CN115411719A - Distributed power supply planning method based on source load uncertainty and voltage stability - Google Patents

Distributed power supply planning method based on source load uncertainty and voltage stability Download PDF

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CN115411719A
CN115411719A CN202110596053.4A CN202110596053A CN115411719A CN 115411719 A CN115411719 A CN 115411719A CN 202110596053 A CN202110596053 A CN 202110596053A CN 115411719 A CN115411719 A CN 115411719A
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张宇华
于艾清
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Shanghai University of Electric Power
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Abstract

The invention relates to a distributed power supply planning method based on source load uncertainty and voltage stability, which mainly comprises the following steps: obtaining uncertainty of solving source load of typical scene by adopting Latin hypercube sampling and improved scene clustering method, and obtaining the front point with minimum variance in clustering processKThe samples were set as initial cluster centers and selected using the elbow methodKThe number of clusters of the means algorithm, avoidingKSubjectivity of value selection; the traditional voltage stability index is improved based on the load grade, and then the multi-purpose distributed power supply is established by combining annual comprehensive costA benchmarking model; and in the address selection process, the voltage stability index is adopted to determine the access range of the distributed power supply. Compared with the prior art, the invention comprehensively considers the uncertainty of DG output and the voltage stability, improves the traditional voltage stability index and better accords with the actual situation.

Description

Distributed power supply planning method based on source load uncertainty and voltage stability
Technical Field
The invention relates to a distributed power supply planning technology, in particular to a planning method based on source load uncertainty and voltage stability.
Background
With the advent of the electricity era, people increasingly demand high-quality electric energy. Most national power systems in the world are powered remotely by a large power grid to supply power to loads, and the power supply mode has many defects, for example, the transmission distance of power supply is too long, the voltage quality at the tail end of a line is easy to cause low, and partial accidents in interconnection of the large power grid can cause the whole large power grid to break down or even break down. The improvement of a power supply mode and the promotion of energy reform are the new direction of the current power grid development when a low-carbon, environment-friendly, efficient and safe power distribution network power supply system is built.
In summary, distributed Generation (DG) has rapidly become a research focus in the power system aspect due to its advantages of various types, low investment, flexible operation mode, cleanness, environmental protection, and the like. What is defined as DG is that the international large power grid Committee (CIGRE) defines it as a small, decentralized, environmentally compatible, small, independent power generation system with power of several kilowatts to several tens of megawatts, which is arranged near the users to supply power, and is generally connected with a power distribution network, and owned by the power department, power users or third-party investors, and can meet the specific requirements of the power department and load users, such as avoiding peak shaving effect when peak power consumption of load occurs, or supplying power to remote users such as mountain users, so that the investment of transmission and transformation can be saved, the power supply safety and reliability of the power system can be improved, and the system can be directly arranged near the users to provide electric energy for a small number of users with specific requirements, and can be connected into a power distribution system to supply power to the load together with a public power grid, independently of the traditional public power distribution network.
The distributed power generation is used as effective supplement of a traditional centralized power supply mode, mutual supplement and coordination between the distributed power generation and a traditional power grid are achieved, and the distributed power generation is an excellent scheme that existing resources, technologies and settings are set to provide safe, stable and reliable electric energy for users. The basic characteristics of the future power grid are environmental friendliness and sustainability, and the DG which utilizes renewable energy sources as main energy sources needs to be accessed and applied in a large scale. DG planning is an extremely important early work in power system development planning, and an optimal planning scheme is obtained under the condition of meeting certain target requirements, and the access position, the type and the unit capacity are required to be determined. Because the random access of DGs to the power distribution network has a wide influence, which mainly shows that the deterioration of the quality of electric energy is aggravated, the reliability of the power supply of the network is influenced, the complexity of a relay protection strategy is increased, the short-circuit capacity of the power distribution network is improved, and the voltage level of the power distribution network is changed, the DGs must be reasonably located and sized when one or more DGs are planned to be accessed to the power distribution network.
Disclosure of Invention
The method is provided aiming at the problems that wind, light and load uncertainty is difficult to process in the locating and sizing planning process of the distributed power supply and the voltage stability is influenced after the distributed power supply is connected to the power grid. A distributed power supply planning method is provided that accounts for wind, light and load uncertainty and voltage stability.
The technical method comprises the following steps: a distributed power supply planning method based on source load uncertainty and voltage stability specifically comprises the following steps:
1. scene construction is carried out on wind, light and load, and then the wind, light and load are usedKAnd (4) reducing the initial scene by using a means clustering algorithm to obtain a typical scene.
1. Scene construction
(1) Wind power probability model
Wind speed is described by adopting Weibull distribution of two parameters, and the probability density function of the wind speed is as follows:
Figure 804882DEST_PATH_IMAGE001
in the formula:vthe actual wind speed;kandcrespectively a shape parameter and a scale parameter.
Actual output of fanP w With wind speedvThe relationship between can be approximated as:
Figure 722023DEST_PATH_IMAGE002
in the formula:P wr representing the rated power of the fan;v ci 、v co andv r respectively the cut-in, cut-out and rated wind speed of the fan.
(2) Photovoltaic probability model
The Beta distribution is adopted to describe the solar radiation degree, and the probability density function is as follows:
Figure 970601DEST_PATH_IMAGE003
in the formula:
Figure 365811DEST_PATH_IMAGE004
two shape parameters for Beta distribution;IandI r respectively the actual and maximum value of the degree of solar radiation.
Actual photovoltaic outputP t Degree of solar radiationIThe relationship between them is as follows:
Figure 649024DEST_PATH_IMAGE005
in the formula:P tr is the photovoltaic rated power.
(3) Load probability model
The magnitude of the load is usually represented by a normal distribution. The probability density function is:
Figure 471487DEST_PATH_IMAGE006
in the formula:P G is an active load;
Figure 738520DEST_PATH_IMAGE007
their expected and standard deviations, respectively;Q G is a reactive load;
Figure 406262DEST_PATH_IMAGE008
is the load power factor angle.
2. Scene generation
The uncertainty of wind, light and load in the distributed power locating and sizing plan is processed by adopting an LHS technology. Suppose, tomSampling random variable at sampling scaleIs composed ofN，
Figure 75141DEST_PATH_IMAGE009
Is shown astA random variablex t Of (2) a cumulative probability density function, whereint=1,2,…,m. The specific sampling steps are as follows:
step 1: will sample the interval [0, 1%]Is divided intoNEqually dividing, then the probability of any interval is equal to1/N；
Step 2: randomly selecting (minimizing the correlation of random variables) sample values in each intervaliHas a cumulative probability density of
Figure 534416DEST_PATH_IMAGE010
In the formula:ris a random number between 0 and 1.
Step 3: obtaining corresponding sampling values by utilizing inverse cumulative distribution transformation, wherein each row represents the sampling value of a random variable when samplingNThe time of day, the sampling matrix is oneT×NOf the matrix of (a).
Figure 288746DEST_PATH_IMAGE011
3. Scene clustering
To avoid a huge amount of computation after generating a large number of initial scenes by LHS techniques, the samples need to be clustered. ConventionalK-The means algorithm has subjectivity for selecting the clustering number, and the clustering number is determined by using an elbow method firstlyK. Then, aim atKThe means algorithm randomly selects the defect of the initial clustering center and leads the variance to be minimumKClustering is carried out on each sample as an initial clustering center scene, and the method specifically comprises the following steps:
step 1: setting a scene set to be clustered as follows:
Figure 494599DEST_PATH_IMAGE012
in the formula: the number of clusters isKSelecting varianceS(x c ) Minimum frontKA scene as an initial clustering center, whereinc=1,2,…,n
Figure 17984DEST_PATH_IMAGE013
In the formula:din the form of the euclidean distance,
Figure 916670DEST_PATH_IMAGE014
is the average of all scenes in the scene set.
Step 2: and calculating Euclidean distances between the remaining scenes and each clustering center, classifying the remaining scenes into the cluster where the closest clustering center is located, and solving the clustering center of each cluster again.
Step 3: then, scenes other than the clustering center are deleted and the scene probability is added to the scene as the clustering center. Thereby obtainingKA representative scene and a corresponding scene probability.
2. The traditional voltage stability index is improved based on the load grade, and a distributed power supply multi-target planning model is established by combining annual comprehensive cost.
1. Objective function
Annual combined costf 1 Minimum sum voltage stability indicatorf 2 Minimum as a target, building a DG planning integrated targetf。
Figure 158296DEST_PATH_IMAGE015
A. The annual comprehensive cost is minimum
f 1 The method comprises DG investment cost, operation maintenance cost and network loss cost, and the concrete form is as follows:
Figure 902261DEST_PATH_IMAGE016
in the formula:P(s) As a scenesThe probability of (c).
For convenient expression, the scene sequence number labeling is omitted in the subsequent formula.
(1) DG investment costC I
Figure 545732DEST_PATH_IMAGE017
In the formula:
Figure 880898DEST_PATH_IMAGE018
a cash conversion factor;r 0 is the return on investment;nto plan for years;krepresents the type of DG;NDG represents a set of nodes capable of installing DG;C I DGkis shown askInvestment cost per unit capacity of the variety DG;P DGkjrepresentation installation at a nodejTokAnd (4) the DG capacity.
(2) DG operation maintenance costC OM
Figure 78661DEST_PATH_IMAGE019
In the formula:COM DGkis as followskOperation and maintenance cost required by DG unit power generation amount is planted;TDGkjis as followskDG at nodejThe annual generation time of the house.
(3) Power distribution network active network loss costC L
Figure 157476DEST_PATH_IMAGE020
In the formula:mthe total number of branches of the distribution network;C e is unit electricity price;T max the annual maximum load loss duration;P j is a branchjNetwork active loss at maximum load;
B. minimum voltage stability index
Original voltage stability indexH ij Comprises the following steps:
Figure 124295DEST_PATH_IMAGE021
H ij is a nodeiTo the nodejThe invention multiplies the load grade coefficient on the basis of the original voltage stability index
Figure 895942DEST_PATH_IMAGE022
，
Figure 846580DEST_PATH_IMAGE022
Representative load pointjThe higher the load level, the more the voltage stability needs to be ensured, so a new voltage stability index is definedL ij ：
Figure 197927DEST_PATH_IMAGE023
L ij The larger the size of the system the worse the stability,L ij the smaller the distribution network, the more stable the distribution network. The voltage stability of the whole distribution network depends on the maximum value of the voltage stability indexes in all the branches, and the maximum value is used as the system voltage stability index, namely:
Figure 550411DEST_PATH_IMAGE024
Lthe voltage index maximum value in the system is the worst voltage stability of the branch circuit, and the branch circuit is most easily affected by disturbance. Thus, according toLThe difference between 1 and 1 may reflect the voltage stability margin of the entire system.
Thus, the objective functionf 2 The mathematical expression is as follows:
Figure 961801DEST_PATH_IMAGE025
2. constraint conditions
(1) Power balance constraint
Figure 399735DEST_PATH_IMAGE026
In the formula:P i andQ i is a nodeiActive and reactive injection power;U i is a nodeiA voltage amplitude;
Figure 289194DEST_PATH_IMAGE027
is the phase angle difference;G ij ，B ij is the branch admittance.
(2) Branch capacity constraint
Figure 761764DEST_PATH_IMAGE028
In the formula:Sijmax is the branchijThe upper limit of the transmission capacity.
(3) Node voltage constraint
Figure 875213DEST_PATH_IMAGE029
In the formula:U imax andU imin are respectively nodesiUpper and lower voltage limits.
(4) DG permeability constraint
Figure 269285DEST_PATH_IMAGE030
In the formula:P iDG andP iDGmax respectively represent nodesiInstalled DG capacity and upper limit of allowable installations;
Figure 493593DEST_PATH_IMAGE031
is the permeability;
Figure 23932DEST_PATH_IMAGE032
represents a set of nodes that allow DG installation;P Ltotal representing the total active load of the distribution network.
3. Selecting a distributed power access range according to a voltage stability indicator
In order to reduce the calculation scale of the distributed power supply planning, the distributed power supplies are screened out according to the sequence of the voltage stability indexes from high to lowL ij The larger branch. And then selecting a node at the tail end of the branch as a node to be planned for the distributed power supply.
4. Solving models using non-dominated sorting genetic algorithms
(1) Integer coding is carried out to generate an initial population;
(2) Calculating an individual fitness value;
(3) Setting a penalty function according to the constraint condition;
(4) Performing non-dominated sorting and congestion degree calculation on the initial population;
(5) Genetic manipulation: selecting, crossing and mutating;
(6) Merging new and old populations;
(7) Chromosome decoding, calculating each target;
(8) Non-dominant fast sorting;
(9) Selecting an elite strategy;
(10) And (4) judging whether an iteration termination condition is met, if so, outputting a pareto optimal solution set and a pareto optimal front edge, and if not, returning to the step (4).
Compared with the prior art, the invention has the following advantages:
(1) The invention provides an improved voltage stability evaluation index. On the basis of the traditional voltage stability assessment according to the existence of the tidal current solution, the load importance degree of the power system is divided and taken into consideration of the assessment, so that a new voltage stability index is defined and the actual condition of the power distribution network is better met.
(2) The invention adopts the improvementKThe means clustering algorithm can adaptively derive the number of clusters from known sample dataThe clustering efficiency is high, the scene is typical, and the traditional method can be avoided by selecting the initial clustering center with the minimum sample varianceKThe drawback of the means algorithm being too sensitive to isolated points.
(3) The distributed power source location and volume planning model established in the method gives consideration to the economy and stability of the power distribution network, and compared with a single-target model, the planning result is more reasonable. And the planning range is selected by utilizing the voltage stability index, so that the planning efficiency is improved, and meanwhile, the planning scheme is more scientific and feasible.
Drawings
Fig. 1 is a flowchart of a distributed power supply planning method based on source load uncertainty and voltage stability according to the present invention.
Fig. 2 is a distribution network of IEEE33 nodes.
Fig. 3 shows a scene clustering result.
FIG. 4 is a typical IEEE33 NSGA-II solution.
Fig. 5 is a comparison of voltage stability indexes before and after DG is switched in.
Fig. 6 shows a comparison of network losses before and after DG access.
Detailed Description
The invention is described in detail below with reference to the figures and the specific embodiments.
The verification analysis is performed by taking an IEEE33 node standard system as an example, the structure of the power distribution network system is shown in fig. 2, and the node load levels and the weighting coefficients thereof are shown in table 1.
TABLE 1 node load rating
Figure 308282DEST_PATH_IMAGE034
DG power factor 0.9, maximum permeability 30%. The planning age limit is set to be 20 years, and the discount rate is 0.1. The maximum installation capacity of each node to be selected DG is 1 000MVA. The fan parameters are as follows: vci =3 m/s, vr =10m/s and vco =20m/s; wind speeds obey a weibull distribution of k =2.17 and c = 8.34. The photovoltaic parameters are: ir =1 kW/m2; the parameters of the beta distribution are: α =1.95 and β =2.28. The load of each node refers to the raw data of the IEEE33 node as an average value, and the standard deviation is 10 percent of the average value. The investment and operation and maintenance costs of the WT are respectively 1 ten thousand yuan/kW and 0.4 ten thousand yuan/kW.h; the investment and operation and maintenance costs of PV are respectively 1.3 ten thousand yuan/kW and 0.25 ten thousand yuan/kW.h. The unit power supply cost of the transformer substation is 0.5 yuan/kW.h.
1. Scene construction is carried out on wind, light and load, and then the wind, light and load are usedKAnd (4) reducing the initial scene by using a means clustering algorithm to obtain a typical scene.
Sampling wind, light and load by using an LHS method, wherein the sampling scale is 800, converting the wind speed and solar irradiance into output efficiencies of PV and WT according to a probability model formula, combining node load data to obtain 800 basic scenes, and then obtaining the clustering number of 8 from the graph 3 through scene clustering, wherein each typical scene is shown in a table 2.
TABLE 2 scene cut results
Figure 189651DEST_PATH_IMAGE036
2. And improving the traditional voltage stability index based on the load grade, and determining the access range of the distributed power supply by adopting the improved voltage stability index in the site selection process.
In order to reduce the planning calculation scale of the distributed power supply, the method screens L according to the sequence of the voltage stability indexes from high to low ij The larger branch. And then selecting a node at the tail end of the branch as a node to be planned for the distributed power supply. When the load level is not considered, L ij The sequence from large to small is as follows: 5,2, 27, 28,3,4, 23,8, 12; after considering the load class, L ij The sequence from large to small is as follows: 2,5,3,4, 23, 30, 28,9.
Therefore, after the load level weight coefficient is multiplied, the branch sorting is greatly changed, and the stability of important loads directly influences the production life and even the life safety of people. Therefore, the load importance degree is a significant factor which is not negligible in the power distribution network planning. In conclusion, distributed power supply optimization configuration is carried out in the range of the end nodes of the branches.
3. And (3) simulating by adopting a non-dominated sorting genetic algorithm to obtain a pareto front edge, and selecting an optimal compromise solution according to a fuzzy membership function.
The Pareto frontier calculated by the model built according to the invention by using NSGA-II is shown in FIG. 4, and each different solution can represent the configuration effect of the current planning scheme. When the decision maker has obvious preference for voltage stability, the annual combined cost is higher, which means that more investment cost needs to be paid to make the voltage index value smaller, and scheme 1 is an extreme scheme of the decision, and the voltage stability index is reduced by 33.1% compared with that before planning, but the annual combined cost is higher. The annual comprehensive cost is the lowest under the condition of the scheme 2, but the voltage stability index is larger, and the voltage stability index is the most unstable compared with other two conditions. Both the annual total cost and voltage stability indicators for scenario 3 are between scenarios 1 and 2. And a decision maker can select different Pareto front end points according to different economic preferences and stability preferences so as to obtain different DG planning schemes.
Table 3 DG plan comparison
Figure 217650DEST_PATH_IMAGE038
Note: the outside of the brackets represents the branch number, and the inside of the brackets represents the photovoltaic capacity and the fan capacity respectively.
And (3) selecting a scheme 3 as an optimal compromise solution by using an equation (30) and an equation (31) in comprehensive consideration of economy and voltage stability. And comparing the voltage stability index of each branch circuit before and after the distributed power supply is connected with the network loss by taking the scheme 3 as a comparison, as shown in fig. 5 and 6. It can be seen from fig. 5 that the voltage index value of branch 2 is decreased from 0.0564 to 0.0407, and at branch 5 is decreased from 0.0380 to 0.0238, indicating that the voltage stability is greatly improved. From fig. 6, it can be seen that the loss of each branch is significantly reduced, and the network loss at the highest-point branch 5 is reduced from 52.09kW to 18.11kW, so that the loss and reliability of the corresponding branch of the distribution network after the DG is optimized are significantly improved.
In order to prove the rationality of the addition of the voltage stability index, the planning model disclosed by the invention is subjected to simulation comparison with a single-target planning model only considering annual comprehensive cost, and the planning result is shown in the following table 4.
TABLE 4 comparison of planning results for different objective functions
Figure 868074DEST_PATH_IMAGE040
As can be seen from table 4, although the annual total cost of the target model is reduced by 13% by using a single target model, the voltage stability index value is higher than 24.4% of the multi-target model, and the system voltage stability is not good. Therefore, the multi-target model provided by the invention can give consideration to both voltage stability and economy and is more reasonable from the comprehensive satisfaction degree.

Claims (5)

1. A distributed power supply planning method based on source load uncertainty and voltage stability is characterized by comprising the following steps:
(1) Obtaining uncertainty of solving source load of typical scene by adopting Latin hypercube sampling and improved scene clustering method, and obtaining the front point with minimum variance in clustering processKThe samples were set as initial cluster centers and selected using the elbow methodK-number of clusters of means algorithm, avoidingKSubjectivity of value selection;
(2) Improving the traditional voltage stability index based on the load grade, and establishing a distributed power supply multi-target planning model by combining annual comprehensive cost;
(3) In the address selection process, an improved voltage stability index is adopted to determine the access range of the distributed power supply;
(4) And (3) simulating by adopting a non-dominated sorting genetic algorithm to obtain a pareto front edge, and selecting an optimal compromise solution according to a fuzzy membership function.
2. The method for planning a distributed power supply based on source load uncertainty and voltage stability as claimed in claim 1, wherein the scene generation and reduction in the step (1) mainly comprises the following steps:
(1) Carrying out probability modeling on wind, light and load, and respectively representing the probability modeling by using Weibull distribution, beta distribution and normal distribution of two parameters;
(2) Sampling each random variable by a Latin hypercube sampling method to generate a large number of initial scenes, and then carrying out improved samplingKAnd clustering and reducing the scenes by means of a means clustering algorithm to obtain typical scenes.
3. The method for planning a distributed power supply based on source load uncertainty and voltage stability as claimed in claim 1, wherein in the step (2), a conventional voltage stability index is improved based on a load class, and the improvement method comprises the following steps:
at the original voltage stabilization indexH ij On the basis of the load level coefficient
Figure 784742DEST_PATH_IMAGE001
，
Figure 342762DEST_PATH_IMAGE001
Representative load pointjThe higher the load level is, the more the stability of the voltage needs to be ensured, so a new voltage stability index is definedL ij ：
Figure 403122DEST_PATH_IMAGE002
L ij The larger the size of the system the worse the stability,L ij the smaller the distribution network is, the more stable the distribution network is;
the voltage stability of the whole distribution network depends on the maximum value of the voltage stability indexes in all the branches, and the maximum value is used as the system voltage stability index, namely:
Figure 97408DEST_PATH_IMAGE003
Lis an indication of voltage in the systemMaximum value, the branch voltage stability being the worst, is most susceptible to disturbances and is therefore based onLThe difference between 1 and 1 may reflect the voltage stability margin of the entire system.
4. The method for planning a distributed power supply based on source load uncertainty and voltage stability as claimed in claim 1, wherein said step (3) employs an improved voltage stability indicator to determine the range of distributed power supply access:
sorting according to the improved voltage stability indexes from high to low, and screeningL ij And a node at the tail end of the branch is selected as a node to be planned of the distributed power supply, so that the calculation scale of the planning model is reduced.
5. The distributed power supply planning method based on source load uncertainty and voltage stability as claimed in claim 1, wherein in the step (4), a pareto frontier is obtained by adopting a non-dominated sorting genetic algorithm simulation, and an optimal compromise solution is selected according to a fuzzy membership function, and the specific steps are as follows:
(1) Calculating optimal fitness values of different schemes consisting of different nodes and different capacities;
(2) Comparing the optimal fitness values of all the schemes to obtain an optimal pareto optimal solution set;
(3) And selecting an optimal compromise solution according to the fuzzy membership function.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117994084A (en) * 2024-04-07 2024-05-07 国网浙江省电力有限公司宁波供电公司 Distributed power supply address selection method, device, computer equipment and medium

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117994084A (en) * 2024-04-07 2024-05-07 国网浙江省电力有限公司宁波供电公司 Distributed power supply address selection method, device, computer equipment and medium
CN117994084B (en) * 2024-04-07 2024-08-09 国网浙江省电力有限公司宁波供电公司 Distributed power supply address selection method, device, computer equipment and medium

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