A smart emergency plan improvement method and system based on five-point cubic smoothing method

Abstract

The present invention relates to a method and system for improving a smart emergency plan based on a five-point cubic smoothing method. The method includes the following steps: establishing an effect verification and evaluation index system for a smart emergency plan, including a short-time scale evaluation index, a medium-time scale evaluation index, and a long-time scale evaluation index; verifying the effect of the smart emergency plan, obtaining data for each indicator, and smoothing the indicator data using a five-point cubic smoothing algorithm; determining the weight of each indicator using a subjective weighting method; calculating a comprehensive evaluation value for the verification of the effect of the smart emergency plan based on the weight of the indicator and the indicator data, and improving the smart emergency plan based on the comprehensive evaluation value. Compared with the prior art, the present invention can well reflect the actual effect of the smart plan, thereby being able to discover and promptly improve the shortcomings of the power emergency plan, so that the smart plan has higher execution efficiency when an emergency occurs.

Description

Technical Field

[0001] The present invention relates to the field of emergency response in power grid enterprises, and in particular to a method and system for improving the performance of intelligent emergency response plans based on a five-point cubic smoothing method. Background Art

[0002] Like all large-scale production activities, power generation carries potential safety risks. Furthermore, with the "dual carbon" goals, higher requirements are being placed on the reliability and safety of regional power grid operations. Currently, the safety production approach prioritizes prevention. Performance evaluation of power emergency plans is a crucial component of power emergency management and a crucial activity within the prevention phase. Therefore, research on this topic is extremely meaningful in the current context.

[0003] Effectiveness verification of power emergency plans is the most effective way to improve and optimize them. A reasonable effectiveness verification evaluation model can identify deficiencies in the emergency response process and verify the effectiveness of the corresponding emergency plan. Therefore, effectiveness verification evaluation models for power emergency plans should be a key focus of power emergency research. However, existing effectiveness verification of power emergency plans is mostly based on the physical verification of classic static plans. Therefore, evaluation models designed for this type of physical verification cannot truly provide guiding improvement suggestions for the plans themselves. Smart power emergency plans, however, are closely linked to the real-time operational status of the power grid. They can better strengthen energy infrastructure construction, enhance the grid's ability to cope with extreme weather, and provide more high-quality, constructive advice for power emergency management. Therefore, for smart plans in integrated power grids, it is necessary to conduct performance evaluations of smart emergency plans to verify their effectiveness and address their shortcomings. Summary of the Invention

[0004] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide an intelligent emergency plan improvement method and system based on the five-point cubic smoothing method.

[0005] The purpose of the present invention can be achieved by the following technical solutions:

[0006] A method for improving an intelligent emergency plan based on a five-point cubic smoothing method comprises the following steps:

[0007] S1. Establish an effect verification and evaluation indicator system for the power emergency smart plan, including short-time scale evaluation indicators, medium- and long-time scale evaluation indicators, and long-time scale evaluation indicators;

[0008] S2. Verify the effectiveness of the power emergency smart plan, obtain data for each indicator, and smooth the indicator data using a quintuple smoothing algorithm;

[0009] S3. Use subjective weighting method to determine the weight of each indicator;

[0010] S4. Combine the indicator weights and indicator data to calculate the comprehensive evaluation value of the verification of the effect of the power emergency smart plan, and improve the power emergency smart plan based on the comprehensive evaluation value.

[0011] Preferably, the short-time scale evaluation index is a toughness index, which has the following specific meanings:

[0012] Its specific meanings are as follows:

[0013]

[0014] Where K(t) represents the resilience index of the power system at time t, t0 is a moment when the power system is not disturbed, that is, the moment when the power system operates normally; t d is the moment when the power system is in the worst operating state after being disturbed; ΔP R (t) represents the degree of imbalance between supply and demand of the power system at time t, and:

[0015]

[0016] in, is the power generation of the thermal power generating unit in the power system at time t; is the power generation of the wind turbine in the power system at time t; is the power generation of the photovoltaic device in the power system at time t; is the power generation of the storage device in the power system at time t; is the total load of the power system at time t;

[0017] The medium and long-term evaluation indicator is the scheduling coefficient, which has the following specific meanings:

[0018]

[0019] Where D(s) represents the scheduling coefficient, υ i represents the importance of the fault at location i, N represents the number of faults, and D i (s) represents the emergency resource scheduling coefficient of the i-th fault at time s, which is defined as:

[0020]

[0021] Where, t irepresents the time when emergency resources arrive at the i-th fault site, t i0 represents the time when the i-th fault occurs, T i represents the time when the fault at the i-th location is restored to normal;

[0022] The long-term evaluation index is a static calculation index fitted by multiple factors through the DEA method. The specific meaning is as follows:

[0023]

[0024] Among them, ρ represents the static calculation index under long time scale, c1 and c2 represent the operating cost and organizational cost of the verification of the power emergency smart plan effect, respectively, and c sum is the total budget cost, e loss is the equipment loss, e total is the total number of electrical equipment, m loss is the consumption of emergency supplies, m total is the total amount of emergency supplies, p use The actual number of mobilized personnel to verify the effectiveness of the power emergency smart plan, p total is the total number of personnel participating in the verification of the effectiveness of the power emergency smart plan, and η is the system recovery percentage.

[0025] Preferably, in step S2, the five-point cubic smoothing algorithm is used to smooth the index data of the short-time scale evaluation index and the medium- and long-time scale evaluation index, and the average of the smoothed index data is calculated, and the average is used as the index data. The smoothing process is specifically as follows:

[0026] Set 2n+1 equally spaced time points: T -n , T -n+1 ,…,T -1 , T0, T1, …, T n-1 , T n , the interval between adjacent time points is h, by rounding t = (T-T0) / h, the original time point is transformed into: t -n =-n,t -n+1 =-n+1,…,t -1 =-1, t0=0, t1=1,…,t n-1 =n-1,t n =n, and the corresponding indicator data are: Y -n , Y -n+1 ,…,Y -1 , Y0, Y1, …, Y n-1 , Y n , using the m-order polynomial to fit the index data, we have:

[0027] Y(t)=a0+a1t+a2t2 +…+a m t m

[0028] Use the least squares method to determine the unknown coefficients a0, a1, ..., a m The value of , from which we can get the five-point cubic smoothing formula:

[0029]

[0030]

[0031]

[0032]

[0033]

[0034] Where Y' represents the data after smoothing.

[0035] Preferably, in step S3, the G1 method is used to determine the weight of each indicator, specifically:

[0036] For m indicators, experts determine the importance of each indicator and rank the indicators according to their importance. The indicator ranked at the mth position is the least important indicator.

[0037] According to the importance of the indicators, the adjacent indicators x i with x i-1 The importance ratio r i Assignment, if the index x i-1 With the indicator x i have the same importance, then r i Equal to 1, if the index x i-1 Ratio index x i Slightly important, then r i Equal to 1.2, if the index x i-1 Ratio index x i Obviously important, then r i Equal to 1.4, if the index x i-1 Ratio index x i Strongly important, then r i Equal to 1.6, if the index x i-1 Ratio index x i Extremely important, then r i =1.8;

[0038] According to the given r i Assign a value and calculate the weight of the mth indicator:

[0039]

[0040] According to the weight of the mth indicator, the weights of the m-1th, m-2th, ...1th indicators are calculated in sequence:

[0041] ω k-1 =r k ω k , k=m,m-1,m-2,...,2

[0042] Where, ω k-1 represents the weight of the k-1th indicator, ω k Indicates the weight of the k-th indicator.

[0043] Preferably, the calculation of the comprehensive evaluation value in step S4 is specifically as follows:

[0044]

[0045] Among them, d is the final comprehensive evaluation value. The smaller the d value, the more ideal the result of the smart plan effect verification is; ω i is the weight of the i-th indicator; is the indicator data of the i-th indicator.

[0046] Preferably, if the size of the comprehensive evaluation value d is [0, 0.25), the result of the smart plan effect verification is excellent; if the size of the comprehensive evaluation value d is [0.25, 0.5), the result of the smart plan effect verification is good; if the size of the comprehensive evaluation value d is [0.5, 0.75), the result of the smart plan effect verification is medium; if the size of the comprehensive evaluation value d is [0.75, 1], the result of the smart plan effect verification is poor.

[0047] An intelligent emergency plan improvement system based on the five-point cubic smoothing method includes: an indicator construction module, a data processing module, a weight determination module and an evaluation optimization module;

[0048] The indicator construction module is used to establish an effect verification and evaluation indicator system for the power emergency smart plan, including short-time scale evaluation indicators, medium- and long-time scale evaluation indicators, and long-time scale evaluation indicators;

[0049] The data processing module is used to obtain the data of various indicators in the effect verification of the power emergency smart plan, and use the quintuple point cubic smoothing algorithm to smooth the indicator data;

[0050] The weight determination module adopts a subjective weighting method to determine the weight of each indicator;

[0051] The evaluation and optimization module combines the weights and indicator data of the indicators to calculate the comprehensive evaluation value of the power emergency smart plan effect verification, and improves the power emergency smart plan based on the comprehensive evaluation value.

[0052] Preferably, among the indicators constructed by the indicator construction module, the short-time scale evaluation indicator is the resilience index, which has the following specific meaning:

[0053] Its specific meanings are as follows:

[0054]

[0055] Where K(t) represents the resilience index of the power system at time t, t0 is a moment when the power system is not disturbed, that is, the moment when the power system operates normally; t d is the moment when the power system is in the worst operating state after being disturbed; ΔP R (t) represents the degree of imbalance between supply and demand of the power system at time t, and:

[0056]

[0057] in, is the power generation of the thermal power generating unit in the power system at time t; is the power generation of the wind turbine in the power system at time t; is the power generation of the photovoltaic device in the power system at time t; is the power generation of the storage device in the power system at time t; is the total load of the power system at time t;

[0058] The medium and long-term evaluation indicator is the scheduling coefficient, which has the following specific meanings:

[0059]

[0060] Where D(s) represents the scheduling coefficient, υ i represents the importance of the fault at location i, N represents the number of faults, and D i (s) represents the emergency resource scheduling coefficient of the i-th fault at time s, which is defined as:

[0061]

[0062] Where, t i represents the time when emergency resources arrive at the i-th fault site, t i0 represents the time when the i-th fault occurs, T i represents the time when the fault at the i-th location is restored to normal;

[0063] The long-term evaluation index is a static calculation index fitted by multiple factors through the DEA method. The specific meaning is as follows:

[0064]

[0065] Among them, ρ represents the static calculation index under long time scale, c1 and c2 represent the operating cost and organizational cost of the verification of the power emergency smart plan effect, respectively, and c sum is the total budget cost, e loss is the equipment loss, e total is the total number of electrical equipment, m loss is the consumption of emergency supplies, m total is the total amount of emergency supplies, p use The actual number of mobilized personnel to verify the effectiveness of the power emergency smart plan, p total is the total number of personnel participating in the verification of the effectiveness of the power emergency smart plan, and η is the system recovery percentage.

[0066] Preferably, the weight determination module uses the G1 method to determine the weight of each indicator, specifically:

[0067] For m indicators, experts determine the importance of each indicator and rank the indicators according to their importance. The indicator ranked at the mth position is the least important indicator.

[0068] According to the importance of the indicators, the adjacent indicators x i with x i-1 The importance ratio r i Assignment, if the index x i-1 With the indicator x i have the same importance, then r i Equal to 1, if the index x i-1 Ratio index x i Slightly important, then r i Equal to 1.2, if the index x i-1 Ratio index x i Obviously important, then r i Equal to 1.4, if the index x i-1 Ratio index x i Strongly important, then r i Equal to 1.6, if the index x i-1 Ratio index x i Extremely important, then r i =1.8;

[0069] According to the given r i Assign a value and calculate the weight of the mth indicator:

[0070]

[0071] According to the weight of the mth indicator, the weights of the m-1th, m-2th, ...1th indicators are calculated in sequence:

[0072] ω k-1 =r k ωk , k=m,m-1,m-2,...,2

[0073] Where, ω k-1 represents the weight of the k-1th indicator, ω k Indicates the weight of the k-th indicator.

[0074] Preferably, in the evaluation and optimization module, the calculation of the comprehensive evaluation value is specifically as follows:

[0075]

[0076] Among them, d is the final comprehensive evaluation value. The smaller the d value, the more ideal the result of the smart plan effect verification is; ω i is the weight of the i-th indicator; is the indicator data of the i-th indicator.

[0077] Compared with the prior art, the present invention has the following beneficial effects:

[0078] Short-time scale evaluation indicators, medium- and long-time scale evaluation indicators, and long-time scale evaluation indicators are constructed. The indicator data of short-time scale evaluation indicators and medium- and long-time scale evaluation indicators are regarded as non-stationary discrete data that change with different time nodes. They are fitted through the five-point cubic smoothing algorithm to realize the verification of the effect of the power emergency smart plan, which can well reflect the actual effect of the smart plan, so that the shortcomings of the power emergency plan can be discovered and improved in time, making the smart plan have higher execution efficiency when an emergency occurs. BRIEF DESCRIPTION OF THE DRAWINGS

[0079] Figure 1 Schematic diagram of the evaluation indicators;

[0080] Figure 2 This is a diagram showing the changes in the supply and demand balance of the power system;

[0081] Figure 3 This is a schematic diagram of using the five-point cubic smoothing method to process indicator data;

[0082] Figure 4 Schematic diagram of the smoothing process of the toughness index;

[0083] Figure 5 Schematic diagram of smoothing processing of scheduling coefficients. DETAILED DESCRIPTION

[0084] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. This embodiment is implemented based on the technical solution of the present invention, and provides a detailed implementation method and specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0085] Example 1:

[0086] A method for improving an intelligent emergency plan based on a five-point cubic smoothing method comprises the following steps:

[0087] S1. Establish an effect verification and evaluation index system for power emergency smart plans, such as Figure 1 As shown, it includes short time scale evaluation indicators, medium and long time scale evaluation indicators and long time scale evaluation indicators;

[0088] S2. Verify the effectiveness of the power emergency smart plan, obtain data for each indicator, and smooth the indicator data using a quintuple smoothing algorithm;

[0089] S3. Use subjective weighting method to determine the weight of each indicator;

[0090] S4. Combine the indicator weights and indicator data to calculate the comprehensive evaluation value of the verification of the effect of the power emergency smart plan, and improve the power emergency smart plan based on the comprehensive evaluation value.

[0091] 1. Short-term evaluation indicators;

[0092] When the power system faces an emergency, the system performance and operating status will change, and the characteristics of the power grid determine that the performance and transient response of the power system will change rapidly when a fault occurs. It will present different states at different time points and has a certain instantaneous nature. Therefore, this application uses the system's performance and state changes as short-time-scale smart plan effect verification and evaluation indicators. The resilience index can more accurately show the changes in power system performance. Based on this, this application uses the resilience index to achieve a quantitative representation of power system performance changes. The short-time-scale evaluation indicator in this application is the resilience index, and its specific meaning is as follows:

[0093]

[0094] Where K(t) represents the resilience index of the power system at time t, t0 is a moment when the power system is not disturbed, that is, the moment when the power system operates normally; t d is the moment when the power system is in the worst operating state after being disturbed; ΔP R (t) represents the degree of imbalance between supply and demand of the power system at time t, and:

[0095]

[0096] in, is the power generation of the thermal power generating unit in the power system at time t; is the power generation of the wind turbine in the power system at time t; is the power generation of the photovoltaic device in the power system at time t; is the power generation of the storage device in the power system at time t; is the total load of the power system at time t;

[0097] like Figure 2 As shown in the figure, at time t0, the system's supply and demand are balanced. When the system receives a sudden disturbance, the system's supply and demand imbalance decreases. d The lowest point at t is the lowest point. When effective emergency response measures are taken, the imbalance between supply and demand in the system gradually increases. f The resilience index is a broadly defined indicator, also known as the elasticity coefficient, that encompasses multiple power system assessments. It can be dynamically modified based on different operating modes. Relevant practitioners will understand this and will not be elaborated on here.

[0098] 2. Medium- and long-term evaluation indicators

[0099] When the power system is disturbed, its load will change due to the operating status of the system. In mild cases, it will cause a short-term power outage for the third type of load, resulting in minor losses; in severe cases, it will cause a long-term power outage for the first type of load, which will have serious adverse effects. Therefore, according to the operating conditions of the system and the degree of the affected load, it is necessary to mobilize the reserved emergency materials as soon as possible for emergency repairs. Since the speed of material dispatch is affected by many factors such as the rationality of its distribution and traffic conditions, the dispatching capacity of the system in a short period of time can be considered to be unchanged. Based on this, this application selects the material dispatch coefficient as a medium- and long-term scale evaluation indicator for verifying the effect of the emergency smart plan. Its specific meaning is as follows:

[0100] When there is only one fault or the user terminal loses power, the value of the dispatch coefficient is defined as:

[0101]

[0102] Where t represents the time when the emergency resource arrives at the fault site, t0 represents the time when the fault occurs, and T represents the time when the fault returns to normal. When the time when the emergency resource arrives at the fault site is less than or equal to the time when the fault occurs, that is, the emergency resource distribution location is consistent with the fault point, the scheduling coefficient is 1. When the time when the emergency resource arrives at the fault site is greater than the time when the fault point returns to normal, the scheduling coefficient is 0.

[0103] Since the above definition represents the dispatch coefficient when the emergency resources arrive at the scene, it is a fixed value and cannot reflect the trend of the dispatch coefficient changing over time. Therefore, we process it and obtain the dispatch coefficient that changes over time:

[0104]

[0105] Where t represents the time when emergency resources arrive at the fault site, t0 represents the time when the fault occurs, and T represents the time when the fault returns to normal.

[0106] Similarly, considering the definition of the dispatch coefficient when multiple faults occur in the system, and processing it, the dispatch coefficient that changes with time is obtained as follows:

[0107]

[0108] Where D i (s) represents the emergency resource scheduling coefficient of the i-th fault at time s, t i represents the time when emergency resources arrive at the i-th fault site, t i0 represents the time when the i-th fault occurs, T i represents the time when the fault at the i-th location is restored to normal;

[0109] By combining the dispatch coefficients of various faults, the comprehensive dispatch coefficient is:

[0110]

[0111] Where D(s) represents the scheduling coefficient, υ i represents the importance of the i-th fault, and N represents the number of faults.

[0112] 3. Long-term evaluation indicators:

[0113] The purpose of verifying the effectiveness of the power emergency smart plan is to enable power companies and emergency personnel to effectively reduce the impact of emergencies on the power grid when emergencies occur. Therefore, power companies need to regularly verify the effectiveness of the plan, which requires that the comprehensive cost of verifying the effectiveness of the plan should not be too high. The comprehensive cost of verifying the effectiveness of the power emergency plan includes operating costs and organizational costs.

[0114] At the same time, all emergency plans hope to minimize the overall losses caused by emergencies, and the response losses in the power emergency plan include the loss of system equipment, the consumption of emergency materials and the number of personnel involved.

[0115] Finally, the emergency plan also hopes that after the emergency response, the power system can be restored to the state before the emergency occurred. In summary, this paper provides long-term evaluation indicators for power emergency effect verification, including: effect verification operation cost, effect verification organization cost, equipment loss, emergency material consumption, number of personnel deployed, and system recovery status. This application uses Data Envelopment Analysis (DEA) to fit these long-term evaluation indicators for emergency plan effect verification into a static calculation indicator. The specific meanings are as follows:

[0116]

[0117] Among them, ρ represents the static calculation index under long time scale, c1 and c2 represent the operating cost and organizational cost of the verification of the effect of the power emergency smart plan, respectively, and c sum is the total budget cost, e loss is the equipment loss, e total is the total number of electrical equipment, m loss is the consumption of emergency supplies, m total is the total amount of emergency supplies, p use The actual number of mobilized personnel to verify the effectiveness of the power emergency smart plan, p total is the total number of personnel participating in the verification of the effectiveness of the power emergency smart plan, and η is the system recovery percentage.

[0118] This application is based on the smart plan of the source-load integrated power grid, and provides key indicators for the performance evaluation of emergency smart plans under three different time scales. The short-time scale evaluation indicators based on the resilience index can always reflect the resilience of the power grid in dealing with emergencies. The medium and long-time scale evaluation indicators based on the dispatch coefficient reflect the rationality of the dispatch. Finally, important static evaluation indicators are extracted for fitting as long-time scale evaluation indicators. For evaluation indicators of different time scales, this application uses the five-point cubic smoothing method to smooth the indicator data, and by constructing an electric power emergency smart plan effect evaluation model, it realizes the verification of the electric power emergency smart plan effect, so that the smart plan has higher execution efficiency when an emergency occurs.

[0119] In step S2, the five-point cubic smoothing algorithm is used to smooth the indicator data of the short-time scale evaluation index and the medium- and long-time scale evaluation index, and the average of the smoothed indicator data is calculated, and the average is used as the indicator data. The smoothing process is specifically as follows:

[0120] Set 2n+1 equally spaced time points: T -n , T -n+1 ,…,T -1 , T0, T1, …, Tn-1 , T n , the interval between adjacent time points is h, by rounding t = (T-T0) / h, the original time point is transformed into: t -n =-n,t -n+1 =-n+1,…,t -1 =-1, t0=0, t1=1,…,t n-1 =n-1,t n =n, and the corresponding indicator data are: Y -n , Y -n+1 ,…,Y -1 , Y0, Y1, …, Y n-1 , Y n , using the m-order polynomial to fit the index data, we have:

[0121] Y(t)=a0+a1t+a2t 2 +…+a m t m

[0122] Use the least squares method to determine the unknown coefficients a0, a1, ..., a m The value of , from which we can get the five-point cubic smoothing formula:

[0123]

[0124]

[0125]

[0126]

[0127]

[0128] Where Y' represents the data after smoothing.

[0129] In the verification and evaluation of the effects of smart plans, the values ​​of the short-time scale evaluation indicators and the medium- and long-time scale evaluation indicators can be considered as non-stationary discrete data that change with different time nodes. The five-point cubic smoothing method can be used to smooth the data of these two evaluation indicators, and the corresponding points can be selected according to the obtained data curve to serve as the final smart plan effect verification and evaluation indicator data.

[0130] The five-point cubic smoothing algorithm is a common method for trend fitting discrete nonlinear data. Its operating principle is equivalent to a multidimensional high-order low-pass filter, which can process high-frequency noise in the signal, reduce interference, and improve data quality. Therefore, the five-point cubic smoothing algorithm also has excellent denoising capabilities. This application briefly describes the steps of the five-point cubic smoothing algorithm. When using it, practitioners can directly call the function in the relevant software or adjust it according to conventional understanding.

[0131] like Figure 3 As shown, the horizontal axis is understood as the time axis, and the vertical axis is the corresponding indicator data, that is, the size of the evaluation value in the figure. It can be seen that when the short-time scale and medium- and long-time scale indicator values ​​are more discrete, the short-time scale and medium- and long-time scale indicators can be regarded as a signal containing high-frequency noise. After denoising the short-time scale evaluation indicator values ​​and the medium- and long-time scale evaluation indicator values ​​through the five-point cubic smoothing algorithm, a relatively stable curve chart that can represent the trend of indicator data changes can be obtained. By selecting the corresponding time point, the corresponding indicator value can be obtained, and the indicator value can be used as the input indicator value for the final smart plan effect verification and evaluation. Before smoothing, Figure 3 The indicator data in is the dynamic indicator value, and the indicator data obtained after processing is Figure 3 The integrated indicator value in .

[0132] In step S3, the G1 method is used to determine the weight of each indicator. The G1 method, namely the ordinal relationship analysis method, is a subjective weighting method. This method has the advantage of not requiring consistency testing, and is therefore more convenient to implement. Specifically,

[0133] For m indicators, experts determine the importance of each indicator and rank the indicators according to their importance. The indicator ranked at the mth position is the least important indicator.

[0134] According to the importance of the indicators, the adjacent indicators x i with x i-1 The importance ratio r i Assignment, if the index x i-1 With the indicator x i have the same importance, then r i Equal to 1, if the index x i-1 Ratio index x i Slightly important, then r i Equal to 1.2, if the index x i-1 Ratio index x i Obviously important, then r i Equal to 1.4, if the index x i-1 Ratio index x i Strongly important, then r iEqual to 1.6, if the index x i-1 Ratio index x i Extremely important, then r i =1.8;

[0135] According to the given r i Assign a value and calculate the weight of the mth indicator:

[0136]

[0137] According to the weight of the mth indicator, the weights of the m-1th, m-2th, ...1th indicators are calculated in sequence:

[0138] ω k-1 =r k ω k , k=m,m-1,m-2,...,2

[0139] Where, ω k-1 represents the weight of the k-1th indicator, ω k Indicates the weight of the k-th indicator.

[0140] The calculation of the comprehensive evaluation value in step S4 is specifically as follows:

[0141]

[0142] Among them, d is the final comprehensive evaluation value. The smaller the d value, the more ideal the result of the smart plan effect verification is; ω i is the weight of the i-th indicator; is the indicator data of the i-th indicator.

[0143] Specifically, if the comprehensive evaluation value d is in the range of [0, 0.25), the result of the smart plan effect verification is excellent; if the comprehensive evaluation value d is in the range of [0.25, 0.5), the result of the smart plan effect verification is good; if the comprehensive evaluation value d is in the range of [0.5, 0.75), the result of the smart plan effect verification is medium; if the comprehensive evaluation value d is in the range of [0.75, 1], the result of the smart plan effect verification is poor.

[0144] Example 2:

[0145] An intelligent emergency plan improvement system based on the five-point cubic smoothing method includes: an indicator construction module, a data processing module, a weight determination module and an evaluation optimization module;

[0146] The indicator construction module is used to establish an effect verification and evaluation indicator system for the power emergency smart plan, including short-time scale evaluation indicators, medium- and long-time scale evaluation indicators, and long-time scale evaluation indicators;

[0147] The data processing module is used to obtain the data of various indicators in the effect verification of the power emergency smart plan, and smooth the indicator data using the quintuple smoothing algorithm;

[0148] The weight determination module uses the subjective weighting method to determine the weight of each indicator;

[0149] The evaluation and optimization module combines the weights and indicator data of the indicators to calculate the comprehensive evaluation value of the verification of the effect of the power emergency smart plan, and improves the power emergency smart plan based on the comprehensive evaluation value.

[0150] Example 3:

[0151] In order to illustrate this application, it is assumed that the effect of a certain smart plan is verified, and the duration of this verification is 2 hours.

[0152] First, the short-time scale evaluation index based on the resilience index is used. The time step is 2 minutes. The grid resilience index under emergencies can be obtained as shown in Table 1:

[0153] Table 1 Short-time scale index values ​​based on resilience index

[0154] T K(t) T K(t) T K(t) T K(t) T K(t) T K(t) 2 1.000 22 0.831 42 0.331 62 0.008 82 0.166 102 0.680 4 1.000 24 0.852 44 0.248 64 0.112 84 0.220 104 0.615 6 1.000 26 0.727 46 0.352 66 0.009 86 0.186 106 0.729 8 1.000 28 0.675 48 0.168 68 0.011 88 0.188 108 0.793 10 0.998 30 0.643 50 0.133 70 0.158 90 0.256 110 0.647 12 0.979 32 0.552 52 0.083 72 0.100 92 0.390 112 0.836 14 0.989 34 0.611 54 0.031 74 0.132 94 0.461 114 0.896 16 0.944 36 0.461 56 0.000 76 0.130 96 0.506 116 0.945 18 0.884 38 0.403 58 0.000 78 0.166 98 0.551 118 1.000 20 0.901 40 0.361 60 0.000 80 0.122 100 0.596 120 0.999

[0155] The five-point cubic smoothing method is used to smooth the toughness index value, which can be obtained as follows: Figure 4 The curve shown in the figure is smoothed to obtain the short-time scale evaluation index value based on the toughness index:

[0156] Then comes the medium and long-term scale indicators based on the dispatch coefficient, with a time step of 10 minutes. 1) Assume that the first fault starts at the 10th minute, the emergency supplies arrive at the 60th minute, and the fault is restored to normal at the 100th minute. 2) Assume that the second and third faults start at the 20th minute, the emergency supplies dispatched to the second location arrive at the 50th minute, and the fault is restored to normal at the 70th minute; the emergency supplies dispatched to the third location arrive at the 40th minute, and the fault is restored to normal at the 80th minute. The dispatch coefficient index values ​​of each fault under the emergency are calculated. The importance of the three faults υ1, υ2, and υ3 can be determined by the G1 method or can be set directly manually. In this embodiment, υ1, υ2, and υ3 are 0.3750, 0.3125, and 0.3125, respectively. The comprehensive dispatch coefficient D(s) can be obtained, as shown in Table 2.

[0157] Table 2 Medium and long time scale indicator values ​​based on scheduling coefficients

[0158] T D(s) T D(s) 10 0.6250 70 0.6875 20 0.0000 80 0.7916 30 0.0000 90 0.7916 40 0.2083 100 1.0000 50 0.3333 110 1.0000 60 0.5000 120 1.0000

[0159] Similarly, the quintuple smoothing algorithm is used to smooth the scheduling coefficient index data, and the following can be obtained: Figure 5 The curve shown in the figure is finally averaged to obtain the medium and long-term scale evaluation index value based on the scheduling coefficient:

[0160] Next, the static index values ​​of the effectiveness verification of this power emergency plan are shown in the following table, and the static fitting index size under long time scale is calculated, as shown in Table 3.

[0161] Table 3 Long time scale indicators

[0162]

[0163] The G1 method is then used to determine the weights of the evaluation indicators, and the weights of the indicators at short time scales, medium and long time scales, and long time scales are: (0.4149, 0.3191, 0.2660).

[0164] Finally, the comprehensive evaluation value of the verification of the power emergency smart plan effect is calculated as follows:

[0165]

[0166] If the comprehensive evaluation value d is in the range of [0, 0.25), the result of the smart plan effect verification is excellent; if the comprehensive evaluation value d is in the range of [0.25, 0.5), the result of the smart plan effect verification is good; if the comprehensive evaluation value d is in the range of [0.5, 0.75), the result of the smart plan effect verification is medium; if the comprehensive evaluation value d is in the range of [0.75, 1], the result of the smart plan effect verification is poor.

[0167] It can be seen that the evaluation level of this time is at an average level, which is far from the ideal level, indicating that the effect verification results of the power emergency smart plan are not very ideal. Through analysis, it is found that the evaluation index values ​​of the three different time scales are all at a medium and low level. Therefore, in future emergency management work, we should strengthen its construction, improve the smart plan, and thus improve the overall emergency level.

[0168] The above describes in detail the preferred embodiments of the present invention. It should be understood that those skilled in the art can make numerous modifications and variations based on the concepts of the present invention without inventive effort. Therefore, any technical solutions that can be derived by those skilled in the art through logical analysis, reasoning, or limited experimentation based on the concepts of the present invention and the prior art should be within the scope of protection defined by the claims.

Claims

1. A smart emergency plan improvement method based on the five-point cubic smoothing method, characterized in that: The following steps are involved: S1. Establish an effect verification and evaluation indicator system for the power emergency smart plan, including short-time scale evaluation indicators, medium- and long-time scale evaluation indicators, and long-time scale evaluation indicators; S2. Verify the effectiveness of the power emergency smart plan, obtain data for each indicator, and smooth the indicator data using a quintuple smoothing algorithm; S3. Use subjective weighting method to determine the weight of each indicator; S4. Calculate the comprehensive evaluation value of the effectiveness verification of the power emergency smart plan based on the indicator weights and indicator data, and improve the power emergency smart plan based on the comprehensive evaluation value; The short-time scale evaluation indicator is the resilience index, which has the following specific meanings: Its specific meanings are as follows: Where K(t) represents the resilience index of the power system at time t, t0 is a moment when the power system is not disturbed, that is, the moment when the power system operates normally; t d is the moment when the power system is in the worst operating state after being disturbed; ΔP R (t) represents the degree of imbalance between supply and demand of the power system at time t, and: in, is the power generation of the thermal power generating unit in the power system at time t; is the power generation of the wind turbine in the power system at time t; is the power generation of the photovoltaic device in the power system at time t; is the power generation of the storage device in the power system at time t; is the total load of the power system at time t; The medium and long-term evaluation indicator is the scheduling coefficient, which has the following specific meanings: Where D(s) represents the scheduling coefficient, υ i represents the importance of the fault at location i, N represents the number of faults, and D i (s) represents the emergency resource scheduling coefficient of the i-th fault at time s, which is defined as: Where, t i represents the time when emergency resources arrive at the i-th fault site, t i0 represents the time when the i-th fault occurs, T i represents the time when the fault at the i-th location is restored to normal; The long-term evaluation index is a static calculation index fitted by multiple factors through the DEA method. The specific meaning is as follows: Among them, ρ represents the static calculation index under long time scale, c1 and c2 represent the operating cost and organizational cost of the verification of the power emergency smart plan effect, respectively, and c sum is the total budget cost, e loss is the equipment loss, e total is the total number of electrical equipment, m loss is the consumption of emergency supplies, m total is the total amount of emergency supplies, p use The actual number of mobilized personnel to verify the effectiveness of the power emergency smart plan, p total is the total number of personnel participating in the verification of the effectiveness of the power emergency smart plan, and η is the system recovery percentage; In step S2, the five-point cubic smoothing algorithm is used to smooth the indicator data of the short-time scale evaluation index and the medium- and long-time scale evaluation index, and the average of the smoothed indicator data is calculated, and the average is used as the indicator data. The smoothing process is specifically as follows: Set 2n+1 equally spaced time points: T -n , T -n+1 ,…,T -1 , T0, T1, …, T n-1 , T n , the interval between adjacent time points is h, by rounding t = (T-T0) / h, the original time point is transformed into: t -n =-n,t -n+1 =-n+1,…,t -1 =-1, t0=0, t1=1,…,t n-1 =n-1,t n =n, and the corresponding indicator data are: Y -n , Y -n+1 ,…,Y -1 , Y0, Y1, …, Y n-1 , Y n , using the m-order polynomial to fit the index data, we have: Y(t)=a0+a1t+a2t 2 +…+a m t m Use the least squares method to determine the unknown coefficients a0, a1, ..., a m The value of , from which we can get the five-point cubic smoothing formula: Where Y' represents the data after smoothing.

2. The intelligent emergency plan improvement method based on the five-point cubic smoothing method according to claim 1 is characterized in that: In step S3, the G1 method is used to determine the weight of each indicator, specifically: For m indicators, experts determine the importance of each indicator and rank the indicators according to their importance. The indicator ranked at the mth position is the least important indicator. According to the importance of the indicators, the adjacent indicators x i with x i-1 The importance ratio r i Assignment, if the index x i-1 With the indicator x i have the same importance, then r i Equal to 1, if the index x i-1 Ratio index x i Slightly important, then r i Equal to 1.2, if the index x i-1 Ratio index x i Obviously important, then r i Equal to 1.4, if the index x i-1 Ratio index x i Strongly important, then r i Equal to 1.6, if the index x i-1 Ratio index x i Extremely important, then r i =1.8; According to the given r i Assign a value and calculate the weight of the mth indicator: According to the weight of the mth indicator, the weights of the m-1th, m-2th, ...1th indicators are calculated in sequence: oh k-1 =r k oh k ,k=m,m-1,m-2,...,2 Where, ω k-1 represents the weight of the k-1th indicator, ω k Indicates the weight of the k-th indicator.

3. The intelligent emergency plan improvement method based on the five-point cubic smoothing method according to claim 1 is characterized in that: The calculation of the comprehensive evaluation value in step S4 is specifically as follows: Among them, d is the final comprehensive evaluation value. The smaller the d value, the more ideal the result of the smart plan effect verification is; ω i is the weight of the i-th indicator; is the indicator data of the i-th indicator.

4. The method for improving an intelligent emergency plan based on the five-point cubic smoothing method according to claim 3 is characterized in that: If the comprehensive evaluation value d is in the range of [0, 0.25), the result of the smart plan effect verification is excellent; if the comprehensive evaluation value d is in the range of [0.25, 0.5), the result of the smart plan effect verification is good; if the comprehensive evaluation value d is in the range of [0.5, 0.75), the result of the smart plan effect verification is medium; if the comprehensive evaluation value d is in the range of [0.75, 1], the result of the smart plan effect verification is poor.

5. A smart emergency plan improvement system based on the five-point cubic smoothing method, characterized in that: A smart emergency plan improvement method based on a five-point cubic smoothing method as described in any one of claims 1 to 4, comprising: an indicator construction module, a data processing module, a weight determination module, and an evaluation optimization module; The indicator construction module is used to establish an effect verification and evaluation indicator system for the power emergency smart plan, including short-time scale evaluation indicators, medium- and long-time scale evaluation indicators, and long-time scale evaluation indicators; The data processing module is used to obtain the data of various indicators in the effect verification of the power emergency smart plan, and use the quintuple point cubic smoothing algorithm to smooth the indicator data; The weight determination module adopts a subjective weighting method to determine the weight of each indicator; The evaluation and optimization module combines the weights and indicator data of the indicators to calculate the comprehensive evaluation value of the power emergency smart plan effect verification, and improves the power emergency smart plan based on the comprehensive evaluation value.

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