CN107093007B - Power distribution network reliability assessment method considering light storage continuous loading capacity - Google Patents

Power distribution network reliability assessment method considering light storage continuous loading capacity Download PDF

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CN107093007B
CN107093007B CN201710208952.6A CN201710208952A CN107093007B CN 107093007 B CN107093007 B CN 107093007B CN 201710208952 A CN201710208952 A CN 201710208952A CN 107093007 B CN107093007 B CN 107093007B
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黄伟
张勇军
陈伯达
陈家超
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South China University of Technology SCUT
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Abstract

The invention provides a power distribution network reliability evaluation method considering light storage continuous loading capacity, which comprises the following specific steps of: firstly, clustering analysis is carried out on photovoltaic output historical data; then establishing photovoltaic output time series models under different weather types; and finally, simulating the running state of the power distribution network considering the continuous loading capacity of the optical storage through a Monte Carlo method to obtain the reliability index of the power distribution network. The innovation points of the invention are three: firstly, the influence of the time sequence of photovoltaic output under different weather types on the reliability of the power distribution network is considered; secondly, obtaining an island dynamic division strategy after the power distribution network fails by calculating the continuous loading capacity of the optical storage under a certain confidence level; and thirdly, providing a power distribution network reliability assessment method considering the island operation characteristics.

Description

Power distribution network reliability assessment method considering light storage continuous loading capacity
Technical Field
The invention relates to the technical field of power distribution network reliability evaluation, in particular to a power distribution network reliability evaluation method considering the light storage continuous loading capacity, wherein the power distribution network comprises a photovoltaic power supply.
Background
In recent years, the energy crisis is further deepened globally due to gradual depletion of fossil energy such as coal and petroleum, and the ecological environment crisis is deepened day by day, so that the development of renewable and distributed energy is vigorously promoted in various countries. With the development of distributed power supply technology and active support of policies, a large number of distributed power supplies are connected to a power distribution network in various forms, and with the increase of permeability, the influence of the distributed power supplies on the operation of the power distribution network cannot be ignored.
The photovoltaic power generation technology is a renewable clean energy with abundant reserves, is an important form of distributed power generation, and large-scale access of the photovoltaic power generation technology can have great influence on the reliability of a power distribution network. In the power distribution network reliability evaluation considering photovoltaic access, two problems need to be focused: the method comprises the following steps of firstly, a reliability model of the photovoltaic power supply and secondly, a power distribution network reliability evaluation algorithm considering photovoltaic access.
For the first problem, the photovoltaic power generation system is influenced by the intensity of solar radiation and the like, and the output of the photovoltaic power generation system is highly random and intermittent, so that the output of the photovoltaic power generation system is uncertain. Most documents describe the probability distribution of the photovoltaic output by only one Beta distribution, and the method neglects the time sequence of the photovoltaic output and the similarity of the output under the same weather type, which is an important characteristic of the photovoltaic output.
For the second problem, after photovoltaic access, the power distribution network is changed from a radial network to a multi-power network, and the traditional power distribution network reliability evaluation method is not applicable. Although there are many studies that photovoltaic access is considered in power distribution network reliability evaluation, many studies use hours as sampling simulation intervals, and it is considered that when a photovoltaic system meets a load demand at a certain moment during island operation, the photovoltaic system can certainly meet the load demand within the hour. The existing method for evaluating the reliability of the power distribution network has the defect, so that the method for accurately evaluating the reliability of the distributed power generation micro-grid connected to the power distribution network is particularly important.
Disclosure of Invention
The invention aims to provide a power distribution network reliability assessment method considering light storage continuous loading capacity, which is based on a photovoltaic output time sequence model and by considering the light storage continuous loading capacity, obtains an island dynamic division strategy after a power distribution network fault, and further provides a systematic active power distribution network reliability assessment method considering island operation characteristics.
The purpose of the invention is realized by the following technical scheme.
A power distribution network reliability assessment method considering the continuous loading capacity of optical storage comprises the following steps:
step 1: photovoltaic power collection for distribution network in target areaStation NpHistorical force data of day with a sampling time interval of Δ TcThe actual output of the photovoltaic at the sampling time t on the ith day is P (i, t); clustering photovoltaic output curves of different days to obtain NePhotovoltaic-like output curve, wherein NeThe number of different weather types obtained by clustering is represented, and the photovoltaic output characteristics under the same weather type are similar; counting to obtain a conversion probability matrix among different weather types;
step 2: respectively establishing photovoltaic output time sequence models under different weather types according to the photovoltaic output historical data under different weather types obtained by clustering;
and step 3: and (3) considering the continuous loading capacity of the light storage under different weather types, and simulating the running state of the power distribution network by adopting a Monte Carlo simulation method to obtain the reliability index of the power distribution network.
Further, clustering the photovoltaic output curves of different days in the step 1, and counting the historical weather process conversion probability matrix includes:
step 1-1: the actual output P (i, t) of the photovoltaic at all the moments t on the ith day is arranged in time sequence to form a row vector, and the row vector is called as a photovoltaic output curve sequence on the ith day. Selecting 4 physical quantities capable of reflecting the output integral level and fluctuation characteristic of the photovoltaic output curve sequence of the ith day to form a characteristic vector
Figure BDA0001260551270000031
As shown in the following formula:
Figure BDA0001260551270000032
in the formula (d)i1The average value of the output of the photovoltaic power station in the ith day and the ith day reflects the whole output level of the weather; di2Is the maximum value of the first order difference component of the photovoltaic output curve sequence on the ith day, di3Is the minimum value of the first order difference component of the photovoltaic output curve sequence on the ith day, di4Is the average value of the first order difference components of the photovoltaic output curve sequence on the ith day, di2、di3And di4The weather fluctuation conditions are reflected together; t is t2And t3Then represents the start-stop time of the time period in the day; p (i, t) is the actual photovoltaic output at time t on day i, NCThe number of sampling points of photovoltaic output data in 1 day;
step 1-2: adopting a fuzzy C-means clustering algorithm, and taking the characteristic vector of the photovoltaic output curve sequence of each day
Figure BDA0001260551270000033
Performing cluster analysis, dividing the photovoltaic output curve sequences with similar output characteristics into the same class, and finally obtaining N of the photovoltaic output curve sequenceseClass classification results, each class curve corresponds to a weather type, namely NeA weather-like type; marking the weather type of the photovoltaic output curve sequence of each day according to the clustering classification result;
step 1-3, according to the weather types of the photovoltaic output curve sequences of each day in the sampling period, a conversion probability matrix between different weather types can be obtained through statistics, wherein the calculation method of the conversion probability between different weather types is as follows:
Figure BDA0001260551270000041
in the formula, P(i-j)Representing the probability of the weather type i transitioning to weather type j, N(i-j)Represents the number of times that the weather type i is transferred to the weather type j in the sampling period, N(i)Indicating the number of days that weather type i occurs.
Further, in the step 2, according to the clustered historical photovoltaic output curve sequence data of different weather types, building photovoltaic output time sequence models under different weather types respectively, specifically including:
step 2-1: clearance force P according to ith sampling time tpPVDCI(i, t) and actual output P (i, t), and calculating the relative output P of the photovoltaic at the sampling time tpV on the ith dayN(i, t) is:
Figure BDA0001260551270000042
step 2-2: the relative output P under the sampling time t of the ith dayN(i, t) is decomposed into a power reference value PS(i, t) and the coefficient of fluctuation DeltaPNSum of (i, t):
PN(i,t)＝PS(i,t)+ΔPN(i,t) (4)
in the formula, PS(i, t) is a reference value of the photovoltaic output at the sampling time t of the ith day, and reflects the overall level of the photovoltaic output, namely delta PNAnd (i, t) is a fluctuation coefficient of the photovoltaic output at the sampling time t of the ith day, and reflects the fluctuation degree of the photovoltaic output under different weather types.
Further, the step 2-2 further comprises:
dividing a photovoltaic reference output curve sequence into three sections within one day of photovoltaic, wherein the three sections are respectively a sunrise time period PS1Day period PS2And sunset period PS3Then, the calculation formula of the photovoltaic output reference value at the sampling time t on the ith day is as follows:
Figure BDA0001260551270000051
in the formula, t1Is the sunrise time; t is t4Is the sunset time; t is t2And t3Then represents the start-stop time of the time period in the day;
corresponding Δ PN(i, t) can then be according to the formula Δ PN(i,t)＝PS(i,t)-PN(i, t), namely calculating the difference between the photovoltaic one-day relative output and each section reference value;
step 2-3: according to the photovoltaic output reference value P of different weather types in the sampling period in the daytime and the middle time periodS2And the fluctuation coefficient Δ PNThe numerical value distribution condition is based on multi-component Gaussian mixture probability distribution, and a nonlinear least square method is adopted to fit the day and middle time period photovoltaic output reference value P according to weather typesS2And the fluctuation coefficient Δ PNIs fitted with a function of
Figure BDA0001260551270000052
In the formula, alphan、σnAnd munAll the parameters are fitting parameters, the numerical values of the parameters are related to the weather type and the type of a fitting variable, and x is the value of an output reference value or a fluctuation coefficient.
Further, the photovoltaic headroom output P in the step 2-1DCI(i, t) the calculating step includes:
step 2-1-1: calculating the solar radiation I outside the atmospheric layer of the ith celestial sphere0(i) It is only related to the relative position between the day and the ground:
Figure BDA0001260551270000053
in the formula: s0Is the solar constant, representing the total amount of solar radiation received per unit area perpendicular to the light rays entering the earth's atmosphere, i being the date number in the year, defined as 1 month and 1 day, i being 1;
step 2-1-2: calculating a space-time relation of a solar incident angle theta (i, t) of the photovoltaic panel according to the geographical position of the photovoltaic power station and the inclination angle of the photovoltaic panel to obtain a function relation of solar radiation with respect to space-time, wherein the solar incident angle theta (i, t) is an included angle between a solar incident line and a normal line of an inclined plane, and the size of the solar incident angle theta (i, t) changes along with the change of the position of the sun:
Figure BDA0001260551270000061
in the formula: theta (i, t) is a solar incident angle at the moment of time t on the ith day, and beta is an inclination angle of the photovoltaic cell panel, namely an included angle between the photovoltaic cell panel and a horizontal plane; (i) is the declination angle; phi is the local latitude; gamma is the azimuth angle of the photovoltaic array, and omega (t) is the solar hour angle;
the declination angle (i), the azimuth angle γ of the photovoltaic array, and the solar time angle ω (t) can be calculated by the following equations:
Figure BDA0001260551270000062
ω(t)＝(12-t)×15°+(120°-ψ) (10)
Figure BDA0001260551270000063
in the formula: psi is the local longitude; t is Beijing time in hours, h (i, t) is the solar altitude, and the calculation formula is as follows:
h(i,t)＝sin-1(sinφ×sin(i)+cosφ×cos(i)×cosω(t)) (12)；
step 2-1-3: calculating the total solar radiation intensity I (I, t) received by the photovoltaic power station at the sampling time t on the ith day by considering atmospheric radiation and scattering action, wherein the total solar radiation intensity I (I, t) is the direct solar radiation intensity Ib(I, t) and scattered radiation solar intensity IdSum of (i, t):
Figure BDA0001260551270000071
in the formula, rho is the surface reflectivity; tau isbIs the atmospheric transparency coefficient of direct solar radiation, which is used to measure the percentage of the atmosphere that allows solar radiation to pass through; tau isdIs the scattering transparency coefficient; tau isrFor the reflection transparency coefficient, the related calculation formula is as follows:
Figure BDA0001260551270000072
m in the formulazThe atmospheric quality parameter corrected according to the altitude is calculated by the following formula:
Figure BDA0001260551270000073
in the formula: h (i, t) is the altitude of the sun, z is the altitude of the area, P (z)/P0Is a large atmosphereA correction factor for the quantity;
step 2-1-4: according to the total solar radiation intensity I (I, t) obtained in the steps, the clearance output P of the photovoltaic at the sampling time t on the ith day is obtainedDCI(i,t)：
Figure BDA0001260551270000074
In the formula IstdLight intensity per unit area under standard conditions, PmThe installed capacity of the photovoltaic power station.
Further, the step 3 of taking into account the continuous loading capacity of the light storage under different weather types and simulating the operation state of the power distribution network by adopting a monte carlo simulation method specifically includes:
step 3-1: collecting typical annual load curve of each load point in power distribution network in target area, and determining simulation times NmaxLet the current simulation number of times NmInitializing a system simulation time T as 1sim＝0；
Step 3-2: determining weather types of days in a current simulation year according to historical weather type conversion probability matrix sampling, and forming a year weather type sequence table;
step 3-3: randomly generating random numbers unified with the number n of power elements of the distribution network1,2…,nSequentially obtaining the non-failure working time T of the ith element according to the random numberi＝-lni/λi(ii) a Wherein, said λiSetting i to 1-n as a failure rate of the ith power element;
step 3-4: selecting the current minimum fault-free working time TTF as min (T)i) Time of simulation TsimTTF; judgment of TsimWhether the time is more than 1 year or not, if not, after sampling to obtain a fault element, generating a random number xi, and calculating the repair time T of the fault elementr-ln ξ/μ as the duration of the system failure, where μ is the component repair rate, and then the next step, step 3-5, is performed; if so, then Nm＝Nm+1 and go to step 3-6;
step 3-5: taking the power element corresponding to the minimum fault-free working time as a fault element, searching through fault history, and analyzing the load types influenced by the fault element, wherein the load types are A, B, C types, and the A type load is a load without influence of the fault element and is a non-power-off load; repairing T for fault element when B-type load is power failure timerThe load of (2); the class C load is the internal load of the microgrid, and the class A load does not need to be subjected to power failure treatment; for the type B load, accumulating the power failure time Rest and the power failure times Fau, and calculating the power shortage LoEng in the power failure time; for the class-C load, the cumulative power failure time Rest, the power failure times Fau and the power shortage LoEng in the power failure time of the load need to be counted after the division and the operation condition of an island region are considered according to the sustainable load carrying capacity of the optical storage in the microgrid, and the processing steps are as follows:
step 3-5-1: let n bez＝1，nzIndicating the nth fault periodzDetermining the island operation range by secondary updating, assuming 365 days every year, and taking 1 month and 1 day zero as a calculation starting point according to TsimConverting to obtain the fault occurrence date and the time t of the daygSetting interval time T for dynamically updating island operation rangez；
Step 3-5-2: calculating at the time t of fault occurrencegFrom [ t ]g+(nz-1)Tz，tg+nzTz]Sustainable load carrying capability of light storage at time t within a time period, t e [ t ∈ [ [ t ]g+(nz-1)Tz，tg+nzTz]Available photovoltaic confidence output P of light storage sustainable load capacity in time periodv(t) and sustainable energy storage output Pe(t) sum Pve(t)＝Pv(t)+Pe(t) wherein said photovoltaic confidence output PvThe calculation process of (t) includes:
1) determining the weather type of the fault occurrence time according to the weather type sequence table of the year, and simulating and generating N under the weather type by adopting a sequential random sampling technology according to the model of the step 2 in the claimtIndividual photovoltaic output day sequence samples, wherein the sampling point time interval of each sampleHas a separation of Delta Tc；
2) Let t be tg+(nz-1)TzSetting the confidence coefficient as N by arranging the photovoltaic output simulation data at the time t in the sequence sample of the sampled photovoltaic output from large to smalltData is then
Figure BDA0001260551270000091
The output value is the confidence output P of the photovoltaic at the moment tv(t) in which
Figure BDA0001260551270000092
Is referred to as multiplying by x NtRounding up backwards;
3) let t be tg+(nz-1)Tz+ΔTcAnd judging t is less than or equal to tg+nzTzIf yes, returning to 2 to continue calculation, if not, finishing calculation and outputting Pv(t)；
Said t e [ t ∈ [ [ T ]g+(nz-1)Tz，tg+nzTz]Energy storage sustainable output P in time periodeThe calculation formula of (t) is as follows:
Figure BDA0001260551270000093
in the formula, E (t)g+(nz-1)Tz) For the battery at time tgWhen the amount of stored electricity is nzWhen 1, the initial amount of energy stored E (t)g)＝EN，ENIs the rated capacity of the energy storage device; eremainFor the lowest permissible remaining battery capacity, P, taking into account the service life of the batteryemaxThe maximum output power of the energy storage device;
step 3-5-3: according to the typical annual load curve of each load point in the power distribution network of the target area, in the fault time period T epsilon [ T ∈ [ ]sim+(nz-1)Tz，Tsim+nzTz]Counting the total load curve currently carried in the island
Figure BDA0001260551270000094
Wherein n isgThe total number of load points currently carried by the island, PLi(t) is the power at the ith load point time t;
step 3-5-4: in a failure period T ∈ [ T ]sim+(nz-1)Tz，Tsim+nzTz]In, judgment of PLSUM(t)≤Pve(t) whether the load is established or not, if not, reducing the load according to the load partition index I in the form of load blocks, accumulating the power failure time Rest and the power failure times Fau of the reduced load point, calculating reliability indexes such as the power shortage LoEng in the power failure time, and returning to the step 3-5-3; if so, then at [ Tsim+(nz-1)Tz，Tsim+nzTz]In time period, all load points in the island after the load is reduced are not powered off;
step 3-5-5: judging time nzTzWhether or not to be greater than or equal to the repair time TrIf yes, the load in the island after the load reduction is not cut off, and the non-fault working time variable T corresponding to the fault element is deletediAnd returning to the step 3-4; if not, nz＝nzAfter +1, returning to the step 3-5-2;
step 3-6: judgment of Nm≤NmaxIf yes, returning to the step 3-2; if not, ending the simulation, outputting the accumulated power failure time Rest and power failure times Fau of each load point, and calculating reliability indexes such as the power shortage LoEng in the power failure time;
step 3-7: and calculating the reliability index of the power distribution network system according to the load point reliability index.
Further, the load reduction in the step 3-5-4 in the form of load blocking should determine the priority level of load blocking according to the size of the load partitioning index I of different load blocks, wherein the smaller the value of I, the higher the priority level is, and the load is firstly removed. The load partitioning is determined by the position of the intelligent switch capable of cutting off the load current, and the load partitioning index I is calculated as follows:
Figure BDA0001260551270000101
in the formula of alphajIs the important degree coefficient of the jth block load, LiIs an importance factor of the ith load point, betajFor the position of the jth load, the coefficient is reduced, diThe electrical distance between the ith load point and the island power supply is independent of the length of a line, and the electrical distance between two adjacent load points is 1.
Compared with the closest prior art, the invention has the following advantages and beneficial effects:
1. in the technical scheme of the invention, the photovoltaic output time sequence model established based on the photovoltaic clearance output not only considers the time sequence of the photovoltaic output, but also considers the volatility characteristic of the photovoltaic output and the photovoltaic output similarity of the same weather type, so that the photovoltaic model in the reliability evaluation of the power distribution network after the photovoltaic access is considered is more comprehensive.
2. In the technical scheme of the invention, the concept and the calculation method of the photovoltaic confidence output are firstly provided, the photovoltaic system is not considered to be constant within one hour of power at a certain moment, but the volatility of the photovoltaic is considered, and T is given under the condition of meeting a certain confidence coefficientzAnd the minimum value of the power output by the photovoltaic energy in the hour enables a photovoltaic model in the reliability evaluation of the power distribution network to be more reasonable.
3. In the technical scheme of the invention, the continuous load carrying capacity of the distributed power supply is defined for the first time, the light storage system is not considered to meet the load demand at a certain moment when the island runs, and the load demand can be considered to be met within the hour, but the dynamic island division strategy after the power distribution network is in fault is obtained by calculating the continuous load carrying capacity of the light storage system under a certain confidence level, so that the reliability evaluation of the light-containing power storage and distribution network considering the island running characteristic is more in line with the actual situation.
Drawings
Fig. 1 is a flowchart of a method for evaluating reliability of a power distribution network in consideration of a continuous loading capability of an optical storage system according to an embodiment of the present invention;
fig. 2 is a power distribution network topology structure diagram of a power distribution network connected to an optical storage complementary microgrid in the embodiment of the present invention;
FIG. 3 is a graph of a photovoltaic confidence output for different weather types on a certain day in an embodiment of the present disclosure;
FIG. 4 is a comparison graph of failure rates of load points according to different evaluation methods in the embodiment of the present invention;
FIG. 5 is a comparison graph of average outage times at various load points for different evaluation methods in an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided in connection with the accompanying drawings and examples, but the invention is not limited thereto.
In the embodiment of the invention, a power distribution network subsystem of IEEE RBTS-BUS6 is taken as an example scene, in order to take load time sequence characteristics into consideration, load data all use actual distribution transformer year operation data, table 3 shows the annual peak load and the number of users of each load node, the simulation times are 10000, and the rest parameters are shown in tables 1 and 2.
Table 1 microelectronic device parameters
Numbering Installed capacity/MW Energy storage capacity/MW h Maximum charge and discharge power/MW
Photovoltaic system 3 ------ ------
Energy storage system ------ 1.5 0.5
TABLE 2 component reliability parameters
Numbering Failure rate Repair time
Line 0.065 5
Transformer device 0.015 200
TABLE 3 load node Peak
Figure BDA0001260551270000121
Figure BDA0001260551270000131
Reliability evaluation is performed on the power distribution network without the light storage complementary microgrid to obtain reliability data of each load point, and the reliability data are shown in table 3:
TABLE 4 reliability data of each load point of distribution network without light storage
Figure BDA0001260551270000132
The load reliability indexes in table 3 reflect the reliability parameters of the power distribution network, annual fault outage rates and average power failure time of three load districts from load 16 to load 23 are large, the districts are connected into the photovoltaic complementary microgrid to improve the power supply reliability indexes of the system, and the topological structure of the connected power distribution network is as shown in fig. 1.
The method comprises the following steps of carrying out full-time simulation reliability evaluation algorithm simulation on a power distribution network after light storage according to a flow chart shown in the attached figure 2, wherein a control strategy based on the sustainable load carrying capacity of a micro-grid power supply is taken as a control strategy 2, compared with a conventional control strategy, the principle of judging the load carrying range of the micro-grid in a control strategy 1 is only whether the power of photovoltaic and stored energy at the moment of a fault t is larger than the load power at the moment, and other simulation conditions and the like are kept consistent, and the simulation result is as follows:
FIG. 3 is a schematic diagram of normalized confidence output of a photovoltaic system on the same day and in different weather conditions during a simulation.
Fig. 4 and 5 are graphs showing a comparison of the failure rate and the average annual power outage time at each load point in the case where the microgrid is not included, the microgrid is included, and the control strategy 1 and the microgrid control strategy 2 are included, respectively.
Calculating the reliability index of the power distribution system according to the reliability index of the load point, wherein SAIFI is the average power failure frequency index of the system as shown in tables 3-4; SAIDI is the average power failure duration index of the system; ASAI is the system effectiveness index; EENS is an annual power shortage indicator.
TABLE 5 Power distribution network reliability index comparison
Figure BDA0001260551270000141
The calculation result shows that:
(1) the access of the micro-grid has great influence on the reliability of the internal load, the fault rate of the load point and the average annual power failure time are greatly reduced under any control strategy, and the system reliability is obviously superior to the condition without the micro-grid, which shows that the access of the micro-grid can effectively improve the reliability of a power distribution system.
(2) Compared with the reliability indexes of each load point under 2 control strategies containing a microgrid, the fault rate of the load point in the microgrid under the strategy 1 is lower than that of the strategy 1, because the fluctuation of a power supply and a load in the microgrid within one hour at the fault moment is considered in the strategy 2, the power supply range of the load can be determined only within one hour after the load is carried, and the strategy 1 only needs to judge the relation between the power supply and the load power at the fault occurrence moment, so the fault rate of the load point under the strategy 1 is lower, but the strategy 2 better accords with the operating characteristics of an island, and the evaluation result is more scientific and credible.
Finally, it should be noted that the described embodiments are only some, not all embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present application without making any creative effort, shall fall within the protection scope of the present application.

Claims (6)

1. A power distribution network reliability assessment method considering the continuous loading capacity of optical storage is characterized by comprising the following steps:
step 1: photovoltaic power station N for collecting power distribution network in target areapHistorical force data of day with a sampling time interval of Δ TcThe actual output of the photovoltaic at the sampling time t on the ith day is P (i, t); clustering photovoltaic output curves of different days to obtain NePhotovoltaic-like output curve, wherein NeThe number of different weather types obtained by clustering is represented, and the photovoltaic output characteristics under the same weather type are similar; counting to obtain a conversion probability matrix among different weather types;
step 2: respectively establishing photovoltaic output time sequence models under different weather types according to the photovoltaic output historical data under different weather types obtained by clustering;
and step 3: the method comprises the steps that the continuous loading capacity of the light storage under different weather types is considered, the operation state of the power distribution network is simulated by adopting a Monte Carlo simulation method, and the reliability index of the power distribution network is obtained; in step 3, the continuous loading capacity of the light storage under different weather types is considered, and the operating state of the power distribution network is simulated by adopting a Monte Carlo simulation method, and the method specifically comprises the following steps:
step 3-1: collecting typical annual load curve of each load point in power distribution network in target area, and determining simulation times NmaxLet the current simulation number of times NmInitializing a system simulation time T as 1sim＝0；
Step 3-2: determining weather types of days in a current simulation year according to historical weather type conversion probability matrix sampling, and forming a year weather type sequence table;
step 3-3: randomly generating random numbers unified with the number n of power elements of the distribution network1,2…,nSequentially obtaining the non-failure working time T of the ith element according to the random numberi＝-lni/λi(ii) a Wherein, said λiSetting i to 1-n as a failure rate of the ith power element;
step 3-4: selecting the current minimum fault-free working time TTF as min (T)i) Time of simulation TsimTTF; judgment of TsimWhether the time is more than 1 year or not, if not, after sampling to obtain a fault element, generating a random number xi, and calculating the repair time T of the fault elementr-ln ξ/μ as the duration of the system failure, where μ is the component repair rate, and then the next step, step 3-5, is performed; if so, then Nm＝Nm+1 and go to step 3-6;
step 3-5: taking the power element corresponding to the minimum fault-free working time as a fault element, searching through fault history, and analyzing the load types influenced by the fault element, wherein the load types are A, B, C types, and the A type load is a load without influence of the fault element and is a non-power-off load; repairing T for fault element when B-type load is power failure timerThe load of (2); the class C load is the internal load of the microgrid, and the class A load does not need to be subjected to power failure treatment; for class B loads, accumulateThe power failure time Rest and the power failure times Fau, and the power shortage amount LoEng in the power failure time is calculated; for the class-C load, the cumulative power failure time Rest, the power failure times Fau and the power shortage LoEng in the power failure time of the load need to be counted after the division and the operation condition of an island region are considered according to the sustainable load carrying capacity of the optical storage in the microgrid, and the processing steps are as follows:
step 3-5-1: let n bez＝1，nzIndicating the nth fault periodzDetermining the island operation range by secondary updating, assuming 365 days every year, and taking 1 month and 1 day zero as a calculation starting point according to TsimConverting to obtain the fault occurrence date and the time t of the daygSetting interval time T for dynamically updating island operation rangez；
Step 3-5-2: calculating at the time t of fault occurrencegFrom [ t ]g+(nz-1)Tz，tg+nzTz]Sustainable load carrying capability of light storage at time t within a time period, t e [ t ∈ [ [ t ]g+(nz-1)Tz，tg+nzTz]Available photovoltaic confidence output P of light storage sustainable load capacity in time periodv(t) and sustainable energy storage output Pe(t) sum Pve(t)＝Pv(t)+Pe(t) wherein said photovoltaic confidence output PvThe calculation process of (t) includes:
1) determining the weather type of the fault occurrence time according to the weather type sequence table of the year, and simulating and generating N under the weather type by adopting a sequential random sampling technology according to the model of the step 2 in the claimtA sequence of photovoltaic output day samples, wherein the sampling point time interval of each sample is Delta Tc；
2) Let t be tg+(nz-1)TzSetting the confidence coefficient as N by arranging the photovoltaic output simulation data at the time t in the sequence sample of the sampled photovoltaic output from large to smalltData is then
Figure FDA0002693143240000021
The output value is the confidence of the photovoltaic at the moment tOutput Pv(t) in which
Figure FDA0002693143240000033
Is referred to as multiplying by x NtRounding up backwards;
3) let t be tg+(nz-1)Tz+ΔTcAnd judging t is less than or equal to tg+nzTzIf yes, returning to 2 to continue calculation, if not, finishing calculation and outputting Pv(t)；
Said t e [ t ∈ [ [ T ]g+(nz-1)Tz，tg+nzTz]Energy storage sustainable output P in time periodeThe calculation formula of (t) is as follows:
Figure FDA0002693143240000031
in the formula, E (t)g+(nz-1)Tz) For the battery at time tgWhen the amount of stored electricity is nzWhen 1, the initial amount of energy stored E (t)g)＝EN，ENIs the rated capacity of the energy storage device; eremainFor the lowest permissible remaining battery capacity, P, taking into account the service life of the batteryemaxThe maximum output power of the energy storage device;
step 3-5-3: according to the typical annual load curve of each load point in the power distribution network of the target area, in the fault time period T epsilon [ T ∈ [ ]sim+(nz-1)Tz，Tsim+nzTz]Counting the total load curve currently carried in the island
Figure FDA0002693143240000032
Wherein n isgThe total number of load points currently carried by the island, PLi(t) is the power at the ith load point time t;
step 3-5-4: in a failure period T ∈ [ T ]sim+(nz-1)Tz，Tsim+nzTz]In, judgment of PLSUM(t)≤Pve(t) whether the load is established or not, if not, reducing the load according to the load partition index I in the form of load blocks, accumulating the power failure time Rest and the power failure times Fau of the reduced load point, calculating reliability indexes such as the power shortage LoEng in the power failure time, and returning to the step 3-5-3; if so, then at [ Tsim+(nz-1)Tz，Tsim+nzTz]In time period, all load points in the island after the load is reduced are not powered off;
step 3-5-5: judging time nzTzWhether or not to be greater than or equal to the repair time TrIf yes, the load in the island after the load reduction is not cut off, and the non-fault working time variable T corresponding to the fault element is deletediAnd returning to the step 3-4; if not, nz＝nzAfter +1, returning to the step 3-5-2;
step 3-6: judgment of Nm≤NmaxIf yes, returning to the step 3-2; if not, ending the simulation, outputting the accumulated power failure time Rest and power failure times Fau of each load point, and calculating reliability indexes such as the power shortage LoEng in the power failure time;
step 3-7: and calculating the reliability index of the power distribution network system according to the load point reliability index.
2. The method according to claim 1, wherein the step 1 of clustering photovoltaic output curves of different days and counting the historical weather process transition probability matrix comprises:
step 1-1: arranging the actual output P (i, t) of the photovoltaic at all the moments t in the ith day in time sequence to form a row vector, and calling the row vector as a photovoltaic output curve sequence of the ith day; selecting 4 physical quantities capable of reflecting the output integral level and fluctuation characteristic of the photovoltaic output curve sequence of the ith day to form a characteristic vector
Figure FDA0002693143240000041
As shown in the following formula:
Figure FDA0002693143240000042
in the formula (d)i1The average value of the output of the photovoltaic power station in the ith day and the ith day reflects the whole output level of the weather; di2Is the maximum value of the first order difference component of the photovoltaic output curve sequence on the ith day, di3Is the minimum value of the first order difference component of the photovoltaic output curve sequence on the ith day, di4Is the average value of the first order difference components of the photovoltaic output curve sequence on the ith day, di2、di3And di4The weather fluctuation conditions are reflected together; t is t2And t3Then represents the start-stop time of the time period in the day; p (i, t) is the actual photovoltaic output at time t on day i, NCThe number of sampling points of photovoltaic output data in 1 day;
step 1-2: adopting a fuzzy C-means clustering algorithm, and taking the characteristic vector of the photovoltaic output curve sequence of each day
Figure FDA0002693143240000043
Performing cluster analysis, dividing the photovoltaic output curve sequences with similar output characteristics into the same class, and finally obtaining N of the photovoltaic output curve sequenceseClass classification results, each class curve corresponds to a weather type, namely NeA weather-like type; marking the weather type of the photovoltaic output curve sequence of each day according to the clustering classification result;
step 1-3, according to the weather types of the photovoltaic output curve sequences of each day in the sampling period, a conversion probability matrix between different weather types can be obtained through statistics, wherein the calculation method of the conversion probability between different weather types is as follows:
Figure FDA0002693143240000051
in the formula, P(i-j)Representing the probability of the weather type i transitioning to weather type j, N(i-j)Represents the number of times that the weather type i is transferred to the weather type j in the sampling period, N(i)Indicating the number of days that weather type i occurs.
3. The method according to claim 1, wherein the step 2 of establishing photovoltaic output time series models under different weather types according to the clustered historical photovoltaic output curve sequence data of different weather types respectively comprises:
step 2-1: clearance force P according to ith sampling time tpPVDCI(i, t) and actual output P (i, t), and calculating the relative output P of the photovoltaic at the sampling time tpV on the ith dayN(i, t) is:
Figure FDA0002693143240000052
step 2-2: the relative output P under the sampling time t of the ith dayN(i, t) is decomposed into a power reference value PS(i, t) and the coefficient of fluctuation DeltaPNSum of (i, t):
PN(i,t)＝PS(i,t)+ΔPN(i,t) (4)
in the formula, PS(i, t) is a reference value of the photovoltaic output at the sampling time t of the ith day, and reflects the overall level of the photovoltaic output, namely delta PNAnd (i, t) is a fluctuation coefficient of the photovoltaic output at the sampling time t of the ith day, and reflects the fluctuation degree of the photovoltaic output under different weather types.
4. The method for evaluating reliability of the power distribution network considering the continuous loading capability of the optical storage according to claim 3, wherein the step 2-2 further comprises:
dividing a photovoltaic reference output curve sequence into three sections within one day of photovoltaic, wherein the three sections are respectively a sunrise time period PS1Day period PS2And sunset period PS3Then, the calculation formula of the photovoltaic output reference value at the sampling time t on the ith day is as follows:
Figure FDA0002693143240000061
in the formula, t1Is the sunrise time; t is t4Is the sunset time; t is t2And t3Then represents the start-stop time of the time period in the day;
corresponding Δ PN(i, t) can then be according to the formula Δ PN(i,t)＝PS(i,t)-PN(i, t), namely calculating the difference between the photovoltaic one-day relative output and each section reference value;
step 2-3: according to the photovoltaic output reference value P of different weather types in the sampling period in the daytime and the middle time periodS2And the fluctuation coefficient Δ PNThe numerical value distribution condition is based on multi-component Gaussian mixture probability distribution, and a nonlinear least square method is adopted to fit the day and middle time period photovoltaic output reference value P according to weather typesS2And the fluctuation coefficient Δ PNIs fitted with a function of
Figure FDA0002693143240000062
In the formula, alphan、σnAnd munAll the parameters are fitting parameters, the numerical values of the parameters are related to the weather type and the type of a fitting variable, and x is the value of an output reference value or a fluctuation coefficient.
5. The method as claimed in claim 3, wherein the photovoltaic net power P in step 2-1 is determined by the method of evaluating reliability of the distribution network considering the light storage continuous loading capabilityDCI(i, t) the calculating step includes:
step 2-1-1: calculating the solar radiation I outside the atmospheric layer of the ith celestial sphere0(i) It is only related to the relative position between the day and the ground:
Figure FDA0002693143240000071
in the formula: s0Is the solar constant, representing the verticality of the incoming earth's atmosphereThe total amount of received solar radiation in the unit area of the light, i is a serial number of a date in one year, and is defined as 1 month and 1 day, i is 1;
step 2-1-2: calculating a space-time relation of a solar incident angle theta (i, t) of the photovoltaic panel according to the geographical position of the photovoltaic power station and the inclination angle of the photovoltaic panel to obtain a function relation of solar radiation with respect to space-time, wherein the solar incident angle theta (i, t) is an included angle between a solar incident line and a normal line of an inclined plane, and the size of the solar incident angle theta (i, t) changes along with the change of the position of the sun:
Figure FDA0002693143240000072
in the formula: theta (i, t) is a solar incident angle at the moment of time t on the ith day, and beta is an inclination angle of the photovoltaic cell panel, namely an included angle between the photovoltaic cell panel and a horizontal plane; (i) is the declination angle; phi is the local latitude; gamma is the azimuth angle of the photovoltaic array, and omega (t) is the solar hour angle;
the declination angle (i), the azimuth angle γ of the photovoltaic array, and the solar time angle ω (t) can be calculated by the following equations:
Figure FDA0002693143240000073
ω(t)＝(12-t)×15+(120°-ψ) (10)
Figure FDA0002693143240000074
in the formula: psi is the local longitude; t is Beijing time in hours, h (i, t) is the solar altitude, and the calculation formula is as follows:
h(i,t)＝sin-1(sinφ×sin(i)+cosφ×cos(i)×cosω(t)) (12)；
step 2-1-3: calculating the total solar radiation intensity I (I, t) received by the photovoltaic power station at the sampling time t on the ith day by considering atmospheric radiation and scattering effect, wherein the total solar radiation intensity I (I, t) is the direct solar radiation intensityDegree Ib(I, t) and scattered radiation solar intensity IdSum of (i, t):
Figure FDA0002693143240000081
in the formula, rho is the surface reflectivity; tau isbIs the atmospheric transparency coefficient of direct solar radiation, which is used to measure the percentage of the atmosphere that allows solar radiation to pass through; tau isdIs the scattering transparency coefficient; tau isrFor the reflection transparency coefficient, the related calculation formula is as follows:
Figure FDA0002693143240000082
m in the formulazThe atmospheric quality parameter corrected according to the altitude is calculated by the following formula:
Figure FDA0002693143240000083
in the formula: h (i, t) is the altitude of the sun, z is the altitude of the area, P (z)/P0A correction factor for atmospheric mass;
step 2-1-4: according to the total solar radiation intensity I (I, t) obtained in the steps, the clearance output P of the photovoltaic at the sampling time t on the ith day is obtainedDCI(i,t)：
Figure FDA0002693143240000091
In the formula IstdLight intensity per unit area under standard conditions, PmThe installed capacity of the photovoltaic power station.
6. The method as claimed in claim 1, wherein the load reduction in steps 3-5-4 in the form of load blocks determines the priority level of load block cutting according to the load partition index I of different load blocks, wherein the smaller the value of I, the higher the priority level, and the first load block is cut; the load partitioning is determined by the position of the intelligent switch capable of cutting off the load current, and the load partitioning index I is calculated as follows:
Figure FDA0002693143240000092
in the formula of alphajIs the important degree coefficient of the jth block load, LiIs an importance factor of the ith load point, betajFor the position of the jth load, the coefficient is reduced, diThe electrical distance between the ith load point and the island power supply is independent of the length of a line, and the electrical distance between two adjacent load points is 1.
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