Maintenance plan safety checking method for electric power system

Abstract

The invention provides a maintenance plan safety checking method for a power system, and the method comprises the steps: collecting new energy related data, and collecting power grid operation data; on the basis of the new energy related data, new energy power multi-scene generation considering new energy power prediction errors is executed, and at least one target new energy power scene is obtained; based on the power grid operation data, constructing a maintenance plan safety checking model based on load flow calculation; and based on a conditional risk mechanism algorithm, performing security check analysis on the target new energy power scene by using the maintenance plan security check model. According to the invention, the uncertainty of new energy is fully considered, risks in different scenes are accurately analyzed and evaluated, and the maintenance plan is optimized and adjusted, so that the reliability of the maintenance plan is improved, the capability of a power grid for coping with new energy fluctuation is enhanced, the safe and stable operation of the power grid in a complex and changeable environment is ensured, and the maintenance cost is reduced. And a solid electric power guarantee is provided for social and economic development.

Description

Technical Field

[0001] The present invention relates to the technical field of power system operation, and in particular to a method for safety verification of a maintenance plan for a power system. Background Art

[0002] Under the background of global efforts to respond to climate change and promote energy transformation, my country has vigorously developed clean energy, and the status of new energy such as wind power and photovoltaics in the power system has become increasingly prominent. Its increasing proportion plays an important role in achieving goals such as energy structure optimization and environmental improvement. The advantages of new energy power generation are significant. Its clean and sustainable characteristics help reduce carbon emissions and alleviate environmental pollution problems, while reducing dependence on traditional fossil energy and enhancing the security and stability of energy supply. However, new energy power generation is closely linked to meteorological conditions. Wind power generation depends on meteorological factors such as wind speed and wind direction, while photovoltaic power generation is subject to light intensity and duration. These meteorological conditions are complex and changeable and difficult to accurately predict, which makes the prediction of new energy power face huge challenges and errors are inevitable.

[0003] In the actual operation of the power system, the maintenance plan is like a "health guide" for the operation of the system. Reasonable arrangement of the maintenance plan is a key link to ensure the safe and stable operation of the power grid. However, in the current maintenance plan formulation process, most of them rely solely on the expected prediction curve of renewable energy power. This method ignores the uncertainty caused by the prediction error. During the maintenance period, the actual power of renewable energy may deviate greatly from the expected prediction value, which in turn causes a series of serious problems. For example, when the actual output of renewable energy is far lower than expected, the power grid may have a power shortage, resulting in a decrease in grid frequency and voltage fluctuations, affecting the quality of electricity. In severe cases, it may even cause large-scale power outages, causing industrial production to stagnate, commercial activities to be interrupted, and residents' lives to fall into chaos, causing huge economic losses and social impacts. As the proportion of renewable energy in the power system continues to rise, this uncertainty poses an increasing threat to the safe and stable operation of the power grid. The traditional maintenance plan formulation method can no longer meet the needs of the rapid development of renewable energy. Summary of the invention

[0004] To this end, the present invention provides a maintenance plan safety verification method for an electric power system, aiming to solve the technical problem that the traditional maintenance plan formulation method cannot adapt to the needs of the rapid development of new energy.

[0005] To achieve the above objectives, the present invention adopts the following technical solutions:

[0006] According to a first aspect of the present invention, the present invention provides a method for safety verification of a maintenance plan for an electric power system, the method comprising:

[0007] Collect data related to new energy and power grid operation data;

[0008] Based on the new energy related data, performing new energy power multi-scenario generation considering new energy power prediction errors to obtain at least one target new energy power scenario;

[0009] Based on the power grid operation data, a maintenance plan safety verification model based on power flow calculation is constructed;

[0010] Based on the conditional risk mechanism algorithm, the maintenance plan safety verification model is used to perform safety verification analysis on the target new energy power scenario;

[0011] Among them, the new energy related data includes at least one of power forecast data, meteorological data and power generation equipment operation data; the power grid operation data includes power grid topology data, power grid real-time operation data and at least one of load history data and load real-time forecast data.

[0012] Further, based on the new energy related data, performing new energy power multi-scenario generation considering new energy power prediction errors to obtain at least one target new energy power scenario includes:

[0013] Based on the new energy related data and actual power generation data, a statistical analysis method is used to calculate the prediction error characteristics to obtain an error analysis result;

[0014] Based on the error analysis results, a new energy power prediction error model for dynamically predicting the error range and probability distribution of new energy power prediction is established;

[0015] Using the Monte Carlo simulation method and combining the new energy power prediction error model, multiple new energy power scenarios are generated;

[0016] The new energy power value considering uncertainty corresponding to each of the new energy power scenarios is calculated, and based on the new energy power value, the multiple new energy power scenarios are classified and screened to obtain a target new energy power scenario.

[0017] Furthermore, the construction of a maintenance plan safety verification model based on power flow calculation based on the power grid operation data includes:

[0018] Define safety verification indicators for evaluating the safety and reliability of maintenance plans in different scenarios; the safety verification indicators include at least one of the grid node voltage over-limit rate, line power flow overload rate, system frequency deviation and system spare capacity shortage rate;

[0019] Based on the power grid operation data, establishing the power system flow calculation algorithm;

[0020] The power system flow calculation algorithm is used to calculate the operating parameters of each node and each line of the power grid; the operating parameters are used to calculate the safety check index value corresponding to each safety check index;

[0021] Among them, the grid node voltage over-limit rate R V The formula is:

[0022]

[0023] Among them, N V is the number of voltage-limited nodes, N T is the total number of nodes;

[0024] and / or,

[0025] The line power flow overload rate R L The formula is:

[0026]

[0027] Among them, N L is the number of power flow overload lines, N L0 is the total number of lines;

[0028] and / or,

[0029] The formula of the system frequency deviation Δf is expressed as:

[0030] Δf=ff N

[0031] Where, f is the actual system frequency, f N is the rated frequency;

[0032] and / or,

[0033] The system spare capacity deficiency rate R B The formula is:

[0034]

[0035] Among them, C B is the required spare capacity of the system, C R is the actual spare capacity.

[0036] Furthermore, the conditional risk mechanism algorithm uses a maintenance plan safety verification model to perform a safety verification analysis on the target new energy power scenario, including:

[0037] For each of the target new energy power scenarios, use the power system power flow calculation algorithm to perform power flow calculation to obtain a power flow calculation result;

[0038] Based on the power flow calculation results, the node voltage over-limit situation and the line power flow overload situation are counted, and the corresponding safety verification index values ​​are calculated to obtain the safety verification result data set of each target new energy power scenario;

[0039] Based on the safety verification result data set, the risk of the maintenance plan under different target new energy power scenarios is evaluated to obtain the expected risk value;

[0040] The conditional risk value of the maintenance plan is calculated, and the risk level of the maintenance plan is analyzed based on the expected risk value and the conditional risk value.

[0041] Furthermore, based on the new energy related data and actual power generation data, a statistical analysis method is used to calculate the prediction error characteristics to obtain the error analysis results, including:

[0042] The prediction error ε(t) in the time period [t1, t2] is calculated using the following formula:

[0043] ε(t)=P f (t)-P r (t)

[0044] Among them, P f (t) is the predicted value of new energy power; P r (t) is the actual power generation value;

[0045] as well as,

[0046] The mean μ of the prediction error is calculated using the following formula:

[0047]

[0048] Where N is the number of samples, N = t1 + t2 + 1;

[0049] as well as,

[0050] The variance σ of the prediction error is calculated using the following formula: 2 :

[0051]

[0052] The probability density function f(ε) of the prediction error is calculated using the following formula:

[0053]

[0054] The probability density function f(ε) is used to reflect the probability distribution of the prediction error.

[0055] Furthermore, based on the error analysis result, a new energy power prediction error model for dynamically predicting the error range and probability distribution of new energy power prediction is established, including:

[0056] Based on real-time meteorological data and historical error data, the time series analysis method is used to establish an autoregressive moving average model for forecasting errors. The formula is as follows:

[0057]

[0058] in, is the autoregressive coefficient, θ j is the moving average coefficient, v(t) is the white noise sequence, p is the autoregressive order, and q is the moving average order.

[0059] Further, the calculating of the uncertainty-considering new energy power value corresponding to each of the new energy power scenarios, and classifying and screening the multiple new energy power scenarios based on the new energy power value to obtain a target new energy power scenario, includes:

[0060] For each of the new energy power scenarios, random sampling is performed according to the probability distribution of the prediction error to obtain the new energy power value P considering uncertainty. s (t), the formula is as follows:

[0061] P s (t) = P f (t)+ε s (t)

[0062] Among them, ε s (t) is the error value obtained by sampling; P f (t) is the predicted value of new energy power;

[0063] Calculate the difference d between each new energy power scenario and the expected scenario i , the formula is as follows:

[0064]

[0065] Among them, the probability of scene i is P i ;P s,i (t) is the new energy power value of the desired scenario;

[0066] Based on the thresholds set for the probability and the difference, target new energy power scenarios that meet the conditions are screened out.

[0067] Furthermore, the power system flow calculation algorithm includes:

[0068] The node voltage method is used to calculate the node voltage of a power system containing n nodes. The formula is as follows:

[0069] YV=I

[0070] Where Y is the node admittance matrix; V is the node voltage vector; I is the node injection current vector;

[0071] The line admittance y connecting nodes i and j ij The calculation formula is:

[0072]

[0073] Among them, z ij Line impedance;

[0074] Node admittance matrix element Y ij The calculation formula is:

[0075]

[0076] Among them, y si Y is the node’s admittance to the ground; ij =-y si ;

[0077] The node injection current vector I of the new energy generation node is calculated using the following formula: i :

[0078]

[0079] Among them, P s,i and Q s,i are respectively the active power and reactive power of the power generation node in the scenario, is the conjugate complex number of the node voltage;

[0080] The node injection current vector I of the load node is calculated using the following formula: i :

[0081]

[0082] Among them, P L,i and Q L,i are the active power and reactive power of the load node in the scenario respectively;

[0083] For the node where the maintenance equipment is located, the corresponding injection current is adjusted according to the maintenance status, and the Newton-Raphson method is used to solve the power flow equation. The iterative formula is expressed as:

[0084] ΔV (k+1) =-(J (k) ) -1 ΔS (k)

[0085] Where, ΔV (k+1) is the voltage correction value of the k+1th iteration; J (k) is the Jacobian matrix of the kth iteration; ΔS (k) is the power imbalance at the kth iteration;

[0086] The Newton-Raphson method is used to solve the power flow equation and iterative calculation is performed until a preset convergence condition is met to obtain the operating parameters of each node and each line of the power grid.

[0087] Furthermore, based on the safety verification result data set, the risk of the maintenance plan under different target new energy power scenarios is evaluated to obtain the expected risk value, including:

[0088] The risk expectation value is calculated using risk quantification indicators, and the formula is as follows:

[0089]

[0090] Among them, P i is the probability of scene i; R i is the risk index value under scenario i, which at least includes the node voltage over-limit rate and the line power flow overload rate.

[0091] Further, the calculating of the conditional risk value of the maintenance plan and analyzing the risk level of the maintenance plan based on the expected risk value and the conditional risk value include:

[0092] The risk index values ​​are sorted from small to large, and the formula is expressed as:

[0093] R (1) ≤R (2) ≤…≤R (M)

[0094] Preset the confidence level a and find the minimum k value that satisfies the following formula:

[0095]

[0096] Among them, 0<a<1;

[0097] The conditional value at risk (CVaR) of the maintenance plan is calculated using the following formula: a :

[0098]

[0099] Based on the risk expectation value and the conditional risk value analysis, under the uncertainty of new energy, the most serious consequences that may be caused by the maintenance plan and the corresponding probability of occurrence;

[0100] and / or,

[0101] Analyze the discovery sources and influencing factors of the maintenance plan to provide adjustment suggestions for the maintenance plan.

[0102] The present invention adopts the above technical solution and has at least the following beneficial effects:

[0103] Through the scheme of the present invention, data related to new energy sources and grid operation data are collected; based on the new energy related data, multiple scenarios of new energy power are generated taking into account the prediction error of new energy power, and at least one target new energy power scenario is obtained; based on the grid operation data, a maintenance plan safety verification model based on flow calculation is constructed; based on the conditional risk mechanism algorithm, the maintenance plan safety verification model is used to perform safety verification analysis on the target new energy power scenario. Therefore, the present invention provides an innovative maintenance plan safety verification method, which fully considers the uncertainty of new energy sources, accurately analyzes and evaluates the risks under different scenarios, optimizes and adjusts the maintenance plan, thereby improving the reliability of the maintenance plan, enhancing the ability of the grid to cope with new energy fluctuations, ensuring the safe and stable operation of the grid in a complex and changeable environment, and providing a solid power guarantee for social and economic development; it overcomes the limitations of traditional verification methods, improves the comprehensiveness, accuracy and reliability of grid safety assessment, builds a solid defense line for the safe and stable operation of the power system in a new energy environment, and effectively guarantees the continuity and stability of power supply for social production and life.

[0104] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0105] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0106] Figure 1 A schematic diagram of a process flow of a maintenance plan safety verification method for a power system provided by an embodiment of the present invention is shown;

[0107] Figure 2 A schematic diagram of a process for executing multi-scenario generation of new energy power provided by an embodiment of the present invention is shown;

[0108] Figure 3 A schematic diagram of a process for performing safety verification analysis on a target new energy power scenario provided by an embodiment of the present invention is shown;

[0109] Figure 4 A schematic diagram of the structure of a maintenance plan safety verification device for a power system provided by an embodiment of the present invention is shown;

[0110] Figure 5 A schematic diagram of the physical structure of a computer device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0111] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0112] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "include..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0113] The embodiment of the present invention provides a maintenance plan safety verification method for a power system, such as Figure 1 As shown, at least the following steps S101 to S104 may be included:

[0114] Step S101, collecting new energy related data and power grid operation data;

[0115] The data related to new energy in the embodiment of the present invention may include power prediction data, meteorological data, and power generation equipment operation data. In practical applications, power prediction data of new energy such as wind power and photovoltaic power generation can be collected from multiple data sources, including prediction values ​​of different time scales (short-term, medium-term and long-term); at the same time, meteorological data closely related to new energy power generation can also be collected, such as historical data and real-time monitoring data such as wind speed, wind direction, light intensity, temperature, and air pressure; and operating parameters of new energy power generation equipment, such as the model, rated power, and pitch angle range of the wind turbine, the conversion efficiency, open circuit voltage, and short circuit current of the photovoltaic module.

[0116] It should be noted that after collecting the new energy related data, the new energy related data can also be preprocessed to obtain complete and accurate new energy related data. Specifically, the collected data can be quality checked to identify and process outliers and missing values ​​in the data. For outliers, they are corrected or deleted according to the distribution characteristics and physical meaning of the data; for missing values, appropriate interpolation methods (such as linear interpolation, spline interpolation, etc.) are used to fill them to ensure the integrity and accuracy of the data.

[0117] The power grid operation data in the embodiment of the present invention includes power grid topology data, real-time power grid operation data, load history data, load real-time forecast data, etc. In practical applications, the topology data of the power grid is collected, which may include the connection relationship and parameter information of substations, transmission lines, distribution equipment, etc., such as line impedance, transformer capacity, etc.; the real-time operation data of the power grid is collected, including the voltage, current, power, etc. of each node; and the historical data and real-time forecast data of the load are collected, including the load characteristics and change rules of different regions and different types of users (industrial, commercial, residential, etc.).

[0118] Step S102, based on the new energy related data, executing new energy power multi-scenario generation considering the new energy power prediction error, and obtaining at least one target new energy power scenario.

[0119] Specifically, the embodiment of the present invention performs multi-scenario generation of new energy power, such as Figure 2 As shown, the following steps S1021 to S1024 may be specifically included:

[0120] Step S1021, based on the new energy related data and the actual power generation data, a statistical analysis method is used to calculate the prediction error characteristics to obtain an error analysis result.

[0121] The embodiment of the present invention can analyze the characteristics of the prediction error by using statistical analysis methods (such as mean, variance, probability distribution function, etc.) based on the collected new energy power prediction data and actual power generation data.

[0122] Specifically, the prediction error ε(t) in the time period [t1, t2] can be calculated using the following formula:

[0123] ε(t)=P f (t)-P r (t)

[0124] Among them, P f (t) is the predicted value of new energy power; P r (t) is the actual power generation value;

[0125] The mean value μ of the prediction error is calculated using the following formula:

[0126]

[0127] Where N is the number of samples, N = t1 + t2 + 1;

[0128] The variance of the prediction error σ is calculated using the following formula 2 :

[0129]

[0130] By analyzing historical data, the probability density function f(ε) of the forecast error can be calculated using the following formula:

[0131]

[0132] The probability density function f(ε) is used to reflect the probability distribution of the prediction error. It can be understood that the wind power prediction error may conform to the normal distribution in some wind speed ranges.

[0133] Step S1022: Based on the error analysis result, a new energy power prediction error model for dynamically predicting the error range and probability distribution of new energy power prediction is established.

[0134] The new energy power prediction error model proposed in the embodiment of the present invention can dynamically predict the error range and probability distribution of new energy power prediction based on real-time meteorological data and historical error data, providing a basis for subsequent multi-scenario generation. Specifically, a time series analysis method can be used to establish an autoregressive moving average (ARIMA) model to predict the error:

[0135]

[0136] in, is the autoregressive coefficient, θ j is the moving average coefficient, v(t) is the white noise sequence, p is the autoregressive order, and q is the moving average order. The embodiment of the present invention estimates the model parameters through historical error data, and then predicts future errors.

[0137] Step S1023, using the Monte Carlo simulation method in combination with the new energy power prediction error model, to generate multiple new energy power scenarios.

[0138] Step S1024, calculating the new energy power value considering uncertainty corresponding to each new energy power scenario, and classifying and screening multiple new energy power scenarios based on the new energy power value to obtain a target new energy power scenario.

[0139] Specifically, for each new energy power scenario, random sampling is performed according to the probability distribution of the prediction error to obtain the new energy power value P considering uncertainty.s (t), the formula is as follows:

[0140] P s (t) = P f (t)+ε s (t)

[0141] Among them, ε s (t) is the error value obtained by sampling, while keeping other power grid operation data unchanged; P f (t) is the predicted value of the new energy power. It can be understood that, assuming that M scenarios are generated, for each time point t, the sampling is repeated times to obtain different new energy power values.

[0142] Furthermore, the embodiments of the present invention can screen and classify the generated scenarios, remove some unreasonable or overly similar scenarios, and reduce the amount of calculation. For example, the scenarios can be screened based on indicators such as the probability of the scenario and the degree of difference from the expected scenario, and representative scenarios can be retained for subsequent safety verification analysis.

[0143] Specifically, calculate the difference d between each new energy power scenario and the expected scenario i , the formula is as follows:

[0144]

[0145] Among them, the probability of scene i is P i ;P s,i (t) is the new energy power value of the expected scenario. By setting thresholds based on probability and difference, target new energy power scenarios that meet the conditions are screened out, such as high probability scenarios, extreme scenarios, or scenarios with extremely low or extremely high new energy power.

[0146] Step S103, based on the power grid operation data, construct a maintenance plan safety verification model based on power flow calculation.

[0147] Define safety verification indicators for evaluating the safety and reliability of maintenance plans in different scenarios. Safety verification indicators may include grid node voltage over-limit rate, line flow overload rate, system frequency deviation, and system reserve capacity shortage rate.

[0148] Among them, the grid node voltage over-limit rate R V The formula is:

[0149]

[0150] N V is the number of voltage-limited nodes, N T is the total number of nodes.

[0151] Line flow overload rate RL The formula is:

[0152]

[0153] Among them, N L is the number of power flow overload lines, N L0 is the total number of lines.

[0154] The formula for system frequency deviation Δf is:

[0155] Δf=ff N

[0156] Where, f is the actual system frequency, f N is the rated frequency.

[0157] System spare capacity insufficiency rate R B The formula is:

[0158]

[0159] Among them, C B is the required spare capacity of the system, C R is the actual spare capacity.

[0160] The above-mentioned safety verification indicators can fully reflect the operating status of the power grid during the maintenance period, and ensure that the power grid can meet the requirements of safe and stable operation under various circumstances. In practical applications, for each safety verification indicator, it is necessary to clarify the reasonable threshold range. When the indicator value exceeds the threshold, it indicates that there is a safety risk in the operation of the power grid, and the maintenance plan needs to be optimized and adjusted. For example, the node voltage over-limit rate threshold is set to 5%, the line flow overload rate threshold is set to 10%, etc. It should be noted that the reasonable threshold range can be set according to actual needs, and the present invention does not limit this.

[0161] Furthermore, based on the power grid operation data, an electric power system flow calculation algorithm is established; the electric power system flow calculation algorithm is used to calculate the operation parameters of each node and each line of the power grid; the operation parameters are used to calculate the safety verification index value corresponding to each safety verification index.

[0162] Specifically, the node voltage method can be used to calculate the node voltage of a power system containing n nodes, and the formula is as follows:

[0163] YV=I

[0164] Among them, Y is the node admittance matrix; V is the node voltage vector; I is the node injection current vector.

[0165] It can be understood that the node admittance matrix element Y ijThe calculation of is related to the line impedance between nodes, transformer parameters, etc. The line admittance y connecting nodes i and j ij The calculation formula is:

[0166]

[0167] Among them, z ij Line impedance.

[0168] Then, the node admittance matrix element Y ij The calculation formula can be:

[0169]

[0170] Among them, y si Y is the node's admittance to ground (such as the admittance of equipment such as parallel capacitors); ij =-y si .

[0171] Furthermore, the node injection current vector I can be determined according to the new energy power scenario and maintenance plan.

[0172] Specifically, for the power generation node, the node injection current vector I of the new energy power generation node can be calculated using the following formula: i :

[0173]

[0174] Among them, P s,i and Q s,i are respectively the active power and reactive power of the power generation node in the scenario, is the conjugate complex number of the node voltage;

[0175] For the load node, the node injection current vector I of the load node can be calculated using the following formula: i :

[0176]

[0177] Among them, P L,i and Q L,i are the active power and reactive power of the load node in the scenario respectively;

[0178] For the node where the maintenance equipment is located, the corresponding injection current is adjusted according to the maintenance status (for example, when the maintenance equipment is powered off, the injection current is zero), and the Newton-Raphson method is used to solve the power flow equation. The iterative formula is expressed as:

[0179] ΔV (k+1) =-(J (k) ) -1 ΔS (k)

[0180] Where, ΔV (k+1) is the voltage correction value of the k+1th iteration; J (k) is the Jacobian matrix of the kth iteration; ΔS (k) The power imbalance of the kth iteration is calculated by the Newton-Raphson method to solve the power flow equation, and the iterative calculation is performed until the preset convergence condition is met (such as the voltage correction is less than the given threshold), and the power flow and other operating parameters of each node and each line of the power grid are obtained, which provides a basis for the calculation of safety verification indicators.

[0181] Step S104: Based on the conditional risk mechanism algorithm, a maintenance plan safety verification model is used to perform a safety verification analysis on the target new energy power scenario.

[0182] Specifically, the embodiment of the present invention performs a safety check analysis for a target new energy power scenario, such as Figure 3 As shown, the following steps S1041 to S1044 may be specifically included:

[0183] Step S1041 , for each target new energy power scenario, a power flow calculation algorithm is used to perform power flow calculation to obtain a power flow calculation result.

[0184] Step S1042, based on the power flow calculation results, statistics are collected on node voltage over-limit situations and line power flow overload situations, and corresponding safety verification index values ​​are calculated to obtain a safety verification result data set for each target new energy power scenario.

[0185] For each selected new energy power scenario, it is substituted into the maintenance plan safety verification model to perform power flow calculation and safety verification index calculation. According to the power flow calculation results, the node voltage limit and line power flow overload conditions are counted, and the corresponding safety verification index values ​​are calculated.

[0186] It can be understood that for the node voltage over-limit rate, traversing all node voltages V i , determine whether the following formula is satisfied:

[0187] V min ≤|V i |≤V max

[0188] Among them, V max and V min are the upper and lower limits of voltage respectively; if they are not met, the voltage of the node exceeds the limit, and the number of nodes exceeding the limit N can be counted V , and then calculate the node voltage over-limit rate R V .

[0189] By recording the calculation results corresponding to each target new energy power scenario, a safety verification result data set is formed. The safety verification result data set contains information on the impact of maintenance plans on the safe operation of the power grid under different scenarios, providing data support for subsequent analysis and decision-making.

[0190] Step S1043, based on the safety verification result data set, the risk of the maintenance plan under different target new energy power scenarios is evaluated to obtain the expected risk value.

[0191] Specifically, the risk expectation value can be calculated using risk quantification indicators, and the formula is as follows:

[0192]

[0193] Among them, P i is the probability of scene i; R i is the risk indicator value under scenario i, such as node voltage over-limit rate, line power flow overload rate, etc.

[0194] Step S1044, calculating the conditional value at risk (CVaR) of the maintenance plan, and analyzing the risk level of the maintenance plan based on the expected value of risk and the conditional value at risk.

[0195] When calculating CVaR in the embodiment of the present invention, it is necessary to first sort the risk index values ​​from small to large. Assume that the risk index values ​​after sorting are:

[0196] R (1) ≤R (2) ≤…≤R (M)

[0197] For a preset confidence level a (0 < a < 1), the minimum k value that satisfies the following formula can be found:

[0198]

[0199] Then, the conditional value at risk (CVaR) of the maintenance plan can be calculated using the following formula: a :

[0200]

[0201] Furthermore, the risk level of the maintenance plan can be measured based on indicators such as risk expectation value and conditional value at risk CVaR to analyze the most serious consequences and corresponding probability of occurrence of the maintenance plan under the uncertainty of new energy.

[0202] In order to minimize the risk of the maintenance plan, the embodiment of the present invention can also analyze the discovery sources and influencing factors of the maintenance plan to provide adjustment suggestions for the maintenance plan. Such as the fluctuation range of new energy power, the importance of maintenance equipment, weak links in the power grid, etc., to provide a basis for making targeted suggestions for adjusting the maintenance plan. For example, if it is found that a certain node frequently exceeds the voltage limit when the new energy power fluctuates greatly, and there are new energy centralized access and maintenance equipment near the node, it can be judged that the risk mainly comes from the impact of new energy power fluctuations and maintenance equipment on the power grid, and the area where the node is located may be a weak link in the power grid.

[0203] The embodiment of the present invention provides a method for safety verification of maintenance plan for power system, which fully considers the uncertainty of new energy and shows excellent performance. It has at least the following advantages:

[0204] 1) In the data collection and preprocessing stage, new energy and power grid operation data are extensively collected and carefully processed to lay a solid foundation for subsequent analysis, effectively solve the problem of uneven and incomplete data quality, and enhance data availability.

[0205] 2) In terms of error analysis and scenario generation for new energy power prediction, the error characteristics are accurately analyzed to build a model, multiple scenarios are generated and reasonably screened through Monte Carlo simulation, fully considering the uncertainty of new energy and greatly improving the comprehensiveness and accuracy of safety verification.

[0206] 3) When constructing the safety verification model for the maintenance plan, scientifically determine multiple types of safety indicators and thresholds, build a power flow calculation model, and accurately simulate the operation status of the power grid, which effectively makes up for the lack of adaptability of traditional models to new energy fluctuations and strengthens the model's ability to evaluate complex working conditions.

[0207] 4) In the multi-scenario safety verification calculation and analysis phase, comprehensively calculate the safety indicators in each scenario and quantify the risks, deeply explore the risk sources and influencing factors, provide detailed basis for decision-making, and greatly improve the accuracy of risk prediction.

[0208] 5) When formulating auxiliary decision-making suggestions for maintenance plan adjustments, flexibly formulate strategies and generate clear suggestions based on risk assessment, assist grid dispatchers in making scientific decisions, and significantly reduce the probability of grid failures caused by the uncertainty of new energy.

[0209] 6) The present invention improves the grid's ability to cope with the uncertainty of new energy sources in all aspects through innovative optimization of the entire process, and ensures the safe and stable operation of the grid. Therefore, the patent of this invention can be widely used in the field of power system safety verification and operation regulation.

[0210] Further, as Figure 1 The specific implementation of the present invention provides a maintenance plan safety verification device for a power system, such as Figure 4As shown, the device may include: a data collection module 410 , a scene generation module 420 , a model construction module 430 and a safety verification module 440 .

[0211] The data collection module 410 can be used to collect data related to new energy sources and power grid operation data;

[0212] The scenario generation module 420 may be used to generate multiple scenarios of new energy power taking into account new energy power prediction errors based on new energy related data to obtain at least one target new energy power scenario;

[0213] The model building module 430 can be used to build a maintenance plan safety verification model based on power flow calculation based on power grid operation data;

[0214] The safety verification module 440 can be used to perform safety verification analysis on the target new energy power scenario based on the conditional risk mechanism algorithm and using the maintenance plan safety verification model.

[0215] It should be noted that for other corresponding descriptions of the functional modules involved in the maintenance plan safety verification device for a power system provided in an embodiment of the present invention, reference can be made to Figure 1 The corresponding description of the method shown will not be repeated here.

[0216] Based on the above Figure 1 The method shown, accordingly, an embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the maintenance plan safety verification method for the power system in any of the above embodiments are implemented.

[0217] Based on the above Figure 1 The method shown and Figure 4 The embodiment of the device shown in the figure, the embodiment of the present invention also provides a physical structure diagram of a computer device, such as Figure 5 As shown, the computer device may include a communication bus, a processor, a memory and a communication interface, and may also include an input / output interface and a display device, wherein each functional unit may communicate with each other through the bus. The memory stores a computer program, and the processor is used to execute the program stored in the memory and execute the steps of the maintenance plan safety verification method for the power system in the above embodiment.

[0218] Those skilled in the art can clearly understand that the specific working processes of the systems, devices, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments, and for the sake of brevity, they are not further described here.

[0219] In addition, the functional units in various embodiments of the present invention may be physically independent of each other, or two or more functional units may be integrated together, or all functional units may be integrated into one processing unit. The above integrated functional units may be implemented in the form of hardware, or in the form of software or firmware.

[0220] Those skilled in the art can understand that if the integrated functional unit is implemented in the form of software and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can essentially or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, which includes a number of instructions to enable a computing device (such as a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention when running the instructions. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk and other media that can store program codes.

[0221] Alternatively, all or part of the steps of implementing the aforementioned method embodiments may be accomplished by hardware associated with program instructions (such as a computing device such as a personal computer, a server, or a network device), and the program instructions may be stored in a computer-readable storage medium. When the program instructions are executed by a processor of a computing device, the computing device executes all or part of the steps of the methods described in the embodiments of the present invention.

[0222] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that within the spirit and principles of the present invention, the technical solutions described in the aforementioned embodiments can still be modified, or some or all of the technical features therein can be replaced by equivalents. However, these modifications or replacements do not deviate from the protection scope of the present invention.

Claims

1. A method for safety verification of maintenance plan for power system, characterized in that: The method comprises: Collect data related to new energy and power grid operation data; Based on the new energy related data, performing new energy power multi-scenario generation considering new energy power prediction errors to obtain at least one target new energy power scenario; Based on the power grid operation data, a maintenance plan safety verification model based on power flow calculation is constructed; Based on the conditional risk mechanism algorithm, the maintenance plan safety verification model is used to perform safety verification analysis on the target new energy power scenario; Among them, the new energy related data includes at least one of power forecast data, meteorological data and power generation equipment operation data; the power grid operation data includes power grid topology data, power grid real-time operation data and at least one of load history data and load real-time forecast data.

2. The method according to claim 1, characterized in that The performing of generating new energy power multi-scenario taking into account new energy power prediction errors based on the new energy related data to obtain at least one target new energy power scenario includes: Based on the new energy related data and actual power generation data, a statistical analysis method is used to calculate the prediction error characteristics to obtain an error analysis result; Based on the error analysis results, a new energy power prediction error model for dynamically predicting the error range and probability distribution of new energy power prediction is established; Using the Monte Carlo simulation method and combining the new energy power prediction error model, multiple new energy power scenarios are generated; A new energy power value considering uncertainty corresponding to each of the new energy power scenarios is calculated, and based on the new energy power value, the multiple new energy power scenarios are classified and screened to obtain a target new energy power scenario.

3. The method according to claim 1, characterized in that The construction of a maintenance plan safety verification model based on power flow calculation based on the power grid operation data includes: Define safety verification indicators for evaluating the safety and reliability of maintenance plans in different scenarios; the safety verification indicators include at least one of the grid node voltage over-limit rate, line power flow overload rate, system frequency deviation and system spare capacity shortage rate; Based on the power grid operation data, establishing the power system flow calculation algorithm; The power system flow calculation algorithm is used to calculate the operating parameters of each node and each line of the power grid; the operating parameters are used to calculate the safety check index value corresponding to each safety check index; Among them, the grid node voltage over-limit rate R V The formula is expressed as: Among them, N V is the number of voltage-limited nodes, N T is the total number of nodes; and / or, The line power flow overload rate R L The formula is expressed as: Among them, N L is the number of power flow overload lines, N L0 is the total number of lines; and / or, The formula of the system frequency deviation Δf is expressed as: Δf=f-f N Where, f is the actual system frequency, f N is the rated frequency; and / or, The system spare capacity deficiency rate R B The formula is expressed as: Among them, C B is the required spare capacity of the system, C R is the actual spare capacity.

4. The method according to claim 3, characterized in that The conditional risk mechanism algorithm is based on the maintenance plan safety verification model to perform safety verification analysis on the target new energy power scenario, including: For each of the target new energy power scenarios, use the power system power flow calculation algorithm to perform power flow calculation to obtain a power flow calculation result; Based on the power flow calculation results, the node voltage over-limit situation and the line power flow overload situation are counted, and the corresponding safety verification index values ​​are calculated to obtain the safety verification result data set of each target new energy power scenario; Based on the safety verification result data set, the risk of the maintenance plan under different target new energy power scenarios is evaluated to obtain the expected risk value; The conditional risk value of the maintenance plan is calculated, and the risk level of the maintenance plan is analyzed based on the expected risk value and the conditional risk value.

5. The method according to claim 2, characterized in that: The method of calculating the prediction error characteristics based on the new energy related data and the actual power generation data and obtaining the error analysis results includes: The prediction error ε(t) in the time period [t1, t2] is calculated using the following formula: ε(t)=P f (t)-P r (t) Among them, P f (t) is the predicted value of new energy power; P r (t) is the actual power generation value; as well as, The mean μ of the prediction error is calculated using the following formula: Where N is the number of samples, N = t1 + t2 + 1; as well as, The variance σ of the prediction error is calculated using the following formula: 2 : The probability density function f(ε) of the prediction error is calculated using the following formula: The probability density function f(ε) is used to reflect the probability distribution of the prediction error.

6. The method according to claim 2, characterized in that The method of establishing a new energy power prediction error model for dynamically predicting the error range and probability distribution of new energy power prediction based on the error analysis result includes: Based on real-time meteorological data and historical error data, the time series analysis method is used to establish an autoregressive moving average model for forecasting errors. The formula is as follows: in, is the autoregressive coefficient, θ j is the moving average coefficient, v(t) is the white noise sequence, p is the autoregressive order, and q is the moving average order.

7. The method according to claim 2, characterized in that The calculating of the uncertainty-considering new energy power value corresponding to each of the new energy power scenarios, and classifying and screening the multiple new energy power scenarios based on the new energy power value to obtain a target new energy power scenario, includes: For each of the new energy power scenarios, random sampling is performed according to the probability distribution of the prediction error to obtain the new energy power value P considering uncertainty. s (t), the formula is as follows: P s (t)=P f (t)+ε s (t) Among them, ε s (t) is the error value obtained by sampling; P f (t) is the predicted value of new energy power; Calculate the difference d between each new energy power scenario and the expected scenario i , the formula is as follows: Among them, the probability of scene i is P i ;P s,i (t) is the new energy power value of the desired scenario; Based on the thresholds set for the probability and the difference, target new energy power scenarios that meet the conditions are screened out.

8. The method according to claim 3, characterized in that The power system flow calculation algorithm includes: The node voltage method is used to calculate the node voltage of a power system containing n nodes. The formula is as follows: YV=I Where Y is the node admittance matrix; V is the node voltage vector; I is the node injection current vector; The line admittance y connecting nodes i and j ij The calculation formula is: Among them, z ij Line impedance; Node admittance matrix element Y ij The calculation formula is: Among them, y si Y is the node’s admittance to the ground; ij =-y si ; The node injection current vector I of the new energy generation node is calculated using the following formula: i : Among them, P s,i and Q s,i are the active power and reactive power of the power generation node in the scenario, is the conjugate complex number of the node voltage; The node injection current vector I of the load node is calculated using the following formula: i : Among them, P L,i and Q L,i are the active power and reactive power of the load node in the scenario respectively; For the node where the maintenance equipment is located, the corresponding injection current is adjusted according to the maintenance status, and the Newton-Raphson method is used to solve the power flow equation. The iterative formula is expressed as: ΔV (k+1) =-(J (k) ) -1 ΔS (k) Where, ΔV (k+1) is the voltage correction value of the k+1th iteration; J (k) is the Jacobian matrix of the kth iteration; ΔS (k) is the power imbalance at the kth iteration; The Newton-Raphson method is used to solve the power flow equation and iterative calculation is performed until a preset convergence condition is met to obtain the operating parameters of each node and each line of the power grid.

9. The method according to claim 4, characterized in that Based on the safety verification result data set, the risk of the maintenance plan under different target new energy power scenarios is evaluated to obtain the expected risk value, including: The risk expectation value is calculated using risk quantification indicators, and the formula is as follows: Among them, P i is the probability of scene i; R i is the risk index value under scenario i, which at least includes the node voltage over-limit rate and the line power flow overload rate.

10. The method according to claim 4, characterized in that The calculating of the conditional risk value of the maintenance plan and analyzing the risk level of the maintenance plan based on the expected risk value and the conditional risk value include: The risk index values ​​are sorted from small to large, and the formula is expressed as: R (1) ≤R (2) ≤…≤R (M) Preset the confidence level a and find the minimum k value that satisfies the following formula: Among them, 0<a<1; The conditional value at risk (CVaR) of the maintenance plan is calculated using the following formula: a : Based on the risk expectation value and the conditional risk value analysis, under the uncertainty of new energy, the most serious consequences that may be caused by the maintenance plan and the corresponding probability of occurrence; and / or, Analyze the discovery sources and influencing factors of the maintenance plan to provide adjustment suggestions for the maintenance plan.

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