CN116799826A - Distributed photovoltaic energy storage system optimal configuration method and related device - Google Patents
Distributed photovoltaic energy storage system optimal configuration method and related device Download PDFInfo
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Abstract
The application discloses an optimal configuration method and a related device of a distributed photovoltaic energy storage system, wherein the method comprises the following steps: respectively predicting illumination intensity and active load based on a preset distribution model to obtain predicted illumination intensity and predicted active load; constructing a distribution network area model according to the predicted illumination intensity and the predicted active load, and configuring an objective function and comprehensive constraint conditions; taking the energy storage access position and the energy storage access capacity as particles, and adopting an improved multi-target particle swarm algorithm to perform optimization solution on the power distribution network area model based on the objective function and the comprehensive constraint condition to obtain an optimal configuration scheme; the improved multi-objective particle swarm algorithm determines inertia weight based on similarity, and the optimal configuration scheme comprises a plurality of groups of optimal access positions and optimal access capacities; and determining a global optimal configuration scheme according to the optimal configuration scheme by adopting a gray correlation analysis method. The application can solve the technical problems that the prior art lacks a reasonable energy storage system optimal configuration scheme, and is not beneficial to the safe and stable operation of the power grid.
Description
Technical Field
The application relates to the field of distributed energy storage systems, in particular to an optimal configuration method and a related device for a distributed photovoltaic energy storage system.
Background
Photovoltaic power generation is used as a new energy power generation mode with higher potential, the new photovoltaic grid-connected capacity of 87.4GW is increased in 2022 years in China by the end of 2022, wherein the concentrated photovoltaic power station 36.29GW and the distributed photovoltaic 51.11GW account for 58.48% of the distributed photovoltaic; meanwhile, the integrated grid-connected capacity 392.04GW of the national photovoltaic power generation is realized, wherein the distributed photovoltaic 157.62GW accounts for 40.2 percent. However, under the condition of large-scale distributed photovoltaic power generation access, photovoltaic has intermittence and randomness, so that the photovoltaic output is unstable, and the distribution network has a larger influence on a station area with weaker bearing capacity.
The rapid development of the energy storage device provides powerful support for the stable development of photovoltaic power generation, and as an important electric energy storage mode, the mode of instant power generation and instant balance in time of traditional power generation can be broken, peak clipping and valley filling can be well realized by combining the energy storage device under the distributed photovoltaic background, and the photovoltaic digestion capacity of a power distribution network station area is enhanced. However, the stability and economy of the power distribution network can be affected by the selection of the access position and the capacity of the energy storage device, and the prior art lacks reasonable optimization configuration, which is not beneficial to the safe and stable operation of the power distribution network.
Disclosure of Invention
The application provides an optimal configuration method and a related device for a distributed photovoltaic energy storage system, which are used for solving the technical problems that the prior art lacks a reasonable optimal configuration scheme for the energy storage system and is not beneficial to safe and stable operation of a power grid.
In view of this, the first aspect of the present application provides a method for optimally configuring a distributed photovoltaic energy storage system, including:
respectively predicting illumination intensity and active load based on a preset distribution model to obtain predicted illumination intensity and predicted active load;
constructing a power distribution network area model according to the predicted illumination intensity and the predicted active load, and simultaneously configuring an objective function and comprehensive constraint conditions, wherein the objective function comprises a voltage stability coefficient, network line loss and energy storage capacity;
taking the energy storage access position and the energy storage access capacity as particles, and adopting an improved multi-objective particle swarm algorithm to perform optimization solution on the power distribution network area model based on the objective function and the comprehensive constraint condition to obtain an optimal configuration scheme;
the improved multi-objective particle swarm algorithm determines inertia weights based on similarity, and the optimal configuration scheme comprises a plurality of groups of optimal access positions and optimal access capacities;
and determining a global optimal configuration scheme according to the optimal configuration scheme by adopting a gray correlation analysis method.
Preferably, the predicting the illumination intensity and the active load based on the preset distribution model respectively, to obtain the predicted illumination intensity and the predicted active load, includes:
the preset distribution model comprises a beta distribution model and a normal distribution model;
predicting illumination intensity by adopting the beta distribution model to obtain predicted illumination intensity;
and predicting the active load by adopting the normal distribution model to obtain a predicted active load.
Preferably, the optimizing and solving the distribution network area model by using the energy storage access position and the energy storage access capacity as particles and adopting an improved multi-target particle swarm algorithm based on the objective function and the comprehensive constraint condition to obtain an optimized configuration scheme includes:
defining initialization parameters based on an improved multi-target particle swarm algorithm, wherein the initialization parameters comprise an initial position vector and an initial speed vector;
taking the energy storage access position and the energy storage access capacity as particles, and calculating an initial fitness value according to the objective function;
and carrying out iterative solving operation on the distribution network station area model according to the comprehensive constraint condition and the inertia weight determined based on the similarity, and updating the initial fitness value until the termination condition is met, so as to obtain an optimal configuration scheme.
Preferably, the determining a global optimal configuration scheme according to the optimal configuration scheme by using a gray correlation analysis method includes:
calculating the priority of each optimal configuration scheme by adopting a gray correlation analysis method;
and determining a global optimal configuration scheme according to the priority.
The second aspect of the present application provides an apparatus for optimally configuring a distributed photovoltaic energy storage system, comprising:
the data prediction unit is used for respectively predicting the illumination intensity and the active load based on a preset distribution model to obtain the predicted illumination intensity and the predicted active load;
the modeling configuration unit is used for constructing a power distribution network platform model according to the predicted illumination intensity and the predicted active load, and simultaneously configuring an objective function and comprehensive constraint conditions, wherein the objective function comprises a voltage stability coefficient, network line loss and energy storage capacity;
the optimization solving unit is used for taking the energy storage access position and the energy storage access capacity as particles, and adopting an improved multi-objective particle swarm algorithm to perform optimization solving on the power distribution network area model based on the objective function and the comprehensive constraint condition to obtain an optimization configuration scheme;
the improved multi-objective particle swarm algorithm determines inertia weights based on similarity, and the optimal configuration scheme comprises a plurality of groups of optimal access positions and optimal access capacities;
the scheme selection unit is used for determining a global optimal configuration scheme according to the optimal configuration scheme by adopting a gray correlation analysis method.
Preferably, the data prediction unit is specifically configured to:
the preset distribution model comprises a beta distribution model and a normal distribution model;
predicting illumination intensity by adopting the beta distribution model to obtain predicted illumination intensity;
and predicting the active load by adopting the normal distribution model to obtain a predicted active load.
Preferably, the optimization solving unit is specifically configured to:
defining initialization parameters based on an improved multi-target particle swarm algorithm, wherein the initialization parameters comprise an initial position vector and an initial speed vector;
taking the energy storage access position and the energy storage access capacity as particles, and calculating an initial fitness value according to the objective function;
and carrying out iterative solving operation on the distribution network station area model according to the comprehensive constraint condition and the inertia weight determined based on the similarity, and updating the initial fitness value until the termination condition is met, so as to obtain an optimal configuration scheme.
Preferably, the scheme selecting unit is specifically configured to:
calculating the priority of each optimal configuration scheme by adopting a gray correlation analysis method;
and determining a global optimal configuration scheme according to the priority.
The application provides a distributed photovoltaic energy storage system optimal configuration device, which comprises a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute the distributed photovoltaic energy storage system optimization configuration method according to the first aspect according to the instructions in the program code.
A fourth aspect of the present application provides a computer readable storage medium for storing program code for performing the distributed photovoltaic energy storage system optimization configuration method of the first aspect.
From the above technical solutions, the embodiment of the present application has the following advantages:
the application provides an optimal configuration method of a distributed photovoltaic energy storage system, which comprises the following steps: respectively predicting illumination intensity and active load based on a preset distribution model to obtain predicted illumination intensity and predicted active load; constructing a power distribution network area model according to the predicted illumination intensity and the predicted active load, and simultaneously configuring an objective function and comprehensive constraint conditions, wherein the objective function comprises a voltage stability coefficient, network line loss and energy storage capacity; taking the energy storage access position and the energy storage access capacity as particles, and adopting an improved multi-target particle swarm algorithm to perform optimization solution on the power distribution network area model based on the objective function and the comprehensive constraint condition to obtain an optimal configuration scheme; the improved multi-objective particle swarm algorithm determines inertia weight based on similarity, and the optimal configuration scheme comprises a plurality of groups of optimal access positions and optimal access capacities; and determining a global optimal configuration scheme according to the optimal configuration scheme by adopting a gray correlation analysis method.
According to the distributed photovoltaic energy storage system optimal configuration method, the electric energy processing condition of the distributed photovoltaic energy storage system is described by constructing a distribution network platform model, and in order to cope with the uncertainty of the photovoltaic, a preset distribution model is adopted to predict important data; solving the model by adopting an improved multi-objective particle swarm algorithm, and preferentially selecting a global optimal configuration scheme; in the process, the similarity is adopted to determine the inertia weight, so that the iterative convergence speed can be increased, and the gray correlation analysis method is adopted, so that the selection of the optimal solution is independent of personal experience influence, the accuracy and the reliability of the result are ensured, and a reasonable energy storage system optimal configuration scheme is provided. The application can solve the technical problems that the prior art lacks a reasonable energy storage system optimal configuration scheme and is not beneficial to the safe and stable operation of the power grid.
Drawings
Fig. 1 is a schematic flow chart of a distributed photovoltaic energy storage system optimizing configuration method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an optimizing configuration device of a distributed photovoltaic energy storage system according to an embodiment of the present application;
fig. 3 is an exemplary diagram of a distribution network area model provided in an embodiment of the present application;
FIG. 4 is a graph showing the predicted illumination intensity and active load provided by an embodiment of the present application;
fig. 5 is a graph of node voltage versus output of the MOPSO algorithm before and after improvement provided by the application example of the present application.
Detailed Description
In order to make the present application better understood by those skilled in the art, the following description will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the accompanying drawings, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
For easy understanding, please refer to fig. 1, an embodiment of a distributed photovoltaic energy storage system optimization configuration method provided by the present application includes:
and 101, respectively predicting the illumination intensity and the active load based on a preset distribution model to obtain the predicted illumination intensity and the predicted active load.
Further, step 101 includes:
the preset distribution model comprises a beta distribution model and a normal distribution model;
predicting illumination intensity by adopting a beta distribution model to obtain predicted illumination intensity;
and predicting the active load by adopting a normal distribution model to obtain a predicted active load.
In the process of predicting illumination intensity by using the beta distribution model, the probability density function is expressed as f [ g (s, t)]Let g (s, t) be the per unit value of the intensity of illumination at time t in s season, lambda be (s, t) and k be (s, t) represents the proportional parameter and the shape parameter at the time of s-season t in the probability density function of the Beta distribution model, respectively, and μ (s, t) represents the average value and the per unit value of the illumination intensity at the time of s-season t, σ be (s, t) represents the standard deviation of illumination intensity at t time of s season, and the prediction process expression can be expressed as：
When the normal distribution model is adopted to predict the active load, P is adopted L Representing the active load of the system, wherein sigma is the standard deviation of the active load of the system, mu is the average value of the active load, and the average comprehensive power consumption load curve is selected as a typical daily load curve for analysis, so that the active load prediction process is expressed as follows:
and 102, constructing a power distribution network area model according to the predicted illumination intensity and the predicted active load, and simultaneously configuring an objective function and comprehensive constraint conditions, wherein the objective function comprises a voltage stability coefficient, network line loss and energy storage capacity.
The power distribution network area model constructed in this embodiment is illustrated in fig. 3, and because the preset distribution model can use the uncertainty of the photovoltaic to predict the accurate and reliable predicted illumination intensity and the predicted active load, the accuracy of the power distribution network area model constructed based on this can be ensured.
The configured objective function mainly comprises a voltage stability coefficient function, a network line loss function and an energy storage capacity function, so that the expression is as follows:
wherein U is ij Is the voltage at the moment of the ith bus bar j, U imax Is the highest voltage of the ith bus, U imin The lowest voltage of the ith bus is N, the number of system nodes is T, the time number is T, VSF is a voltage stability coefficient function, and the voltage stability coefficient VSF can be obtained total ，R m To connect the equivalent resistance of bus i and bus i+1, P DG,i+1 Active power output, Q, of distributed power supply at busbar i+1 DG,i+1 Reactive power output, P, of distributed power supply at busbar i+1 i+1 、Q i+1 Active power load and reactive power load at bus i+1, U i+1 Is the voltage amplitude at bus i+1, P loss As a network line loss function, t 0 For maximum charge or discharge start time, t 1 For maximum charge or discharge end time, P storej (i) For the charging or discharging power at the j-th energy storage system i moment, deltat is the time interval between two moments, N store P is the number of energy storage systems store As a function of energy storage capacity.
The comprehensive constraint conditions mainly comprise node voltage constraint, power balance constraint, power supply output constraint and energy storage power constraint, and other related constraint conditions can be configured according to actual situation requirements, and the comprehensive constraint conditions are not limited herein.
And 103, taking the energy storage access position and the energy storage access capacity as particles, and adopting an improved multi-objective particle swarm algorithm to perform optimization solution on the power distribution network area model based on the objective function and the comprehensive constraint condition to obtain an optimal configuration scheme.
The improved multi-objective particle swarm algorithm determines inertia weights based on the similarity, and the optimal configuration scheme comprises a plurality of groups of optimal access positions and optimal access capacities.
Further, step 103 includes:
defining initialization parameters based on an improved multi-target particle swarm algorithm, wherein the initialization parameters comprise an initial position vector and an initial speed vector;
taking the energy storage access position and the energy storage access capacity as particles, and calculating an initial fitness value according to an objective function;
and carrying out iterative solving operation on the distribution network area model according to the comprehensive constraint condition and the inertia weight determined based on the similarity, and updating the initial fitness value until the termination condition is met, so as to obtain an optimal configuration scheme.
It should be noted that, the Multi-objective particle swarm algorithm (Multi-Objective Particle Swarm Optimization, MOPSO) has been widely used in solving the optimization problem due to its advantages of fast convergence, simple parameter setting, etc., but the Multi-objective particle swarm algorithm lacks corresponding guidance for the weighting value of the inertia factor, and lacks theoretical support in the selection of the final global optimal solution.
In the specific iterative optimization process, the initial position vector and the initial speed vector are respectively expressed as X i ＝(x i1 ,x i2 ,...,x iD )、V i ＝(v i1 ,v i2 ,...,v iD ) pBest represents the optimal position of the ith particle, the optimal position in all examples is gBest, the energy storage access position and the energy storage access capacity are used as the continuous updating position of the particle in the iteration process, the particle is an optimization variable, and the coding format is X= [ X ] 1 ,x 2 ,...,x N ,y 1 ,y 2 ,...,y N ) Wherein X represents the total particle number, X i For the access position of the energy storage device, y i For the rated access capacity of the energy storage device, N represents the number of access energy storage devices, and the iterative solution process can be expressed as:
where k is the current number of iterations,optimal position pBest for particle i iteration to k generations,/>Optimal position gBest for all particles iterated to the kth generation +>Representing the position and velocity of particle i at the kth generation, ω being the inertial weight, c being determined based on similarity 1 、c 2 R is the learning factor 1 、r 2 Is two mutually independent and uniformly positioned in [0,1 ]]Random numbers in between.
The above-mentioned inertial weight ω is determined based on the similarity,particles i of the kth generation, +.>Optimal position pBest for the generation i to k of particles,/and/or>For all particles iterated to the optimal position gBest of the kth generation, the similarity and inertial weight ω of the ith particle to the global optimal solution gBest of the kth generation can be expressed as:
wherein omega max 、ω min Representing the maximum and minimum values of the inertial weights respectively,then the inertial weight of the ith particle in the kth generation.
It should be noted that, the obtained optimal configuration scheme includes multiple groups of optimal access positions and optimal access capacities, that is, multiple optimal configuration schemes can be obtained, and a specific scheme selection strategy is also required to complete the optimal selection operation, so as to obtain the final optimal scheme.
And 104, determining a global optimal configuration scheme according to the optimal configuration scheme by adopting a gray correlation analysis method.
Further, step 104 includes:
calculating the priority of each optimal configuration scheme by adopting a gray correlation analysis method;
and determining a global optimal configuration scheme according to the priority.
The multiple optimal configuration schemes, namely multiple pareto solutions, can measure the similarity of the development trend among the factors in each scheme by using a gray correlation analysis method, and discuss the factors according to the gray correlation analysis theory, wherein the projection of the first scheme on an ideal reference scheme is V l Then the priority of the first scheme can be calculated as:
wherein V is l The superscript "+" - "of (a) indicates positive/negative schemes, respectively, and NI is an index number, gamma lk Is the gray correlation coefficient, w, between the kth indexes in the ith scheme k Weight of each index in a certain scheme, D l Is the priority of the first scheme.
It is understood that the globally optimal configuration scheme may be selected according to the scheme priority order. According to the embodiment, the gray correlation analysis method is introduced into the energy storage system optimization configuration process, so that the selection of the optimal solution is not too much dependent on the influence of personal experience, the similarity is adopted to adjust the value of the inertia weight in the multi-target particle swarm algorithm, the convergence speed is increased, and the access position and the access capacity of the final energy storage system are selected to obtain the global optimal solution.
For easy understanding, the application provides an application example of the distribution network area model shown in fig. 3, and the predicted illumination intensity and active load based on the preset distribution model are shown in fig. 4, and the active load output is 10:00-14:00 and 19:00-21:00 is higher, photovoltaic output is at 10:00-15:00 is higher and at 13:00 reaches a maximum. The maximum iteration number in the multi-target particle swarm algorithm is set to be 50, the number of initial populations is set to be 50, the maximum inertia weight is set to be 0.9, the minimum inertia weight is set to be 0.4, the SOC value range is set to be 0.2-0.9, and the model is solved under the traditional multi-target particle swarm algorithm and the improved multi-target particle swarm algorithm respectively based on the maximum iteration number.
Referring to fig. 5 and table 1, it can be seen from the analysis that the voltage stability factor f of the multi-objective particle swarm algorithm is improved compared with the conventional multi-objective particle swarm algorithm 1 0.3329pu, 0.27% lower; and network line loss f 2 1.4978pu, 3.97% lower; capacity f of energy storage device 3 Is 3.711 MW.h, which is reduced by 4.31%. It can be seen that the embodiment provides a more reasonable and reliable optimal configuration scheme of the distributed photovoltaic energy storage system.
TABLE 1 optimization results for different MOPSO algorithms
According to the distributed photovoltaic energy storage system optimal configuration method provided by the embodiment of the application, the electric energy processing condition of the distributed photovoltaic energy storage system is described by constructing a distribution network platform model, and in order to cope with the uncertainty of the photovoltaic, a preset distribution model is adopted to predict important data; solving the model by adopting an improved multi-objective particle swarm algorithm, and preferentially selecting a global optimal configuration scheme; in the process, the similarity is adopted to determine the inertia weight, so that the iterative convergence speed can be increased, and the gray correlation analysis method is adopted, so that the selection of the optimal solution is independent of personal experience influence, the accuracy and the reliability of the result are ensured, and a reasonable energy storage system optimal configuration scheme is provided. The embodiment of the application can solve the technical problems that the prior art lacks a reasonable energy storage system optimal configuration scheme and is not beneficial to the safe and stable operation of the power grid.
For ease of understanding, referring to fig. 2, the present application provides an embodiment of an apparatus for optimizing a distributed photovoltaic energy storage system, including:
a data prediction unit 201, configured to predict an illumination intensity and an active load based on a preset distribution model, respectively, to obtain a predicted illumination intensity and a predicted active load;
the modeling configuration unit 202 is configured to construct a power distribution network platform model according to the predicted illumination intensity and the predicted active load, and simultaneously configure an objective function and comprehensive constraint conditions, wherein the objective function comprises a voltage stability coefficient, a network line loss and an energy storage capacity;
the optimization solving unit 203 is configured to perform optimization solving on the power distribution network platform area model by using the energy storage access position and the energy storage access capacity as particles and adopting an improved multi-objective particle swarm algorithm based on an objective function and a comprehensive constraint condition, so as to obtain an optimal configuration scheme;
the improved multi-objective particle swarm algorithm determines inertia weight based on similarity, and the optimal configuration scheme comprises a plurality of groups of optimal access positions and optimal access capacities;
the scheme selection unit 204 is configured to determine a global optimal configuration scheme according to the optimal configuration scheme by using a gray correlation analysis method.
Further, the data prediction unit 201 is specifically configured to:
the preset distribution model comprises a beta distribution model and a normal distribution model;
predicting illumination intensity by adopting a beta distribution model to obtain predicted illumination intensity;
and predicting the active load by adopting a normal distribution model to obtain a predicted active load.
Further, the optimization solving unit 203 is specifically configured to:
defining initialization parameters based on an improved multi-target particle swarm algorithm, wherein the initialization parameters comprise an initial position vector and an initial speed vector;
taking the energy storage access position and the energy storage access capacity as particles, and calculating an initial fitness value according to an objective function;
and carrying out iterative solving operation on the distribution network area model according to the comprehensive constraint condition and the inertia weight determined based on the similarity, and updating the initial fitness value until the termination condition is met, so as to obtain an optimal configuration scheme.
Further, the scheme selection unit 204 is specifically configured to:
calculating the priority of each optimal configuration scheme by adopting a gray correlation analysis method;
and determining a global optimal configuration scheme according to the priority.
The application also provides a distributed photovoltaic energy storage system optimal configuration device, which comprises a processor and a memory;
the memory is used for storing the program codes and transmitting the program codes to the processor;
the processor is used for executing the distributed photovoltaic energy storage system optimal configuration method in the method embodiment according to the instructions in the program codes.
The application also provides a computer readable storage medium, which is used for storing program codes, and the program codes are used for executing the distributed photovoltaic energy storage system optimal configuration method in the method embodiment.
In the several embodiments provided by the present application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown 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 units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in essence or a part contributing to the prior art or all or part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for executing all or part of the steps of the method according to the embodiments of the present application by means of a computer device (which may be a personal computer, a server, or a network device, etc.). And the aforementioned storage medium includes: u disk, mobile hard disk, read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), magnetic disk or optical disk, etc.
The above embodiments are only for illustrating the technical solution of the present application, and not for limiting the same; although the application 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 scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application.
Claims (10)
1. The method for optimally configuring the distributed photovoltaic energy storage system is characterized by comprising the following steps of:
respectively predicting illumination intensity and active load based on a preset distribution model to obtain predicted illumination intensity and predicted active load;
constructing a power distribution network area model according to the predicted illumination intensity and the predicted active load, and simultaneously configuring an objective function and comprehensive constraint conditions, wherein the objective function comprises a voltage stability coefficient, network line loss and energy storage capacity;
taking the energy storage access position and the energy storage access capacity as particles, and adopting an improved multi-objective particle swarm algorithm to perform optimization solution on the power distribution network area model based on the objective function and the comprehensive constraint condition to obtain an optimal configuration scheme;
the improved multi-objective particle swarm algorithm determines inertia weights based on similarity, and the optimal configuration scheme comprises a plurality of groups of optimal access positions and optimal access capacities;
and determining a global optimal configuration scheme according to the optimal configuration scheme by adopting a gray correlation analysis method.
2. The method for optimizing configuration of a distributed photovoltaic energy storage system according to claim 1, wherein the predicting illumination intensity and active load based on a preset distribution model respectively to obtain the predicted illumination intensity and the predicted active load comprises:
the preset distribution model comprises a beta distribution model and a normal distribution model;
predicting illumination intensity by adopting the beta distribution model to obtain predicted illumination intensity;
and predicting the active load by adopting the normal distribution model to obtain a predicted active load.
3. The method for optimizing configuration of a distributed photovoltaic energy storage system according to claim 1, wherein the optimizing solution is performed on the power distribution network area model by using an improved multi-objective particle swarm algorithm based on the objective function and the comprehensive constraint condition by taking the energy storage access position and the energy storage access capacity as particles, so as to obtain an optimizing configuration scheme, and the method comprises the following steps:
defining initialization parameters based on an improved multi-target particle swarm algorithm, wherein the initialization parameters comprise an initial position vector and an initial speed vector;
taking the energy storage access position and the energy storage access capacity as particles, and calculating an initial fitness value according to the objective function;
and carrying out iterative solving operation on the distribution network station area model according to the comprehensive constraint condition and the inertia weight determined based on the similarity, and updating the initial fitness value until the termination condition is met, so as to obtain an optimal configuration scheme.
4. The method for optimizing configuration of a distributed photovoltaic energy storage system according to claim 1, wherein the determining a global optimal configuration scheme according to the optimal configuration scheme by using a gray correlation analysis method comprises:
calculating the priority of each optimal configuration scheme by adopting a gray correlation analysis method;
and determining a global optimal configuration scheme according to the priority.
5. An optimal configuration device for a distributed photovoltaic energy storage system, comprising:
the data prediction unit is used for respectively predicting the illumination intensity and the active load based on a preset distribution model to obtain the predicted illumination intensity and the predicted active load;
the modeling configuration unit is used for constructing a power distribution network platform model according to the predicted illumination intensity and the predicted active load, and simultaneously configuring an objective function and comprehensive constraint conditions, wherein the objective function comprises a voltage stability coefficient, network line loss and energy storage capacity;
the optimization solving unit is used for taking the energy storage access position and the energy storage access capacity as particles, and adopting an improved multi-objective particle swarm algorithm to perform optimization solving on the power distribution network area model based on the objective function and the comprehensive constraint condition to obtain an optimization configuration scheme;
the improved multi-objective particle swarm algorithm determines inertia weights based on similarity, and the optimal configuration scheme comprises a plurality of groups of optimal access positions and optimal access capacities;
the scheme selection unit is used for determining a global optimal configuration scheme according to the optimal configuration scheme by adopting a gray correlation analysis method.
6. The distributed photovoltaic energy storage system optimizing configuration device according to claim 5, wherein the data prediction unit is specifically configured to:
the preset distribution model comprises a beta distribution model and a normal distribution model;
predicting illumination intensity by adopting the beta distribution model to obtain predicted illumination intensity;
and predicting the active load by adopting the normal distribution model to obtain a predicted active load.
7. The distributed photovoltaic energy storage system optimizing configuration device according to claim 5, wherein the optimizing solving unit is specifically configured to:
defining initialization parameters based on an improved multi-target particle swarm algorithm, wherein the initialization parameters comprise an initial position vector and an initial speed vector;
taking the energy storage access position and the energy storage access capacity as particles, and calculating an initial fitness value according to the objective function;
and carrying out iterative solving operation on the distribution network station area model according to the comprehensive constraint condition and the inertia weight determined based on the similarity, and updating the initial fitness value until the termination condition is met, so as to obtain an optimal configuration scheme.
8. The device for optimizing configuration of a distributed photovoltaic energy storage system according to claim 5, wherein the scheme selecting unit is specifically configured to:
calculating the priority of each optimal configuration scheme by adopting a gray correlation analysis method;
and determining a global optimal configuration scheme according to the priority.
9. An optimized configuration device for a distributed photovoltaic energy storage system, which is characterized by comprising a processor and a memory;
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to execute the distributed photovoltaic energy storage system optimization configuration method of any one of claims 1-4 according to instructions in the program code.
10. A computer readable storage medium for storing program code for performing the distributed photovoltaic energy storage system optimization configuration method of any of claims 1-4.
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