Reliability evaluation method for novel power distribution network containing high-permeability new energy

Abstract

The invention relates to a reliability evaluation method for a novel power distribution network containing high-permeability new energy. The method comprises the following steps: S1, constructing a wind power generation probability distribution model based on Weibull distribution; s2, constructing a photovoltaic power generation probability distribution model based on Beta distribution; s3, defining a reliability evaluation index of the power distribution network; and S4, based on the wind power generation probability distribution model, the photovoltaic power generation probability distribution model and the power distribution network reliability evaluation index, performing reliability evaluation on the operation of the power distribution network by using a sequential Monte Carlo method. According to the method, higher adaptability and accuracy can be shown in a high-permeability new energy scene, the dynamic behavior of the power distribution network can be reflected more truly, and the operation state and potential risk of the power distribution network under the high permeability of new energy can be known more comprehensively.

Description

Technical Field

[0001] The present invention relates to the field of power systems, in particular to distribution network reliability assessment technology, and more particularly to a distribution network operation reliability assessment method suitable for access of a high proportion of distributed renewable energy. Background Art

[0002] As the global energy structure transforms to clean energy, the access ratio of distributed renewable energy such as wind power generation and photovoltaic power generation continues to increase. While reducing carbon emissions, this change also poses new challenges to the operational reliability of distribution networks. Distributed renewable energy has significant intermittent and random characteristics, which increases the power flow fluctuations and uncertainty of the distribution network, thereby affecting the stability and security of the system. Therefore, how to accurately evaluate the operational reliability of distribution networks with high penetration of renewable energy has become a hot issue in current power system research.

[0003] In the prior art, the distribution network reliability assessment methods used are mostly for traditional passive distribution systems, which are difficult to effectively deal with the complex power flow and uncertainty after the access of new energy. For example, the invention patent with the publication number "CN 113673085 A" proposes a distribution network reliability assessment method considering switch control failure, by analyzing the power supply path between the load point and the fault component in the distribution network, calculating the probability of switch failure and its impact on system reliability, and thus evaluating the power supply reliability index of the distribution network under various automation modes; the invention patent with the publication number "CN 117669176 A" proposes a distribution network reliability assessment method based on network simplification method, which realizes comprehensive data collection and processing through Internet of Things technology, simplifies the distribution network structure by using graph theory simplification method, and combines deep neural network model for evaluation and optimization.

[0004] However, the above two solutions still have the following problems: they lack sufficient consideration of the dynamic changes in the distribution network and the randomness of new energy sources, and may not be able to make effective adjustments in a timely manner when faced with the dynamic uncertainties brought about by new energy sources. Summary of the invention

[0005] The purpose of the present invention is to provide a method for more accurately and comprehensively evaluating the reliability of a distribution network in recent years.

[0006] In order to achieve the above object, the technical solution adopted by the present invention is:

[0007] A new distribution network reliability assessment method containing high-penetration renewable energy sources comprises the following steps:

[0008] Step S1: constructing a wind power generation probability distribution model based on Weibull distribution;

[0009] Step S2: constructing a photovoltaic power generation probability distribution model based on Beta distribution;

[0010] Step S3: defining distribution network reliability evaluation indicators;

[0011] Step S4: Based on the wind power generation probability distribution model, the photovoltaic power generation probability distribution model and the distribution network reliability evaluation index, the reliability of the distribution network operation is evaluated using the sequential Monte Carlo method.

[0012] In the step S1, the wind power generation probability distribution model includes a probability distribution function of wind power output power and a probability density of wind power output power;

[0013] The probability distribution function of the wind power output is:

[0014]

[0015] Where P w and P w,r Respectively represent the actual power and rated power output of the wind turbine; v, v r 、v c,in and v c,out They represent the actual wind speed, rated wind speed, cut-in and cut-out wind speeds of wind turbines, respectively. c and k represent the scale parameter and shape parameter of Weibull distribution, respectively.

[0016] Then v c,in ≤v≤v r When , the probability density of wind power output is:

[0017]

[0018] The wind power generation probability distribution model is obtained by using the relationship function between the output power of the wind turbine and the wind speed and the probability distribution function and probability density function of the actual wind speed represented by the Weibull distribution;

[0019] The relationship function between the output power of the wind turbine and the wind speed is:

[0020]

[0021] The probability distribution function and probability density function of the actual wind speed expressed by Weibull distribution are:

[0022]

[0023] The scale parameter c and shape parameter k of the Weibull distribution satisfy:

[0024]

[0025] In the formula, and are the mean and standard deviation of wind speed respectively.

[0026] In step S2, the photovoltaic power generation probability distribution model includes a probability distribution function of photovoltaic power generation output power and a probability density of photovoltaic power generation output power;

[0027] The probability distribution function of the photovoltaic power generation output power and the probability density of the photovoltaic power generation output power are:

[0028]

[0029] In the formula, p pv,max represents the maximum output power of photovoltaic power generation; α and β are the parameters of the Beta distribution Γ of solar radiation intensity, expressed as:

[0030]

[0031] Where μ and σ are the mean and standard deviation of the solar radiation intensity ratio, respectively.

[0032] The output power of the photovoltaic power generation system is expressed as:

[0033]

[0034] In the formula, p pv 、i pv and i 0 They represent the output power, photocurrent and equivalent diode reverse saturation current of the photovoltaic system respectively; T, G and T n , G n are the actual ambient temperature, radiation intensity and the nominal ambient temperature, radiation intensity respectively; i pv,n and V t,n T = 25 ° C and G = 1000 W / m 2 The photocurrent of the equivalent diode and N s The thermal voltage output by the series-connected photovoltaic units; R s and R p Respectively represent n s The series resistance of the photovoltaic cells and n p The parallel resistance of each photovoltaic unit; q, a and k are the electron charge constant, diode constant and Boltzman constant respectively; K V , K I and V oc,n ,I sc,n They are voltage, current coefficient, open circuit voltage and short circuit current respectively.

[0035] In step S3, the distribution network reliability assessment index includes the reliability assessment index of the distribution network components, the reliability assessment index of the load point, the reliability assessment index of the distribution network as a whole and the reliability assessment index of the power consumption of the distribution network users;

[0036] The reliability evaluation indicators of the distribution network components include the failure rate, repair rate, trouble-free working time and repair time of the distribution network components; the reliability evaluation indicators of the load points include the average failure rate, fault-related power outage time and average annual power outage time of the load points; the reliability evaluation indicators of the distribution network as a whole include the system power supply shortage indicator; the reliability evaluation indicators of the distribution network users' electricity consumption include the average power outage time, average power outage frequency and power supply availability.

[0037] The reliability evaluation index of the distribution network components is described as:

[0038]

[0039] Where λ and ν represent the failure rate and repair rate of distribution network components respectively; n m ,t o 、n r and t r They represent the number of failures of distribution network components, the total operating time, the number of repairs during the maintenance time, and the total maintenance time; t nor and t rep represents the trouble-free working time and the repair time respectively; num is a random number uniformly distributed between (0,1);

[0040] The reliability evaluation index of the load point is described as:

[0041]

[0042] In the formula, λ i represents the average number of power outages at load point i in a year; j and V i They represent the failure rate of distribution network component j and the set of components in the distribution network that can cause failure at load point i; t i,out represents the average fault outage time of load point i within one year; t' i,out represents the average fault outage time of load point i;

[0043] The reliability evaluation index of the distribution network as a whole is described as:

[0044] ρ=Σ i∈I t i,out l i ,

[0045] Where ρ represents the average power loss caused by all power outage load points due to faults in the distribution network within one year; l i represents the average load at load point i; I represents the set of all fault load points in the distribution network;

[0046] The reliability evaluation index of the power consumption of the distribution network users is described as:

[0047]

[0048] In the formula, τ time It represents the average power outage time of each user in the distribution network within one year; n i and M represent the number of users at load point i and the set of all load points in the distribution network system respectively; τ freq Represents the average number of power outages for each user in the distribution network within a year; τ use It indicates the ratio of normal power supply time without faults to users in the distribution network within a year.

[0049] The step S4 comprises the following steps:

[0050] Step 1): Initialize the network parameters, structure and simulation time t of the distribution network sim ;

[0051] Step 2): Calculate the fault-free working time t of each component in the distribution network nor , and form a time series within the simulation duration;

[0052] Step 3): Compare the fault-free working time t of each component in the distribution network nor , determine the trouble-free working time t nor The smallest component is the faulty component, and the simulation time is accumulated t sim =t sim +t nor，min , where t nor，min is the trouble-free working time of the faulty component;

[0053] Step 4): Calculate the repair time t of the faulty component rep and the repair time t of the switching element rep , and then determine the fault conditions of each load point in the distribution network;

[0054] Step 5): Simulate the distribution network. When the distribution network forms an island operation state, generate the output power of the renewable energy power generation through random sampling; when the power output of the renewable energy in the island is greater than or equal to the load, each load point operates normally; when the power output of the renewable energy in the island is less than the load, the loads are removed in order of importance from small to large until the balance between power supply and demand is achieved, and the fault time and number of the load points are increased in turn; the above process is repeated until the faulty component is repaired;

[0055] Step 6): When the set simulation time is reached, the simulation is terminated, and the reliability evaluation index of the distribution network is calculated according to each state quantity in the simulation process.

[0056] Due to the application of the above-mentioned technical scheme, the present invention has the following advantages compared with the prior art: the present invention can show higher adaptability and accuracy in new energy scenarios with high penetration rates, can more realistically reflect the dynamic behavior of the distribution network, and help to more comprehensively understand the operating status and potential risks of the distribution network under high penetration rates of new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 It is a schematic flow chart of the method of the present invention.

[0058] Figure 2 It is a flow chart of reliability assessment of active distribution network based on SMC of the present invention.

[0059] Figure 3 This is the distribution network model under high penetration rate of new energy of the present invention.

[0060] Figure 4 is the average failure rate λ of each load point of the present invention i .

[0061] Figure 5 is the average annual power outage time t of each load point in the present invention i,out . DETAILED DESCRIPTION

[0062] The present invention will be further described below in conjunction with the embodiments shown in the accompanying drawings.

[0063] Embodiment 1: As shown in the attached Figure 1 As shown, a new distribution network reliability assessment method containing high penetration rate new energy includes the following steps:

[0064] Step S1: construct a wind power generation probability distribution model based on Weibull distribution. The wind power generation probability distribution model includes a probability distribution function of wind power output power and a probability density of wind power output power.

[0065] The specific steps are as follows: the output power of the distributed wind power generation system in the distribution network is mainly related to the wind speed. The relationship between the output power of the wind turbine and the wind speed can be approximately expressed by a piecewise function as follows:

[0066] The relationship function between the output power of a wind turbine and wind speed is:

[0067]

[0068] In formula (1), P w and P w,r Respectively represent the actual power and rated power output of the wind turbine; v, v r 、v c,in and v c,out They respectively represent the actual wind speed, rated wind speed, and the cut-in and cut-out wind speeds of the wind turbine.

[0069] The probability distribution function and probability density function of the actual wind speed expressed by Weibull distribution are:

[0070]

[0071] In formula (2), c and k represent the scale parameter and shape parameter of Weibull distribution respectively, satisfying:

[0072]

[0073] In formula (3), and are the mean and standard deviation of wind speed respectively.

[0074] The wind power generation probability distribution model is obtained by using the relationship function between the output power and wind speed of the wind turbine and the probability distribution function and probability density function of the actual wind speed represented by Weibull distribution. That is, from equations (1) and (2), the probability distribution function of wind power output power is:

[0075]

[0076] Then we can get v c,in ≤v≤v r When , the probability density of wind power output is:

[0077]

[0078] Step S2: construct a photovoltaic power generation probability distribution model based on Beta distribution. The photovoltaic power generation probability distribution model includes a probability distribution function of photovoltaic power generation output power and a probability density of photovoltaic power generation output power.

[0079] The specific steps are as follows: the output power of photovoltaic power generation equipment in the distribution network is mainly affected by the ambient temperature and the intensity of solar radiation. The output power of the photovoltaic power generation system is expressed as:

[0080]

[0081] In formula (6), p pv 、i pv and i 0 They represent the output power, photocurrent and equivalent diode reverse saturation current of the photovoltaic system respectively; T, G and T n , G n are the actual ambient temperature, radiation intensity and the nominal ambient temperature, radiation intensity respectively; i pv,n and V t,n T = 25 ° C and G = 1000 W / m 2 The photocurrent of the equivalent diode and N s The thermal voltage output by the series-connected photovoltaic units; R s and R p Respectively represent n s The series resistance of the photovoltaic cells and n p The parallel resistance of each photovoltaic unit; q, a and k are the electron charge constant, diode constant and Boltzman constant respectively; K V , K I and V oc,n ,I sc,n They are voltage, current coefficient, open circuit voltage and short circuit current respectively.

[0082] In general, due to the limited change of ambient temperature, the output power of photovoltaic power generation is mainly affected by the radiation intensity. The Beta distribution is used to describe the solar radiation intensity, and the probability distribution function and probability density of photovoltaic power generation output power are obtained as follows:

[0083]

[0084] In formula (7), p pv,max represents the maximum output power of photovoltaic power generation; α and β are the parameters of the Beta distribution Γ of solar radiation intensity, expressed as:

[0085]

[0086] In formula (8), μ and σ are the mean and standard deviation of the solar radiation intensity ratio, respectively.

[0087] Step S3: Define distribution network reliability evaluation indicators.

[0088] The specific steps are as follows: the factors related to the reliability assessment of the distribution network under the high penetration rate of new energy mainly include the reliability of the distribution network components, the reliability of the load points and the reliability of the system as a whole. The distribution network reliability assessment indicators include the reliability assessment indicators of the distribution network components, the reliability assessment indicators of the load points, the reliability assessment indicators of the distribution network as a whole and the reliability assessment indicators of the electricity consumption of the distribution network users.

[0089] The reliability evaluation indicators of distribution network components include the failure rate, repair rate, fault-free working time and repair time of distribution network components, which are described by formula (9):

[0090]

[0091] In formula (9), λ and ν represent the failure rate and repair rate of distribution network components respectively; n m ,t o 、n r and t r They represent the number of failures of distribution network components, the total operating time, the number of repairs during the maintenance time, and the total maintenance time; t nor and t rep They represent the trouble-free working time and the repair time respectively; num is a random number uniformly distributed between (0,1).

[0092] Based on the definition of component failure rate and repair rate, the reliability of the load point can be further described. The reliability evaluation indicators of the load point include the average failure rate of the load point, the failure power outage time and the average annual power outage time. The reliability evaluation indicators of the load point are described as follows:

[0093]

[0094] In formula (10), λ i It represents the average number of power outages at load point i in a year. The more power outages there are, the lower the power supply reliability is. j and V i They represent the failure rate of distribution network component j and the set of components in the distribution network that can cause failure at load point i; t i,out It represents the average fault outage time of load point i in one year. The longer the outage time, the lower the power supply reliability; t' i,out It represents the average fault outage time of load point i.

[0095] Based on the reliability index of formula (9) and (10), the reliability of the overall power supply and utilization of the distribution network under high penetration of new energy can be further defined. The reliability evaluation index of the overall distribution network includes the system power supply shortage index. The reliability evaluation index of the overall distribution network is described as:

[0096] ρ=Σ i∈I ti,out l i (11)

[0097] In formula (11), ρ represents the average value of power loss caused by all power outage load points due to faults in the distribution network within one year; l i represents the average load at load point i; I represents the set of all fault load points in the distribution network.

[0098] The reliability evaluation indicators of power consumption of distribution network users include average power outage time, average power outage frequency and power supply availability. The description of each indicator is shown in formula (12):

[0099]

[0100] In formula (12), τ time It represents the average power outage time of each user in the distribution network within one year; n i and M represent the number of users at load point i and the set of all load points in the distribution network system respectively; τ freq represents the average power outage frequency, that is, the average number of power outages for each user in the distribution network within a year; τ use It indicates the ratio of normal power supply time without faults to users in the distribution network within a year. The larger the ratio, the greater the reliability of the power supply of the distribution network.

[0101] Step S4: Based on the wind power generation probability distribution model, the photovoltaic power generation probability distribution model and the distribution network reliability evaluation index, the reliability of the distribution network operation is evaluated using the Sequential Monte Carlo method (SMC).

[0102] As attached Figure 2 As shown, step S4 includes the following steps:

[0103] Step 1): Initialize the network parameters, structure and simulation time t of the distribution network sim ;

[0104] Step 2): Calculate the trouble-free working time t of each component in the distribution network nor , and form a time series within the simulation duration;

[0105] The elements in this step refer to various key devices and components in the power grid. They mainly include:

[0106] Line: Power line responsible for the transmission of electrical energy;

[0107] Transformer: A device that regulates voltage, ensuring that electrical energy can be transferred between different voltage levels;

[0108] Circuit breakers and switches: used to control the flow of electrical current and protect systems from short circuits or other electrical faults;

[0109] Distributed power sources (such as wind turbines and photovoltaic arrays): act as power generation units in active distribution networks.

[0110] Switching components are also a subcategory within "Components".

[0111] The time series composition within the simulation duration: fault-free working time (t nor ), repair time (t rep ), simulation time (t sim ). Its function:

[0112] ① Simulate the operation and fault status of components: Time series can accurately simulate the operation cycle of each component, from trouble-free operation to failure, and then to re-operation after repair. Through this sequential simulation, the state changes of different components in the distribution network can be captured to understand their reliability performance in long-term operation;

[0113] ② Help determine the timing of system failure events: Through time series, it is possible to determine when component failures occur, as well as the duration and impact of these failures. This helps analyze how failure events propagate in the system and which components are susceptible to chain reactions;

[0114] ③ Evaluate the overall reliability of the system: By analyzing the failure frequency and repair time during the entire simulation period, the overall reliability indicators of the system can be statistically calculated, such as the average failure rate, average power outage time, etc. These indicators are an important basis for evaluating the long-term reliability of the system. The trouble-free working time and repair time in the time series can provide detailed data to help calculate the average power supply reliability of the load point and the system.

[0115] Step 3): Compare the fault-free working time t of each component in the distribution network nor , determine the trouble-free working time t nor The smallest component is the faulty component, and the simulation time is accumulated t sim =t sim +t nor，min , where t nor，min is the trouble-free working time of the faulty component;

[0116] Step 4): Calculate the repair time t of the faulty component rep and the repair time t of the switching element rep , and then determine the fault conditions of each load point in the distribution network;

[0117] Determining the fault conditions of each load point in the distribution network mainly includes:

[0118] Analyze the repair time: In the SMC simulation, whenever a component failure is detected, the status of all load points related to it will be recorded. If the repair time of the faulty component is long, these load points will be in a long-term power outage state;

[0119] Determine the scope of the power outage: By analyzing the faulty components and their associated lines and nodes, the system can determine which load points will be affected;

[0120] Function of the switch element: Assume that when a fault occurs, the switch element acts to isolate the fault area. If the switch element works properly, it can ensure that only a specific area is out of power, not the entire system. On the contrary, if the switch element itself fails, its repair time will directly affect the fault recovery speed of the load point;

[0121] Island operation processing: In some cases, if the distribution network forms an island operation after a fault occurs (i.e., the distributed generation continues to supply power), it is necessary to decide which load points can continue to operate based on the power generation capacity and load demand within the island. If the power generation is insufficient, the system will prioritize the removal of less important load points according to their importance until power balance is achieved.

[0122] Step 5): Simulate the distribution network. When the distribution network forms an island operation state, generate the output power of renewable energy power generation through random sampling; when the output power of renewable energy in the island is greater than or equal to the load, each load point operates normally; when the output power of renewable energy in the island is less than the load, the loads are removed in order of importance from small to large until the balance between power supply and demand is achieved, and the fault time and number of load points are increased in turn; the above process is repeated until the faulty component is repaired;

[0123] The specific method of increasing the fault time of a load point is as follows: When the distribution network enters the island operation state, the system first determines the total power generation in the island based on the renewable energy power generated by random sampling. This power generation is compared with the total power demand of all load points in the island. If the power generation is less than the total load demand, it means that the current power supply is insufficient and load shedding is required. Whenever a load point is shedding, the system will accumulate the fault time of the load point. Specifically, the current simulation time t sim The time difference between the time when the load is removed and the time when the fault is restored is the fault time to be added. During the entire simulation process, each time a load is removed, the fault time needs to be accumulated continuously. If a load point is removed multiple times during the entire simulation, the fault time of each power outage will be accumulated into the total fault time of the load point.

[0124] When a component is detected to be faulty during simulation, the system immediately calculates a repair time t for the component. repThis time represents the time required from the occurrence of a fault to the restoration of the component to normal operation. The repair time is usually determined by the repair rate of the component, which may be set based on statistical data or historical experience. In simulation, the repair time can be simulated by random sampling, probability distribution (such as exponential distribution), etc. During the simulation process, whenever a component fails, the system records the current simulation time t current And start accumulating time. The system will calculate the repair time according to the set repair time t rep To calculate the estimated repair completion time t current +t rep As the simulation time advances, the current simulation time t sim Reach or exceed t current +t rep When it is detected, it means that the repair time of the faulty component has arrived and the component can resume normal operation.

[0125] Step 6): When the set simulation time is reached, the simulation is terminated, and the reliability evaluation index of the distribution network is calculated according to each state quantity during the simulation process.

[0126] A specific example is as follows:

[0127] The present invention uses a 10kV active distribution network model for generating electricity using renewable energy Figure 3 As shown in the figure, the main parameters of the active distribution network model are as follows: The system has 15 lines k 1 -k 15 , 8 load points L 1 -L 8 , 8 fuses f 1 -f 8 , 3 circuit breakers cb 1 -cb 3 , transformer capacity is 50MVA, ratio is 35kV / 10.5kV, load is L 1 =2MW, L 2 =L 3 =1MW, L 4 =L 5 =0.5MW, L 6 =L 7 =L 8 =1MW, the power factor of each load is 0.9, the line parameter k 3 to k 4 is 3km, k 5 to k 6 The main line is 4km, and the other lines are 2km. The line parameters of each branch line are 0.27+0.335iΩ / km. 1 and DG 2They are wind turbines and photovoltaic units respectively. Two groups of parameters are set during the experiment: the rated capacity of the first group is DG 1 =1.5MW, DG 2 =1MW, the rated total capacity of DG accounts for about 25% of the total load; the rated capacity parameters are DG 1 =2.5MW, DG 2 =2MW, the rated total capacity of distributed generation (DG) accounts for about 45% of the total load. During the experiment, the simulation duration was set to 5 years, the island formation time was about 0.25h, the power change of each load point obeyed the normal distribution, and the standard deviation was 10% of the expected load of each load point. The wind speed of wind power generation and the solar radiation intensity and temperature of photovoltaic power generation used the measured data of a certain place within 1 year as a reference. Considering the long simulation time, the same wind speed, radiation intensity and temperature data were used every year; during the experiment, the main focus was on the impact of the penetration rate of new energy on the reliability of distribution network operation, so DG was ignored. 1 and DG 2 Own failure factors.

[0128] The present invention provides three schemes for calculating and analyzing the reliability of the above distribution network: 1 To not access DG 1 and DG 2 In the case of (DG permeability is 0), S 2 For DG 1 =1.5MW and DG 2 =1MW (DG penetration is about 25%), S 3 For DG 1 =2.5MW and DG 2 =2MW case (DG penetration is about 45%).

[0129] Figure 4 and Figure 5 Shown is S 1 ~S 3 Comparison of reliability indicators of each load point in the distribution network under the following circumstances. Figure 4 It can be seen that in S 1 ~S 3 Under the three new energy penetration rates, the average failure rate index of each load point did not change significantly. This is because the main target of the experiment in this paper is the impact of new energy penetration on the reliability of the distribution network, and the change in system reliability caused by the failure of DG itself after access is not considered. Figure 5 It can be seen that in S 1 ~S 3Under the three new energy penetration rates, after DG access, the average fault outage time of load points 1 to 5 is reduced, and the reliability is improved with the increase of new energy penetration rate, while the reliability of load points 6 to 8 has not changed significantly. The reason is that after DG access, load points 1 to 5 can continue to operate in an isolated manner after an operation failure of the distribution network occurs, that is, DG 1 and DG 2 It can continue to supply power to the load points in the island in the event of a fault. The higher penetration rate of new energy can ensure that there are fewer loads that lose power in the island, providing higher reliability. However, load points 6 to 8 cannot form an island operation state, so the average failure time has not changed significantly. The above reliability indicators show that after the new energy is connected to the distribution network, the operation reliability of the relevant load points of the distribution network can be improved, especially the load points that can form an island operation with the connected DG.

[0130] Table 1 shows the 1 ~S 3 Comparison of reliability indicators of the overall operation of the distribution network under three scenarios of renewable energy penetration. As shown in Table 1, for the power supply end, the access of renewable energy reduces the average power outage value ρ of the distribution network as a whole, and as the renewable energy penetration rate increases, the reliability indicator gradually decreases. At the same time, for the user end, the average power outage time indicator τ time and the average available power supply rate index τ use The reason is that after the access of new energy, the power supply reliability of some load points (load points 1 to 5) that can form an island operation state is improved, which brings about the improvement of the overall operation reliability of the distribution network system, and the average power outage frequency index τ of the system as a whole freq No significant changes occurred because the main goal of the experiment was to examine the impact of penetration on reliability, without considering the impact of new energy failure factors. The above experiments show that with the increase of new energy penetration in the distribution network, the reliability of some load points has been improved due to the existence of island operation, thereby improving the overall operation reliability of the distribution network to a certain extent.

[0131] Table 1 Comparison of distribution network reliability under different penetration rates

[0132]

[0133] The beneficial effects of the present invention are:

[0134] 1. The evaluation based on the Sequential Monte Carlo method adopted in this invention can simulate various operating scenarios after the access of high-proportion distributed renewable energy, and capture the uncertainty caused by the fluctuation and randomness of renewable energy output in the distribution network. This makes the method more adaptable and accurate in the scenario of high penetration of renewable energy, and can more realistically reflect the dynamic behavior of the distribution network;

[0135] 2. The present invention can not only simulate a variety of faults and operating scenarios, but also provide comprehensive reliability indicators based on the simulation results, such as system outage frequency, outage time, load point failure rate, etc. The comprehensiveness and accuracy of these indicators are much higher than the traditional method based on simplified models, which helps to more comprehensively understand the operating status and potential risks of the distribution network under high penetration of new energy.

[0136] The above embodiments are only for illustrating the technical concept and features of the present invention, and their purpose is to enable people familiar with the technology to understand the content of the present invention and implement it accordingly, and they cannot be used to limit the protection scope of the present invention. Any equivalent changes or modifications made according to the spirit of the present invention should be included in the protection scope of the present invention.

Claims

1. A new distribution network reliability assessment method containing high penetration rate new energy, characterized by: The reliability assessment method of the new distribution network containing high-penetration new energy sources comprises the following steps: Step S1: constructing a wind power generation probability distribution model based on Weibull distribution; Step S2: constructing a photovoltaic power generation probability distribution model based on Beta distribution; Step S3: defining distribution network reliability evaluation indicators; Step S4: Based on the wind power generation probability distribution model, the photovoltaic power generation probability distribution model and the distribution network reliability evaluation index, the reliability of the distribution network operation is evaluated using the sequential Monte Carlo method.

2. The reliability assessment method of a new distribution network containing high-penetration new energy according to claim 1 is characterized in that: In the step S1, the wind power generation probability distribution model includes a probability distribution function of wind power output power and a probability density of wind power output power; The probability distribution function of the wind power output is: Where P w and P w,r Respectively represent the actual power and rated power output of the wind turbine; v, v r 、v c,in and v c,out They represent the actual wind speed, rated wind speed, cut-in and cut-out wind speeds of wind turbines, respectively. c and k represent the scale parameter and shape parameter of Weibull distribution, respectively. Then v c,in ≤v≤v r When , the probability density of wind power output is:

3. The reliability assessment method of a new distribution network containing high-penetration new energy according to claim 2 is characterized in that: The wind power generation probability distribution model is obtained by using the relationship function between the output power of the wind turbine and the wind speed and the probability distribution function and probability density function of the actual wind speed represented by the Weibull distribution; The relationship function between the output power of the wind turbine and the wind speed is: The probability distribution function and probability density function of the actual wind speed expressed by Weibull distribution are:

4. The reliability assessment method of a new distribution network containing high-penetration new energy according to claim 3 is characterized in that: The scale parameter c and shape parameter k of the Weibull distribution satisfy: In the formula, and are the mean and standard deviation of wind speed respectively.

5. The reliability assessment method of a new distribution network containing high-penetration new energy according to claim 1 is characterized in that: In step S2, the photovoltaic power generation probability distribution model includes a probability distribution function of photovoltaic power generation output power and a probability density of photovoltaic power generation output power; The probability distribution function of the photovoltaic power generation output power and the probability density of the photovoltaic power generation output power are: In the formula, p pv,max represents the maximum output power of photovoltaic power generation; α and β are the parameters of the Beta distribution Γ of solar radiation intensity, expressed as: Where μ and σ are the mean and standard deviation of the solar radiation intensity ratio, respectively.

6. The reliability assessment method of a new distribution network containing high-penetration new energy according to claim 5 is characterized in that: The output power of the photovoltaic power generation system is expressed as: In the formula, p pv 、i pv and i0 represent the output power, photocurrent and equivalent diode reverse saturation current of the photovoltaic system respectively; T, G and T n , G n are the actual ambient temperature, radiation intensity and the nominal ambient temperature, radiation intensity respectively; i pv,n and V t,n T = 25 ° C and G = 1000 W / m 2 The photocurrent of the equivalent diode and N s The thermal voltage output by the series-connected photovoltaic units; R s and R p Respectively represent n s The series resistance of the photovoltaic cells and n p The parallel resistance of each photovoltaic unit; q, a and k are the electron charge constant, diode constant and Boltzman constant respectively; K V , K I and V oc,n ,I sc,n They are voltage, current coefficient, open circuit voltage and short circuit current respectively.

7. The reliability assessment method of a new distribution network containing high-penetration new energy according to claim 1 is characterized in that: In step S3, the distribution network reliability assessment index includes the reliability assessment index of the distribution network components, the reliability assessment index of the load point, the reliability assessment index of the distribution network as a whole and the reliability assessment index of the power consumption of the distribution network users; The reliability evaluation indicators of the distribution network components include the failure rate, repair rate, trouble-free working time and repair time of the distribution network components; the reliability evaluation indicators of the load points include the average failure rate, fault-related power outage time and average annual power outage time of the load points; the reliability evaluation indicators of the distribution network as a whole include the system power supply shortage indicator; the reliability evaluation indicators of the distribution network users' electricity consumption include the average power outage time, average power outage frequency and power supply availability.

8. The reliability assessment method of a new distribution network containing high-penetration new energy according to claim 7 is characterized in that: The reliability evaluation index of the distribution network components is described as: Where λ and ν represent the failure rate and repair rate of distribution network components respectively; n m ,t o 、n r and t r They represent the number of failures of distribution network components, the total operating time, the number of repairs during the maintenance time, and the total maintenance time; t nor and t rep represents the trouble-free working time and the repair time respectively; num is a random number uniformly distributed between (0,1); The reliability evaluation index of the load point is described as: In the formula, λ i represents the average number of power outages at load point i in a year; j and V i They represent the failure rate of distribution network component j and the set of components in the distribution network that can cause failure at load point i; t i,out It represents the average fault outage time of load point i in one year; t' i,out represents the average fault outage time of load point i; The reliability evaluation index of the distribution network as a whole is described as: p=∑ i∈I t i,out l i , Where ρ represents the average power loss caused by all power outage load points due to faults in the distribution network within one year; l i represents the average load at load point i; I represents the set of all fault load points in the distribution network; The reliability evaluation index of the power consumption of the distribution network users is described as: In the formula, τ time It represents the average power outage time of each user in the distribution network within one year; n i and M represent the number of users at load point i and the set of all load points in the distribution network system respectively; τ freq Represents the average number of power outages for each user in the distribution network within a year; τ use It indicates the proportion of normal power supply time without faults to users in the distribution network within a year.

9. The reliability assessment method of a new distribution network containing high-penetration new energy according to claim 1 is characterized in that: The step S4 comprises the following steps: Step 1): Initialize the network parameters, structure and simulation time t of the distribution network sim ; Step 2): Calculate the fault-free working time t of each component in the distribution network nor , and form a time series within the simulation duration; Step 3): Compare the fault-free working time t of each component in the distribution network nor , determine the trouble-free working time t nor The smallest component is the faulty component, and the simulation time is accumulated t sim =t sim +t nor，min , where t nor，min is the trouble-free working time of the faulty component; Step 4): Calculate the repair time t of the faulty component rep and the repair time t of the switching element rep , and then determine the fault conditions of each load point in the distribution network; Step 5): Simulate the distribution network. When the distribution network forms an island operation state, generate the output power of the renewable energy power generation through random sampling; when the power output of the renewable energy in the island is greater than or equal to the load, each load point operates normally; when the power output of the renewable energy in the island is less than the load, the loads are removed in order of importance from small to large until the balance between power supply and demand is achieved, and the fault time and number of the load points are increased in turn; the above process is repeated until the faulty component is repaired; Step 6): When the set simulation time is reached, the simulation is terminated, and the reliability evaluation index of the distribution network is calculated according to each state quantity in the simulation process.

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