CN115630880A - Distribution transformer area distributed photovoltaic access capacity calculation method and system - Google Patents

Distribution transformer area distributed photovoltaic access capacity calculation method and system Download PDF

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CN115630880A
CN115630880A CN202211265302.2A CN202211265302A CN115630880A CN 115630880 A CN115630880 A CN 115630880A CN 202211265302 A CN202211265302 A CN 202211265302A CN 115630880 A CN115630880 A CN 115630880A
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distributed photovoltaic
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唐成虹
廖辉
赵福林
贾向博
戴维
蒋亦凡
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NARI Nanjing Control System Co Ltd
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Nari Technology Co Ltd
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Abstract

The invention discloses a distributed photovoltaic access capacity calculation method and a distributed photovoltaic access capacity calculation system for a power distribution area.

Description

Distribution transformer area distributed photovoltaic access capacity calculation method and system
Technical Field
The invention relates to a distributed photovoltaic access capacity calculation method and system for a power distribution transformer area, and belongs to the technical field of distributed power sources and power distribution networks.
Background
A novel power system taking new energy as a main body is constructed, the rapid development of distributed photovoltaic is promoted by the construction target, the distributed photovoltaic is mainly connected into a medium-low voltage distribution transformer area, if the capacity of the connected distributed photovoltaic exceeds the saturated bearing capacity of the transformer area, the voltage quality problem of the transformer area can be caused, and the phenomena of high voltage in the daytime and low voltage at night can occur. Therefore, the distributed photovoltaic access capacity of the distribution area needs to be calculated to meet the requirement of the saturated bearing capacity of the distribution area, and no accurate calculation method exists at present.
Disclosure of Invention
The invention provides a method and a system for calculating distributed photovoltaic access capacity of a power distribution station area, which solve the problems disclosed in the background technology.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
a power distribution station distributed photovoltaic access capacity calculation method comprises the following steps:
acquiring rated capacity, wiring topology, load capacity, distributed photovoltaic quantity and power information of planned access of a power distribution station area;
inputting rated capacity, wiring topology, load capacity, distributed photovoltaic quantity and power information of planned access of a power distribution area into a pre-constructed capacity decision model, and solving the capacity decision model to obtain distributed photovoltaic access capacity meeting the requirement of saturated bearing capacity of the power distribution area; the capacity decision model aims at the maximum distributed photovoltaic access capacity, and takes target probability constraint, power flow constraint, power distribution network node voltage constraint, line capacity constraint, power factor constraint, reactive compensation device switching quantity constraint, distributed photovoltaic construction capacity constraint, power supply radius constraint and transformer load capacity constraint into consideration.
The objective function of the capacity decision model is:
Figure BDA0003892885950000021
wherein the content of the first and second substances,
Figure BDA0003892885950000022
as an objective function, M is the number of distributed photovoltaics planned to be accessed,
Figure BDA0003892885950000023
for accessing node m of distribution network k Distributed photovoltaic capacity of m k Number of distribution network node accessed by kth distributed photovoltaic k ∈[1,N]And N is the total number of the nodes of the power distribution network.
The target probability constraint is:
Figure BDA0003892885950000024
Figure BDA0003892885950000025
wherein x is photovoltaic state, y is load state, x and y are decision variables, zeta is operation state variable of power distribution system, zeta max F (x, y, zeta) is the photovoltaic saturation bearing capacity of the distribution network in the state zeta for the maximum state number of the distribution system, N is the total number of nodes of the distribution network, x ζ Probability of photovoltaic at State ζ, y ζ Probability of load in State ζ, P ζn The photovoltaic active output of the nth distribution network node under the state zeta is shown, alpha is the confidence level of the objective function,
Figure BDA0003892885950000028
f (x, y, ζ) takes a minimum value at a probability level of at least α, and P { } represents a probability value.
The power flow constraint is as follows:
Figure BDA0003892885950000026
Figure BDA0003892885950000027
wherein, P pv For distributed photovoltaic capacity, beta s The distributed power output accounts for the percentage of the rated power of the distributed power supply under the scene s,
Figure BDA0003892885950000031
for the active power output reduction of the distributed power supply under the scene s, chi s Is the percentage of the load power to the load peak value under the scene s, U i,s Is the voltage amplitude, U, of the ith power distribution network node under the scene s j,s Is the voltage amplitude theta of the jth power distribution network node under the scene s ij,s The voltage phase angle difference between the ith power distribution network node and the jth power distribution network node under the scene s is P Gi,s Is the active load, Q, of the conventional power supply at the ith power distribution network node under the scene s Gi,s Is the reactive load, P, of the conventional power supply at the ith distribution network node under the scene s Di,s Is the active load, Q, of the ith distribution network node under the scene s Di,s Is the reactive load of the ith distribution network node under the scene s, G i,j Is the real part of the ith row and jth column of the admittance matrix, B i,j Is the imaginary part of the ith row and jth column of the admittance matrix.
The line capacity constraints are:
Figure BDA0003892885950000032
wherein, P ij,s Is the active power, Q, flowing from the ith distribution network node to the jth distribution network node ij,s For the reactive power flowing from the ith network node to the jth network node,
Figure BDA0003892885950000033
for the ith power distributionThe maximum power value which can be carried by the branch between the network node and the jth distribution network node.
The transformer load capacity constraints are:
at normal room temperature, under normal operation:
Figure BDA0003892885950000034
under normal room temperature, overload operation:
Figure BDA0003892885950000035
wherein, P is active power, Q is active power, S is apparent power, T is overload running time, T is maximum allowable time of overload running, and alpha' is overload ratio.
Inputting the rated capacity, wiring topology, load capacity, the number of distributed photovoltaic devices planned to be accessed and power information of a distribution substation into a pre-constructed capacity decision model, solving the capacity decision model, and obtaining the distributed photovoltaic access capacity meeting the requirement of the saturated bearing capacity of the distribution substation, wherein the method comprises the following steps:
inputting rated capacity, wiring topology, load capacity, planned access distributed photovoltaic quantity and power information of a distribution substation area into a pre-constructed capacity decision model, and solving the capacity decision model by adopting a particle swarm algorithm to obtain distributed photovoltaic access capacity meeting the requirement of saturated bearing capacity of the distribution substation area; in the particle swarm optimization, the distributed photovoltaic capacity of the access power distribution network node is used as a decision variable.
A power distribution grid distributed photovoltaic access capacity calculation system, comprising:
the information acquisition module is used for acquiring rated capacity, wiring topology, load capacity, the number of distributed photovoltaic devices planned to be accessed and power information of a power distribution station area;
the model calculation module is used for inputting the rated capacity, wiring topology, load capacity, the number of distributed photovoltaic devices planned to be accessed and power information of a power distribution station area into a pre-constructed capacity decision model, solving the capacity decision model and obtaining the distributed photovoltaic access capacity meeting the saturated bearing capacity requirement of the power distribution station area; the capacity decision model takes the maximum distributed photovoltaic access capacity as a target, and takes target probability constraint, power flow constraint, power distribution network node voltage constraint, line capacity constraint, power factor constraint, reactive compensation device switching quantity constraint, distributed photovoltaic construction capacity constraint, power supply radius constraint and transformer load capacity constraint into consideration.
A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform a power distribution substation distributed photovoltaic access capacity calculation method.
A computing device comprising one or more processors, one or more memories, and one or more programs stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs comprising instructions for performing a power distribution substation distributed photovoltaic access capacity calculation method.
The invention has the following beneficial effects: according to the distributed photovoltaic access capacity calculation method, the distributed photovoltaic access capacity is obtained by solving the capacity decision model, the capacity decision model takes the maximum distributed photovoltaic access capacity as a target, constraints such as power supply capacity and electric energy quality of a distribution station area are considered, the saturated bearing capacity requirement of the distribution station area can be met scientifically and accurately, accurate calculation of the distributed photovoltaic access capacity can be achieved, the problem that the distribution station area has high electric energy quality such as high daytime voltage and low nighttime voltage due to the fact that a large number of distributed photovoltaics are randomly accessed into the distribution station area is avoided, and the distributed photovoltaic access capacity calculation method has important significance for reducing potential operation risks of a power distribution network and guaranteeing safe and reliable operation of a power distribution system.
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FIG. 1 is a flow chart of the method of the present invention;
fig. 2 is a flow chart of the construction of a capacity decision module.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
As shown in fig. 1, a method for calculating distributed photovoltaic access capacity of a distribution substation includes the following steps:
step 1, acquiring rated capacity, wiring topology, load capacity, distributed photovoltaic quantity and power information of planned access of a power distribution station area;
step 2, inputting rated capacity, wiring topology, load capacity, planned accessed distributed photovoltaic quantity and power information of the distribution substation area into a pre-constructed capacity decision model, and solving the capacity decision model to obtain distributed photovoltaic access capacity meeting the saturated bearing capacity requirement of the distribution substation area; the capacity decision model aims at the maximum distributed photovoltaic access capacity, and takes target probability constraint, power flow constraint, power distribution network node voltage constraint, line capacity constraint, power factor constraint, reactive compensation device switching quantity constraint, distributed photovoltaic construction capacity constraint, power supply radius constraint and transformer load capacity constraint into consideration.
According to the method, the distributed photovoltaic access capacity is obtained by solving the capacity decision model, the capacity decision model takes the maximum distributed photovoltaic access capacity as a target, constraints such as power supply capacity and electric energy quality of a distribution station area are considered, the saturated bearing capacity requirement of the distribution station area can be met scientifically and accurately, accurate calculation of the distributed photovoltaic access capacity can be achieved, the problem that the distribution station area has high electric energy quality such as high voltage in the daytime and low voltage at night due to the fact that a large number of distributed photovoltaics are randomly accessed into the distribution station area is avoided, and the method has important significance for reducing potential operation risks of a power distribution network and guaranteeing safe and reliable operation of a power distribution system.
Before the method is implemented, a capacity decision model needs to be constructed, as shown in fig. 2, parameter information such as rated capacity, wiring topology, load and the like of a distribution substation area can be obtained, information such as the number of distributed photovoltaics planned to be accessed and power and the like is obtained, then an objective function with the maximum distributed photovoltaic access capacity as a target is established, a constraint function of target probability constraint, power flow constraint, distribution network node voltage constraint, line capacity constraint, power factor constraint, switching quantity constraint of a reactive power compensation device, distributed photovoltaic construction capacity constraint, power supply radius constraint and transformer load capacity constraint is established, and finally the capacity decision model is constructed on the basis of the objective function and the constraint function.
The objective function of the capacity decision model can be formulated as:
Figure BDA0003892885950000061
wherein the content of the first and second substances,
Figure BDA0003892885950000062
as an objective function, M is the number of distributed photovoltaics planned to be accessed,
Figure BDA0003892885950000063
for accessing node m of distribution network k Distributed photovoltaic capacity of m k Numbering nodes of power distribution network accessed by kth distributed photovoltaic k ∈[1,N]And N is the total number of the nodes of the power distribution network.
The target probability constraint can be formulated as:
Figure BDA0003892885950000064
Figure BDA0003892885950000065
wherein x is photovoltaic state, y is load state, x and y are decision variables, zeta is operation state variable of power distribution system, zeta max ＝M s ×N s F (x, y, zeta) is the photovoltaic saturation bearing capacity of the distribution network under the state zeta, N is the total number of nodes of the distribution network, x ζ Probability of photovoltaic at state ζ, y ζ Probability of load in State ζ, P ζn Is the nth distribution network node under the state zetaThe photovoltaic active power output, alpha is the confidence level of the objective function,
Figure BDA0003892885950000075
take the minimum value for f (x, y, ζ) at a probability level of at least α, P { } represents a probability value.
The power flow constraint can be formulated as:
Figure BDA0003892885950000071
Figure BDA0003892885950000072
wherein, P pv For distributed photovoltaic capacity, beta s The distributed power output accounts for the percentage of the rated power of the distributed power supply under the scene s,
Figure BDA0003892885950000073
for the active power output reduction, chi, of the distributed power supply under the scene s s Is the percentage of the load power to the load peak value under the scene s, U i,s The voltage amplitude value, U, of the ith power distribution network node under the scene s j,s The voltage amplitude value theta of the jth power distribution network node under the scene s ij,s The voltage phase angle difference between the ith power distribution network node and the jth power distribution network node under the scene s is P Gi,s Is the active load, Q, of the conventional power supply at the ith power distribution network node under the scene s Gi,s Is the reactive load, P, of the conventional power supply at the ith distribution network node under the scene s Di,s Is the active load, Q, of the ith distribution network node under the scene s Di,s Is the reactive load of the ith distribution network node under the scene s, G i,j Is the real part of the ith row and jth column of the admittance matrix, B i,j Is the imaginary part of the ith row and the jth column of the admittance matrix.
The distribution network node voltage constraint can be formulated as:
Figure BDA0003892885950000074
wherein the content of the first and second substances,
Figure BDA0003892885950000081
is U i,s The upper limit of (a) is,
Figure BDA0003892885950000082
is U i,s The lower limit of (3).
The line capacity constraint can be formulated as:
Figure BDA0003892885950000083
wherein, P ij,s Is the active power, Q, flowing from the ith distribution network node to the jth distribution network node ij,s For the reactive power flowing from the ith network node to the jth network node,
Figure BDA0003892885950000084
the maximum power value which can be carried by a branch between the ith distribution network node and the jth distribution network node.
The power factor constraint can be formulated as:
Figure BDA0003892885950000085
wherein the content of the first and second substances,
Figure BDA0003892885950000086
the angle is a power angle, and the angle is a power angle,
Figure BDA0003892885950000087
is composed of
Figure BDA0003892885950000088
The lower limit of (a) is,
Figure BDA0003892885950000089
is composed of
Figure BDA00038928859500000810
The upper limit of (2).
The switching amount constraint of the reactive power compensation device can be expressed by a formula as follows:
Figure BDA00038928859500000811
wherein, Q' Gi,s The switching amount of the reactive power compensation device is changed,
Figure BDA00038928859500000812
is Q' Gi,s The lower limit of (a) is,
Figure BDA00038928859500000813
is Q' Gi,s The upper limit of (2).
The construction capacity constraint of the distributed photovoltaic power supply can be expressed by the following formula:
S PV,i ≤S C,i
wherein S is PV,i For distributed power access capacity, S, at the ith distribution network node C,i Representing the maximum capacity of the photovoltaic power allowed to be built at the ith distribution network node influenced by geographical, economic and policy factors.
The supply radius constraint can be formulated as:
Figure BDA00038928859500000814
wherein l k The line length from the kth distribution network node to the k-1 th distribution network node at the upstream, l max The maximum power supply radius of the power distribution station area.
The transformer can run for a long time with rated load, light planting or no load, and also has certain overload capacity, and the constraint of the transformer load capacity can be expressed by a formula as follows:
at normal room temperature, under normal operation:
Figure BDA0003892885950000091
under normal room temperature, overload operation:
Figure BDA0003892885950000092
wherein, P is active power, Q is active power, S is apparent power, T is overload running time, T is maximum allowable time of overload running, and alpha' is overload ratio.
On the basis of the model, the rated capacity, the wiring topology, the load capacity, the planned accessed distributed photovoltaic quantity and the power information of the distribution substation are obtained, the rated capacity, the wiring topology, the load capacity, the planned accessed distributed photovoltaic quantity and the power information of the distribution substation are input into a pre-constructed capacity decision model, the capacity decision model is solved by adopting a particle swarm algorithm, and the distributed photovoltaic access capacity meeting the saturated bearing capacity requirement of the distribution substation is obtained, wherein in the particle swarm algorithm, the distributed photovoltaic capacity accessed to a node of the distribution network is used as a decision variable.
The process of solving by using the particle swarm algorithm can be as follows:
1) Distributed photovoltaic capacity to be accessed
Figure BDA0003892885950000093
As a decision variable;
2) Initializing the optimizing positions and optimizing speeds of the N particles by adopting a particle swarm algorithm, wherein the optimizing positions represent the state of each decision variable, and the optimizing speeds represent the advancing speeds of the particles towards the optimal extreme value;
3) Calculating photovoltaic capacity values of all the particles respectively, and solving an individual optimal state and an individual optimal extreme value;
4) Obtaining a global optimal state and a global optimal extreme value;
5) Updating the optimizing speed and the optimizing position of the particles according to the individual and global optimal extreme values and optimal states;
6) Judging whether the optimizing speed is converged or not, and if not, skipping and executing 3); and if the photovoltaic capacity is converged, outputting a global optimal state and a global optimal extreme value to obtain the optimal capacity of photovoltaic access.
The problem that the voltage of a distribution transformer area exceeds the limit and the like due to the fact that a large number of distributed photovoltaic devices are randomly accessed into the transformer area at present is solved, the safe operation of a power grid is greatly influenced, and meanwhile, the photovoltaic inverter quits the operation due to the fact that the voltage exceeds the limit, and loss is brought to photovoltaic income. The capacity maximization objective function of the accessed distributed power supply is established, the saturated bearing capacity constraint function of the power distribution station area is established, and a capacity decision model is established; the model comprehensively considers constraints such as power supply capacity and electric energy quality of a power distribution area, can meet the saturated bearing capacity requirement of the power distribution area more scientifically and accurately, achieves accurate calculation of distributed photovoltaic access capacity meeting the saturated bearing capacity requirement of the power distribution area, avoids the problem of electric energy quality such as high daytime voltage and low nighttime voltage of the power distribution area, and has important significance for reducing potential operation risks of a power distribution network and ensuring safe and reliable operation of a power distribution system.
Based on the same technical scheme, the invention also discloses a software device of the method, and a power distribution station distributed photovoltaic access capacity calculation device comprises:
the information acquisition module is used for acquiring rated capacity, wiring topology, load capacity, the number of distributed photovoltaic devices planned to be accessed and power information of a power distribution station area;
the model calculation module is used for inputting the rated capacity, wiring topology, load capacity, the number of distributed photovoltaic devices planned to be accessed and power information of a power distribution station area into a pre-constructed capacity decision model, solving the capacity decision model and obtaining the distributed photovoltaic access capacity meeting the saturated bearing capacity requirement of the power distribution station area; the capacity decision model takes the maximum distributed photovoltaic access capacity as a target, and takes target probability constraint, power flow constraint, node voltage constraint, line capacity constraint, power factor constraint, reactive compensation device switching quantity constraint, distributed photovoltaic construction capacity constraint, power supply radius constraint and transformer load capacity constraint into consideration.
The data processing flow of each module in the device is consistent with that of the method, and the description is not repeated here.
Based on the same technical solution, the present invention also discloses a computer-readable storage medium storing one or more programs, the one or more programs including instructions, which when executed by a computing device, cause the computing device to perform a power distribution substation distributed photovoltaic access capacity calculation method.
Based on the same technical solution, the present invention also discloses a computing device, which includes one or more processors, one or more memories, and one or more programs, where the one or more programs are stored in the one or more memories and configured to be executed by the one or more processors, and the one or more programs include instructions for executing the power distribution substation distributed photovoltaic access capacity calculation method.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The present invention is not limited to the above embodiments, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention are included in the scope of the claims of the present invention as filed.

Claims (10)

1. A distribution station distributed photovoltaic access capacity calculation method is characterized by comprising the following steps:
acquiring rated capacity, wiring topology, load capacity, distributed photovoltaic quantity and power information of planned access of a power distribution station area;
inputting rated capacity, wiring topology, load capacity, distributed photovoltaic quantity and power information of planned access of a power distribution area into a pre-constructed capacity decision model, and solving the capacity decision model to obtain distributed photovoltaic access capacity meeting the requirement of saturated bearing capacity of the power distribution area; the capacity decision model aims at the maximum distributed photovoltaic access capacity, and takes target probability constraint, power flow constraint, power distribution network node voltage constraint, line capacity constraint, power factor constraint, reactive compensation device switching quantity constraint, distributed photovoltaic construction capacity constraint, power supply radius constraint and transformer load capacity constraint into consideration.
2. The method for calculating the distributed photovoltaic access capacity of the power distribution substation according to claim 1, wherein an objective function of the capacity decision model is as follows:
Figure FDA0003892885940000011
wherein the content of the first and second substances,
Figure FDA0003892885940000012
as an objective function, M is the number of distributed photovoltaics planned to be accessed,
Figure FDA0003892885940000013
for accessing node m of distribution network k Distributed photovoltaic capacity of m k Numbering nodes of power distribution network accessed by kth distributed photovoltaic k ∈[1,N]And N is the total number of the nodes of the power distribution network.
3. The method for calculating the distributed photovoltaic access capacity of the power distribution substation according to claim 1, wherein the target probability constraint is as follows:
Figure FDA0003892885940000014
Figure FDA0003892885940000015
wherein x is photovoltaic state, y is load state, x and y are decision variables, zeta is operation state variable of power distribution system, zeta max F (x, y, zeta) is the photovoltaic saturation bearing capacity of the distribution network in the state zeta for the maximum state number of the distribution system, N is the total number of nodes of the distribution network, x ζ Probability of photovoltaic at state ζ, y ζ At state ζProbability of load, P ζn The photovoltaic active output of the nth distribution network node under the state zeta is shown, alpha is the confidence level of the objective function,
Figure FDA0003892885940000024
f (x, y, ζ) takes a minimum value at a probability level of at least α, and P { } represents a probability value.
4. The method for calculating distributed photovoltaic access capacity of the power distribution substation according to claim 1, wherein the power flow constraint is as follows:
Figure FDA0003892885940000021
Figure FDA0003892885940000022
wherein, P pv For distributed photovoltaic capacity, beta s The distributed power output accounts for the percentage of the rated power of the distributed power supply under the scene s,
Figure FDA0003892885940000023
for the active power output reduction, chi, of the distributed power supply under the scene s s Is the percentage of the load power to the load peak value under the scene s, U i,s Is the voltage amplitude, U, of the ith power distribution network node under the scene s j,s Is the voltage amplitude theta of the jth power distribution network node under the scene s ij,s The voltage phase angle difference between the ith power distribution network node and the jth power distribution network node under the scene s is P Gi,s Is the active load, Q, of the conventional power supply at the ith distribution network node under scene s Gi,s Is the reactive load, P, of the conventional power supply at the ith distribution network node under the scene s Di,s Is the active load, Q, of the ith distribution network node under the scene s Di,s Is the reactive load of the ith distribution network node under the scene s, G i,j Is the real part of the ith row and jth column of the admittance matrix, B i,j Is the first of admittance matrixThe imaginary part of the i row and the j column.
5. The distribution substation distributed photovoltaic access capacity calculation method according to claim 1, wherein the line capacity constraint is:
Figure FDA0003892885940000031
wherein, P ij,s Is the active power, Q, flowing from the ith distribution network node to the jth distribution network node ij,s For the reactive power flowing from the ith network node to the jth network node,
Figure FDA0003892885940000032
the maximum power value which can be carried by a branch between the ith distribution network node and the jth distribution network node.
6. The distribution substation distributed photovoltaic access capacity calculation method according to claim 1, wherein the transformer load capacity constraint is:
at normal room temperature, under normal operation:
Figure FDA0003892885940000033
under normal room temperature, overload operation:
Figure FDA0003892885940000034
wherein, P is active power, Q is active power, S is apparent power, T is overload running time, T is maximum allowable time of overload running, and alpha' is overload ratio.
7. The method for calculating the distributed photovoltaic access capacity of the power distribution station area according to claim 1, wherein the method comprises the steps of inputting rated capacity, wiring topology, load capacity, the number of distributed photovoltaic devices planned to be accessed and power information of the power distribution station area into a pre-constructed capacity decision model, solving the capacity decision model, and obtaining the distributed photovoltaic access capacity meeting the requirement of the saturated bearing capacity of the power distribution station area, and comprises the following steps:
inputting rated capacity, wiring topology, load capacity, distributed photovoltaic number and power information of planned access of a power distribution area into a pre-constructed capacity decision model, solving the capacity decision model by adopting a particle swarm algorithm, and obtaining distributed photovoltaic access capacity meeting the requirement of saturated bearing capacity of the power distribution area; in the particle swarm optimization, the distributed photovoltaic capacity of a node accessed to the power distribution network is used as a decision variable.
8. A power distribution substation distributed photovoltaic access capacity calculation system, comprising:
the information acquisition module is used for acquiring rated capacity, wiring topology, load capacity, the number of distributed photovoltaic devices planned to be accessed and power information of a power distribution station area;
the model calculation module is used for inputting the rated capacity, wiring topology, load capacity, the number of distributed photovoltaic devices planned to be accessed and power information of a power distribution station area into a pre-constructed capacity decision model, solving the capacity decision model and obtaining the distributed photovoltaic access capacity meeting the saturated bearing capacity requirement of the power distribution station area; the capacity decision model takes the maximum distributed photovoltaic access capacity as a target, and takes target probability constraint, power flow constraint, power distribution network node voltage constraint, line capacity constraint, power factor constraint, reactive compensation device switching quantity constraint, distributed photovoltaic construction capacity constraint, power supply radius constraint and transformer load capacity constraint into consideration.
9. A computer readable storage medium storing one or more programs, the one or more programs comprising instructions, which when executed by a computing device, cause the computing device to perform any of the methods of claims 1-7.
10. A computing device, comprising:
one or more processors, one or more memories, and one or more programs stored in the one or more memories and configured to be executed by the one or more processors, the one or more programs including instructions for performing any of the methods of claims 1-7.
CN202211265302.2A 2022-10-17 2022-10-17 Distribution transformer area distributed photovoltaic access capacity calculation method and system Pending CN115630880A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116054265A (en) * 2023-03-23 2023-05-02 国网山西省电力公司营销服务中心 Metering method and system for photovoltaic accessible capacity in transformer area
CN116561485A (en) * 2023-06-28 2023-08-08 国网北京市电力公司 Distribution transformer area photovoltaic capacity calculation method, device, equipment and medium

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116054265A (en) * 2023-03-23 2023-05-02 国网山西省电力公司营销服务中心 Metering method and system for photovoltaic accessible capacity in transformer area
CN116054265B (en) * 2023-03-23 2023-06-20 国网山西省电力公司营销服务中心 Metering method and system for photovoltaic accessible capacity in transformer area
CN116561485A (en) * 2023-06-28 2023-08-08 国网北京市电力公司 Distribution transformer area photovoltaic capacity calculation method, device, equipment and medium
CN116561485B (en) * 2023-06-28 2023-09-15 国网北京市电力公司 Distribution transformer area photovoltaic capacity calculation method, device, equipment and medium

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