CN111884254B - Distributed photovoltaic absorption access method and device based on double random simulation - Google Patents

Distributed photovoltaic absorption access method and device based on double random simulation Download PDF

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CN111884254B
CN111884254B CN202010738932.1A CN202010738932A CN111884254B CN 111884254 B CN111884254 B CN 111884254B CN 202010738932 A CN202010738932 A CN 202010738932A CN 111884254 B CN111884254 B CN 111884254B
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photovoltaic
node
access
load
load node
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CN111884254A (en
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吴悦
张忠会
赵升学
李国栋
赵永明
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State Grid Gansu Electric Power Co Longnan Power Supply Co
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/381—Dispersed generators
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J3/00—Circuit arrangements for ac mains or ac distribution networks
    • H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
    • H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
    • H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
    • H02J2300/20—The dispersed energy generation being of renewable origin
    • H02J2300/22—The renewable source being solar energy
    • H02J2300/24—The renewable source being solar energy of photovoltaic origin
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/50—Photovoltaic [PV] energy
    • Y02E10/56—Power conversion systems, e.g. maximum power point trackers

Abstract

The embodiment of the invention provides a distributed photovoltaic absorption access method and a distributed photovoltaic absorption access device based on double random simulation, wherein the method comprises the following steps: determining the moment of limiting the maximum photovoltaic access absorption capacity, performing double random simulation and capacity simulation based on the load level and photovoltaic output characteristics of the moment of limiting the maximum photovoltaic access absorption capacity to obtain the photovoltaic absorption capacity of each access load node of the whole network and the maximum voltage value of the load node of the whole network, and determining a distributed photovoltaic absorption access scheme according to the photovoltaic absorption capacity of each access load node of the whole network and the maximum voltage value of the load node of the whole network. In the random simulation process provided by the embodiment of the invention, the load data and the photovoltaic output at the maximum photovoltaic access limiting moment are used for random simulation without considering the randomness of the load and the photovoltaic output, so that the photovoltaic consumption capability of the power distribution network can be solved, and the uncertainty of the number of the access points and the specific access position is only needed to be considered.

Description

Distributed photovoltaic absorption access method and device based on double random simulation
Technical Field
The invention relates to the field of distribution network distributed power supply planning, in particular to a distributed photovoltaic absorption access method and device based on double random simulation.
Background
In recent years, with the support of our country to the construction of distributed photovoltaic and the subsidy policy, the demand for the grid connection of distributed photovoltaic of our country's rural distribution network increases year by year, which brings great challenges to the safe, stable and economic operation of the power grid, including power quality, network loss, power grid reliability and the like, wherein the most serious influence is caused by the voltage out-of-limit problem. Meanwhile, two uncertainties of the number of access nodes and the specific photovoltaic access position exist during distributed photovoltaic grid connection, and in the existing random simulation method, when a complex optimization problem is solved, a main problem exists: unnecessary computational overhead is caused by improper selection of the sampling function, which increases the probability of repeated sampling and increases the computation time.
Disclosure of Invention
Aiming at the problems in the prior art, the embodiment of the invention provides a distributed photovoltaic absorption access method and device based on double random simulation.
In a first aspect, an embodiment of the present invention provides a distributed photovoltaic absorption access method based on dual random simulation, including:
s1, determining the moment with the maximum limit on photovoltaic access absorption capacity based on the limit value of the node voltage of the power distribution network, all load nodes in the range of the distribution photovoltaic power distribution network to be accessed, the annual load characteristic and the annual unit capacity photovoltaic output characteristic;
s2, acquiring the load level and photovoltaic output characteristics at the moment when the photovoltaic access absorption capacity limit is maximum;
s3, based on the load level and photovoltaic output characteristics of each load node at the moment of maximum photovoltaic access limit, performing first random sampling simulation by adopting a beta distribution function as a first random sampling function, and determining the number of photovoltaic access points;
s4, based on the number of the photovoltaic access points, performing second random sampling simulation by adopting a uniform distribution function as a second random sampling function to determine the positions of the photovoltaic access points;
s5, carrying out capacity simulation on each access load node at the position of the photovoltaic access point, and determining the photovoltaic absorption capacity of each access load node of the whole network and the maximum voltage value of the load node of the whole network;
s6, determining a distributed photovoltaic absorption access scheme according to the photovoltaic access point position, the photovoltaic absorption capacity of each access load node of the whole network and the maximum voltage value of the load node of the whole network;
and S7, repeating the steps from S3 to S6, and determining a plurality of distributed photovoltaic absorption access schemes.
Further, the performing a first re-random sampling simulation by using a β distribution function as a first re-random sampling function based on the load level and the photovoltaic output characteristics of each load node at the time when the photovoltaic access limit is maximum, to determine the number of photovoltaic access points specifically includes:
and performing first random sampling simulation by adopting a beta (2,5) distribution function as a first random sampling function based on the load level and the photovoltaic output characteristics of each load node at the moment of maximum photovoltaic access limit, and determining the number of photovoltaic access points.
Further, the performing a second random sampling simulation by using a uniform distribution function as a second random sampling function based on the number of the photovoltaic access points to determine the position of the photovoltaic access point specifically includes:
and based on the number of the photovoltaic access points, adopting a uniform distribution function as a second random sampling function, and repeatedly performing second random sampling simulation until different load nodes meeting the number of the photovoltaic access points are extracted, and determining the position of the photovoltaic access points.
Further, the determining, based on the limit value of the node voltage of the power distribution network, all load nodes within the range of the distribution-type photovoltaic power distribution network to be accessed, the annual load characteristic and the annual unit capacity photovoltaic output characteristic, a time at which the limit on the photovoltaic access absorption capacity is maximum specifically includes:
evaluating the photovoltaic access absorption capacity of each load node at each moment by adopting a dichotomy according to a first relation model based on the limit value of the voltage of the power distribution network node, all load nodes in the range of the distribution-type photovoltaic power distribution network to be accessed, the annual load characteristic and the annual unit capacity photovoltaic output characteristic;
determining the moment with the maximum limit on the photovoltaic access absorption capacity according to the photovoltaic access absorption capacity of each load node at each moment;
wherein the bisection method comprises:
according to the voltage value of the load node, the consumption capacity of the load node is repeatedly divided into two parts according to the following rule, and the voltage value of each load node of the whole network is correspondingly checked once every two parts;
if any load node voltage value exceeds the limit, taking an upper boundary of a binary interval for calculating the consumption capacity of the load node;
if any load node voltage value is out of limit, taking a lower boundary of a binary interval for calculating the consumption capacity of the load node;
wherein the first relational model comprises:
Figure GDA0003309726930000021
wherein P is the load level and photovoltaic output characteristic at a timelim(n)(k) The consumption capacity of the node k after n dichotomy is shown, wherein n is a positive integer greater than or equal to 0 and represents the dichotomy, in each verification, the consumption capacity is an interval median value, and P islim(n+1)(k) Denotes the digestion capability of node k after n +1 dichotomy, Pdown(k) Representing the lower boundary, P, of a bipartite interval containing the load node k's absorption capabilityup(k) Representing the upper boundary of the bipartite interval containing the load node k's capacity to absorb.
Further, the repeatedly dividing the consumption capability of the load node by two according to the voltage value of the load node according to the following rule specifically includes:
calculating a voltage value of a load node according to a second relation model, and repeatedly dividing the absorption capacity of the load node into two parts according to the voltage value of the load node;
wherein the second relational model comprises:
Figure GDA0003309726930000031
Figure GDA0003309726930000032
wherein, Ue(pu)Representing a voltage per unit value of a node e in a network after a load node c is accessed to a photovoltaic, wherein the node e is before being accessed to the load node c; u shapef(pu)Representing the voltage per unit value of a node f in the network after the load node c is connected to the photovoltaic, wherein the node f is connected to the load nodeAfter point c; r represents the resistance of the basic parameter unit length of the line, x represents the reactance of the basic parameter unit length of the line, i and j represent the number variables of the nodes, and LiRepresenting the length of the line between node i and node i-1, QjRepresenting real-time reactive power, U, of node j0Representing the nominal voltage, P, of the networklim1(c) Representing the power injected into the network by the photovoltaic connected to the load node c, PjRepresenting the real-time active power of node j.
Further, the capacity simulation is performed on each access load node at the photovoltaic access point position, and the photovoltaic absorption capacity of each access load node in the whole network and the maximum voltage value of the load node in the whole network are determined, which specifically includes:
carrying out capacity simulation on each access load node on the photovoltaic access point, and determining the absorption capacity of each access load node of the whole network and the maximum voltage value of the load node of the whole network according to a third relation model;
wherein the third relationship model comprises:
Figure GDA0003309726930000041
wherein, in a certain capacity simulation, A represents the number sequence of nodes accessed to the photovoltaic, A (i) represents the number of the ith node in the sequence, D (i) represents the photovoltaic capacity accessed by the sampled ith node,
Figure GDA0003309726930000042
represents the electrical distance coefficient, N represents the total number of nodes of the network, min A (i) represents the node with the smallest number among all the nodes connected to the photovoltaic, PL(i) Representing the load level of the load node, z representing the accumulation coefficient, b representing the number of capacity simulations;
Figure GDA0003309726930000043
wherein D isPV(b) The photovoltaic absorption capacity of the capacity simulation is shown,m represents the number of nodes actually accessed to the photovoltaic, and D (i) represents the photovoltaic capacity accessed by the sampled ith node;
Figure GDA0003309726930000044
wherein, Ug(pu)Expressing the voltage per unit value of a node g in the network, r expressing the resistance of a basic parameter unit length of the line, x expressing the reactance of the basic parameter unit length of the line, i and j expressing node number variables, LiRepresenting the length of the line between node i and node i-1, QjRepresenting real-time reactive power, U, of node j0Representing the nominal voltage, P, of the networklim1(j) Representing the power injected into the network by the photovoltaic connected to the load node j, PjRepresenting the real-time active power of the node j;
if the voltage per unit value of each load node is less than or equal to a preset value, voltage constraint is met; determining the photovoltaic absorption capacity of each access load node of the whole network and the maximum voltage value of the load node of the whole network;
and if the voltage per unit value of any load node is larger than the preset value, repeating the steps from S3 to S5 after 5 times of capacity simulation until the preset times of simulation process is completed, and determining the photovoltaic consumption capacity of each access load node of the whole network and the maximum voltage value of the load node of the whole network.
Further, the method further comprises:
taking a plurality of distributed photovoltaic absorption access schemes as a photovoltaic absorption access scheme database;
screening a matched distributed photovoltaic absorption access scheme in the photovoltaic absorption database based on the target access photovoltaic absorption capacity;
and based on the matched distributed photovoltaic absorption access scheme, taking the distributed photovoltaic absorption access scheme with the minimum voltage maximum value change degree of the load nodes of the whole network as the optimal distributed photovoltaic absorption access scheme.
In a second aspect, an embodiment of the present invention provides a distributed photovoltaic absorption access apparatus based on dual random simulation, including:
the first determining module is used for determining the moment of limiting the maximum photovoltaic access absorption capacity based on the limit value of the node voltage of the power distribution network, all load nodes in the range of the distributed photovoltaic power distribution network to be accessed, the annual load characteristic and the annual unit capacity photovoltaic output characteristic;
the acquisition module is used for acquiring the load level and the photovoltaic output characteristic at the moment when the photovoltaic access absorption capacity limit is maximum;
the second determining module is used for performing first re-random sampling simulation by adopting a beta distribution function as a first re-random sampling function based on the load level and the photovoltaic output characteristics of each load node at the moment of maximum photovoltaic access limit to determine the number of photovoltaic access points;
the third determining module is used for performing second random sampling simulation by adopting a uniform distribution function as a second random sampling function based on the number of the photovoltaic access points to determine the positions of the photovoltaic access points;
the fourth determining module is used for carrying out capacity simulation on each access load node on the photovoltaic access point position, and determining the photovoltaic absorption capacity of each access load node of the whole network and the maximum voltage value of the whole network load node;
a fifth determining module, configured to determine a distributed photovoltaic absorption access scheme according to the photovoltaic access point position, the photovoltaic absorption capacity of each access load node in the entire network, and the maximum voltage value of the load node in the entire network;
and the sixth determining module is used for repeating the second determining module to the fifth determining module and determining a plurality of distributed photovoltaic absorption access schemes.
In a third aspect, an embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the program to implement the steps of the distributed photovoltaic absorption access method based on dual random simulation according to the first aspect.
In a fourth aspect, the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the distributed photovoltaic absorption access method based on dual stochastic simulation as described in the first aspect above.
It can be known from the above technical solutions that, according to the distributed photovoltaic absorption access method and apparatus based on dual random simulation provided in the embodiments of the present invention, by determining the time at which the limitation on the photovoltaic absorption capacity is maximum, performing dual random simulation based on the load level and the photovoltaic output characteristics of each load node at the time at which the limitation on the photovoltaic absorption capacity is maximum, and determining the number of photovoltaic access points and the specific access positions of the photovoltaic access points, the random simulation process does not need to consider the randomness of the load and the photovoltaic output, but can solve the photovoltaic absorption capacity of the power distribution network by using the load data and the photovoltaic output at the time at which the limitation on the photovoltaic access is maximum for random simulation, and only needs to consider the uncertainties of the number of access points and the specific access positions, so that the random simulation can better cope with the load fluctuation and the uncertainty of the distributed power supply, meanwhile, the beta distribution function is used as a first random sampling function, the uniform distribution function is used as a second random sampling function, the photovoltaic output prediction is more suitable for being carried out, the photovoltaic output prediction random scene can be better responded, the evaluation result is more comprehensive, the solving process is clear, the calculation efficiency is improved, various distributed photovoltaic absorption access schemes are easy to generate, and guidance is provided for the actual power distribution network planning.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
Fig. 1 is a schematic flowchart of a distributed photovoltaic absorption access method based on dual random simulation according to an embodiment of the present invention;
fig. 2-9 are schematic diagrams of a distributed photovoltaic absorption access method based on dual random simulation according to another embodiment of the present invention;
fig. 10 is a schematic structural diagram of a distributed photovoltaic absorption access apparatus based on dual random simulation according to an embodiment of the present invention;
fig. 11 is a schematic physical structure diagram of an electronic device according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments, but not all embodiments, of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a schematic flowchart of a distributed photovoltaic absorption access method based on dual random simulation according to an embodiment of the present invention; as shown in fig. 1, the method includes:
step S1: and determining the moment with the maximum limit on the photovoltaic access absorption capacity based on the limit value of the node voltage of the power distribution network, all load nodes in the range of the distribution-type photovoltaic power distribution network to be accessed, the annual load characteristics and the annual unit capacity photovoltaic output characteristics.
In the step, according to a target power distribution network and network architecture parameters thereof, limiting values of the node voltages of the power distribution network are determined, all load nodes (namely N load nodes to be accessed to the photovoltaic power distribution network) within the range of the distributed photovoltaic power distribution network are determined, and the annual load characteristic P is determinedL(time scale is 1 hour, unit kW), and annual unit capacity photovoltaic output characteristic PV(time scale 1 hour, unit kW), random simulation parameters were set: if the total frequency S is adopted, the initial random simulation frequency a is equal to 1, the capacity simulation frequency b is equal to 1, and the limit value of the node voltage of the distribution network, all load nodes in the range of the distributed photovoltaic distribution network to be accessed and the annual load characteristics are determined based on the limit value, the load nodes and the annual load characteristics of the node voltage of the distribution networkAnd a unit capacity photovoltaic output characteristic of the whole year, for example, a dichotomy is adopted to evaluate the photovoltaic access absorption capacity of each load node at each moment, and the minimum value in the evaluation result is obtained.
Step S2: and acquiring the load level and the photovoltaic output characteristic at the moment when the photovoltaic access absorption capacity is limited to the maximum.
In this step, it should be noted that, by evaluating the photovoltaic access absorption capacity of each load node at each moment in step S1, the load level and the photovoltaic output characteristic at the corresponding moment can be obtained, and therefore, in this step, the load level and the photovoltaic output characteristic at the moment at which the photovoltaic access absorption capacity is most limited can be directly determined.
Step S3: and performing first random sampling simulation by taking a beta distribution function as a first random sampling function based on the load level and the photovoltaic output characteristics of each load node at the moment of maximum photovoltaic access limit, and determining the number of photovoltaic access points.
In the step, the first random sampling of the random simulation for the a time is carried out, a beta distribution function is adopted as a first random sampling function, the number M of the photovoltaic access points is determined, and the random sampling combination function is used
Figure GDA0003309726930000071
And (3) sampling characteristics, wherein the values of the sampling characteristics are distributed from 1 to N along with the value of M, and the distribution trend is that the middle part is large and the two ends are small.
For example, a first re-random sampling of the random simulation of the a th time is performed, a β distribution function is used as the first re-random sampling function, the sampling principle is as shown in fig. 3, N load nodes are projected onto the β distribution function in equal proportion according to numbers of 1 to N, and the value obtained by each random sampling is rounded up to obtain the number M of photovoltaic access points or the node number value (see 85 node or 84 node and the like in fig. 4, 85 or 84 is the node number value), that is, if the extracted value H is located in the interval [ H-1, H ] (H ≧ 2), then M ═ H or the node with the node number H is extracted.
Step S4: and based on the number of the photovoltaic access points, performing second random sampling simulation by adopting a uniform distribution function as a second random sampling function to determine the positions of the photovoltaic access points.
In this step, a second random sampling of the random simulation is performed for the a-th time to determine the specific photovoltaic access position aM×1For example, for M load nodes specifically connected to the photovoltaic system, in order to ensure the probability of each node being extracted, various types of sampling functions are contrastively analyzed, and finally, uniform distribution is selected as a second random sampling function. The sampling principle is similar to that of the first repeated random sampling, the difference is that the sampling functions are different, the second repeated random sampling is repeated for multiple times, the sampling is not finished until M different load nodes are sampled, and the specific photovoltaic access point position A can be determined through the second repeated random sampling simulationM×1。
Step S5: and carrying out capacity simulation on each access load node on the position of the photovoltaic access point, and determining the photovoltaic absorption capacity of each access load node of the whole network and the maximum voltage value of the load node of the whole network.
In this step, a b-th capacity simulation of the a-th random simulation is performed for each access load node at the photovoltaic access point location, since the load node i (i.e., each access load node at the photovoltaic access point location) is inversely related to its electrical distance from the head end of the network and its load level PL(i) Positive correlation is formed, the photovoltaic consumption of the b-th capacity simulation is calculated, the photovoltaic consumption capacity of each access load node of the whole network is determined, the photovoltaic consumption capacity of each access load node of the whole network is the sum of the photovoltaic consumption capacities, (see figure 4), if the photovoltaic access point positions are determined to be nodes 65, 71, 75, 77 and 85 in figure 4, the photovoltaic consumption of the b-th capacity simulation of the computing node 65 is 305.78, the photovoltaic consumption of the b-th capacity simulation of the computing node 71 is 584.45, the photovoltaic consumption of the b-th capacity simulation of the computing node 75 is 389.29, the photovoltaic consumption of the b-th capacity simulation of the computing node 77 is 242.71, and the photovoltaic consumption of the b-th capacity simulation of the computing node 82 is 87.43, the photovoltaic consumption of each access load node of the whole network is determinedThe photovoltaic absorption capacity of the load node is 1609.66KW, and the full-network voltage after the b-th capacity simulation is verified at the same time to determine the maximum value of the full-network load node voltage, such as the maximum value of the full-network load node voltage of 1.069.
Step S6: and determining a distributed photovoltaic absorption access scheme according to the position of the photovoltaic access point, the photovoltaic absorption capacity of each access load node of the whole network and the maximum voltage value of the load nodes of the whole network.
In this step, it can be understood that the result of the capacity simulation is recorded, the random simulation is completed, and the result of the random simulation is used as a distributed photovoltaic absorption access scheme.
Step S7: and repeating the steps from S3 to S6 to determine a plurality of distributed photovoltaic absorption access schemes.
In this step, the steps from S3 to S6 are repeated, that is, the first stochastic simulation, the second stochastic simulation and the capacity simulation are repeated, so as to obtain a plurality of distributed photovoltaic absorption access schemes, the distributed photovoltaic absorption access schemes are used as a database, see fig. 9, for example, 2000 stochastic simulations are performed, so as to obtain 3599 different photovoltaic access schemes, the processes of each stochastic simulation and the capacity simulation thereof are shown in fig. 7, and each scatter point in the figure represents one absorption scheme.
From the above technical solutions, it can be seen that the distributed photovoltaic absorption access method based on dual random simulation provided in the embodiments of the present invention determines the number of photovoltaic access points and the specific access positions of the photovoltaic access points by determining the time at which the limitation on the photovoltaic access absorption capability is maximum, performing dual random simulation based on the load level and the photovoltaic output characteristics of each load node at the time at which the limitation on the photovoltaic access is maximum, so that the load data and the photovoltaic output at the time at which the limitation on the photovoltaic access is maximum are used for random simulation to solve the photovoltaic absorption capability of the distribution network without considering the randomness of the load and the photovoltaic output, and only considering the uncertainty of the number of the access points and the specific access positions, thereby the random simulation can better cope with the load fluctuation and the uncertainty of the distributed power source, and meanwhile, using the β distribution function as the first re-random sampling function, the uniform distribution function is used as the second random sampling function, the photovoltaic output prediction is more suitable to be carried out, the photovoltaic output prediction random scene can be better responded, the evaluation result is more comprehensive, the solving process is clear, the calculation efficiency is improved, various distributed photovoltaic absorption access schemes are easy to generate, and guidance is provided for the planning of the actual power distribution network.
On the basis of the foregoing embodiment, in this embodiment, the determining, based on the limit value of the node voltage of the power distribution network, all load nodes within the range of the distribution-type photovoltaic power distribution network to be accessed, the annual load characteristic, and the annual unit capacity photovoltaic output characteristic, the time at which the limit on the photovoltaic access absorption capacity is maximum specifically includes:
evaluating the photovoltaic access absorption capacity of each load node at each moment by adopting a dichotomy according to a first relation model based on the limit value of the voltage of the power distribution network node, all load nodes in the range of the distribution-type photovoltaic power distribution network to be accessed, the annual load characteristic and the annual unit capacity photovoltaic output characteristic;
determining the moment with the maximum limit on the photovoltaic access absorption capacity according to the photovoltaic access absorption capacity of each load node at each moment;
wherein the bisection method comprises:
according to the voltage value of the load node, the consumption capacity of the load node is repeatedly divided into two parts according to the following rule, and the voltage value of each load node of the whole network is correspondingly checked once every two parts;
if any load node voltage value exceeds the limit, taking an upper boundary of a binary interval for calculating the consumption capacity of the load node;
if any load node voltage value is out of limit, taking a lower boundary of a binary interval for calculating the consumption capacity of the load node;
wherein the first relational model comprises:
Figure GDA0003309726930000091
wherein P is the load level and photovoltaic output characteristic at a timelim(n)(k) The consumption capacity of the node k after n dichotomy is shown, wherein n is a positive integer greater than or equal to 0 and represents the dichotomy, in each verification, the consumption capacity is an interval median value, and P islim(n+1)(k) Denotes the digestion capability of node k after n +1 dichotomy, Pdown(k) Representing the lower boundary, P, of a bipartite interval containing the load node k's absorption capabilityup(k) Representing the upper boundary of the bipartite interval containing the load node k's capacity to absorb.
In the present embodiment, an 85-node actual power distribution system in a certain area is taken as an example for explanation, and a topological diagram is shown in fig. 4.
Determining a target distribution network and a network architecture thereof as shown in fig. 4, wherein the network architecture is in a linear form LJG-35; determining the range of a power distribution network (all load nodes in the nodes 63-85, 12 in total) capable of accessing the distributed photovoltaic, the annual load characteristic PL (annual hourly load taking the end node 85 as an example is shown in figure 5), and the annual unit capacity photovoltaic output characteristic PV(the annual output characteristics of a 100kW photovoltaic are shown in fig. 6); setting random simulation parameters: the total frequency S is 20000, the initial random analog frequency spreading is 1, and the capacity analog frequency b is 1.
Evaluating the absorption capacity of the single node accessed to the photovoltaic. Setting an initial binary interval as [0, 5000] unit kW, setting the number of times of division T as 15, and solving the photovoltaic absorption capacity of each node from 63 to 85 at different moments day by day. Taking node 85 as an example, the daily evaluation results are shown in fig. 7.
As can be seen from FIG. 7, the photovoltaic absorption is least limited from month 1 to month 1 and from month 1 to month 11 to month 31; month 2 to month 4, month 1 and month 9 to month 11, month 1; the limitation of No. 4/month 1 to No. 9/month 1 on photovoltaic absorption is the largest, and similarly, the photovoltaic absorption capacity of each load node from 63 to 85 can be obtained, as shown in FIG. 8.
As can be seen from fig. 8, the photovoltaic absorption capacity of a node is inversely related to its electrical distance and positively related to the load level. Comparing the annual limit consumption capacity of each node, the time with the maximum photovoltaic access limit all the year around is 6 months, 2 days, 11 noon (see fig. 6), and the load level and photovoltaic output characteristics (shown in table 1) of each point at the time are brought into a random simulation process to evaluate a consumption scheme when the photovoltaic is consumed by multiple nodes.
Table 1 relevant parameters for maximum moment of photovoltaic access restriction
Figure GDA0003309726930000101
Figure GDA0003309726930000111
On the basis of the foregoing embodiment, in this embodiment, the repeatedly dividing the consumption capability of the load node by two according to the following rule according to the voltage value of the load node specifically includes:
calculating a voltage value of a load node according to a second relation model, and repeatedly dividing the absorption capacity of the load node into two parts according to the voltage value of the load node;
wherein the second relational model comprises:
Figure GDA0003309726930000112
Figure GDA0003309726930000113
wherein, Ue(pu)Representing a voltage per unit value of a node e in a network after a load node c is accessed to a photovoltaic, wherein the node e is before being accessed to the load node c; u shapef(pu)Representing a voltage per unit value of a node f in a network after a load node c is accessed to a photovoltaic, wherein the node f is accessed to the load node c; r represents the resistance of the basic parameter unit length of the line, x represents the reactance of the basic parameter unit length of the line, i and j represent the number variables of the nodes, and LiRepresenting the length of the line between node i and node i-1, QjRepresenting real-time reactive power, U, of node j0Representing the nominal voltage, P, of the networklim1(c) Representing access load node cPower injected into the network by photovoltaic, PjRepresenting the real-time active power of node j.
In the embodiment of the present invention, it should be noted that the voltage value of each load node is obtained according to a second relation model, where the basic parameters of the line include a unit length resistance r, a unit length reactance x, a line length L, a load power P + jQ, and a network rated voltage UNAre known quantities and can be given by the network architecture and load data.
According to the technical scheme, the distributed photovoltaic absorption and access method based on the double random simulation, provided by the embodiment of the invention, has the advantages that the photovoltaic access absorption capacity of each load node at each moment is evaluated through the dichotomy, the calculation efficiency can be improved, the calculation process is simple, and the evaluated absorption capacity of a single load node for accessing the photovoltaic is accurate.
On the basis of the foregoing embodiment, in this embodiment, the performing a first re-random sampling simulation by using a β distribution function as a first re-random sampling function based on the load level and the photovoltaic output characteristics of each load node at the time when the photovoltaic access limit is maximum, to determine the number of photovoltaic access points specifically includes:
and performing first random sampling simulation by adopting a beta (2,5) distribution function as a first random sampling function based on the load level and the photovoltaic output characteristics of each load node at the moment of maximum photovoltaic access limit, and determining the number of photovoltaic access points.
In this embodiment, referring to fig. 3, a β (2,5) distribution function is selected as a first re-random sampling function, N load nodes are projected onto the distribution function β (2,5) in equal proportion according to numbers 1 to N, and the value sampled at each time randomly is rounded up to obtain M or a node number value, that is, if the sampled value H is in the interval [ H-1, H ] (H ≧ 2), then M ═ H is present, or a node with the node number H is sampled, for example, by β (2,5), and M ═ 5 is present.
The embodiment of the invention selects the beta (2,5) distribution function as the first re-random sampling function, thereby reducing unnecessary calculation cost and reducing the probability of repeated sampling.
On the basis of the foregoing embodiment, in this embodiment, the performing a second random sampling simulation by using a uniform distribution function as a second random sampling function based on the number of the photovoltaic access points to determine the position of the photovoltaic access point specifically includes:
and based on the number of the photovoltaic access points, adopting a uniform distribution function as a second random sampling function, and repeatedly performing second random sampling simulation until different load nodes meeting the number of the photovoltaic access points are extracted, and determining the position of the photovoltaic access points.
In this embodiment, it should be noted that a uniform distribution is selected as the second random sampling function. The sampling principle is similar to that of the first repeated random sampling, the difference is that the sampling functions are different, the second repeated random sampling is repeated for multiple times, the sampling is not finished until M different load nodes are sampled, and the M nodes can form a photovoltaic access position sequence AM×1For example, after uniform distributed sampling, the M nodes sampled successively are 82, 65, 75, 77, 71, i.e. the sequence a of photovoltaic specific access positionsM×1Is [65, 71, 75, 77, 82 ]]T。
The embodiment of the invention adopts the uniform distribution function as the second random sampling function, and repeatedly performs the second random sampling simulation, thereby reducing unnecessary calculation cost, reducing the probability of repeated sampling, and simultaneously ensuring the probability of each node being sampled.
The double random sampling of the embodiment of the invention has two different objects, namely the number of the photovoltaic access points and the specific photovoltaic access load node sequence, and the two objects respectively correspond to the sampling results of the first double random sampling and the second double random sampling. Combining functions
Figure GDA0003309726930000131
The values of (1) are distributed along with the distribution trend that the values of M are from 1 to N and are large in the middle and small in two ends, the closer M is to N/2, the more the combination possibility is, namely, the more photovoltaic multi-node access schemes are, and when M is equal to N, the scheme is the least and only one scheme is adopted. Obviously, for the first re-random sampling, if the number of times M equals to N/2 is greater in several random samples, the sampling result of the photovoltaic access scheme is re-determinedThe lower the complex rate, and conversely, if M ═ N is extracted multiple times, only one of the sampled effective photovoltaic access schemes is selected, and the other M ═ N sampling results are repeated sampling. After the number of the photovoltaic access points is determined, the second random sampling determines the specific M load nodes accessed to the photovoltaic, equal sampling probability of each load node needs to be guaranteed, otherwise, the sampling probability of individual nodes is too high or too low, and the sampling repetition rate is obviously higher than that of the equal-probability second sampling.
By analyzing the two randomly sampled objects of the embodiment of the present invention, the conclusion can be drawn: the first re-random sampling function should be as heavy as possible as the combined function
Figure GDA0003309726930000132
The second random sampling function should make the probability of each load node being sampled to the access photovoltaic equal as much as possible. Further, by comparing and analyzing the distribution characteristics of the various sampling functions, it was found that the distribution characteristics of β (2,5) are closest to the combinatorial function
Figure GDA0003309726930000133
The uniform distribution function can ensure equal probability sampling of each node, and then the conclusion that beta (2,5) and the uniform distribution function are respectively used as a first double sampling function and a second double sampling function is obtained.
On the basis of the foregoing embodiment, in this embodiment, the performing capacity simulation on each access load node at the photovoltaic access point position to determine the photovoltaic absorption capacity of each access load node in the entire network and the maximum voltage value of the entire network load node specifically includes:
carrying out capacity simulation on each access load node on the photovoltaic access point, and determining the absorption capacity of each access load node of the whole network and the maximum voltage value of the load node of the whole network according to a third relation model;
wherein the third relationship model comprises:
Figure GDA0003309726930000134
wherein, in a certain capacity simulation, A represents the number sequence of nodes accessed to the photovoltaic, A (i) represents the number of the ith node in the sequence, D (i) represents the photovoltaic capacity accessed by the sampled ith node,
Figure GDA0003309726930000135
represents the electrical distance coefficient, N represents the total number of nodes of the network, min A (i) represents the node with the smallest number among all the nodes connected to the photovoltaic, PL(i) Representing the load level of the load node, z representing the accumulation coefficient, b representing the number of capacity simulations;
Figure GDA0003309726930000141
wherein D isPV(b) Representing the photovoltaic consumption capacity of the current capacity simulation, M representing the number of nodes actually accessed to the photovoltaic, and D (i) representing the photovoltaic capacity accessed to the ith node sampled;
Figure GDA0003309726930000142
wherein, Ug(pu)Expressing the voltage per unit value of a node g in the network, r expressing the resistance of a basic parameter unit length of the line, x expressing the reactance of the basic parameter unit length of the line, i and j expressing node number variables, LiRepresenting the length of the line between node i and node i-1, QjRepresenting real-time reactive power, U, of node j0Representing the nominal voltage, P, of the networklim1(j) Representing the power injected into the network by the photovoltaic connected to the load node j, PjRepresenting the real-time active power of the node j;
if the voltage per unit value of each load node is less than or equal to a preset value, voltage constraint is met; determining the photovoltaic absorption capacity of each access load node of the whole network and the maximum voltage value of the load node of the whole network;
and if the voltage per unit value of any load node is larger than the preset value, repeating the steps from S3 to S5 after 5 times of capacity simulation until the preset times of simulation process is completed, and determining the photovoltaic consumption capacity of each access load node of the whole network and the maximum voltage value of the load node of the whole network.
In this embodiment, it should be noted that, referring to fig. 2, the photovoltaic absorption capacity of the node i is approximately inversely related to the electrical distance from the head end of the network and the load level P thereof in the b-th capacity simulation of the a-th stochastic simulationL(i) Is positively correlated, so in the b-th capacity simulationM×1The photovoltaic access capacity d (i) of the load node i is calculated according to a third relation model, wherein in the present embodiment, the capacity accumulation parameter is 2, and d (i) is multiplied by the unit capacity photovoltaic output (i.e. the power of the photovoltaic injection node i) at the determined time.
In this embodiment, it should be noted that the voltage of the entire network is verified after the b-th capacity simulation. When multiple nodes are connected into the photovoltaic, the photovoltaic power is considered according to the negative load, and the voltage per unit value U of a certain node g in the networkg(pu)And if the voltage per unit value of each node is not more than 1.07, the voltage constraint is met. If the voltage per unit value of any node of the network exceeds 1.07, simulating for 5 times, stopping immediately, and starting to perform random simulation for the (a + 1) th time until all random simulations for S times are completed.
For example, for AM×1Capacity simulations were performed and the limit elimination scheme is shown in table 2.
Table 2 limit resolution for 1 st random simulation
Figure GDA0003309726930000151
The capacity simulation result shows that the final capacity simulation times of the first random simulation are 190 times, the maximum value of the voltage of the node of the whole network is 1.069, and the maximum value of the voltage can be absorbed by 1609.66 kW.
The process of each random simulation and the capacity simulation thereof is shown in fig. 9, wherein each scatter point represents a digestion scheme, and grading evaluation can be realized according to the influence degree of the scatter points on the voltage. It is clear that the photovoltaic absorption capacity of the selected distribution network is 1789.44kW at the maximum.
On the basis of the foregoing embodiment, in this embodiment, the method further includes:
taking a plurality of distributed photovoltaic absorption access schemes as a photovoltaic absorption access scheme database;
screening a matched distributed photovoltaic absorption access scheme in the photovoltaic absorption database based on the target access photovoltaic absorption capacity;
and based on the matched distributed photovoltaic absorption access scheme, taking the distributed photovoltaic absorption access scheme with the minimum voltage maximum value change degree of the load nodes of the whole network as the optimal distributed photovoltaic absorption access scheme.
In this embodiment, it should be noted that the distributed photovoltaic absorption access scheme is compared and analyzed. And screening a feasible scheme data set of the distributed photovoltaic access power distribution network in S times of random simulation according to a database generated by a plurality of distributed photovoltaic absorption access schemes, wherein the feasible scheme data set comprises specific photovoltaic access nodes and respective access capacities. The capacity and the maximum value in all the schemes are the distributed photovoltaic absorption capacity of the target power distribution network, and the corresponding access position sequence is the optimal access scene under the maximum absorption capacity. In practical application, according to actual factors such as photovoltaic installation space and installation capacity declared to a power grid company by a user, an optimal solution can be screened out in a data set, for example, when the installation capacity of photovoltaic received by the power grid company is 1500kW, a plurality of solutions can be screened out through a database. In contrast to all the absorption schemes, the scheme in which the influence on the voltage is minimal may be selected.
In this implementation, it should be noted that, with reference to fig. 9, the method provided in the embodiment of the present invention may further implement a hierarchical evaluation of the photovoltaic grid-connected capacity: the optimal region in the graph shows that when the photovoltaic capacity is less than 521.29kW, no matter how the photovoltaic capacity is accessed, the grid voltage is not affected, and the optimal access capacity is obtained; the safe regions in the graph show that when the photovoltaic capacity is 521.29-1352.26kW, the grid voltage is increased by the photovoltaic grid connection, but no matter how the grid voltage is connected, the grid voltage is always safe, and the safe connection capacity is obtained; the dangerous area in the graph shows that when the photovoltaic capacity is 1352.26-1789.44, the grid voltage is out of limit due to photovoltaic grid connection, and 1789.44kW is the limit absorption capacity of the target grid; when the capacity exceeds 1789.44kW, no matter how the capacity is accessed, the grid voltage is out of limit, and the photovoltaic grid connection is forbidden at the moment corresponding to the dangerous area in the graph.
According to the technical scheme, the distributed photovoltaic absorption access method based on the double random simulation provided by the embodiment of the invention can compare all absorption schemes, and select the absorption scheme which has the smallest influence on the voltage and meets the target access photovoltaic absorption capacity.
The distributed photovoltaic absorption access method based on the double random simulation provided by the embodiment of the invention can realize the graded evaluation of the distributed photovoltaic absorption capacity, provides a corresponding optimal absorption scheme aiming at different access capacity requirements, and provides guidance for the actual power distribution network planning.
Fig. 10 is a schematic structural diagram of a distributed photovoltaic absorption access apparatus based on dual random simulation according to an embodiment of the present invention, as shown in fig. 10, the apparatus includes: a first determining module 201, an obtaining module 202, a second determining module 203, a third determining module 204, a fourth determining module 205, a fifth determining module 206, and a sixth determining module 207, wherein:
the first determining module 201 is configured to determine, based on a limit value of a node voltage of the distribution network, all load nodes within a range of a distribution-type photovoltaic distribution network to be accessed, an annual load characteristic, and an annual unit capacity photovoltaic output characteristic, a time at which the limit on the photovoltaic access absorption capacity is maximum;
an obtaining module 202, configured to obtain a load level and a photovoltaic output characteristic at a time when the photovoltaic access absorption capability is most limited;
a second determining module 203, configured to perform a first re-random sampling simulation by using a β distribution function as a first re-random sampling function based on the load level and the photovoltaic output characteristics of each load node at the time when the photovoltaic access limit is maximum, and determine the number of photovoltaic access points;
a third determining module 204, configured to perform a second random sampling simulation by using a uniform distribution function as a second random sampling function based on the number of the photovoltaic access points, and determine a position of the photovoltaic access point;
a fourth determining module 205, configured to perform capacity simulation on each access load node in the photovoltaic access point position, and determine a photovoltaic absorption capacity of each access load node in the entire network and a maximum voltage value of the entire network load node;
a fifth determining module 206, configured to determine a distributed photovoltaic absorption access scheme according to the photovoltaic access point position, the photovoltaic absorption capacity of each access load node in the entire network, and the maximum voltage value of the load node in the entire network;
a sixth determining module 207, configured to repeat the second determining module to the fifth determining module, and determine a plurality of distributed photovoltaic absorption access schemes.
The risk assessment system provided by the embodiment of the present invention may be specifically configured to execute the risk assessment method of the above embodiment, and the technical principle and the beneficial effect thereof are similar to each other.
Based on the same inventive concept, an embodiment of the present invention provides an electronic device, and referring to fig. 11, the electronic device specifically includes the following contents: a processor 301, a communication interface 303, a memory 302, and a communication bus 304;
the processor 301, the communication interface 303 and the memory 302 complete mutual communication through the bus 304; the communication interface 303 is used for realizing information transmission between related devices such as modeling software, an intelligent manufacturing equipment module library and the like; the processor 301 is used for calling the computer program in the memory 302, and the processor executes the computer program to implement the method provided by the above method embodiments, for example, the processor executes the computer program to implement the following steps: s1, determining the moment with the maximum limit on photovoltaic access absorption capacity based on the limit value of the node voltage of the power distribution network, all load nodes in the range of the distribution photovoltaic power distribution network to be accessed, the annual load characteristic and the annual unit capacity photovoltaic output characteristic; s2, acquiring the load level and photovoltaic output characteristics at the moment when the photovoltaic access absorption capacity limit is maximum; s3, based on the load level and photovoltaic output characteristics of each load node at the moment of maximum photovoltaic access limit, performing first random sampling simulation by adopting a beta distribution function as a first random sampling function, and determining the number of photovoltaic access points; s4, based on the number of the photovoltaic access points, performing second random sampling simulation by adopting a uniform distribution function as a second random sampling function to determine the positions of the photovoltaic access points; s5, carrying out capacity simulation on each access load node at the position of the photovoltaic access point, and determining the photovoltaic absorption capacity of each access load node of the whole network and the maximum voltage value of the load node of the whole network; s6, determining a distributed photovoltaic absorption access scheme according to the photovoltaic access point position, the photovoltaic absorption capacity of each access load node of the whole network and the maximum voltage value of the load node of the whole network; and S7, repeating the steps from S3 to S6, and determining a plurality of distributed photovoltaic absorption access schemes.
Based on the same inventive concept, yet another embodiment of the present invention further provides a non-transitory computer-readable storage medium, on which a computer program is stored, the computer program, when executed by a processor, is implemented to perform the methods provided by the above method embodiments, for example, S1, determining a time when the limitation on the photovoltaic access absorption capacity is maximum based on the limit value of the distribution network node voltage, all load nodes within the range of the distributed photovoltaic distribution network to be accessed, the annual load characteristics, and the annual unit capacity photovoltaic output characteristics; s2, acquiring the load level and photovoltaic output characteristics at the moment when the photovoltaic access absorption capacity limit is maximum; s3, based on the load level and photovoltaic output characteristics of each load node at the moment of maximum photovoltaic access limit, performing first random sampling simulation by adopting a beta distribution function as a first random sampling function, and determining the number of photovoltaic access points; s4, based on the number of the photovoltaic access points, performing second random sampling simulation by adopting a uniform distribution function as a second random sampling function to determine the positions of the photovoltaic access points; s5, carrying out capacity simulation on each access load node at the position of the photovoltaic access point, and determining the photovoltaic absorption capacity of each access load node of the whole network and the maximum voltage value of the load node of the whole network; s6, determining a distributed photovoltaic absorption access scheme according to the photovoltaic access point position, the photovoltaic absorption capacity of each access load node of the whole network and the maximum voltage value of the load node of the whole network; and S7, repeating the steps from S3 to S6, and determining a plurality of distributed photovoltaic absorption access schemes.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (8)

1. A distributed photovoltaic absorption access method based on double random simulation is characterized by comprising the following steps:
s1, determining the moment with the maximum limit on the photovoltaic access absorption capacity based on the limit value of the node voltage of the power distribution network, all load nodes in the range of the distribution photovoltaic power distribution network to be accessed, the annual load characteristic and the annual unit capacity photovoltaic output characteristic, and specifically comprising the following steps:
evaluating the photovoltaic access absorption capacity of each load node at each moment by adopting a dichotomy according to a first relation model based on the limit value of the voltage of the power distribution network node, all load nodes in the range of the distribution-type photovoltaic power distribution network to be accessed, the annual load characteristic and the annual unit capacity photovoltaic output characteristic;
determining the moment with the maximum limit on the photovoltaic access absorption capacity according to the photovoltaic access absorption capacity of each load node at each moment;
wherein the bisection method comprises:
according to the voltage value of the load node, the consumption capacity of the load node is repeatedly divided into two parts according to the following rule, and the voltage value of each load node of the whole network is correspondingly checked once every two parts;
if any load node voltage value exceeds the limit, taking an upper boundary of a binary interval for calculating the consumption capacity of the load node;
if any load node voltage value is out of limit, taking a lower boundary of a binary interval for calculating the consumption capacity of the load node;
wherein the first relational model comprises:
Figure FDA0003314529240000011
wherein P is the load level and photovoltaic output characteristic at a timelim(n)(k) The consumption capacity of the load node k after n dichotomy is shown, wherein n is a positive integer greater than or equal to 0 and represents the dichotomy times, in each verification, the consumption capacity is an interval median value, and P islim(n+1)(k) Denotes the load node k's ability to absorb after n +1 dichotomies, Pdown(k) Representing the lower boundary, P, of a bipartite interval containing the load node k's absorption capabilityup(k) Representing an upper bound of a binary interval containing the load node k's capacity to absorb;
the repeated halving of the consumption capability of the load node according to the voltage value of the load node and the following rule specifically comprises the following steps:
calculating a voltage value of a load node according to a second relation model, and repeatedly dividing the absorption capacity of the load node into two parts according to the voltage value of the load node;
wherein the second relationship model comprises:
Figure FDA0003314529240000021
Figure FDA0003314529240000022
wherein, Ue(pu)Representing a voltage per unit value of a node e in a network after a load node c is accessed to a photovoltaic, wherein the node e is before being accessed to the load node c; u shapef(pu)Representing a voltage per unit value of a node f in a network after a load node c is accessed to a photovoltaic, wherein the node f is accessed to the load node c; r represents the resistance of the basic parameter unit length of the line, x represents the reactance of the basic parameter unit length of the line, i and j represent the number variables of the nodes, and LiRepresenting the length of the line between node i and node i-1, QjRepresenting real-time reactive power, U, of node j0Representing the nominal voltage, P, of the networklim1(c) Representing the power injected into the network by the photovoltaic connected to the load node c, PjRepresenting the real-time active power of the node j, and N representing the total number of nodes of the network;
s2, acquiring the load level and photovoltaic output characteristics at the moment when the photovoltaic access absorption capacity limit is maximum;
s3, based on the load level and photovoltaic output characteristics of each load node at the moment when the photovoltaic access absorption capacity is limited to the maximum, performing first random sampling simulation by taking a beta distribution function as a first random sampling function, and determining the number of photovoltaic access points;
s4, based on the number of the photovoltaic access points, performing second random sampling simulation by adopting a uniform distribution function as a second random sampling function to determine the positions of the photovoltaic access points;
s5, carrying out capacity simulation on each access load node at the position of the photovoltaic access point, and determining the photovoltaic absorption capacity of each access load node of the whole network and the maximum voltage value of the load node of the whole network;
s6, determining a distributed photovoltaic absorption access scheme according to the photovoltaic access point position, the photovoltaic absorption capacity of each access load node of the whole network and the maximum voltage value of the load node of the whole network;
and S7, repeating the steps from S3 to S6, and determining a plurality of distributed photovoltaic absorption access schemes.
2. The distributed photovoltaic absorption access method based on the dual random simulation as claimed in claim 1, wherein the performing a first re-random sampling simulation to determine the number of photovoltaic access points based on the load level and the photovoltaic output characteristics of each load node at the time when the photovoltaic access absorption capacity limit is maximum by using a β distribution function as a first re-random sampling function specifically comprises:
and performing first random sampling simulation by adopting a beta (2,5) distribution function as a first random sampling function based on the load level and the photovoltaic output characteristics of each load node at the moment of maximum photovoltaic access absorption capacity limit, and determining the number of photovoltaic access points.
3. The distributed photovoltaic absorption access method based on the double random simulation as claimed in claim 1, wherein the second random sampling simulation is performed by using a uniform distribution function as a second random sampling function based on the number of the photovoltaic access points to determine the position of the photovoltaic access point, specifically comprising:
and based on the number of the photovoltaic access points, adopting a uniform distribution function as a second random sampling function, and repeatedly performing second random sampling simulation until different load nodes meeting the number of the photovoltaic access points are extracted, and determining the position of the photovoltaic access points.
4. The distributed photovoltaic absorption access method based on the double stochastic simulation according to claim 1, wherein the capacity simulation is performed on each access load node at the position of the photovoltaic access point, and the photovoltaic absorption capacity of each access load node in the whole network and the maximum voltage value of the load node in the whole network are determined, specifically comprising:
carrying out capacity simulation on each access load node on the position of the photovoltaic access point, and determining the photovoltaic absorption capacity of each access load node of the whole network and the maximum voltage value of the load node of the whole network according to a third relation model;
wherein the third relationship model comprises:
Figure FDA0003314529240000041
wherein, in a certain capacity simulation, A represents the number sequence of load nodes accessed to the photovoltaic, A (i) represents the number of the ith load node in the sequence, D (i) represents the photovoltaic capacity accessed by the sampled ith load node,
Figure FDA0003314529240000042
represents the electrical distance coefficient, N represents the total number of nodes of the network, min A (i) represents the lowest numbered load node of all load nodes accessing the photovoltaic, PL(i) Representing the load level of the load node, z representing the accumulation coefficient, b representing the number of capacity simulations;
Figure FDA0003314529240000043
wherein D isPV(b) Representing the photovoltaic absorption capacity of the current capacity simulation, M representing the number of load nodes actually accessed to the photovoltaic, and D (i) representing the photovoltaic capacity accessed to the ith sampled load node;
Figure FDA0003314529240000044
wherein, Ug(pu)Expressing the voltage per unit value of a node g in the network, r expressing the resistance of a basic parameter unit length of the line, x expressing the reactance of the basic parameter unit length of the line, i and j expressing node number variables, LiRepresenting node i and nodeLine length between i-1, QjRepresenting real-time reactive power, U, of node j0Representing the nominal voltage, P, of the networklim1(j) Representing the power injected into the network by the photovoltaic connected to the load node j, PjRepresenting the real-time active power of the node j;
if the voltage per unit value of each load node is less than or equal to a preset value, voltage constraint is met; determining the photovoltaic absorption capacity of each access load node of the whole network and the maximum voltage value of the load node of the whole network;
and if the voltage per unit value of any load node is larger than the preset value, repeating the steps from S3 to S5 after 5 times of capacity simulation until the preset times of simulation process is completed, and determining the photovoltaic consumption capacity of each access load node of the whole network and the maximum voltage value of the load node of the whole network.
5. The distributed photovoltaic absorption access method based on the double random simulation as recited in claim 1, further comprising:
taking a plurality of distributed photovoltaic absorption access schemes as a photovoltaic absorption access scheme database;
screening a matched distributed photovoltaic absorption access scheme from the photovoltaic absorption access scheme database based on the target access photovoltaic absorption capacity;
and based on the matched distributed photovoltaic absorption access scheme, taking the distributed photovoltaic absorption access scheme with the minimum voltage maximum value change degree of the load nodes of the whole network as the optimal distributed photovoltaic absorption access scheme.
6. A distributed photovoltaic absorption access device based on dual stochastic simulation, comprising:
the first determining module is used for determining the moment with the maximum limit on the photovoltaic access absorption capacity based on the limit value of the node voltage of the power distribution network, all load nodes in the range of the distribution photovoltaic power distribution network to be accessed, the annual load characteristic and the annual unit capacity photovoltaic output characteristic, and is specifically used for:
evaluating the photovoltaic access absorption capacity of each load node at each moment by adopting a dichotomy according to a first relation model based on the limit value of the voltage of the power distribution network node, all load nodes in the range of the distribution-type photovoltaic power distribution network to be accessed, the annual load characteristic and the annual unit capacity photovoltaic output characteristic;
determining the moment with the maximum limit on the photovoltaic access absorption capacity according to the photovoltaic access absorption capacity of each load node at each moment;
wherein the bisection method comprises:
according to the voltage value of the load node, the consumption capacity of the load node is repeatedly divided into two parts according to the following rule, and the voltage value of each load node of the whole network is correspondingly checked once every two parts;
if any load node voltage value exceeds the limit, taking an upper boundary of a binary interval for calculating the consumption capacity of the load node;
if any load node voltage value is out of limit, taking a lower boundary of a binary interval for calculating the consumption capacity of the load node;
wherein the first relational model comprises:
Figure FDA0003314529240000061
wherein P is the load level and photovoltaic output characteristic at a timelim(n)(k) The consumption capacity of the load node k after n dichotomy is shown, wherein n is a positive integer greater than or equal to 0 and represents the dichotomy times, in each verification, the consumption capacity is an interval median value, and P islim(n+1)(k) Denotes the load node k's ability to absorb after n +1 dichotomies, Pdown(k) Representing the lower boundary, P, of a bipartite interval containing the load node k's absorption capabilityup(k) Representing an upper bound of a binary interval containing the load node k's capacity to absorb;
the repeated halving of the consumption capability of the load node according to the voltage value of the load node and the following rule specifically comprises the following steps:
calculating a voltage value of a load node according to a second relation model, and repeatedly dividing the absorption capacity of the load node into two parts according to the voltage value of the load node;
wherein the second relationship model comprises:
Figure FDA0003314529240000062
Figure FDA0003314529240000063
wherein, Ue(pu)Representing a voltage per unit value of a node e in a network after a load node c is accessed to a photovoltaic, wherein the node e is before being accessed to the load node c; u shapef(pu)Representing a voltage per unit value of a node f in a network after a load node c is accessed to a photovoltaic, wherein the node f is accessed to the load node c; r represents the resistance of the basic parameter unit length of the line, x represents the reactance of the basic parameter unit length of the line, i and j represent the number variables of the nodes, and LiRepresenting the length of the line between node i and node i-1, QjRepresenting real-time reactive power, U, of node j0Representing the nominal voltage, P, of the networklim1(c) Representing the power injected into the network by the photovoltaic connected to the load node c, PjRepresenting the real-time active power of the node j, and N representing the total number of nodes of the network;
the acquisition module is used for acquiring the load level and the photovoltaic output characteristic at the moment when the photovoltaic access absorption capacity limit is maximum;
the second determining module is used for performing first re-random sampling simulation by adopting a beta distribution function as a first re-random sampling function based on the load level and the photovoltaic output characteristics of each load node at the moment when the photovoltaic access absorption capacity is limited to the maximum, and determining the number of photovoltaic access points;
the third determining module is used for performing second random sampling simulation by adopting a uniform distribution function as a second random sampling function based on the number of the photovoltaic access points to determine the positions of the photovoltaic access points;
the fourth determining module is used for carrying out capacity simulation on each access load node on the photovoltaic access point position, and determining the photovoltaic absorption capacity of each access load node of the whole network and the maximum voltage value of the whole network load node;
a fifth determining module, configured to determine a distributed photovoltaic absorption access scheme according to the photovoltaic access point position, the photovoltaic absorption capacity of each access load node in the entire network, and the maximum voltage value of the load node in the entire network;
and the sixth determining module is used for repeatedly calling the second determining module to the fifth determining module to determine a plurality of distributed photovoltaic absorption access schemes.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor executes the program to implement the dual random simulation-based distributed photovoltaic absorption access method according to any one of claims 1 to 5.
8. A non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the distributed photovoltaic absorption access method based on dual stochastic simulation according to any of claims 1 to 5.
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