CN114037157A - Distributed photovoltaic locating and sizing optimization method, system, equipment and storage medium - Google Patents
Distributed photovoltaic locating and sizing optimization method, system, equipment and storage medium Download PDFInfo
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Abstract
The invention relates to a distributed photovoltaic locating and sizing optimization method, a system, equipment and a storage medium, wherein the method comprises the following steps: calculating a correlation coefficient between the load of a feeder line-mounted distribution transformer of the medium-voltage distribution network and distributed photovoltaic on the basis of a Pearson model; determining a distribution transformer load position complementary with the distributed photovoltaic as a distributed photovoltaic access position; and solving the optimal power flow problem based on a second-order cone programming theory on the basis of the distributed photovoltaic access positions, and determining the photovoltaic access capacity of each distributed photovoltaic access position. The optimal power flow problem solving method based on the second-order cone theory can determine the optimal access position and capacity of the distributed photovoltaic system so as to optimize the operation economy of the power distribution network.
Description
Technical Field
The invention relates to a distributed photovoltaic locating and sizing optimization method, system, equipment and storage medium considering load characteristics, and relates to the technical field of power distribution networks.
Background
With the gradual increase of global energy shortage, the environmental pollution problem becomes more serious. Under the double-carbon target, the new energy construction is accelerated, and the active construction of a novel power system taking new energy as a main body is the current primary task. Photovoltaic is taken as typical green low carbon clean energy, how effectively to accelerate photovoltaic industry development, how to consume photovoltaic power generation in order to reduce and abandon light phenomenon, how to rationally optimize the problem such as access distribution network of distributed photovoltaic awaits urgent need to be solved.
The peak-valley difference can be effectively stabilized by reasonable access of distributed photovoltaic. But the capacity of access and the position of the access point can have adverse effects on the safety and stability of the system, such as the change of the system power flow, the out-of-limit of voltage, three-phase imbalance harmonic waves and the like. Therefore, the location and volume optimization of the distributed photovoltaic is necessary, meanwhile, the power load characteristics are considered to be diversified day by day, a large number of distribution and transformation loads are connected to the medium-voltage feeder, and the load curve is possibly degraded due to the existence of complementary distribution and transformation loads, so that the utilization rate of the power distribution network equipment is reduced.
Disclosure of Invention
In view of the above problems, an object of the present invention is to provide a distributed photovoltaic siting and sizing optimization method, system, device, and storage medium that are beneficial to accessing a distribution network for distributed photovoltaic optimization, effectively improve a system peak-valley difference, and improve a distribution network device utilization rate and take load characteristics into account.
In order to achieve the purpose, the invention adopts the following technical scheme:
in a first aspect, the distributed photovoltaic siting and sizing optimization method provided by the invention comprises the following steps:
calculating a correlation coefficient between the load of a feeder line-mounted distribution transformer of the medium-voltage distribution network and distributed photovoltaic on the basis of a Pearson model;
determining a distribution transformer load position complementary with the distributed photovoltaic as a distributed photovoltaic access position;
and solving the optimal power flow problem based on a second-order cone programming theory on the basis of the distributed photovoltaic access positions, and determining the photovoltaic access capacity of each distributed photovoltaic access position.
The distributed photovoltaic locating and sizing optimization method further calculates a correlation coefficient between the load of a feeder line-mounted distribution transformer of the medium-voltage distribution network and the distributed photovoltaic based on a Pearson model, and comprises the following steps:
acquiring a distribution and transformation load curve and a distributed photovoltaic power generation curve of each load point of a distribution and transformation terminal of a distribution transformer in each ring network structure of a medium-voltage distribution network;
taking all values of the distributed photovoltaic power generation curve as negative values, and regarding the distributed photovoltaic power generation curve as a distribution load curve;
per-unit transformation is carried out on all the distribution and transformation load curves;
and analyzing the correlation between the load of the distribution transformer connected with the feeder in the medium-voltage distribution network ring network structure and the distributed photovoltaic according to the per-unit distribution and transformation load curve based on the Pearson model, and determining the load position of the distribution transformer which is complementary with the distributed photovoltaic.
The distributed photovoltaic siting constant volume optimization method further comprises the following steps:
x, Y are distribution transformation load curves before and after per-unit transformation respectively; n is the point number of the distribution and transformation load curve; r is a correlation coefficient, r is more than or equal to-1 and less than or equal to + 1.
The distributed photovoltaic siting constant volume optimization method further comprises the following conditions of determining the load position of the distribution transformer which is complementary with the distributed photovoltaic through the r value: when r <0, it indicates that the two variables X, Y are negatively correlated, indicating that there is a complementary behavior between the two distribution load curves.
The distributed photovoltaic locating and sizing optimization method is further characterized in that an optimal power flow problem is solved based on a second-order cone programming theory and distributed photovoltaic access positions are used as a basis, and the photovoltaic access capacity of each distributed photovoltaic access position is determined, and comprises the following steps:
establishing a node power optimal power flow model;
transforming the node power optimal power flow model to obtain an optimal power flow model based on cone transformation, and solving;
and according to the solving result, carrying out distributed power supply capacity configuration at each distributed photovoltaic access position.
The distributed photovoltaic siting constant volume optimization method further establishes a node power optimal power flow model, and comprises the following steps:
establishing an objective function with minimum network loss;
and setting constraint conditions including node power flow balance constraint, node voltage amplitude constraint, branch current constraint and distributed power supply planning output constraint.
The distributed photovoltaic siting constant volume optimization method further comprises the steps of transforming the node power optimal power flow model to obtain an optimal power flow model based on cone transformation, and solving the optimal power flow model, wherein the steps comprise:
carrying out cone transformation on the target function;
setting constraint conditions and performing second-order cone constraint transformation;
the solution is performed by the solution toolkit mosek.
In a second aspect, the present invention provides a distributed photovoltaic siting and sizing optimization system, including:
a correlation coefficient calculation unit; configured to calculate a correlation coefficient of a medium voltage distribution network feeder attached distribution transformer load with distributed photovoltaics based on a pearson model;
a photovoltaic access position determination unit configured to determine a distribution transformer load position having complementarity with the distributed photovoltaics as a distributed photovoltaic access position;
and the photovoltaic access determining unit is configured to solve the optimal power flow problem based on the distributed photovoltaic access positions and based on a second-order cone programming theory, and determine the photovoltaic access capacity of each distributed photovoltaic access position.
In a third aspect, the present invention provides an electronic device, which includes at least a processor and a memory, where the memory stores a computer program, and the processor executes the computer program when executing the computer program to implement the method.
In a fourth aspect, the present invention provides a computer storage medium having computer-readable instructions stored thereon, the computer-readable instructions being executable by a processor to implement the method.
Due to the adoption of the technical scheme, the invention has the following advantages:
1. according to the distributed photovoltaic load selection method, correlation coefficients are calculated based on a Pearson model, the correlation between each distribution transformer load and distributed photovoltaic is identified, the distribution transformer load suitable for distributed photovoltaic access can be preliminarily screened out by the model, and a foundation is laid for the problem of location and volume of distributed photovoltaic;
2. the optimal power flow problem solving method based on the second-order cone theory can determine optimal access positions and optimal access capacity of distributed photovoltaic power to optimize the operation economy of the power distribution network;
in conclusion, the method and the device can be widely applied to the process of accessing the distributed photovoltaic into the power distribution network.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Like reference numerals refer to like parts throughout the drawings. In the drawings:
FIG. 1 is a schematic diagram of electrical connections of a 10kV power distribution network in an embodiment of the invention;
FIG. 2 is a schematic diagram of typical distribution load curves for residential, office and commercial types in accordance with an embodiment of the present invention;
FIG. 3 is a schematic diagram of typical per unit distribution load curves of residents, offices and businesses in the embodiment of the present invention;
fig. 4 is a schematic diagram of per unit of a distributed photovoltaic power generation curve in the embodiment of the present invention;
fig. 5 is a block diagram of an electronic device according to an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
It is to be understood that the terminology used herein is for the purpose of describing particular example embodiments only, and is not intended to be limiting. As used herein, the singular forms "a", "an" and "the" may be intended to include the plural forms as well, unless the context clearly indicates otherwise. The terms "comprises," "comprising," "including," and "having" are inclusive and therefore specify the presence of stated features, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, steps, operations, elements, components, and/or groups thereof. The method steps, processes, and operations described herein are not to be construed as necessarily requiring their performance in the particular order described or illustrated, unless specifically identified as an order of performance. It should also be understood that additional or alternative steps may be used.
For convenience of description, spatially relative terms, such as "inner", "outer", "lower", "upper", and the like, may be used herein to describe one element or feature's relationship to another element or feature as illustrated in the figures. Such spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures.
In consideration of the fact that second-order cone programming has a good effect on solving a global optimum value for a convex optimization problem, the optimization method solves the optimization of the distributed photovoltaic site selection constant volume problem based on a second-order cone programming theory by identifying the load distribution and distribution correlation of the distributed photovoltaic and the medium-voltage feeder line. The invention provides a distributed photovoltaic siting constant volume optimization method, a system, equipment and a storage medium, wherein the method comprises the following steps: calculating a correlation coefficient between the load of a feeder line-mounted distribution transformer of the medium-voltage distribution network and distributed photovoltaic on the basis of a Pearson model; determining a distribution transformer load position complementary with the distributed photovoltaic as a distributed photovoltaic access position; and solving the optimal power flow problem based on a second-order cone programming theory on the basis of the distributed photovoltaic access positions, and determining the photovoltaic access capacity of each distributed photovoltaic access position. The optimal power flow problem solving method based on the second-order cone theory can determine the optimal access position and capacity of the distributed photovoltaic system so as to optimize the operation economy of the power distribution network.
Example one
The distributed photovoltaic siting and sizing optimization method considering the load characteristics provided by the embodiment includes the following steps:
s1, calculating a correlation coefficient between the load of a feeder-mounted distribution transformer of the medium-voltage distribution network and distributed photovoltaic based on a Pearson model, wherein the correlation coefficient comprises the following steps:
s11, acquiring a distribution and transformation load curve and a distributed photovoltaic power generation curve of each load point of a distribution and transformation terminal of a distribution transformer in each ring network structure of the medium-voltage distribution network ring network structure;
and S12, taking negative values of all values of the distributed photovoltaic power generation curve, and performing correlation calculation by taking the negative values as a special distribution transformation load curve for conveniently performing correlation solution with other distribution transformation conforming curves.
S13, performing per unit transformation on all distribution and transformation load curves:
wherein y is a distribution load curve set; y isi*The per unit value of the distribution and transformation load curve at the ith point is obtained; y isiThe actual value of the distribution and transformation load curve at the ith point is represented by MW; max (y) is the maximum value in MW in the distribution load curve set y.
S14, analyzing the correlation between the distribution transformer load and the distributed photovoltaic which are connected with the feeder in the medium-voltage distribution network ring network structure according to the per-unit distribution transformer load curve based on the Pearson model, and determining the distribution transformer load position which is complementary with the distributed photovoltaic.
The Pearson model is:
x, Y are distribution transformation load curves before and after per-unit transformation respectively; n is the point number of the distribution and transformation load curve; r is a correlation coefficient.
Further, the value of r is between-1 and +1, namely-1 is not less than r and not more than +1, the load position of the distribution transformer which is complementary with the distributed photovoltaic is determined through the value of r and is used as the distributed photovoltaic access position, the load position of the distributed photovoltaic access can effectively stabilize the peak-valley difference of the medium voltage feeder, the utilization efficiency of the equipment is improved, and the method comprises the following steps:
when r >0, it indicates that the two variables X, Y are positively correlated (i.e., when the value of X increases or decreases, the value of Y increases or decreases).
When r <0, it indicates that the two variables X, Y are negatively correlated, indicating that the two variables, i.e., the two distribution load curves, have complementary characteristics.
When r 1, the two variables X, Y are fully linearly related.
When r is 0, a wireless correlation between the two variables X, Y is indicated.
When 0< | r | <1, it indicates that there is some degree of linear correlation between the two variables X, Y, and the closer | r | is to 1, the more closely the linear relationship between the two variables X, Y, and the closer | r | is to 0, the weaker the linear correlation between the two variables X, Y. Generally, the method can be divided into three stages: the linear correlation is low in | r | <0.4, the significant correlation is 0.4 ≦ r | <0.7, and the linear correlation is high in | r | <1 of 0.7.
S2, solving an optimal power flow problem based on a second-order cone programming theory on the basis of the distributed photovoltaic access positions, and calculating the distributed photovoltaic optimal access capacity with the minimum network loss as a target function, wherein the method comprises the following steps:
s21, establishing a node power optimal power flow model
(1) Establishing an objective function
Considering the time sequence change, taking the minimum network loss as an objective function, establishing the objective function as follows:
wherein T is the number of time sequence points; n is a system node; piThe sum of active power injected at the node i is obtained, and delta t is an optimized time step;
(2) setting constraint conditions
A. And node power flow balance constraint:
wherein, PLDiActive power, Q, injected for node i loadLDiReactive power injected for the load on node i, Ω (i) is the set of adjacent nodes to node i, Vi、Vj、θijRespectively the voltage amplitude and phase angle difference of the node i and the node j under the normal operation condition of the system, Gii、BiiSelf-conductance and self-susceptance, G, respectively, in a nodal admittance matrixijFor mutual conductance in a nodal admittance matrix, BijAre the mutual susceptances in the nodal admittance matrix.
B. Node voltage amplitude constraint:
Vmin≤Vi,t≤Vmax (5)
in the formula, VminIs the lower limit of the voltage amplitude of node i, VmaxIs the voltage amplitude of node iThe upper limit of (d);
C. and (3) branch current constraint:
wherein, IijIs the current amplitude, I, of branch ijijmaxIs the upper limit of the current magnitude for branch ij.
D. Distributed power supply planning output constraint:
wherein the content of the first and second substances,
for the distributed power output curve of access node i,
the maximum capacity of the distributed power supply is accessible for node i.
S22 optimal power flow model based on cone transformation
(1) Linearization of an objective function:
the conversion of the model mainly comprises introducing new variables, changing the original equality and inequality constraints into linear equality constraints or rotation secondary cone constraints, and eliminating the objective function and V in the constraint conditions in a variable replacement modei(t)、Vj(t)、θij(t) the non-linear form of the product, three variables introduced are specifically as follows:
further, the objective function with minimum network loss can be linearized in the form:
at the same time, the voltage amplitude V of the nodei(t)、Vj(t) and phase angle θij(t) the system's own constraints in the form of the product also change accordingly:
(2) setting a constraint condition:
A. and (3) node tidal current power constraint:
B. node voltage amplitude constraint:
C. and (3) branch current constraint:
D. the distributed power supply planning output constraint is the same as the formula (7).
By the above-mentioned cone transformation, the decision variable is determined by the amplitude V of the node voltagei(t) and phase angle difference θij(t) is changed to Xi(t)、Yij(t) and Zij(t) of (d). Wherein Xi(t) for nodes in the network, and Yij(t)、Zij(t) for a leg in the network.
(3) Second order cone constrained transformation
Using X for phase angle difference and voltage amplitude of each nodei(t)、Yij(t)、Zij(t) after replacement, the three naturally satisfy the Cartesian product form of the rotating cone as the voltage auxiliary variable:
2Xi(t)Xj(t)＝Yij(t)2+Zij(t)2 (13)
i＝1,...,N,j∈Ω(i),t＝0,...,T
the following cone constraint equations can be solved using the mature solver package mosek:
2Xi(t)Xj(t)≥Yij(t)2+Zij(t)2 (14)
i＝1,...,N,j∈Ω(i),t＝0,...,T
according to the acceptable data format of the mosek interface, assigning the information such as the constraint, the upper and lower variable limits and the objective function matrix, then calling a solver to solve, and completing the optimal configuration of the capacity of the distributed photovoltaic access position by the distribution transformer load which has complementarity with the distributed photovoltaic
And S23, carrying out distributed power source capacity configuration at the distributed photovoltaic access positions according to the optimized configuration result.
The following describes the distributed photovoltaic location and capacity optimization method considering the load characteristics in detail by taking a certain feeder of a 10kV power distribution network in a certain area and the load of the feeder as specific embodiments.
As shown in fig. 1, the basic situation of the 10kV distribution network is as follows: the transformer substation A for supplying power in the region supplies power to the distribution transformer distributed by the ring net cage through a 10kV feeder, the material of the feeder cable is copper, and the section of the feeder cable is 400mm2The ring main unit adopts single bus connection, the scale of the incoming and outgoing lines is two-in and four-out, wherein, the cable single ring main unit # 1 consists of a feeder A1 and a feeder B1, and the load point 1, the load point 2, the load point 6 and the load point 7 are supplied with power through the ring main unit # 1, the ring main unit # 2, the ring main unit # 6 and the ring main unit # 7. The load levels and load characteristics of the distribution network are detailed in table 1. The characteristic curves of the loads of the resident, office and business categories are shown in fig. 2; the per-unit characteristic curves of the residential, office and business loads are shown in fig. 3; the distributed photovoltaic power generation curve is shown in FIG. 4; a schematic diagram of the per unit of the distributed photovoltaic power generation curve is shown in fig. 5.
Meter 110 kV power distribution network maximum load level and load characteristics
| Load point numbering | Highest load (MW) | |
| 1 | 2.5 | |
| 2 | 2.6 | |
| 3 | 2.3 | Working in |
| 4 | 2.5 | |
| 5 | 2.3 | |
| 6 | 1.9 | |
| 7 | 2.5 | |
| 8 | 2.1 | Commerce |
| Total up to | 18.7 | - |
By adopting the distributed photovoltaic locating and sizing optimization method, the correlation between the 10 kilovolt feeder line connection distribution load and the distributed photovoltaic is analyzed by taking a typical 24-point-per-day load curve as an object
(1) Calculating the correlation coefficient of distribution transformer load and distributed photovoltaic: correlation calculation is carried out on distribution transformer load and distributed photovoltaic on the basis of pearson model, and the obtained result is shown in Table 2
Table 2 distribution transformer load and photovoltaic correlation calculation result table
The calculation results in the table show that the distributed photovoltaic is complementary to the loads 3, 4, 5, 7 and 8, and has stronger complementarity to the office class loads 3, the commercial class loads 5 and 8 and the school class loads 7, so that the peak-valley difference of the medium-voltage feeder line can be effectively stabilized by connecting the distributed photovoltaic to the loads, and the utilization efficiency of the equipment is improved.
(2) Distributed site selection and volume fixing based on second order cone programming theory
According to the result, the 10MVA distributed photovoltaic is accessed to the distribution transformer load 3, the load 5, the load 7 and the load 8 (namely, the screened alternative points) in the ring network, and the optimization times of the algorithm are effectively reduced by considering the screening of the load characteristic distributed photovoltaic access points based on the pearson model. Optimal configuration of distributed photovoltaic access positions and capacities is performed for 4 load points, and specific algorithm parameter settings are shown in tables 3 and 4:
table 3 planning parameter settings
| Reference voltage (kV) | 10.0 |
| Reference capacity (kVA) | 100.0 |
| Upper limit of voltage allowance (p.u.) | 1.05 |
| Lower limit of allowable voltage (p.u.) | 0.95 |
| Line maximum load rate | 0.8 |
| |
1 |
| |
24 |
| Total planning capacity (MVA) for distributed |
10 |
TABLE 4 Algorithm parameter set
| Algorithm selection | Second order cone programming algorithm |
| Maximum number of iterations | 50 |
| Allowing precision | 0.00001 |
Configuring a 10MVA distributed photovoltaic power supply to the 10kV power distribution network by taking the minimum network loss as an objective function, setting related parameters and performing distributed power supply optimization configuration based on a DSIM simulation platform, wherein the configuration result is shown in Table 5, and the comparison result before and after power distribution network optimization is shown in Table 6:
TABLE 5 distributed photovoltaic optimization configuration results
| Planning location | Type (B) | Distributed power capacity (MVA) |
| |
|
0 |
| |
|
0 |
| |
Photovoltaic power generation | 2.32 |
| |
|
0 |
| |
Photovoltaic power generation | 2.99 |
| |
|
0 |
| |
Photovoltaic power generation | 2.03 |
| |
Photovoltaic power generation | 2.66 |
As can be seen from the above table, the office load 3 is connected to the 2.32MVA distributed photovoltaic, and the loads 5, 7 and 8 have larger configuration capacities, i.e., 2.99MVA, 2.03MVA and 2.66MVA, respectively, because the commercial load curve is closer to the distributed photovoltaic power generation curve, and more distributed photovoltaic connections help to suppress the peak-valley difference.
TABLE 6 comparison before and after optimization of distribution network
| Is not configured | After being configured | |
| Network loss (kWh) | 1752.52775 | 1411.43324 |
| Total voltage deviation (p.u.) | 0.01788 | 0.01439 |
| Maximum value of System load (%) | 80.2851 | 68.90399 |
The above table shows that when the optimal configuration of the distributed photovoltaic is performed at each distribution and transformation load point, the network loss is reduced by 341.09451kWh, the voltage deviation is reduced to 0.01439 from 0.01788, and the load rate of the system is reduced to 69%, so that the distributed photovoltaic is optimally accessed to the power distribution network, the peak-valley difference can be effectively stabilized, the network loss is reduced, and the utilization efficiency of the equipment is improved.
Example two
Correspondingly, the embodiment provides a distributed photovoltaic siting and sizing optimization system considering the load characteristics. The system provided by this embodiment can implement the distributed photovoltaic siting optimization method considering load characteristics of the first embodiment, and the system can be implemented by software, hardware, or a combination of software and hardware. For convenience of description, the present embodiment is described with the functions divided into various units, which are described separately. Of course, the functions of the units may be implemented in the same software and/or hardware or in one or more pieces. For example, the system may comprise integrated or separate functional modules or units to perform the corresponding steps in the method of an embodiment. Since the system of the present embodiment is substantially similar to the method embodiment, the description process of the present embodiment is relatively simple, and reference may be made to part of the description of the first embodiment for relevant points.
The distributed photovoltaic siting and sizing optimization system provided by this embodiment includes:
a correlation coefficient calculation unit; configured to calculate a correlation coefficient of a medium voltage distribution network feeder attached distribution transformer load with distributed photovoltaics based on a pearson model;
a photovoltaic access position determination unit configured to determine a distribution transformer load position having complementarity with the distributed photovoltaics as a distributed photovoltaic access position;
and the photovoltaic access determining unit is configured to solve the optimal power flow problem based on the distributed photovoltaic access positions and based on a second-order cone programming theory, and determine the photovoltaic access capacity of each distributed photovoltaic access position.
EXAMPLE III
The present embodiment provides an electronic device corresponding to the distributed photovoltaic siting volume optimization method considering load characteristics provided in the first embodiment, where the electronic device may be an electronic device for a client, such as a mobile phone, a notebook computer, a tablet computer, a desktop computer, and the like, to execute the method of the first embodiment.
As shown in fig. 5, the electronic device includes a processor, a memory, a communication interface, and a bus, and the processor, the memory, and the communication interface are connected by the bus to complete communication therebetween. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The memory stores a computer program that can be executed on the processor, and the processor executes the distributed photovoltaic siting optimization method considering load characteristics provided in this embodiment when executing the computer program. Those skilled in the art will appreciate that the architecture shown in fig. 5 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects may be applied, and that a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In some implementations, the logic instructions in the memory may be implemented in software functional units and stored in a computer readable storage medium when sold or used as a stand-alone product. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), an optical disk, and various other media capable of storing program codes.
In other implementations, the processor may be various general-purpose processors such as a Central Processing Unit (CPU), a Digital Signal Processor (DSP), and the like, and is not limited herein.
Example four
The distributed photovoltaic siting optimization method taking into account load characteristics according to this embodiment may be implemented as a computer program product, which may include a computer readable storage medium on which computer readable program instructions for executing the distributed photovoltaic siting optimization method taking into account load characteristics according to this embodiment are loaded.
The computer readable storage medium may be a tangible device that retains and stores instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any combination of the foregoing.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment. In the description herein, references to the description of "one embodiment," "some implementations," or the like, mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of an embodiment of the specification. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. 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.
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 (10)
1. A distributed photovoltaic site selection constant volume optimization method is characterized by comprising the following steps:
calculating a correlation coefficient between the load of a feeder line-mounted distribution transformer of the medium-voltage distribution network and distributed photovoltaic on the basis of a Pearson model;
determining a distribution transformer load position complementary with the distributed photovoltaic as a distributed photovoltaic access position;
and solving the optimal power flow problem based on a second-order cone programming theory on the basis of the distributed photovoltaic access positions, and determining the photovoltaic access capacity of each distributed photovoltaic access position.
2. The distributed photovoltaic siting volume optimization method according to claim 1, wherein calculating correlation coefficients of medium voltage distribution network feeder attached distribution transformer load and distributed photovoltaic based on a pearson model comprises:
acquiring a distribution and transformation load curve and a distributed photovoltaic power generation curve of each load point of a distribution and transformation terminal of a distribution transformer in each ring network structure of a medium-voltage distribution network;
taking all values of the distributed photovoltaic power generation curve as negative values, and regarding the distributed photovoltaic power generation curve as a distribution load curve;
per-unit transformation is carried out on all the distribution and transformation load curves;
and analyzing the correlation between the load of the distribution transformer connected with the feeder in the medium-voltage distribution network ring network structure and the distributed photovoltaic according to the per-unit distribution and transformation load curve based on the Pearson model, and determining the load position of the distribution transformer which is complementary with the distributed photovoltaic.
3. The distributed photovoltaic siting and sizing optimization method according to claim 2, characterized in that the pearson model is:
x, Y are distribution transformation load curves before and after per-unit transformation respectively; n is the point number of the distribution and transformation load curve; r is a correlation coefficient, r is more than or equal to-1 and less than or equal to + 1.
4. The distributed photovoltaic siting volume optimization method according to claim 3, characterized in that the condition for determining the distribution transformer load position complementary to the distributed photovoltaic by r value is: when r <0, it indicates that the two variables X, Y are negatively correlated, indicating that there is a complementary behavior between the two distribution load curves.
5. The distributed photovoltaic siting and sizing optimization method according to any one of claims 1 to 4, wherein an optimal power flow problem is solved based on a second-order cone programming theory on the basis of the distributed photovoltaic access positions, and the photovoltaic access capacity of each distributed photovoltaic access position is determined, and the method comprises the following steps:
establishing a node power optimal power flow model;
transforming the node power optimal power flow model to obtain an optimal power flow model based on cone transformation, and solving;
and according to the solving result, carrying out distributed power supply capacity configuration at each distributed photovoltaic access position.
6. The distributed photovoltaic siting and sizing optimization method according to claim 5, wherein establishing a node power optimal power flow model comprises:
establishing an objective function with minimum network loss;
and setting constraint conditions including node power flow balance constraint, node voltage amplitude constraint, branch current constraint and distributed power supply planning output constraint.
7. The distributed photovoltaic siting and sizing optimization method according to claim 6, wherein the optimal power flow model of the node power is transformed to obtain an optimal power flow model based on cone transformation, and the solving is performed, comprising:
carrying out cone transformation on the target function;
setting constraint conditions and performing second-order cone constraint transformation;
the solution is performed by the solution toolkit mosek.
8. A distributed photovoltaic siting and sizing optimization system, characterized in that the system comprises:
a correlation coefficient calculation unit; configured to calculate a correlation coefficient of a medium voltage distribution network feeder attached distribution transformer load with distributed photovoltaics based on a pearson model;
a photovoltaic access position determination unit configured to determine a distribution transformer load position having complementarity with the distributed photovoltaics as a distributed photovoltaic access position;
and the photovoltaic access determining unit is configured to solve the optimal power flow problem based on the distributed photovoltaic access positions and based on a second-order cone programming theory, and determine the photovoltaic access capacity of each distributed photovoltaic access position.
9. An electronic device comprising at least a processor and a memory, the memory having stored thereon a computer program, characterized in that the processor, when executing the computer program, executes to carry out the method of any of claims 1 to 7.
10. A computer storage medium having computer readable instructions stored thereon which are executable by a processor to implement the method of any one of claims 1 to 7.
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| CN117196180A (en) * | 2023-10-17 | 2023-12-08 | 无锡市广盈电力设计有限公司 | Distribution line photovoltaic collection point site selection method containing high-proportion distributed photovoltaic |
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| CN111431173A (en) * | 2020-03-30 | 2020-07-17 | 国网经济技术研究院有限公司 | Medium-voltage distribution network ring network structure determination method and system considering load complementary characteristics |
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| CN108629499A (en) * | 2018-04-25 | 2018-10-09 | 国家电网公司 | A kind of power distribution network photovoltaic plant addressing constant volume method based on second order cone theazy |
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