Consider the reliability evaluation method and device of power system operation under extremely cold weather

Abstract

The application provides a power system operation reliability evaluation method and device considering extremely cold weather, which comprises the following steps: obtaining weather data; calculating the icing amount on the wind turbine blade according to the weather data, and determining a wind turbine operation reliability model based on the icing amount; constructing a power transmission line operation reliability model based on ice wind load; constructing a generator operation reliability model based on the functional relationship between the generator failure rate and the operating state; sampling the working states of the wind turbine, the power transmission line and the generator by using the Monte Carlo method to establish a system state set; calculating the reliability index and the wind power curtailment under each working state in the system state set by using an optimal load shedding model; evaluating the operation reliability of the power system according to the reliability index to obtain a reliability evaluation result. The application solves the problem in the prior art that there is a lack of system operation reliability model under extremely cold weather for the current high-proportion new energy system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power system reliability evaluation, in particular to a method for evaluating the reliability of power system operation under extremely cold weather, a device for evaluating the reliability of power system operation under extremely cold weather, a computer readable storage medium and a computer program product. BACKGROUND

[0002] Some scholars have studied the seasonal impact of wind and light on the reliability of distribution systems, and proposed a time-varying failure rate model based on the severity level of zoned weather. Some scholars have established a storm probability model based on historical data to evaluate the reliability of power systems considering different levels of storms. Some scholars have proposed a device operation reliability model considering multiple meteorological factors, but have not modeled in detail for icing weather factors. Some scholars have used wind speed, precipitation rate and ice load to establish a bad weather mathematical model, which links failure risk to weather conditions. However, no scholars have systematically modeled system operation reliability under extremely cold weather, which poses a serious challenge to accurately quantifying system reliability under extremely cold weather. It is necessary to accurately quantify the impact of time-varying conditions under extremely cold weather on system component failure rates, accurately evaluate the reliability of power systems under extremely cold weather, and then develop the correct power generation strategy to improve system reliability. SUMMARY

[0003] The main purpose of the present application is to provide a method for evaluating the reliability of power system operation under extremely cold weather, a device for evaluating the reliability of power system operation under extremely cold weather, a computer readable storage medium and a computer program product, to at least solve the problem of lack of system operation reliability model under extremely cold weather for current high proportion of new energy system in the prior art.

[0004] To achieve the above object, according to one aspect of the present application, a method for evaluating the reliability of power system operation under extremely cold weather is provided, comprising: obtaining weather data, the weather data including at least freezing rain data, temperature and wind speed, the freezing rain data including at least freezing rain mass, freezing rain volume, freezing rain speed and freezing rain duration; calculating the icing amount on the blades of a wind turbine according to the weather data, and determining a wind turbine operation reliability model based on the icing amount, the wind turbine operation reliability model being used to calculate the probability of the blades of the wind turbine stopping due to icing under the influence of extremely cold weather; calculating ice wind load according to the weather data, and constructing a transmission line operation reliability model based on the ice wind load, the ice wind load being the combined force load of the horizontal force of wind on the transmission line and the vertical force of ice on the transmission line, the transmission line operation reliability model representing the relationship between the probability of transmission line outage and the ice wind load; constructing a generator operation reliability model under the extremely cold weather based on the functional relationship between generator failure rate and operating state, the operating state being the state of the power system operation under the extremely cold weather, the generator operation reliability model being used to calculate the probability of generator outage due to the influence of the extremely cold weather; sampling the working states of the wind turbine, the transmission line and the generator by using the Monte Carlo method based on the wind turbine operation reliability model, the transmission line operation reliability model and the generator operation reliability model, and establishing a system state set; calculating the reliability index and the amount of abandoned wind power in each working state in the system state set by using an optimal load shedding model, the optimal load shedding model being a model with the minimum load shedding amount as the objective function, and power balance constraint, generator output constraint and line power flow constraint as the constraint conditions, the reliability index including loss of load probability and expected energy not supplied, and the amount of abandoned wind power being used to reflect the degree of abandoned wind power under the extremely cold weather; evaluating the reliability of the power system operation according to the reliability index, and obtaining a reliability evaluation result, the reliability evaluation result being low reliability, medium reliability or high reliability, so that a corresponding power generation strategy is executed according to the reliability evaluation result.

[0005] Optionally, calculating the icing amount on the blades of the wind turbine according to the weather data, and determining a wind turbine operation reliability model based on the icing amount, comprises: calculating the flow field distribution on the surface of the blades of the wind turbine based on the Navier-Stokes equation; and calculating the movement trajectory of the freezing rain according to a first formula, the first formula being m represents the freezing rain mass, V represents the freezing rain volume, V = 4 / 3π(D r / 2) 3 , D r represents the diameter of the freezing rain, and ρ rrepresents the density of freezing rain, g represents the acceleration of gravity, A represents the windward area of the freezing rain, A = π(D r / 2) 2 , c d represents the drag coefficient, c l represents the buoyancy coefficient, represents the air velocity vector, represents the freezing rain velocity vector, p a represents the air density; dividing the blade surface into a plurality of grid control bodies based on a Messinger icing model; establishing a mass conservation equation and an energy conservation equation for water droplets in each of the grid control bodies based on the flow field distribution and the motion trajectory, calculating the icing amount of the blade surface, the mass conservation equation being m1+m2=m3+m4+m5, m1 being the mass of water droplets impacting the current grid control body, m2 being the mass of water droplets flowing into the current grid control body, m3 being the mass of water droplets evaporating or sublimating in the current grid control body, m4 being the mass of water droplets flowing out of the current grid control body, and m5 being the mass of the current grid control body frozen into ice, m5 representing the icing amount, the current grid control body being any one of the plurality of grid control bodies, the energy conservation equation being Q1+Q2+Q3=Q4+Q5+Q6+Q7+Q8, Q1 being the heat generated by convective heat exchange between the airflow and the surface of the current grid control body, Q2 being the heat absorbed by water droplets or icing surfaces in the current grid control body during evaporation and sublimation, Q3 being the sensible heat absorbed by water droplets when heated to 0°C when the blade surface is supercooled, Q4 being the kinetic energy conversion heat when the water droplets impact the surface of the current grid control body, Q5 being the heat released when the water droplets condense into ice in the current grid control body, Q6 being the heat generated by the heating effect of air friction on the surface of the current grid control body, Q7 being the heat released when the water frozen on the blade is cooled from 0°C to the temperature of the blade surface, and Q8 being the heat released when the water flowing to the next grid control body is cooled from 0°C to the temperature of the blade surface; constructing the wind turbine operation reliability model based on the icing amount, the expression of the wind turbine operation reliability model being FOR w represents the outage rate of the wind turbine, k1, k2 and k3 are respectively the contribution coefficients of wind, icing and temperature to the outage rate of the wind turbine, v w is the real-time wind speed at the blade of the wind turbine, m5 represents the icing amount, T c is the real-time temperature at the blade of the wind turbine, is the statistical value of the forced outage rate of the wind turbine.

[0006] Optionally, the ice wind load is calculated according to the weather data, and a power transmission line operation reliability model is constructed based on the ice wind load, including: the weather data is brought into a second formula to calculate the ice thickness of the power transmission line, the second formula being R eq , T is the freezing rain duration, q represents the rainfall rate, p I is the density of ice, p W is the density of water, v represents the wind speed, and W is the water content in the air; the ice thickness is substituted into a third formula to calculate the ice wind load of the power transmission line, the third formula being wherein F IW represents the ice wind load, 9.8*10 -3 represents a conversion factor for unit conversion, D is the cable diameter, C is a constant factor, K represents a span factor, v g represents the wind speed of the gust; a failure probability of each of the power transmission lines is calculated according to the ice wind load in combination with a fourth formula, the fourth formula being wherein f l is the failure probability of the lth power transmission line, Th1 is an upper limit critical value of the ice wind load, and Th2 is a lower limit critical value of the ice wind load; the power transmission line operation reliability model is constructed according to the failure probabilities of all the power transmission lines and the line lengths corresponding to the power transmission lines, and an expression of the power transmission line operation reliability model is FIFOR l = f l L l , FIFOR l is the forced outage probability of the lth power transmission line, and L l represents the line length of the lth power transmission line.

[0007] Optionally, a generator operation reliability model under the extremely cold weather is constructed based on a functional relationship between a generator failure rate and an operation state, including: the functional relationship is determined, the functional relationship being that a distribution of the generator failure rate is a piecewise exponential function of the operation state; the generator operation reliability model is constructed according to the functional relationship, and an expression of the generator operation reliability model is i represents an index of a generator, I i is a load current of the ith generator, is a rated current of the ith generator, is a maximum tripping current of the ith generator, is a statistical value of a shutdown probability of the ith generator, and a and b are both parameters of an exponential distribution.

[0008] Optionally, before calculating the reliability index and the wind power curtailment of each of the working states in the system state set by using the optimal load shedding model, the method further comprises: determining a target function with the minimum load shedding amount as the target, and the expression of the target function is ENS m,s is the load shedding amount of the mth load in the scenario s, p s is the probability of the scenario s, NS is the total number of scenarios, the scenario represents a scenario formed by various working states, and NM is the total number of loads; constructing a power balance constraint condition according to the reference output of the generator, the output of the wind turbine, the predicted output of the load, and the load shedding amount; constructing a generator output constraint condition according to the upper limit and the lower limit of the output of the generator; constructing a wind turbine output constraint condition according to the predicted output of the wind turbine and the actual output of the wind turbine; constructing a line power flow constraint condition according to the power flow between the generator, the wind turbine, the load, and the node on the transmission line; and constructing the optimal load shedding model according to the target function, the power balance constraint condition, the generator output constraint condition, the wind turbine output constraint condition, and the line power flow constraint condition.

[0009] Optionally, calculating the reliability index and the wind power curtailment of each of the working states in the system state set by using the optimal load shedding model comprises: calculating the load shedding amount of each of the working states in the system state set by using the optimal load shedding model to obtain a load shedding state set, the load shedding state set being a set of working states in which load shedding occurs and which are calculated by using the optimal load shedding model; calculating the load shedding probability of the power system according to a fifth formula, the fifth formula being LOLP represents the load shedding probability, G1 represents the load shedding state set, p k represents the probability of the working state k in the load shedding state set, and k represents the working state index; calculating the energy not supplied expectation value of the power system according to a sixth formula, the sixth formula being EENS represents the energy not supplied expectation value, ENS m,k represents the load shedding amount of the mth load in the working state k, and NM is the total number of loads; and calculating the wind power curtailment according to a seventh formula, the seventh formula being EWC represents the wind power curtailment, G2 represents a set in which wind power curtailment occurs in the system state set, p o represents the probability of the current wind power curtailment state o, represents the current output of the rth wind turbine in the current wind power curtailment state o, is the predicted output of the rth wind turbine, and NR is the total number of wind turbines.

[0010] Optionally, the operation reliability of the power system is evaluated according to the reliability index, and a reliability evaluation result is obtained, including: in a case where the loss of load probability is greater than or equal to a first loss of load probability threshold or the energy not supplied expectation value is greater than or equal to a first energy not supplied expectation threshold, it is determined that the reliability evaluation result is that the reliability is low; in a case where the loss of load probability is less than the first loss of load probability threshold and the loss of load probability is greater than or equal to a second loss of load probability threshold, or the energy not supplied expectation value is less than the first energy not supplied expectation threshold and the energy not supplied expectation value is greater than or equal to a second energy not supplied expectation threshold, it is determined that the reliability evaluation result is that the reliability is medium, the first loss of load probability threshold is greater than the second loss of load probability threshold, and the first energy not supplied expectation threshold is greater than the second energy not supplied expectation threshold; in a case where the loss of load probability is less than the second loss of load probability threshold or the energy not supplied expectation value is less than the second energy not supplied expectation threshold, it is determined that the reliability evaluation result is that the reliability is high.

[0011] According to another aspect of the present application, there is provided an apparatus for evaluating operation reliability of a power system under extremely cold weather, comprising: an acquisition unit configured to acquire weather data, the weather data comprising at least freezing rain data, temperature and wind speed, the freezing rain data comprising at least freezing rain quality, freezing rain volume, freezing rain speed and freezing rain duration; a first determination unit configured to calculate icing amount on a blade of a wind turbine according to the weather data, and determine a wind turbine operation reliability model based on the icing amount, the wind turbine operation reliability model being used to calculate a probability that the blade of the wind turbine is frozen and stopped due to the extremely cold weather; a first construction unit configured to calculate ice wind load according to the weather data, and construct a transmission line operation reliability model based on the ice wind load, the ice wind load being a resultant force load of horizontal force of wind on the transmission line and vertical force of ice on the transmission line, the transmission line operation reliability model representing a relationship between a power outage probability of the transmission line and the ice wind load; a second construction unit configured to construct a generator operation reliability model under the extremely cold weather based on a functional relationship between generator failure rate and operation state, the operation state being a state of the power system under the extremely cold weather, the generator operation reliability model being used to calculate a probability that the generator is frozen and stopped due to the extremely cold weather; a sampling unit configured to sample working states of the wind turbine, the transmission line and the generator by using a Monte Carlo method based on the wind turbine operation reliability model, the transmission line operation reliability model and the generator operation reliability model, and establish a system state set; a calculation unit configured to calculate a reliability index and wind curtailment amount in each of the working states in the system state set by using an optimal load shedding model, the optimal load shedding model being a model with a minimum load shedding amount as an objective function, and power balance constraint, generator output constraint and line power flow constraint as constraint conditions, the reliability index comprising loss of load probability and expected energy not supplied, and the wind curtailment amount being used to reflect a wind curtailment degree of the power system under the extremely cold weather; and an evaluation unit configured to evaluate operation reliability of the power system according to the reliability index, and obtain a reliability evaluation result, so as to execute a corresponding power generation strategy according to the reliability evaluation result, the reliability evaluation result being low reliability, medium reliability or high reliability.

[0012] According to still another aspect of the present application, there is provided a computer readable storage medium comprising a stored program, wherein the program, when executed, controls a device in which the computer readable storage medium is located to perform any of the methods.

[0013] According to yet another aspect of the present application, there is provided a computer program product comprising computer instructions which, when executed by a processor, implement any of the methods.

[0014] By applying the technical solution of the present application, in the method for evaluating the reliability of the power system operation under extremely cold weather, the icing amount of the wind turbine blade under extremely cold weather is calculated by using weather data, a wind turbine operation reliability model is constructed to determine the probability of the wind turbine stopping, a transmission line operation reliability model is established based on the metal deformation theory, a generator operation reliability model is established based on the relationship between the operating state of the power system under extremely cold weather and the failure rate of the generator, the working state of the wind turbine, the generator and the transmission line is sampled by using the Monte Carlo method according to the component failure probability, a system state set is established, the reliability index and the wind curtailment amount under each state in the system state set are calculated by using the optimal load shedding model, and then the reliability index of the system under extremely cold weather is calculated, and the power system under extremely cold weather is accurately and reliably evaluated according to the reliability index. The present application solves the problem that the existing technology lacks a system operation reliability model under extremely cold weather for the current high-proportion new energy system. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 A hardware structure block diagram of a mobile terminal for executing the method for evaluating the reliability of the power system operation under extremely cold weather is shown according to an embodiment of the present application;

[0016] Figure 2 A flowchart of the method for evaluating the reliability of the power system operation under extremely cold weather is shown according to an embodiment of the present application;

[0017] Figure 3 A system topology diagram of IEEE 30 nodes is shown according to an embodiment of the present application;

[0018] Figure 4 A structure block diagram of the device for evaluating the reliability of the power system operation under extremely cold weather is shown according to an embodiment of the present application.

[0019] Among the above drawings, the following reference signs are included:

[0020] 102, processor; 104, memory; 106, transmission device; 108, input and output device. DETAILED DESCRIPTION

[0021] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other without conflict. The present application will be described in detail below with reference to the drawings and in combination with the embodiments.

[0022] In the following, the technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative effort should fall into the protection scope of the present application.

[0023] It should be noted that the terms "first", "second", and the like in the description of the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product or device.

[0024] For the convenience of description, the following describes some nouns or terms related to the embodiments of the present application:

[0025] Expected Energy Not Served (EENS): the expected number of load demand reductions due to generation capacity shortages or grid constraints within a given time period;

[0026] Loss of Load Probability (LOLP): the probability that the available generation capacity of the system is less than or equal to a certain constant load demand;

[0027] Forced Outage Rate: the probability of unplanned outage of power generation equipment or power transmission equipment.

[0028] As introduced in the background, there is currently no scholar in the prior art who has systematically modeled the system reliability under extremely cold weather, which poses a serious challenge to accurately quantifying the system reliability under extremely cold weather. To solve the problem in the prior art that there is currently a lack of system reliability model under extremely cold weather for the current high proportion of new energy system, the embodiments of the present application provide a method for evaluating the reliability of a power system under extremely cold weather, a device for evaluating the reliability of a power system under extremely cold weather, a computer readable storage medium and a computer program product.

[0029] In the following, the technical solutions in the embodiments of the present application will be described clearly and completely in conjunction with the drawings in the embodiments of the present application.

[0030] The methods and embodiments provided in this application can be executed on a mobile terminal, computer terminal, or similar computing device. Taking running on a mobile terminal as an example, Figure 1 This is a hardware structure block diagram of a mobile terminal for a power system operation reliability assessment method considering extremely cold weather, according to an embodiment of the present invention. Figure 1 As shown, a mobile terminal may include one or more ( Figure 1 Only one is shown in the diagram. A processor 102 (which may include, but is not limited to, a microprocessor MCU or a programmable logic device FPGA, etc.) and a memory 104 for storing data are also shown. The mobile terminal may further include a transmission device 106 for communication functions and an input / output device 108. Those skilled in the art will understand that... Figure 1 The structure shown is for illustrative purposes only and does not limit the structure of the mobile terminal described above. For example, the mobile terminal may also include components that are more... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown.

[0031] The memory 104 can be used to store computer programs, such as application software programs and modules, like the computer program corresponding to the power system operation reliability assessment method under extremely cold weather in this embodiment of the invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, thereby implementing the above-described method. The memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include memory remotely located relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-described networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or send data via a network. Specific examples of the above-described networks may include wireless networks provided by the mobile terminal's communication provider. In one example, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0032] A method for evaluating the reliability of a power system in extremely cold weather is provided in this embodiment, which is run on a mobile terminal, a computer terminal or a similar computing device. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical sequence is shown in the flowchart, in some cases, the steps shown or described herein can be executed in a different order.

[0033] Figure 2 is a flowchart of a method for evaluating the reliability of a power system in extremely cold weather according to an embodiment of the present application. As shown in Figure 2 , the method comprises the following steps:

[0034] Step S201, obtaining weather data, the weather data at least including freezing rain data, temperature and wind speed, the freezing rain data at least including freezing rain quality, freezing rain volume, freezing rain speed and freezing rain duration hours.

[0035] Specifically, freezing rain data, temperature and wind speed data and other data are obtained in real time from a meteorological bureau or other meteorological data service. The freezing rain data includes freezing rain quality, freezing rain volume, freezing rain speed and freezing rain duration hours. These data will be used for subsequent wind turbine and transmission line operation reliability analysis.

[0036] Step S202, calculating the amount of ice on the wind turbine blade according to the weather data, and determining a wind turbine operation reliability model based on the amount of ice, the wind turbine operation reliability model being used to calculate the probability of the wind turbine blade stalling due to icing under the influence of extremely cold weather.

[0037] Specifically, the amount of ice on the wind turbine blade is calculated according to the weather data, and a wind turbine operation reliability model is determined based on the amount of ice, which is used to calculate the probability of the wind turbine blade stalling due to icing under the influence of extremely cold weather, so as to evaluate the influence of extremely cold weather on the wind turbine and provide a basis for formulating the operation strategy of the wind turbine.

[0038] Step S203, calculating the ice wind load according to the weather data, and constructing a transmission line operation reliability model based on the ice wind load, the ice wind load being the resultant force of the horizontal force of wind on the transmission line and the vertical force of ice on the transmission line, and the transmission line operation reliability model representing the relationship between the outage probability of the transmission line and the ice wind load.

[0039] Specifically, the ice wind load is calculated according to the weather data, and a transmission line operation reliability model is constructed. For transmission lines of different lengths, the forced outage probability of the transmission line under extremely cold weather can be calculated according to the transmission line operation reliability model, so as to evaluate the influence of extremely cold weather on the transmission line and provide a basis for formulating the operation strategy of the transmission line.

[0040] Step S204, based on the function relationship between the generator failure rate and the operating state, a generator operating reliability model under the above extreme cold weather is constructed, the operating state is the state of the power system operation under the above extreme cold weather, and the generator operating reliability model is used to calculate the probability of the generator stopping due to the influence of the extreme cold weather.

[0041] Specifically, according to the function relationship between the generator failure rate and the operating state, a generator operating reliability model under the extreme cold weather is constructed, which is used to calculate the probability of the generator stopping due to the influence of the extreme cold weather, so as to evaluate the influence of the extreme cold weather on the generator and provide a basis for formulating the operation strategy of the generator.

[0042] Step S205, based on the wind turbine operating reliability model, the power transmission line operating reliability model and the generator operating reliability model, the working state of the wind turbine, the power transmission line and the generator is sampled by using the Monte Carlo method, and a system state set is established.

[0043] Specifically, according to the element failure probability, the working state of the wind turbine, the generator and the power transmission line is sampled by using the Monte Carlo method, and a system state set is established, so as to determine the possible working state of the power system and provide a basis for subsequent reliability index calculation and evaluation. Specifically, step 1), for each element (wind turbine, generator, power transmission line), according to its failure rate and maintenance time and other parameters, the probability distribution of its working (normal operation) and failure (shutdown) state is defined. These distributions can be empirical distributions based on historical data, or theoretical distributions derived from element characteristics and external conditions (such as extreme cold weather); step 2), a series of random numbers are generated using a random number generator, which are usually uniformly distributed between 0 and 1; step 3), the generated random numbers are mapped to the working or failure state of the element, which is usually realized by comparing the random number with the cumulative distribution function (CDF) of the element state, if the random number is less than or equal to the CDF value of the element in a certain state, it is considered that the element is in this state; otherwise, the element is in another state. Step 4), for each element, steps 2) and 3) are repeated multiple times to generate a large number of state samples. Each sampling represents a possible system state. Step 5), the state samples of all elements are combined to construct a system state set. Each system state is composed of the combination of all elements in that state. One of the key advantages of the Monte Carlo method is its flexibility and adaptability, which can be used to handle complex systems and uncertain conditions. However, it usually requires a large amount of computing resources because a large number of random samples need to be generated and processed. In practical applications, high-performance computing devices or parallel computing techniques may be needed to improve the efficiency of Monte Carlo simulation.

[0044] In step S206, the reliability index and the wind power curtailment in each working state in the system state set are calculated by using an optimal load shedding model. The optimal load shedding model takes the minimum load shedding amount as an objective function and takes power balance constraints, generator output constraints, and line flow constraints as constraint conditions. The reliability index includes a loss of load probability and an expected energy not supplied. The wind power curtailment is used to reflect the wind power curtailment degree of the power system in the extremely cold weather.

[0045] Specifically, for each system state, the performance of the system is evaluated, such as calculating the power supply capacity, loss of load amount, and the like of the system. This usually involves power flow calculation and optimization problem solving of the power system. By statistically analyzing the performance evaluation results of a large number of system states, system reliability indexes such as a loss of load probability (LOLP) and an expected energy not supplied (EENS) are calculated. The reliability index and the wind power curtailment in each working state in the system state set are calculated by using an optimal load shedding model, including a loss of load probability and an expected energy not supplied. The reliability index of the system is calculated according to the working state of the power system, and the operation of the power system in the extremely cold weather is evaluated, thereby providing a basis for formulating a response strategy.

[0046] In step S207, the operation reliability of the power system is evaluated according to the reliability index, and a reliability evaluation result is obtained. According to the reliability evaluation result, a corresponding power generation strategy is executed. The reliability evaluation result is low reliability, medium reliability, or high reliability.

[0047] Specifically, the operation reliability of the power system is evaluated according to the reliability index, and a reliability evaluation result is obtained. According to the reliability evaluation result, a corresponding power generation strategy is formulated. When the reliability is low, a standby power supply is increased, and when the reliability is high, resource allocation is optimized, so as to guarantee the safe and reliable operation of the power system in the extremely cold weather.

[0048] To verify the effectiveness of the solving method of the present application, an IEEE 30-node system is taken as an example for simulation verification. The detailed parameters (such as generator and line specifications) of the IEEE 30-node system are shown in Table 1. Figure 3 The wind farm is connected to nodes 20 and 24, with a capacity of 80 MW. The new energy penetration rate is 30%. The system operation reliability indexes considering the extremely cold weather and not considering the extremely cold weather are compared. For a high-proportion new energy power system, in the extremely cold weather, the large-scale off-grid of the wind turbine has a great influence on the power supply of the power system. The reduction of new energy power supply leads to more loss of load of the system. Due to the influence of the extremely cold weather, the system device failure rate rises, leading to a decrease in the reliable power supply capacity of the system. After considering the icing failure of the wind turbine, due to the decrease in the ability of the wind turbine to convert wind energy into electrical energy, the actual wind power curtailment is reduced.

[0049] Table 1 result comparison

[0050]

[0051] As can be seen in Table 1, in a high proportion of new energy penetration system, the load loss probability and the expected value of power shortage are obviously higher after considering the system operation reliability model under extremely cold weather, which shows that extremely cold weather has a greater impact on the reliability of the power system. For the amount of wind power loss, there is a slight decrease after considering the icing of wind turbines. It shows that it is necessary to consider the power system operation reliability model under extremely cold weather for power system operation reliability evaluation. From the experimental results, it can be seen that the system operation reliability under extremely cold weather is modeled, and the evaluation results show that after considering the icing of wind turbines and the fault of power transmission line, the load loss of power system increases significantly, which shows that the icing of wind turbines and the fault of power transmission line have a greater impact on the stability of the power system. For wind power loss, there is a certain degree of reduction after considering the impact of extremely cold weather. Overall, although the reliability index has decreased, these evaluation results more truly reflect the actual situation and have a guiding role for formulating actual power generation strategies.

[0052] In this embodiment, the icing amount of the wind turbine blade under extremely cold weather is calculated using weather data, a wind turbine operation reliability model is constructed to determine the probability of wind turbine shutdown, a power transmission line operation reliability model is established based on the metal deformation theory, a generator operation reliability model is established based on the relationship between the operating state of the power system under extremely cold weather and the failure rate of the generator, the working state of the wind turbine, the generator and the power transmission line is sampled using the Monte Carlo method according to the component failure probability, and a system state set is established. The reliability index and the amount of abandoned wind power in each state in the system state set are calculated using the optimal load shedding model, and then the reliability index of the system under extremely cold weather is calculated, and the power system under extremely cold weather is accurately and reliably evaluated according to the reliability index. The present application solves the problem of lack of system operation reliability model under extremely cold weather for high proportion of new energy system in the prior art.

[0053] In order for those skilled in the art to more clearly understand the technical solutions of the present application, the implementation process of the method for considering the reliability evaluation of the power system under extremely cold weather of the present application will be described in detail below in combination with specific embodiments.

[0054] In order to improve the accuracy of calculating the shutdown rate of the wind turbine, in an alternative embodiment, the above step S202 comprises:

[0055] Step S2021, calculate the flow field distribution on the surface of the above-mentioned wind turbine blade based on the Navier-Stokes equation;

[0056] Step S2022, calculating the motion trajectory of the freezing rain according to a first formula, the first formula being m represents the mass of the freezing rain, V represents the volume of the freezing rain, V = 4 / 3π(D r / 2) 3 , D r represents the diameter of the freezing rain, p r represents the density of the freezing rain, g represents the acceleration of gravity, A represents the windward area of the freezing rain, A = π(D r / 2) 2 , c d represents the drag coefficient, c l represents the buoyancy coefficient, represents the air velocity vector, represents the freezing rain velocity vector, p a represents the air density;

[0057] Step S2023, dividing the blade surface into a plurality of grid control bodies based on a Messinger icing model;

[0058] Step S2024, establishing a mass conservation equation and an energy conservation equation for water droplets in each grid control body based on the flow field distribution and the motion trajectory, calculating the icing amount of the blade surface, the mass conservation equation being m1+m2=m3+m4+m5, m1 being the mass of water droplets impacting the current grid control body, m2 being the mass of water droplets flowing into the current grid control body, m3 being the mass of water droplets evaporating or sublimating in the current grid control body, m4 being the mass of water droplets flowing out of the current grid control body, m5 being the mass of ice frozen in the current grid control body, representing the icing amount, the current grid control body being any one of the plurality of grid control bodies, the energy conservation equation being Q1+Q2+Q3=Q4+Q5+Q6+Q7+Q8, Q1 being the heat generated by convective heat exchange between the airflow and the surface of the current grid control body, Q2 being the heat absorbed by water droplets or ice surface evaporation and sublimation in the current grid control body, Q3 being the sensible heat absorbed by water droplets when heated to 0°C when the blade surface is supercooled, Q4 being the kinetic energy conversion heat when the water droplets impact the surface of the current grid control body, Q5 being the heat released when the water droplets condense into ice in the current grid control body, Q6 being the heat generated by the heating effect of air friction on the surface of the current grid control body, Q7 being the heat released when the frozen water on the blade is cooled from 0°C to the blade surface temperature, and Q8 being the heat released when the water flowing to the next grid control body is cooled from 0°C to the blade surface temperature;

[0059] Step S2025, constructing the wind turbine operation reliability model based on the icing amount, the expression of the wind turbine operation reliability model being FOR w represents the outage rate of the wind turbine, k1, k2 and k3 are the contribution coefficients of wind, icing and temperature to the outage rate of the wind turbine, v w is the real-time wind speed at the blade of the wind turbine, m5 represents the icing amount, T c is the real-time temperature at the blade of the wind turbine, is the statistical value of the forced outage rate of the wind turbine.

[0060] In the above embodiment, the flow field calculation of the blade surface is carried out by using the N-S equation (Navier-Stokes equation), i.e. the Navier-Stokes equation. The N-S equation represents that the total external force acting on the object is equal to the momentum change rate of the object in the force direction, thereby ensuring the momentum conservation in the freezing rain movement process. The expressions of the N-S equation in x, y and z directions are as follows: In the formula, p is the fluid density; is the fluid velocity vector; v x , v y and v z are the components of the velocity vector along the x, y and z directions; PRESS is the pressure on the surface of the fluid; F x , F y and F z are the body forces along the x, y and z directions. Since only gravity is affected, F x = 0, F y = 0, F z = -pg, g is the acceleration of gravity. S is the generalized source term containing viscous force; is the Hamiltonian operator, which can be expressed as follows: The expression of the generalized source term S is as follows: Based on the first formula, the trajectory of the freezing rain is calculated according to Newton's law. The icing amount on the blade surface depends on the trajectory of the water droplets and the amount of water droplets hitting the blade surface, and the amount of water droplets hitting the blade surface depends on the flow field distribution on the blade surface. Based on the Messinger model, the blade surface is divided into a plurality of grid control bodies, and in each grid control body, the mass and energy conservation equations for water droplets are established to calculate the icing amount on the blade surface. Considering the effects of temperature, icing and wind speed, the outage rate calculation of the wind turbine is as follows: It should be noted that, is a statistical value calculated from the forced outage rate at all historical time points, which can be an average value, a mean square error, a standard deviation, etc. A wind turbine operation reliability model is constructed based on the icing amount, which is used to evaluate the operation reliability of the wind turbine under different environmental conditions. Considering the effects of temperature, icing and wind speed on the performance of the wind turbine, the operation efficiency and safety of the wind turbine are improved, and the downtime and failure caused by icing are reduced.

[0061] To improve the accuracy of calculating the forced outage probability of the power transmission line, in an alternative embodiment, the step S203 comprises:

[0062] Step S2031, the above weather data into the second formula to calculate the ice thickness of the power transmission line, the second formula is R eq For the ice thickness, T is the duration of freezing rain hours, q represents the rainfall rate, p I is the density of ice, p W is the density of water, v represents the wind speed, W is the water content in the air;

[0063] Step S2032, the above ice thickness into the third formula to calculate the ice wind load of the power transmission line, the third formula is Where, F IW represents the ice wind load, 9.8x10 -3 represents the conversion factor, for unit conversion, D is the cable diameter, C is the constant, K represents the span factor, v g represents the wind speed of the gust;

[0064] Step S2033, according to the ice wind load combined with the fourth formula to calculate the failure probability of each of the power transmission line, the fourth formula is Where, f l is the failure probability of the lth power transmission line, Th1 is the upper limit of the ice wind load critical value, Th2 is the lower limit of the ice wind load critical value;

[0065] Step S2034, according to the failure probability of all the power transmission line and the line length corresponding to the power transmission line to build the power transmission line operation reliability model, the expression of the power transmission line operation reliability model is FIFOR l = f l L l , FIFOR l is the forced outage probability of the lth power transmission line, L l represents the line length of the lth power transmission line.

[0066] In the above embodiment, according to the freezing rain rainfall, freezing rain hours, wind speed, etc., based on the metal deformation theory, considering the horizontal force of wind on the power transmission line and the vertical force of ice on the line, the new ice wind load F IW, to establish a functional relationship between the transmission line failure rate and the ice wind load, and to obtain a transmission line operation reliability model under extremely cold weather conditions. This transmission line operation reliability model integrates the failure probability and the length of all transmission lines, which can help to evaluate the failure risk of the transmission line under extremely cold weather conditions and provide operation reliability evaluation of the entire power system.

[0067] To improve the accuracy of calculating the outage rate of the generator, in an alternative embodiment, the above step S204 includes:

[0068] Step S2041, determining the above functional relationship, the above functional relationship is that the distribution of the above generator failure rate is a piecewise exponential function of the above operating state;

[0069] Step S2042, constructing the above generator operation reliability model according to the above functional relationship, the expression of the above generator operation reliability model is i represents the index of the generator, I i is the load current of the i-th generator, is the rated current of the i-th generator, is the maximum tripping current of the i-th generator, is the outage probability statistical value of the i-th generator, and a and b are both parameters of the exponential distribution.

[0070] In the above embodiment, the generator failure probability distribution is a piecewise exponential function of its operating state, and the failure rate of the generator can be expressed as: That is, the generator reliability model describes the generator failure probability distribution through a piecewise exponential function. This generator reliability model takes into account multiple parameters such as the load current, rated current, and maximum tripping current of the generator, thereby enabling more accurate prediction of the failure rate of the generator. This prediction is crucial for the stable operation of the power system, as the failure of the generator will directly affect power supply. Through accurate assessment of the reliability of the generator, the reliability of the entire power system can be improved.

[0071] To ensure the balance between power generation, wind power and load within the system, while ensuring the safe operation of the transmission line, in an alternative embodiment, before the above step S206, the method further includes:

[0072] Step S301, determining a target function with the minimum load reduction as the target, the expression of the above target function is ENS m,s is the load loss of the m-th load under scenario s, p s is the probability of the occurrence of the above scenario s, NS is the total number of scenarios, the scenario represents the scenario formed by various operating states, and NM is the total number of the above loads;

[0073] Step S302, constructing a power balance constraint condition according to the reference output of the generator, the output of the wind turbine, the predicted output of the load, and the load shedding amount;

[0074] Step S303, constructing a generator output constraint condition according to the upper limit and the lower limit of the output of the generator;

[0075] Step S304, constructing a wind turbine output constraint condition according to the predicted output of the wind turbine and the actual output of the wind turbine;

[0076] Step S305, constructing a line power flow constraint condition according to the power flow between the generator, the wind turbine, the load, and the node on the transmission line;

[0077] Step S306, constructing the optimal load shedding model according to the objective function, the power balance constraint condition, the generator output constraint condition, the wind turbine output constraint condition, and the line power flow constraint condition.

[0078] In the above embodiment, the optimal load shedding model takes the minimum load shedding amount as the target, and the constraint conditions include the power balance constraint, the generator output constraint, the line power flow constraint, and the like. According to the predicted output and the actual output of the wind turbine, the output constraint condition of the wind turbine is constructed to ensure that the output of the wind turbine meets the system demand, and the wind turbine output constraint condition is is the wind power predicted output of the rth wind turbine. According to the reference output of the generator, the output of the wind turbine, the predicted output of the load, and the load shedding amount, the power balance equation is constructed to ensure the balance between power generation and load in the system and the output of the wind turbine meets the load demand, and the expression of the power balance constraint condition is is the output of the rth wind turbine under the scenario s, P i,s is the reference output of the ith generator under the scenario s, d m,s is the predicted output of the mth load under the scenario s, NR is the total number of the wind turbines, and NI is the total number of the generators. According to the power flow relationship between the generator, the wind turbine, the load, and the node on the transmission line, the line power flow constraint condition is constructed to ensure that the transmission line in the system operates within a safe and stable range, according to the upper limit and the lower limit of the output of the generator, the generator output constraint condition is constructed to ensure that the output of the generator is within a reasonable range, and the generator output constraint condition is P i min ≤ P i,s ≤ P i max , P i minPmin,i is the lower limit of the output of the i-th generator in any scenario i max Pmax,i is the upper limit of the output of the i-th generator in any scenario; the line flow constraint condition is π ln is the power transfer distribution factor of node n to transmission line l, is the associated parameter of generator i to node n, is the associated parameter of wind turbine r to node n, is the associated matrix parameter of load m to node n, FLOW l is the flow power limit value of the transmission line l. According to the objective function, the power balance constraint condition, the generator output constraint condition, the wind turbine output constraint condition and the line flow constraint condition, an optimal load shedding model is constructed to achieve the goal of minimizing the load shedding amount, ensure the balance between generation, wind power and load in the system, and ensure the safe operation of the transmission line. In this way, the operation of the power system can be effectively optimized, the energy utilization efficiency can be improved, the cost of load shedding can be reduced, and the reliability and stability of the power grid can be improved.

[0079] In order to ensure the stable operation of the power system, in an alternative embodiment, the step S206 includes:

[0080] Step S2061, calculating the load shedding amount of each working state in the system state set by using the optimal load shedding model, obtaining a load shedding state set, the load shedding state set is a set of working states in which load shedding phenomenon exists calculated by the optimal load shedding model;

[0081] Step S2062, calculating the load shedding probability of the power system according to the fifth formula, the fifth formula is LOLP represents the load shedding probability, G1 represents the load shedding state set, p k represents the probability of working state k in the load shedding state set, k represents the working state index;

[0082] Step S2063, calculating the energy deficiency expectation value of the power system according to the sixth formula, the sixth formula is EENS represents the energy deficiency expectation value, ENS m,k represents the load shedding amount of the m-th load in the working state k, NM is the total number of loads;

[0083] Step S2064, calculating the wind power curtailment according to the seventh formula, the seventh formula is EWC represents the wind power curtailment, G2 represents a set in which wind power curtailment phenomenon occurs in the system state set, p oa probability of being in the current wind curtailment state o, a current output of the rth wind turbine in the current wind curtailment state o, a predicted output of the rth wind turbine, and NR is the total number of wind turbines.

[0084] In the above embodiment, the Loss of Load Probability (LOLP) and the Expected Energy Not Served (EENS) are used to reflect the reliability level of the power system in the extremely cold weather. The Expected Wind Curtailment (EWC), i.e., the wind curtailment amount, is used to reflect the wind curtailment degree of the power system in the extremely cold weather. It should be noted that the wind curtailment amount cannot be used to evaluate the reliability of the power system, but the wind curtailment degree can reflect whether the wind turbine fails or whether a model considering the reliability of the power system in the extremely cold weather needs to be deployed. In this way, the current operation state of the power system can be better understood, the potential energy shortage can be predicted, and the utilization efficiency of the wind turbine can be evaluated. Through these calculations, measures can be taken in time to adjust the system operation, reduce the probability of energy shortage, and improve the utilization rate of the wind turbine, thereby ensuring the stable operation of the power system.

[0085] In order to improve the accuracy of the reliability evaluation of the power system, in an alternative embodiment, the step S207 comprises:

[0086] In step S2071, if the loss of load probability is greater than or equal to a first loss of load probability threshold or the expected energy not served is greater than or equal to a first expected energy not served threshold, it is determined that the reliability evaluation result is that the reliability is low.

[0087] In step S2072, if the loss of load probability is less than the first loss of load probability threshold and greater than or equal to a second loss of load probability threshold, or the expected energy not served is less than the first expected energy not served threshold and greater than or equal to a second expected energy not served threshold, it is determined that the reliability evaluation result is that the reliability is medium, the first loss of load probability threshold is greater than the second loss of load probability threshold, and the first expected energy not served threshold is greater than the second expected energy not served threshold.

[0088] In step S2073, if the loss of load probability is less than the second loss of load probability threshold or the expected energy not served is less than the second expected energy not served threshold, it is determined that the reliability evaluation result is that the reliability is higher.

[0089] In the above embodiments, it is determined whether the loss of load probability is greater than or equal to the first loss of load probability threshold or whether the energy deficiency expectation value is greater than or equal to the first energy deficiency expectation threshold. If so, it is determined that the reliability evaluation result is low reliability. It is determined whether the loss of load probability is less than the first loss of load probability threshold and greater than or equal to the second loss of load probability threshold, or whether the energy deficiency expectation value is less than the first energy deficiency expectation threshold and greater than or equal to the second energy deficiency expectation threshold. If so, it is determined that the reliability evaluation result is medium reliability. If the loss of load probability is less than the second loss of load probability threshold or the energy deficiency expectation value is less than the second energy deficiency expectation threshold, it is determined that the reliability evaluation result is low reliability. The reliability of the power system is evaluated and classified. By setting different loss of load probability thresholds and energy deficiency expectation thresholds, the system can be evaluated according to the actual situation, and the reliability of the system can be classified. This classification can help power system managers better understand the operating state of the system and take timely measures to improve the reliability of the system to ensure the stability and reliability of power supply.

[0090] It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described herein can be executed in an order different from that shown.

[0091] The embodiments of the present application also provide a device for evaluating the reliability of a power system operating in extremely cold weather. It should be noted that the device for evaluating the reliability of a power system operating in extremely cold weather according to the embodiments of the present application can be used to execute the method for evaluating the reliability of a power system operating in extremely cold weather according to the embodiments of the present application. The device is used to implement the above embodiments and preferred embodiments, which have been described. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware or a combination of software and hardware can also be implemented and conceived.

[0092] The device for evaluating the reliability of a power system operating in extremely cold weather according to the embodiments of the present application is described below.

[0093] Figure 4 is a structural block diagram of the device for evaluating the reliability of a power system operating in extremely cold weather according to the embodiments of the present application.

[0094] As shown in Figure 4 , the device includes:

[0095] The acquisition unit 100 is configured to acquire weather data, wherein the weather data at least includes freezing rain data, temperature and wind speed, and the freezing rain data at least includes freezing rain quality, freezing rain volume, freezing rain speed and freezing rain duration hours.

[0096] Specifically, the freezing rain data, temperature and wind speed data and other data are acquired in real time from a meteorological bureau or other meteorological data service. The freezing rain data includes freezing rain quality, freezing rain volume, freezing rain speed and freezing rain duration hours. These data will be used for subsequent wind turbine and transmission line operation reliability analysis.

[0097] The first determination unit 200 is configured to calculate the icing amount on the wind turbine blade according to the weather data, and determine a wind turbine operation reliability model based on the icing amount, wherein the wind turbine operation reliability model is used to calculate the probability of the wind turbine blade icing and stopping due to the influence of the extremely cold weather.

[0098] Specifically, the icing amount on the wind turbine blade is calculated according to the weather data, and the wind turbine operation reliability model is determined based on the icing amount, which is used to calculate the probability of the wind turbine blade icing and stopping due to the influence of the extremely cold weather, so as to evaluate the influence of the extremely cold weather on the wind turbine and provide a basis for formulating the operation strategy of the wind turbine.

[0099] The first construction unit 300 is configured to calculate ice wind force load according to the weather data, and construct a transmission line operation reliability model based on the ice wind force load, wherein the ice wind force load is the force load composed of the horizontal force of wind force on the transmission line and the vertical force of ice force on the transmission line, and the transmission line operation reliability model represents the relationship between the transmission line outage probability and the ice wind force load.

[0100] Specifically, the ice wind force load is calculated according to the weather data, and the transmission line operation reliability model is constructed, which can be used to calculate the forced outage probability of the transmission line under the extremely cold weather for transmission lines of different lengths, so as to evaluate the influence of the extremely cold weather on the transmission line and provide a basis for formulating the operation strategy of the transmission line.

[0101] The second construction unit 400 is configured to construct a generator operation reliability model under the extremely cold weather based on the functional relationship between the generator failure rate and the operation state, wherein the operation state is the state of the power system when it is running under the extremely cold weather, and the generator operation reliability model is used to calculate the probability of the generator stopping due to the influence of the extremely cold weather.

[0102] Specifically, according to the functional relationship between the generator failure rate and the operating state, a generator operating reliability model in extremely cold weather is constructed to calculate the probability of the generator stopping due to the influence of extremely cold weather, so as to evaluate the influence of extremely cold weather on the generator and provide a basis for formulating the operation strategy of the generator.

[0103] The sampling unit 500 is configured to sample the working states of the wind turbine, the power transmission line and the generator in the system state set by using the Monte Carlo method based on the wind turbine operating reliability model, the power transmission line operating reliability model and the generator operating reliability model.

[0104] Specifically, the working states of the wind turbine, the generator and the power transmission line are sampled by using the Monte Carlo method according to the element failure probability, and a system state set is established to determine the possible working states of the power system and provide a basis for subsequent reliability index calculation and evaluation. Specifically, step 1), for each element (wind turbine, generator, power transmission line), the probability distribution of the working (normal operation) and failure (shutdown) states is defined according to the failure rate and maintenance time and other parameters of the element. These distributions can be empirical distributions based on historical data, or theoretical distributions derived from element characteristics and external conditions (such as extremely cold weather); step 2), a series of random numbers are generated using a random number generator, which are usually uniformly distributed between 0 and 1; step 3), the generated random numbers are mapped to the working or failure state of the element, which is usually achieved by comparing the random number with the cumulative distribution function (CDF) of the element state, if the random number is less than or equal to the CDF value of the element in a certain state, it is considered that the element is in this state; otherwise, the element is in another state. Step 4), for each element, steps 2) and 3) are repeated multiple times to generate a large number of state samples. Each sampling represents a possible system state. Step 5), the state samples of all elements are combined to construct a system state set. Each system state is composed of the combination of all elements in that state. A key advantage of the Monte Carlo method is its flexibility and adaptability, which can be used to handle complex systems and uncertain conditions. However, it usually requires a large amount of computing resources because a large number of random samples need to be generated and processed. In practical applications, high-performance computing devices or parallel computing techniques may be used to improve the efficiency of Monte Carlo simulation.

[0105] The computing unit 600 is configured to calculate the reliability index and the wind curtailment in each working state in the system state set by using an optimal load shedding model, the optimal load shedding model is a model with the minimum load shedding amount as the objective function and the power balance constraint, the generator output constraint and the line power flow constraint as the constraint conditions, the reliability index includes the loss of load probability and the expected energy not supplied, and the wind curtailment is used to reflect the degree of wind curtailment of the power system in extremely cold weather.

[0106] Specifically, for each system state, the performance of the system is evaluated, such as calculating the power supply capacity, load loss amount, etc. of the system. This usually involves power flow calculation and optimization problem solving of the power system. By statistically analyzing the performance evaluation results of a large number of system states, system reliability indicators such as loss of load probability (LOLP), expected energy not supplied (EENS), etc. are calculated. The optimal load shedding model is used to calculate the reliability indicators and wind curtailment of each operating state in the system state set, including the loss of load probability and the expected energy not supplied. The reliability indicators of the system are calculated according to the operating state of the power system, and the operation of the power system under extremely cold weather is evaluated, which provides a basis for formulating response strategies.

[0107] The evaluation unit 700 is configured to evaluate the operation reliability of the power system according to the reliability indicators to obtain a reliability evaluation result, and execute a corresponding power generation strategy according to the reliability evaluation result. The reliability evaluation result is low, medium or high.

[0108] Specifically, the operation reliability of the power system is evaluated according to the reliability indicators to obtain a reliability evaluation result, and a corresponding power generation strategy is formulated according to the reliability evaluation result. When the reliability is low, the standby power supply is increased, and when the reliability is high, the resource allocation is optimized to ensure the safe and reliable operation of the power system under extremely cold weather.

[0109] In this embodiment, the icing amount of the wind turbine blade under extremely cold weather is calculated using weather data, a wind turbine operation reliability model is constructed to determine the probability of wind turbine shutdown; a transmission line operation reliability model is established based on metal deformation theory; a generator operation reliability model is established based on the relationship between the operating state of the power system under extremely cold weather and the generator failure rate; the operating state of the wind turbine, the generator and the transmission line is sampled using the Monte Carlo method according to the component failure probability, and a system state set is established; the optimal load shedding model is used to calculate the reliability indicators and wind curtailment of each state in the system state set, and then the reliability indicators of the system under extremely cold weather are calculated, and the power system under extremely cold weather is accurately and reliably evaluated according to the reliability indicators. The present application solves the problem of lack of system operation reliability model under extremely cold weather for the current high proportion of new energy system in the prior art.

[0110] In order to improve the accuracy of calculating the shutdown rate of the wind turbine, in an alternative embodiment, the first determination unit comprises:

[0111] The first calculation module is configured to calculate the flow field distribution on the surface of the blade of the wind turbine based on the Navier-Stokes equation.

[0112] A second calculation module is configured to calculate the movement trajectory of the freezing rain according to a first formula, wherein the first formula is m represents the mass of the freezing rain, V represents the volume of the freezing rain, V = 4 / 3π(D r / 2) 3 , D r represents the diameter of the freezing rain, p r represents the density of the freezing rain, g represents the acceleration of gravity, A represents the windward area of the freezing rain, A = π(D r / 2) 2 , c d represents the drag coefficient, c l represents the buoyancy coefficient, represents the air velocity vector, represents the freezing rain velocity vector, p a represents the air density;

[0113] A division module is configured to divide the blade surface into a plurality of grid control bodies based on a Messinger icing model;

[0114] A third calculation module is configured to establish a mass conservation equation and an energy conservation equation for water droplets in each grid control body based on the flow field distribution and the movement trajectory, and calculate the icing amount of the blade surface, wherein the mass conservation equation is m1+m2=m3+m4+m5, m1 represents the mass of water droplets impinging on a current grid control body, m2 represents the mass of water droplets flowing into the current grid control body, m3 represents the mass of water droplets evaporated or sublimated in the current grid control body, m4 represents the mass of water droplets flowing out of the current grid control body, and m5 represents the mass of ice frozen in the current grid control body, and the current grid control body is any one of the plurality of grid control bodies, and the energy conservation equation is Q1+Q2+Q3=Q4+Q5+Q6+Q7+Q8, Q1 represents heat generated by convective heat exchange between airflow and the surface of the current grid control body, Q2 represents heat absorbed by water droplets or ice surface evaporation and sublimation in the current grid control body, Q3 represents the sensible heat absorbed by water droplets when heated to 0℃ in the case of supercooling of the blade surface, Q4 represents kinetic energy conversion heat generated by water droplets impinging on the surface of the current grid control body, Q5 represents heat released by water droplets when condensing into ice in the current grid control body, Q6 represents heat generated by the heating effect of air friction on the surface of the current grid control body, Q7 represents heat released by water frozen on the blade when cooled from 0℃ to the temperature of the blade surface, and Q8 represents heat released by water flowing to the next grid control body when cooled from 0℃ to the temperature of the blade surface;

[0115] A first construction module is configured to construct a wind turbine operation reliability model based on the icing amount, and the expression of the wind turbine operation reliability model is FORw represents the outage rate of the wind turbine, k1, k2 and k3 are the contribution coefficients of wind, icing and temperature to the outage rate of the wind turbine, v w is the real-time wind speed at the blade of the wind turbine, m5 represents the icing amount, T c is the real-time temperature at the blade of the wind turbine, is the statistical value of the forced outage rate of the wind turbine.

[0116] In the above embodiment, the flow field calculation of the blade surface is carried out by using the N-S equation (Navier-Stokes equation), i.e. the Navier-Stokes equation. The N-S equation represents that the total external force acting on the object is equal to the momentum change rate of the object in the force direction, thereby ensuring the momentum conservation in the freezing rain movement process. The expressions of the N-S equation in x, y and z directions are as follows: In the formula, p is the fluid density; is the fluid velocity vector;v x , v y and v z are the components of the velocity vector along the x, y and z directions; PRESS is the pressure on the surface of the fluid; F x , F y and F z are the body forces along the x, y and z directions. Since only gravity is affected, F x = 0, F y = 0, F z = -pg, g is the acceleration of gravity. S is the generalized source term containing viscous force; is the Hamiltonian operator, which can be expressed as follows: The expression of the generalized source term S is as follows: Based on the first formula, the trajectory of the freezing rain is calculated according to Newton's law. The icing amount on the blade surface depends on the trajectory of the water droplets and the amount of water droplets hitting the blade surface, and the amount of water droplets hitting the blade surface depends on the flow field distribution of the blade surface. Based on the Messinger model, the blade surface is divided into a plurality of grid control bodies, and in each grid control body, the mass conservation and energy conservation equations of the water droplets are established, thereby calculating the icing amount on the blade surface. Considering the influence of temperature, icing and wind speed, the outage rate calculation of the wind turbine is as formula It should be noted that, is a statistical value calculated from the forced outage rate of all historical time points, which can be an average value, a mean square deviation, a standard deviation, etc. A wind turbine operation reliability model is constructed based on the icing amount, which is used to evaluate the operation reliability of the wind turbine under different environmental conditions. Considering the influence of temperature, icing and wind speed on the performance of the wind turbine, the operation efficiency and safety of the wind turbine are improved, and the failure and downtime caused by icing are reduced.

[0117] To improve the accuracy of calculating the forced outage probability of the power transmission line, in an alternative embodiment, the first constructing unit comprises:

[0118] A fourth calculating module is configured to calculate the icing thickness of the power transmission line by using the weather data in a second formula, wherein the second formula is R eq wherein R is the icing thickness, T is the duration of freezing rain, q represents the rainfall rate, p I is the density of ice, p W is the density of water, v represents the wind speed, and W represents the water content in the air;

[0119] A fifth calculating module is configured to calculate the ice wind load of the power transmission line by using the icing thickness in a third formula, wherein the third formula is wherein F IW represents the ice wind load, 9.8*10 -3 represents a conversion factor for unit conversion, D is the cable diameter, C is a constant factor, K represents a span factor, v g represents the wind speed of the gust;

[0120] A sixth calculating module is configured to calculate the failure probability of each power transmission line according to the ice wind load and a fourth formula, wherein the fourth formula is wherein f l is the failure probability of the lth power transmission line, Th1 is the upper limit critical value of the ice wind load, and Th2 is the lower limit critical value of the ice wind load;

[0121] A second constructing module is configured to construct a power transmission line operation reliability model according to the failure probability of all the power transmission lines and the line length of the corresponding power transmission lines, wherein the expression of the power transmission line operation reliability model is FIFOR l = f l L l , FIFOR l is the forced outage probability of the lth power transmission line, and L l represents the line length of the lth power transmission line.

[0122] In the above embodiment, according to the freezing rain rainfall, the freezing rain duration, the wind speed, and the like, a new ice wind load F IW, to establish a functional relationship between the fault rate of the transmission line and the ice wind load, and to obtain a transmission line operation reliability model under extremely cold weather conditions. This transmission line operation reliability model integrates the fault probability and line length of all transmission lines, and can help evaluate the fault risk of the transmission line under extremely cold weather conditions and provide operation reliability evaluation of the entire power system.

[0123] To improve the accuracy of calculating the outage rate of the generator, in an alternative embodiment, the second construction unit comprises:

[0124] A determination module for determining the above-mentioned functional relationship, wherein the distribution of the above-mentioned generator failure rate is a piecewise exponential function of the above-mentioned operating state;

[0125] A third construction module for constructing the above-mentioned generator operation reliability model according to the above-mentioned functional relationship, wherein the expression of the above-mentioned generator operation reliability model is i represents the index of the generator, I i is the load current of the i-th generator, is the rated current of the i-th generator, is the maximum tripping current of the i-th generator, is the outage probability statistical value of the i-th generator, and a and b are both parameters of the exponential distribution.

[0126] In the above embodiment, the distribution of the generator failure probability is a piecewise exponential function of its operating state, and the failure rate of the generator can be expressed as: That is, the generator reliability model describes the distribution of the generator failure probability through a piecewise exponential function. This generator reliability model takes into account multiple parameters such as the load current, rated current, and maximum tripping current of the generator, thereby enabling more accurate prediction of the failure rate of the generator. Such prediction is crucial for the stable operation of the power system, as the failure of the generator directly affects power supply. Through accurate assessment of the reliability of the generator, the reliability of the entire power system can be improved.

[0127] To ensure the balance between power generation, wind power and load within the system, while ensuring the safe operation of the transmission line, in an alternative embodiment, the device further comprises:

[0128] A second determination unit for determining an objective function with the minimum load reduction amount as the target before calculating the reliability index and the amount of abandoned wind power in each of the above-mentioned operating states in the above-mentioned system state set using the optimal load reduction model, wherein the expression of the above-mentioned objective function is ENS m,s is the loss of load of the m-th load under scenario s, p sThe probability of the above-mentioned scenario s, NS is the total number of scenarios, the above-mentioned scenario represents various scenarios formed by the above-mentioned working state, NM is the total number of the above-mentioned load;

[0129] The third construction unit is used for constructing a power balance constraint condition according to the reference output of the generator, the output of the wind turbine, the predicted output of the load, and the load loss amount;

[0130] The fourth construction unit is used for constructing a generator output constraint condition according to the upper limit and the lower limit of the output of the generator;

[0131] The fifth construction unit is used for constructing a wind turbine output constraint condition according to the predicted output of the wind turbine and the actual output of the wind turbine;

[0132] The sixth construction unit is used for constructing a line power flow constraint condition according to the power flow between the generator, the wind turbine, the load and the node on the transmission line;

[0133] The seventh construction unit is used for constructing the optimal load shedding model according to the target function, the power balance constraint condition, the generator output constraint condition, the wind turbine output constraint condition and the line power flow constraint condition.

[0134] In the above-mentioned embodiment, the optimal load shedding model: taking the minimum load shedding amount as the target, the constraint conditions include power balance constraint, generator output constraint, line power flow constraint and the like. According to the predicted output and the actual output of the wind turbine, the output constraint condition of the wind turbine is constructed to ensure that the output of the wind turbine meets the system demand, and the wind turbine output constraint condition is The wind power predicted output of the rth wind turbine. According to the reference output of the generator, the output of the wind turbine, the predicted output of the load and the load loss amount, the power balance equation is constructed to ensure the balance between power generation and load in the system and the output of the wind turbine meets the load demand, and the expression of the power balance constraint condition is The output of the rth wind turbine under the scenario s, P i,s The reference output of the ith generator under the scenario s, d m,s The predicted output of the mth load under the scenario s, NR is the total number of wind turbines, and NI is the total number of generators. According to the power flow relationship between the generator, the wind turbine, the load and the node on the transmission line, the line power flow constraint condition is constructed to ensure that the transmission line in the system operates within a safe and stable range, according to the upper limit and the lower limit of the output of the generator, the generator output constraint condition is constructed to ensure that the output of the generator is within a reasonable range, and the generator output constraint condition is P i min ≤P i,s ≤Pi max P i min Pil is the lower limit of the output of the i-th generator in any scenario, i max Pil is the upper limit of the output of the i-th generator in any scenario; the line flow constraint condition is π ln πnl is the power transfer distribution factor of node n to transmission line l, βin is the association parameter of generator i to node n, βrn is the association parameter of wind turbine r to node n, βmn is the association matrix parameter of load m to node n, FLOW l Pil is the power flow limit value of the transmission line l. According to the objective function, the power balance constraint condition, the generator output constraint condition, the wind turbine output constraint condition and the line flow constraint condition, an optimal load shedding model is constructed to achieve the goal of minimizing the load shedding amount, ensuring the balance between generation, wind power and load in the system, and ensuring the safe operation of the transmission line. This can effectively optimize the operation of the power system, improve energy utilization efficiency, reduce the cost of load shedding, and improve the reliability and stability of the power grid.

[0135] In order to ensure the stable operation of the power system, in an alternative embodiment, the calculation unit comprises:

[0136] A seventh calculation module is configured to calculate the load shedding amount of each operating state in the system state set by using the optimal load shedding model, and obtain a load shedding state set, wherein the load shedding state set is a set of operating states in which load shedding occurs calculated by the optimal load shedding model.

[0137] An eighth calculation module is configured to calculate the load shedding probability of the power system according to a fifth formula, wherein the fifth formula is LOLP represents the load shedding probability, G1 represents the load shedding state set, p k p(k) represents the probability of operating state k in the load shedding state set, and k represents the operating state index.

[0138] A ninth calculation module is configured to calculate the energy deficiency expectation value of the power system according to a sixth formula, wherein the sixth formula is EENS represents the energy deficiency expectation value, ENS m,k p(k) represents the probability of operating state k in the load shedding state set, and k represents the operating state index.

[0139] A tenth calculation module is configured to calculate the wind power curtailment amount according to a seventh formula, wherein the seventh formula is EWC represents the above-mentioned wind curtailment, G2 represents the above-mentioned system state set in which the wind curtailment occurs, p o represents the probability of being in the current wind curtailment state o, represents the current output of the rth wind turbine in the current wind curtailment state o, is the predicted output of the rth wind turbine, and NR is the total number of wind turbines.

[0140] In the above embodiment, the Loss of Load Probability (LOLP) and the Expected Energy Not Served (EENS) are used to reflect the reliability level of the power system in extremely cold weather. The Expected Wind Curtailment (EWC), i.e., the above-mentioned wind curtailment, is used to reflect the degree of wind curtailment of the power system in extremely cold weather. It should be noted that the wind curtailment cannot be used to evaluate the reliability of the power system, and the degree of wind curtailment can reflect whether the wind turbine has failed and whether a model considering the reliability of the power system in extremely cold weather needs to be deployed. The current operation state of the power system can be better understood, the potential energy shortage can be predicted, and the utilization efficiency of the wind turbine can be evaluated. Through these calculations, measures can be taken in time to adjust the system operation, reduce the probability of energy shortage, and improve the utilization rate of the wind turbine, thereby ensuring the stable operation of the power system.

[0141] In order to improve the accuracy of the reliability evaluation of the power system, in an alternative embodiment, the evaluation unit comprises:

[0142] The first determination module is configured to determine that the reliability evaluation result is that the reliability is low when the loss of load probability is greater than or equal to a first loss of load probability threshold or the expected energy not served is greater than or equal to a first expected energy not served threshold.

[0143] The second determination module is configured to determine that the reliability evaluation result is that the reliability is medium when the loss of load probability is less than the first loss of load probability threshold and greater than or equal to a second loss of load probability threshold, or the expected energy not served is less than the first expected energy not served threshold and greater than or equal to a second expected energy not served threshold. The first loss of load probability threshold is greater than the second loss of load probability threshold, and the first expected energy not served threshold is greater than the second expected energy not served threshold.

[0144] The third determination module is configured to determine that the reliability evaluation result is high reliability when the load loss probability is less than the second load loss probability threshold or the power shortage expectation value is less than the second power shortage expectation threshold.

[0145] In the above embodiment, it is determined whether the load loss probability is greater than or equal to the first load loss probability threshold or the power shortage expectation value is greater than or equal to the first power shortage expectation threshold. If yes, it is determined that the reliability evaluation result is low reliability. It is determined whether the load loss probability is less than the first load loss probability threshold and greater than or equal to the second load loss probability threshold or the power shortage expectation value is less than the first power shortage expectation threshold and greater than or equal to the second power shortage expectation threshold. If yes, it is determined that the reliability evaluation result is medium reliability. If the load loss probability is less than the second load loss probability threshold or the power shortage expectation value is less than the second power shortage expectation threshold, it is determined that the reliability evaluation result is low reliability. The reliability of the power system is evaluated and classified. By setting different load loss probability thresholds and power shortage expectation thresholds, the system can be evaluated according to the actual situation, and the reliability of the system can be classified. This classification can help power system managers better understand the operation state of the system and take timely measures to improve the reliability of the system to ensure the stability and reliability of power supply.

[0146] The above power system reliability evaluation device considering extreme cold weather includes a processor and a memory, and the above acquisition unit, the first determination unit and the first construction unit are all stored in the memory as program units. The corresponding functions are realized by the processor executing the above program units stored in the memory. The above modules are all located in the same processor; or, the above modules are located in different processors in any combination.

[0147] The processor contains a core, and the core retrieves the corresponding program unit from the memory. The core can be set to one or more, and the problem of lacking a system operation reliability model under extreme cold weather for the current high proportion of new energy system in the prior art can be solved by adjusting the core parameters.

[0148] The memory can include non-persistent memory in a computer readable medium, random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.

[0149] The embodiment of the application provides a computer readable storage medium, and the computer readable storage medium includes a stored program. When the program runs, the device where the computer readable storage medium is located executes the power system reliability evaluation method considering extreme cold weather.

[0150] The embodiment of the present application provides a processor for running a program, wherein the processor is used for executing the method for evaluating the reliability of power system operation under extremely cold weather.

[0151] The embodiment of the present application provides a system for evaluating the reliability of power system operation under extremely cold weather, which comprises a processor, a memory, and a program stored in the memory and capable of running on the processor, and the processor realizes at least the following steps when executing the program:

[0152] In step S201, weather data is acquired, wherein the weather data at least comprises freezing rain data, temperature and wind speed, and the freezing rain data at least comprises freezing rain quality, freezing rain volume, freezing rain speed and freezing rain duration hours;

[0153] In step S202, the icing amount on the blade of a wind turbine is calculated according to the weather data, and a wind turbine operation reliability model is determined based on the icing amount, wherein the wind turbine operation reliability model is used for calculating the probability of blade icing and shutdown of the wind turbine caused by the influence of extremely cold weather;

[0154] In step S203, ice wind load is calculated according to the weather data, and a transmission line operation reliability model is constructed based on the ice wind load, wherein the ice wind load is the force load composed of the horizontal force of wind on the transmission line and the vertical force of ice on the transmission line, and the transmission line operation reliability model represents the relationship between the outage probability of the transmission line and the ice wind load;

[0155] In step S204, a generator operation reliability model under the extremely cold weather is constructed based on the functional relationship between the generator failure rate and the operation state, wherein the operation state is the state of the power system operation under the extremely cold weather, and the generator operation reliability model is used for calculating the probability of generator shutdown caused by the influence of the extremely cold weather on the generator;

[0156] In step S205, the working states of the wind turbine, the transmission line and the generator are sampled by using the Monte Carlo method based on the wind turbine operation reliability model, the transmission line operation reliability model and the generator operation reliability model, and a system state set is established;

[0157] Step S206, the reliability index and the wind power curtailment in each of the working states in the system state set are calculated by using an optimal load shedding model, the optimal load shedding model is a model taking the minimum load shedding amount as an objective function, and taking power balance constraint, generator output constraint, and line power flow constraint as constraint conditions, the reliability index includes loss of load probability and expected energy not supplied, and the wind power curtailment is used for reflecting the wind power curtailment degree of the power system in the extremely cold weather;

[0158] Step S207, the operation reliability of the power system is evaluated according to the reliability index, and a reliability evaluation result is obtained, the corresponding power generation strategy is executed according to the reliability evaluation result, and the reliability evaluation result is low reliability, medium reliability, or high reliability.

[0159] The application also provides a computer program product suitable for executing a program having at least one step of a method for evaluating the operation reliability of a power system in extremely cold weather when executed on a data processing device.

[0160] Obviously, those skilled in the art should understand that the modules or steps of the application can be realized by a general computing device, which can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, which can be realized by program codes executable by the computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in different order, or they can be manufactured into individual integrated circuit modules, or multiple modules or steps can be manufactured into a single integrated circuit module. Therefore, the application is not limited to any specific combination of hardware and software.

[0161] Those skilled in the art should understand that the embodiments of the application can be provided as a method, a system, or a computer program product. Therefore, the application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the application can be in the form of a computer program product implemented on one or more computer usable storage media containing computer usable program codes (including but not limited to disk storage, CD-ROM, optical storage, etc.).

[0162] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof.

[0163] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof.

[0164] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof. Figure 1 one or more flowcharts and / or blocks in the flowcharts and / or combination thereof.

[0165] In one typical configuration, the computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0166] The memory can include non-persistent memory and / or volatile memory, such as a random access memory (RAM) including a cache area for the temporary storage of data. A

[0167] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory computer-readable media such as modulated data signals and carriers.

[0168] It should also be noted that the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that processes, methods, articles or devices including a series of elements not only include those elements, but also include other elements not explicitly listed or inherent to such processes, methods, articles or devices. Without more limitations, the element defined by the statement "including a" does not exclude the presence of additional identical elements in the process, method, article or device including the element.

[0169] From the above description, it can be seen that the above-mentioned embodiments of the present application achieve the following technical effects:

[0170] 1) The method for evaluating the reliability of the power system in extremely cold weather according to the present application calculates the icing amount of the wind turbine blade in extremely cold weather using weather data, constructs a wind turbine operation reliability model to determine the probability of wind turbine shutdown, establishes a transmission line operation reliability model based on metal deformation theory, establishes a generator operation reliability model based on the relationship between the operating state of the power system in extremely cold weather and the failure rate of the generator, samples the working state of the wind turbine, the generator and the transmission line according to the component failure probability using the Monte Carlo method to establish a system state set, calculates the reliability index and the amount of abandoned wind power in each state in the system state set using the optimal load shedding model, and further calculates the reliability index of the system in extremely cold weather, thereby accurately evaluating the reliability of the power system in extremely cold weather according to the reliability index. The present application solves the problem of lack of system operation reliability model in extremely cold weather for the current high proportion of new energy system in the prior art.

[0171] 2) The application considers the reliability evaluation device of the power system operation under extremely cold weather, calculates the icing amount of the wind turbine blade under extremely cold weather by using weather data, constructs a wind turbine operation reliability model to determine the probability of wind turbine stop; establishes a transmission line operation reliability model based on the metal deformation theory; establishes a generator operation reliability model based on the relationship between the operating state of the power system under extremely cold weather and the generator failure rate; according to the component failure probability, the Monte Carlo method is used to sample the working state of the wind turbine, the generator and the transmission line, and the system state set is established; the optimal load shedding model is used to calculate the reliability index and the wind curtailment under each state in the system state set, and then the reliability index of the system under extremely cold weather is calculated, and the power system under extremely cold weather is accurately and reliably evaluated according to the reliability index. In view of the problem that the current high proportion of new energy system lacks a system operation reliability model under extremely cold weather. The application solves the problem that the current high proportion of new energy system lacks a system operation reliability model under extremely cold weather in the prior art.

[0172] The above only describes the preferred embodiments of the application and is not intended to limit the application. Those skilled in the art can make various modifications and changes to the application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the application shall be included in the protection scope of the application.

Claims

1. A method for evaluating the reliability of power system operation under extremely cold weather, characterized in that, The method comprises: acquiring weather data, the weather data comprising at least freezing rain data, temperature and wind speed, the freezing rain data comprising at least freezing rain mass, freezing rain volume, freezing rain speed and freezing rain duration; calculating icing amount on blades of a wind turbine according to the weather data, and determining a wind turbine operation reliability model based on the icing amount, the wind turbine operation reliability model being used to calculate a probability of the wind turbine stopping due to icing of the blades under extremely cold weather; calculating ice wind force according to the weather data, and constructing a transmission line operation reliability model based on the ice wind force, the ice wind force being a resultant force of horizontal force of wind on the transmission line and vertical force of ice on the transmission line, the transmission line operation reliability model representing a relationship between a power outage probability of the transmission line and the ice wind force; constructing a generator operation reliability model under the extremely cold weather based on a functional relationship between a generator failure rate and an operation state, the operation state being a state of the power system under the extremely cold weather, the generator operation reliability model being used to calculate a probability of the generator stopping due to the extremely cold weather; sampling working states of the wind turbine, the transmission line and the generator by using a Monte Carlo method based on the wind turbine operation reliability model, the transmission line operation reliability model and the generator operation reliability model, to establish a system state set; calculating a reliability index and wind curtailment in each of the working states in the system state set by using an optimal load shedding model, the optimal load shedding model being a model with a minimum load shedding amount as an objective function, and power balance constraint, generator output constraint and line power flow constraint as constraint conditions, the reliability index comprising a loss of load probability and an energy not supplied expectation value, and the wind curtailment being used to reflect a wind curtailment degree of the power system under the extremely cold weather; evaluating operation reliability of the power system according to the reliability index to obtain a reliability evaluation result, and executing a corresponding power generation strategy according to the reliability evaluation result, the reliability evaluation result being low reliability, medium reliability or high reliability, before calculating the reliability index and the wind curtailment in each of the working states in the system state set by using the optimal load shedding model, the method further comprises: A target function is determined with a minimum load reduction as a target, and an expression of the target function is , is a load loss amount of the mth load in the scenario s, p s is a probability of the scenario s occurring, NS is a total number of scenarios, the scenario represents a scenario formed by various working states, and NM is a total number of the loads; A power balance constraint condition is constructed according to the reference output of the generator, the output of the wind generator, the predicted output of the load and the loss load amount, and the expression of the power balance constraint condition is , is the output of the rth wind generator under the scene s, is the reference output of the ith generator under the scene s, is the predicted output of the mth load under the scene s, NR is the total number of the wind generators, and NI is the total number of the generators. A generator output constraint condition is constructed according to the upper limit and the lower limit of the output of the generator, and the generator output constraint condition is , is the lower limit of the output of the i-th generator under any scenario, is the upper limit of the output of the i-th generator under any scenario; A wind turbine output constraint condition is constructed according to the predicted output of the wind turbine and the actual output of the wind turbine, and the wind turbine output constraint condition is , is the wind power predicted output of the rth wind turbine. According to the power flow between the generators, the wind generators, the loads and the nodes on the transmission line, a line power flow constraint condition is constructed, and the line power flow constraint condition is , is a power transfer distribution factor of a node n to a transmission line l, is an associated parameter of an i th generator to a node n, is an associated parameter of an r th wind generator to a node n, is an associated matrix parameter of an m th load to a node n, is a power flow limit value of the transmission line l; constructing the optimal load shedding model according to the objective function, the power balance constraint condition, the generator output constraint condition, the wind turbine output constraint condition and the line power flow constraint condition.

2. The method of claim 1, wherein, calculating icing amount on blades of a wind turbine according to the weather data, and determining a wind turbine operation reliability model based on the icing amount, comprises: calculating flow field distribution on a blade surface of the wind turbine based on Navier-Stokes equation; According to a first formula for calculating the trajectory of the freezing rain, the first formula is , m represents the mass of the freezing rain, V represents the volume of the freezing rain, , represents the diameter of the freezing rain, represents the density of the freezing rain, represents the acceleration of gravity, A represents the frontal area of the freezing rain, , represents the drag coefficient, represents the buoyancy coefficient, represents the air velocity vector, represents the freezing rain velocity vector, represents the air density; dividing the blade surface into a plurality of grid control bodies based on a Messinger icing model; The mass conservation equation and the energy conservation equation are established for water droplets in each grid control body based on the flow field distribution and the motion trajectory, and the ice amount on the blade surface is calculated, the mass conservation equation is m1 is the mass of water droplets impacting on the current grid control body, m2 is the mass of water droplets flowing into the current grid control body, m3 is the mass of water droplets evaporating or sublimating in the current grid control body, m4 is the mass of water droplets flowing out of the current grid control body, m5 is the mass of the current grid control body frozen into ice, and m represents the ice amount, the current grid control body is any one of the plurality of grid control bodies, and the energy conservation equation is , is the heat generated by the convection heat exchange between the air flow and the surface of the current grid control body, is the heat absorbed by the evaporation and sublimation of water droplets or ice surface in the current grid control body, is the sensible heat absorbed by water droplets when heated to 0℃ when the blade surface is supercooled, is the kinetic energy conversion heat when the water droplets impact the surface of the current grid control body, is the heat released when water droplets condense into ice in the current grid control body, is the heat generated by the heating effect of air friction on the surface of the current grid control body, is the heat released when the frozen water on the blade is cooled from 0℃ to the temperature of the blade surface, is the heat released when the water flowing to the next grid control body is cooled from 0℃ to the temperature of the blade surface. constructing the wind turbine operation reliability model based on the icing amount, an expression of the wind turbine operation reliability model is , represents an outage rate of the wind turbine, , and are contribution coefficients of wind, icing and temperature to the outage rate of the wind turbine respectively, is a real-time wind speed at a blade of the wind turbine, represents the icing amount, is a real-time temperature at the blade of the wind turbine, is a forced outage rate statistical value of the wind turbine.

3. The method of claim 1, wherein, calculating ice wind force according to the weather data, and constructing a transmission line operation reliability model based on the ice wind force, comprises: The weather data is substituted into a second formula to calculate the ice thickness of the power transmission line, the second formula being , is the ice thickness, T is the duration of freezing rain in hours, q represents the rainfall rate, is the density of ice, is the density of water, represents the wind speed, represents the water content in the air; substituting the ice thickness into a third formula for calculating the ice wind load of the power transmission line, the third formula being wherein, represents the ice wind load, represents a conversion factor for unit conversion, D is a cable diameter, C is a constant factor, and K represents a span factor, represents a wind speed of a gust; According to the ice wind load, a failure probability of each of the transmission lines is calculated by a fourth formula, the fourth formula being wherein, is the failure probability of the lth transmission line, is an upper limit critical value of the ice wind load, is a lower limit critical value of the ice wind load; The power transmission line operation reliability model is constructed according to the fault probability of all the power transmission lines and the line length corresponding to the power transmission lines, and the expression of the power transmission line operation reliability model is , is the forced outage probability of the power transmission line of the lth item, represents the line length of the power transmission line of the lth item.

4. The method of claim 1, wherein, The function relationship is determined, and the function relationship is that the distribution of the generator failure rate is a segmented exponential function of the operating state. The function relationship is determined, and the function relationship is that the distribution of the generator failure rate is a segmented exponential function of the operating state. According to the function relationship, a generator operation reliability model is constructed, and an expression of the generator operation reliability model is , i represents an index of a generator, is a load current of the i-th generator, is a rated current of the i-th generator, is a maximum tripping current of the i-th generator, is a statistical value of a shutdown probability of the i-th generator, and a and b are both parameters of an exponential distribution.

5. The method of claim 1, wherein, The reliability index and the wind power curtailment of each operating state in the system state set are calculated by using the optimal load shedding model, including: The loss of load quantity of each operating state in the system state set is calculated by using the optimal load shedding model to obtain a loss of load state set, and the loss of load state set is a set of operating states in which the loss of load phenomenon exists and is calculated by using the optimal load shedding model. calculating the loss of load probability of the power system according to a fifth formula , LOLP represents the loss of load probability, G1 represents the loss of load state set, represents the probability of operating state k in the loss of load state set, k represents the index of the operating state; The power system power shortage expectation value is calculated according to a sixth formula , The power system power shortage expectation value is calculated according to a sixth formula The power system power shortage expectation value is calculated according to a sixth formula The amount of wind curtailment is calculated according to the seventh formula, which is: EWC represents the amount of wind curtailment, and G2 represents the set of systems in which wind curtailment occurs. This indicates the current state of wind curtailment. The probability, This indicates that the r-th wind turbine is in the current wind curtailment state. The current output of the next step Let NR be the predicted output of the r-th wind turbine, and NR be the total number of wind turbines.

6. The method of claim 1, wherein, The operating reliability of the power system is evaluated according to the reliability index to obtain a reliability evaluation result, including: In a case where the loss of load probability is greater than or equal to a first loss of load probability threshold or the energy deficiency expectation value is greater than or equal to a first energy deficiency expectation threshold, it is determined that the reliability evaluation result is low reliability. In a case where the loss of load probability is less than the first loss of load probability threshold and greater than or equal to a second loss of load probability threshold, or the energy deficiency expectation value is less than the first energy deficiency expectation threshold and greater than or equal to a second energy deficiency expectation threshold, it is determined that the reliability evaluation result is medium reliability, the first loss of load probability threshold is greater than the second loss of load probability threshold, and the first energy deficiency expectation threshold is greater than the second energy deficiency expectation threshold. In a case where the loss of load probability is less than the second loss of load probability threshold or the energy deficiency expectation value is less than the second energy deficiency expectation threshold, it is determined that the reliability evaluation result is high reliability.

7. A device for assessing the reliability of power system operation under extremely cold weather conditions, characterized in that, The device includes: An acquisition unit is configured to acquire weather data, the weather data including at least freezing rain data, temperature, and wind speed, and the freezing rain data including at least freezing rain quality, freezing rain volume, freezing rain speed, and freezing rain duration hours; A first determination unit is configured to calculate an icing amount on a wind turbine blade according to the weather data, and determine a wind turbine operating reliability model based on the icing amount, the wind turbine operating reliability model being used to calculate a probability of wind turbine blade icing and shutdown caused by the extremely cold weather; A first construction unit is configured to calculate an ice wind load according to the weather data, and construct a transmission line operating reliability model based on the ice wind load, the ice wind load being a resultant force load of a horizontal force of wind on a transmission line and a vertical force of ice on the transmission line, and the transmission line operating reliability model representing a relationship between a transmission line outage probability and the ice wind load; A second construction unit is configured to construct a generator operating reliability model in the extremely cold weather based on a function relationship between a generator failure rate and an operating state, the operating state being a state of a power system during operation in the extremely cold weather, and the generator operating reliability model being used to calculate a probability of generator shutdown caused by the extremely cold weather. a sampling unit configured to sample working states of the wind turbine, the power transmission line and the generator by using a Monte Carlo method based on the wind turbine operation reliability model, the power transmission line operation reliability model and the generator operation reliability model, and to establish a system state set; a calculating unit configured to calculate a reliability index and a wind curtailment amount in each of the working states in the system state set by using an optimal load shedding model, the optimal load shedding model being a model with a minimum load shedding amount as an objective function and with a power balance constraint, a generator output constraint and a line power flow constraint as constraint conditions, the reliability index including a loss of load probability and an expected energy not supplied, and the wind curtailment amount being used to reflect a wind curtailment degree of the power system in the extremely cold weather; an evaluating unit configured to evaluate the operation reliability of the power system according to the reliability index to obtain a reliability evaluation result, the reliability evaluation result being low reliability, medium reliability or high reliability, and to execute a corresponding power generation strategy according to the reliability evaluation result, The device further comprises a second determining unit, configured to determine a target function with the minimum load reduction amount as the target before calculating the reliability index of each of the working states in the system state set and the wind power curtailment amount by using the optimal load reduction model, and the expression of the target function is , is the load loss amount of the mth load under the scenario s, p s is the probability of the scenario s occurring, NS is the total number of scenarios, the scenario represents a scenario formed by various working states, and NM is the total number of loads. a third constructing unit configured to construct a power balance constraint condition according to the reference output of the generator, the output of the wind turbine, the predicted output of the load, and the load loss amount, wherein the power balance constraint condition is expressed as , is the output of the rth wind turbine in the s scene, is the reference output of the ith generator in the s scene, is the predicted output of the mth load in the s scene, NR is the total number of the wind turbines, and NI is the total number of the generators. a fourth constructing unit configured to construct a generator output constraint condition according to the upper limit of output and the lower limit of output of the generator, the generator output constraint condition being , the lower limit of output of the i-th generator in any scenario, the upper limit of output of the i-th generator in any scenario; A fifth constructing unit is configured to construct a wind turbine output constraint condition according to the predicted output of the wind turbine and the actual output of the wind turbine, wherein the wind turbine output constraint condition is , is the wind power predicted output of the rth wind turbine. A sixth constructing unit is configured to construct a line power flow constraint condition according to power flow between the generators, the wind power generators, the loads and nodes on the transmission line, and the line power flow constraint condition is , is a power transfer distribution factor of a node n to a transmission line l, is an associated parameter of an i-th generator to a node n, is an associated parameter of an r-th wind power generator to a node n, is an associated matrix parameter of an m-th load to a node n, is a power flow limit value of the transmission line l; a seventh constructing unit configured to construct the optimal load shedding model according to the objective function, the power balance constraint condition, the generator output constraint condition, the wind turbine output constraint condition and the line power flow constraint condition.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises a stored program, wherein the program controls a device where the computer readable storage medium is located to execute the method in any one of claims 1 to 6 when the program is running.

9. A computer program product comprising computer instructions, characterized in that, The computer instructions are executed by the processor to implement the method in any one of claims 1 to 6.

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