy sources, A e is the electricity production of different renewable energy sources; According to formula (14), the power supply margin index of the power grid is calculated: In formula (14), ψ represents the power supply margin of the power grid, P sup Indicates the power supply capacity of the partition. Indicates the maximum load of the partition; According to formula (15), the general frequency is expressed based on the power system inertia constant, generator turbine parameters and governor regulation constant, In formula (15), Δf max represents the maximum frequency disturbance, ΔP represents the power disturbance, D represents the load damping constant, R represents the speed regulator adjustment constant, T R represents the time constant, H represents the system inertia constant, F H represents the turbine parameter, ω n , α, ω r , t nadir They are all intermediate variables with no clear physical meaning; Given the lowest frequency point, the maximum power disturbance at this time is obtained. According to formula (16), this power is defined as the frequency safety margin: According to formula (17), the line overload rate index is calculated: The line overload rate describes the line's carrying capacity for transmission power, and is specifically defined as follows: δ=n br / N br (17) In formula (17), δ represents the line overload rate, n br Indicates the number of lines with transmission power exceeding 80% of capacity, N br Indicates the total number of lines; According to formula (18), the main transformer load factor index is calculated: β=P i sub / P i max (18) In formula (18), β represents the load factor of the main transformer, P i sub Represents the power transmitted through the main transformer i, P i max Represents the transmission capacity of main transformer i.

5. The method for evaluating the operation status of a power grid according to claim 1, characterized in that In step 3), the q-order orthogonal fuzzy number is constructed according to formula (19), Let X be the domain, and define the q-order orthogonal fuzzy sets on X according to equation (19): A={<x,u A (x),v A (x)>|x∈X} (19) In formula (19), u A (x) represents the membership of x to the domain X, v A (x) indicates the non-membership degree of x and satisfies: Assume α = (u, v) is a q-order orthogonal fuzzy number. The score function of any q-order orthogonal fuzzy number α is defined as S(α) = u q -v q , the exact function is defined as H(α)=u q +v q .

6. The method for evaluating the operation status of a power grid according to claim 5, characterized in that In order to make the experts' judgment on the weights of evaluation indicators more comprehensive, a group decision-making model for power grid operation status evaluation is constructed to determine the expert weights. The specific group decision-making model is as follows: Set A = {A j |j=1,2,…,n} is the evaluation index set, e={e t |t=1,2,…,s} is the expert set, is the corresponding expert weight and satisfies ωt=[0,1], then expert e t The decision matrix can be expressed as in Indicates expert t For the evaluation index A j The judgment of weight is described by q-order orthogonal fuzzy numbers. The steps to solve the expert weight are as follows: Step 3-1) Establish expert t The decision matrix in is a q-order orthogonal fuzzy number; Step 3-2) Build a standardized decision matrix The evaluation indicators are divided into benefit type b and cost type λ c In order to facilitate the unified processing of evaluation indicators of different natures, the cost-based indicators are transformed into benefit-based indicators according to formula (20). Step 3-3) Calculate the index score matrix set S under the expert et decision-making = {S t |t=1,2,…,s}, where Step 3-4) Calculate the indicator average score matrix according to the indicator score matrix set S Among them, indicator A j The average score is Step 3-5) Calculate the expert e according to formula (21) t The score matrix St(t=1,2,…,s) and the average score matrix The relationship between The calculation formula is: Step 3-6) Determine the expert weights Get the expert weight matrix W e ={ω t |t=1,2,…,s}.

7. The method for evaluating the operation status of a power grid according to claim 6, characterized in that In step 3), the mathematical definition of the improved q-ROFEOWA operator is: Setting α i =(u i ,v i )(i=1,2,…,m) is a set of q-order orthogonal fuzzy numbers, parameter q≥1, and the q-ROFEOWA operator is calculated according to equation (22), In formula (22), ξ represents the improved q-ROFEOWA operator, ω = (ω1, ω2, …, ω m ) T is α1,α2,…,α m exponentially weighted vector, and satisfying ω j ∈[0,1], The specific steps for calculating the subjective weight of the evaluation index are as follows: Combining expert weights and improved q-ROFEOWA operator, the expert decision matrix B t (t=1,2,…,s) is integrated into a comprehensive decision matrix Β=(b j ) 1×n ; According to formula (23), the support degree Sup(k l ,k p ), and the support matrix (Sup) is obtained n×n , In formula (23), u l 、v l It is indicator A l The membership and non-membership values ​​of u p 、v p It is indicator A p The membership and non-membership values ​​of ; Through the support matrix (Sup) n×n According to formula (24), the T matrix is ​​calculated. In formula (24), T(k l ) is the indicator A l T value; Calculate the subjective weight value of the evaluation index by T matrix The corresponding subjective weight matrix of the evaluation index is W s ={ω sj |j=1,2,…,n}.

8. The method for evaluating the operation status of a power grid according to claim 1, characterized in that: In step 4), the four-level membership cloud model is: For each indicator, data sampling is performed n times. When the indicator is a quantitative numerical indicator and its numerical boundary is known, the indicator approximation method is used according to formula (25) to solve the digital characteristics (E x ,E n ,H e ), In formula (25), E x is the expected value, E n is entropy, H e is the super entropy, q is a constant; The steps of the generation algorithm of the membership cloud model are: Generate E n For expectation, H e 2 E' is the random number with standard deviation n ; Generate E x For expectations, is a random number c with standard deviation; calculate Then (c, μ) is a cloud droplet; repeat the above steps until a cloud droplet that meets the requirements is generated.

9. The method for evaluating the operation status of a power grid according to claim 1, characterized in that: In step 5), the fuzzy comprehensive evaluation principle is: using the weighted average principle to perform fuzzy comprehensive evaluation according to formula (26), In formula (26), b i A represents the membership of the i-th indicator to the evaluation vocabulary; j Represents the standard vectors of different levels, which are usually determined manually. The level standard vector is taken as the evaluation vocabulary V = {V1, V2, V3, V4}, and k is the description of minb j To secb j Wait for small b j Ignoring the degree, when k = 1, it means that all b j Neither can be ignored.

10. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program, wherein the computer program is stored in the memory and is configured to be executed by the processor to implement the method for evaluating the operation status of a power grid according to any one of claims 1 to 9.

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