CN114204547B - Power distribution network multi-measure combination loss reduction optimization method considering source network load storage cooperative optimization - Google Patents
Power distribution network multi-measure combination loss reduction optimization method considering source network load storage cooperative optimization Download PDFInfo
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Abstract
The invention provides a power distribution network multi-measure combination loss reduction optimization method considering source network load storage cooperative optimization, which predicts photovoltaic output by utilizing a neural network model according to actually measured meteorological data; according to the acquired historical data of the illumination intensity, a photovoltaic output random model is constructed, a prediction result is corrected by using a classical scene obtained by the photovoltaic output random model, and a corrected photovoltaic output curve is obtained; according to the acquired storage battery parameter data, a storage battery energy storage model based on the residual electric quantity level and the charge and discharge power is constructed; obtaining an optimal network structure and compensation capacity according to the corrected photovoltaic output curve, the storage battery energy storage model, the power distribution network parameter data and a preset source network, power storage and distribution network collaborative optimization loss reduction model; the invention realizes the internal reactive voltage stabilization and the active power flow optimization of the power distribution network and achieves the overall aims of safe and reliable operation of the power distribution network, reduction of technical loss and improvement of regional low-carbon energy-saving benefits.
Description
Technical Field
The invention relates to the technical field of power distribution network optimization, in particular to a power distribution network multi-measure combination loss reduction optimization method considering source network load storage cooperative optimization.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
The regional low-voltage power distribution network is located at the tail end of the whole power grid and is directly connected with terminal power users, and the regional low-voltage power distribution network has the advantages of being low in voltage level, thin in line diameter of line conducting wires, wide in line distribution, provided with more branches and the like, so that the loss problem of the regional power grid is outstanding, and the loss reduction potential is huge.
The inventor finds that the current solution of the loss reduction problem of the power distribution network technology usually adopts a single element transformation mode, the loss problem of a regional power grid cannot be solved fundamentally, and the comprehensive treatment benefit is not high.
Disclosure of Invention
In order to solve the defects of the prior art, the invention provides a power distribution network multi-measure combined loss reduction optimization method considering source network charge storage cooperative optimization, the cooperative matching sequence of loss reduction measures, the compensation capacity limit of loads, the operation constraint of an energy storage device, the switching frequency limit, the voltage and current fluctuation limit of each node, the distributed energy capacity limit and other factors are comprehensively considered, the calculation cost and the feasible solution development optimization are considered, a power distribution network multi-measure combined loss reduction optimization model and a loss reduction method considering source network charge storage cooperative optimization are designed, a comprehensive control strategy with strong robust characteristics is formulated based on an optimization result, the internal reactive voltage stability and active power flow optimization of a power distribution network are realized, and the overall targets of safe and reliable operation, technical loss reduction and regional low-carbon energy-saving benefit improvement of the power distribution network are reached.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides a power distribution network multi-measure combination loss reduction optimization method considering source network load storage cooperative optimization.
A power distribution network multi-measure combination loss reduction optimization method considering source network load storage cooperative optimization comprises the following processes:
according to the measured meteorological data, photovoltaic output prediction is carried out by utilizing a neural network model, and a photovoltaic output curve is obtained;
according to the acquired historical data of the illumination intensity, a photovoltaic output random model is constructed, a prediction result is corrected by using a classical scene obtained by the photovoltaic output random model, and a corrected photovoltaic output curve is obtained;
according to the acquired storage battery parameter data, a storage battery energy storage model based on the residual electric quantity level and the charge and discharge power is constructed;
and obtaining an optimal network structure and compensation capacity according to the corrected photovoltaic output curve, the storage battery energy storage model, the power distribution network parameter data and the preset source network charge storage and power distribution network collaborative optimization loss reduction model.
Further, the source network load storage and distribution power grid collaborative optimization loss reduction model is as follows: and respectively carrying out normalization processing on the quantities by using a multi-objective optimization model established by taking the minimum grid loss, the minimum three-phase unbalanced current and the minimum action times of a line interconnection switch containing photovoltaic and energy storage access as targets, and taking the geometric mean value of each target as a target function.
Further, photovoltaic output prediction is carried out by utilizing a BP neural network according to actually measured meteorological data, and a photovoltaic output curve is obtained.
Further, according to the acquired historical data of the illumination intensity, parameters of the photovoltaic output random model are fitted by using a moment estimation method, the photovoltaic output random model is established, and the photovoltaic output curve is corrected according to the classical scene obtained by the model, so that the corrected photovoltaic output curve is obtained.
Further, the storage battery energy storage model based on the residual power level and the charging and discharging power comprises the following steps:
obtaining the residual electric quantity level of the storage battery in the charging process according to the self-discharge rate of the storage battery, the charging power and the charging efficiency of the storage battery, the rated capacity of the storage battery and the sampling interval;
and obtaining the residual electric quantity level of the storage battery in the discharging process according to the self-discharging rate of the storage battery, the discharging power and the discharging efficiency of the storage battery, the rated capacity of the storage battery and the sampling interval.
Furthermore, the source network, load storage and distribution network collaborative optimization loss reduction model comprises power flow constraint, voltage constraint, current constraint, storage battery operation constraint and photovoltaic operation constraint.
Further, the source network load storage and distribution power grid collaborative optimization loss reduction model is solved by using an improved mixed integer differential evolution algorithm, and the method comprises the following steps:
initializing parameters of a differential evolution algorithm;
generating an initial population;
carrying out mutation and cross operation;
evaluating the individual fitness value, judging whether a termination condition is met, if so, terminating and outputting a result; otherwise, judging whether to accelerate operation;
when the acceleration operation is required, judging whether the operation is transferred or not after the acceleration is finished, and when the acceleration operation is not required, directly judging whether the operation is transferred or not;
and when the transfer operation is not needed, the step is directly returned to the mutation and crossover operation step.
The second aspect of the present invention provides a power distribution network multi-measure combination loss reduction optimization system considering source network load storage collaborative optimization, including:
a photovoltaic contribution prediction generation module configured to: according to the actually measured meteorological data, photovoltaic output prediction is carried out by utilizing a neural network model, and a photovoltaic output curve is obtained;
a photovoltaic output prediction correction module configured to: according to the acquired historical data of the illumination intensity, a photovoltaic output random model is constructed, a prediction result is corrected by using a classical scene obtained by the photovoltaic output random model, and a corrected photovoltaic output curve is obtained;
a battery energy storage model generation module configured to: according to the acquired storage battery parameter data, a storage battery energy storage model based on the residual electric quantity level and the charge and discharge power is constructed;
a multi-action combined loss reduction optimization module configured to: and obtaining an optimal network structure and compensation capacity according to the corrected photovoltaic output curve, the storage battery energy storage model, the power distribution network parameter data and the preset source network charge storage and power distribution network collaborative optimization loss reduction model.
A third aspect of the present invention provides a computer-readable storage medium, on which a program is stored, where the program, when executed by a processor, implements the steps in the method for optimizing multi-measure combination loss reduction of a power distribution network in consideration of source network load-storage cooperative optimization according to the first aspect of the present invention.
A fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, where the processor executes the program to implement the steps in the power distribution network multi-measure combined loss reduction optimization method considering source network load and storage cooperative optimization according to the first aspect of the present invention.
Compared with the prior art, the invention has the beneficial effects that:
the method comprehensively considers the factors of the cooperative matching sequence of the loss reduction measures, the compensation capacity limit of the load, the operation constraint of the energy storage device, the switching frequency limit, the voltage and current fluctuation limit of each node, the capacity limit of the distributed energy source and the like, and realizes the optimization of the loss reduction strategy by considering the calculation cost and feasible solution.
The invention designs a power distribution network multi-measure combination loss reduction optimization model and a loss reduction method considering source network load storage cooperative optimization, and formulates a loss reduction comprehensive control strategy with strong robust characteristics based on an optimization result, so that the internal reactive voltage stabilization and active power flow optimization of the power distribution network are realized, and the overall targets of safe and reliable operation, technical loss reduction and regional low-carbon energy-saving benefit improvement of the power distribution network are achieved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are included to illustrate an exemplary embodiment of the invention and not to limit the invention.
Fig. 1 is a schematic flow chart of a power distribution network multi-measure combination loss reduction optimization method considering source network load storage cooperative optimization according to embodiment 1 of the present invention.
Fig. 2 is a schematic flowchart of an improved mixed integer differential evolution algorithm provided in embodiment 1 of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
Example 1:
as shown in fig. 1, embodiment 1 of the present invention provides a power distribution network multi-measure combination loss reduction optimization method considering source network load and storage cooperative optimization, including the following processes:
s1: according to the measured meteorological data, photovoltaic output prediction is carried out by utilizing a BP neural network to obtain a photovoltaic output curve, wherein the BP neural network prediction specifically comprises the following steps:
selecting characteristic quantities such as atmospheric temperature, wind speed and day type, classifying samples by using a fuzzy C-mean method, selecting historical data with the maximum similarity to a predicted day as a sample, and establishing a gray GM (1, 1) model on the basis, wherein a prediction equation can be expressed as follows:
in the formula, a is a development coefficient, u is an ash action amount, and the data can be obtained by solving according to a least square method.
And training the grey prediction result as a BP neural network sample to obtain a predicted photovoltaic output curve.
S2: according to the acquired historical data of the illumination intensity, preferably fitting parameters of a photovoltaic output stochastic model by using a moment estimation method, establishing the photovoltaic output stochastic model, sampling a formed photovoltaic output stochastic curve to obtain a photovoltaic output classical scene, and correcting a photovoltaic output prediction result by using the classical scene to obtain a more accurate photovoltaic output curve, wherein the photovoltaic output model specifically comprises the following steps:
according to the beta distribution, the illumination intensity can be expressed as:
in the formula, r and r max Actual and maximum illumination intensity, respectively; a and b are shape parameters of the distribution, and Γ represents the gamma function.
Has M blocks with an area of A m Efficiency is eta m (M = 1.... M) solar cell module having a total area of
The weighted efficiency is
Total active power P m Can be expressed as:
P m ＝r·A·η
the photovoltaic output stochastic model can be expressed as:
in the formula, R M The maximum available power of the M solar cell modules is obtained.
Sampling the photovoltaic output stochastic model to obtain a large number of stochastic scenes, and reducing by using an optimal k-means clustering method to obtain a classical scene describing the randomness of the photovoltaic output.
S3: and establishing a storage battery energy storage device mathematical model based on the residual electric quantity level (SOC) and the charge-discharge power. The method comprises the following specific steps:
Where SOC (t) is the residual charge level of the battery at the tth sampling interval, ε is the self-discharge rate of the battery, P ES，c (t)、P ES，dis (t) the charging and discharging power of the storage battery, alpha and beta are the charging and discharging efficiency, E e And delta t is the sampling interval and is taken as 1h for the rated capacity of the storage battery.
S4: and establishing a source network load, storage and distribution power grid collaborative optimization loss reduction model considering distributed photovoltaic output and energy storage regulation and control and considering three-phase imbalance and line switch cost.
S4.1: network loss P accessed by photovoltaic and energy storage LOSS Three-phase unbalanced current I ub Number of times of switching operation in line connection k And (3) building a multi-target optimization model for the target at minimum, respectively carrying out normalization processing on all the quantities, and taking the geometric mean value of each target as a target function, wherein the method specifically comprises the following steps:
in the formula, a, b and c are weights of each objective function, and satisfy a + b + c =3, and the values of the weights can be adjusted according to requirements to determine the importance of each optimization objective; p is LOSS，N 、I ub，N 、λ k，N The result of normalization for each target can be obtained as follows:
in the formula,
is the grid loss and three-phase unbalanced current of the initial system, k n The total number of the line switches is;
target with photovoltaic and energy storage access network loss P LOSS The method specifically comprises the following steps:
in the formula, N is the total branch number of the power distribution network; k is a radical of n，t For the closed state of all the switches on the branch n in the period t, the value of 0 or 1,0 represents that the switches are in the open state, and 1 represents that the switches are in the closed state; r n Resistance for branch n; p n，t Active power, Q, of branch n for a period of t n，t The reactive power of the branch n is t time period; p PVn，t Photovoltaic active power, Q, for a branch n connected at time t PVn，t The photovoltaic reactive power accessed to the branch n in the t period; mu.s t The charging and discharging condition of the energy storage device in the t time period is 1 or-1, 1 represents that the energy storage device is in a discharging state, and-1 represents that the energy storage device is in a charging state; p ESn，t 、Q ESn，t Active power and reactive power which are sent out or absorbed by the energy storage device on the branch n at the t time period; i V n，t And | is the voltage amplitude at the tail end of the branch n in the period t.
Three-phase unbalanced current I in target ub Comprises the following steps:
in the formula,
for each phase current on line n for a period t,
representing the average current on line n over time t.
Number of switching operations in target λ k Comprises the following steps:
in the formula, x k，s 、x k，s-1 The state of switch k for period t and its preceding period.
S4.2: determining the constraint conditions of the optimization model, specifically:
the power flow constraint condition, the voltage constraint condition, the current constraint condition, the storage battery operation constraint condition and the photovoltaic operation constraint condition.
(1) And (3) power flow constraint conditions:
in the formula, P i，t 、Q i，t Respectively the active power and the reactive power injected at the node i in the period t; p is PVi，t 、Q PVi，t Respectively the active power and the reactive power of the photovoltaic injected at the node i in the period t; p ESi，t 、Q ESi，t Is the power of the energy storage device at node i during the period t, mu t 1 represents the energy storage device delivering power, -1 represents the energy storage device absorbing power; u shape i，t 、U j，t Voltages at nodes i and j in the period t respectively; g ij 、B ij 、θ ij Respectively are the conductance, susceptance and phase angle difference of the nodes i and j; n is a radical of hydrogen b Is the number of nodes.
(2) The voltage constraint conditions are as follows:
in the formula of U i，t 、
The current value, the allowed minimum value and the maximum value of the voltage of the node i, respectively.
(3) The current constraint conditions are as follows:
in the formula I ij 、
The current value and the maximum value allowed for the current of branch ij, respectively; where branch ij is the branch between nodes i, j.
(4) Energy storage operation constraint conditions:
P ES min ≤P Es，t ≤P ES max ，t∈T
-S inv，ES ≤P Es，t ≤P inv，ES ，t∈T
SOC min ≤SOC t ≤SOC max ，t∈T
in the formula, P ES max 、P ES min Maximum and minimum active power of the stored energy respectively; s. the inv，ES To the capacity of the storage inverter, P ES，t 、Q ES，t Respectively charging and discharging active power and reactive power at t time period on the energy storage alternating current side; SOC max 、SOC min Respectively an upper limit and a lower limit of the charge core number of the energy storage device.
(5) Photovoltaic operation constraint conditions:
P PV，t 2 +Q PV，t 2 ≤S PV 2 ，t∈T
in the formula, P PV，t And Q PV，t Active and reactive power for photovoltaic output during a period t; s. the PV Is the capacity of the photovoltaic inverter and,
is the minimum power factor of the photovoltaic output.
S5: and (4) inputting the corrected photovoltaic output curve obtained in the step (S2), the storage battery energy storage model in the step (S3) and system data into the power distribution network optimization loss reduction model established in the step (S4), and solving the power distribution network optimization loss reduction model by using an improved mixed integer differential evolution algorithm to obtain an optimal network structure and compensation capacity. The system data comprises node data and branch data of the power distribution network system; the improved differential evolution algorithm is a mixed differential evolution algorithm capable of processing variables containing mixed integers.
As shown in fig. 2, the improved differential evolution algorithm process specifically includes:
s5.1: and (5) inputting the corrected photovoltaic output curve obtained in the S2, the storage battery energy storage model in the S3 and the system data into the power distribution network optimization loss reduction model established in the S4.
S5.2: initializing parameters of a differential evolution algorithm; in order to enable the differential evolution algorithm to solve the problem of integer programming or mixed integer programming, the algorithm is improved:
the differential evolution algorithm is composed of NP parameter vectors
(i =1,2, \8230;, NP, where G denotes the G-th generation) constituent population is subjected to parallel direct optimization in the search space, assuming that the dimension of the integer variable is D, then
Can be expressed as
At initialization time, according to
And initializing an integer variable x, wherein the integer variable is obtained by randomly taking a value in a real number space and then rounding down. F is [0,1 ]]Random number, rou, uniform among othersnd (b) represents an integer closest to the real number b.
In the differential evolution process, the accelerated descent usually causes finding a local minimum or convergence early, and a local optimal solution cannot be skipped. In order to enlarge the search space and relieve the selection pressure, a migration operation is introduced to regenerate a plurality of new diversified population individuals, wherein the new individuals are in the optimal individuals
On the basis of the above-mentioned characteristics, by non-uniform random selection.
The j gene of the i individuals was generated as follows.
In the formula, σ i And delta is randomly generated and uniformly distributed in [0,1 ]]A number in between; the new population generated by the migration operation is a candidate solution with higher quality, and is easier to get rid of the constraint of the local minimum value, so that the algorithm is prevented from falling into the local optimum.
The parameter ρ is used to check whether a migration operation needs to be performed:
wherein,
ε 1 representing the expected deviation, ε, between population diversity and the best individual 2 Indicates the expected deviation between gene diversity and optimal individuals, in [0,1]To select between. Eta ji Referred to as the gene diversity ratio index. If p is less than ε 1 And executing the migration operation to generate a new population which can increase the search space, and otherwise, keeping the current search direction until an optimal target is found.
By adopting the migration operation, the search space can be improved, and the possibility of global search can be improved. However, after population expansion, convergence speed is slow, and in this case, acceleration is used to overcome this drawback. When the current solution cannot be improved any further, a descent strategy is introduced to force the current best individual towards a better direction, which can be achieved by:
in the formula,
step size p for the current optimal solution a ∈[0，1]Determined according to the droop characteristics. Start p a Set to 1 to obtain a new individual
Then the fitness value
And with
A comparison is made. If the droop characteristics are satisfied,
then
And replacing the worst individual in the population to become a candidate solution of the next generation. Otherwise, if the step-down characteristic is not satisfied, the step-down is reduced to 0.5 or 0.8, and the method is repeated to search
Up to
Small enough or up to the number of iterations.
S5.3: randomly generating each individual in the initial population;
s5.4: calculating a fitness function value of each individual;
taking the fitness function Fit (f (x)) as:
wherein, C max For the maximum estimation of f (x), the maximum value of f (x) occurring in the calculation process or the maximum value in the current population can be used.
S5.5: carrying out variation and cross operation on individuals in the population;
the cross operation adopts two-phase cross, and the equation is as follows:
in the formula, C R To characterize the cross probability of the evolution parameters, taking values between 0 and 1, rand () means [0,1 ]]Uniformly distributed random numbers in between. In order to improve algorithm precision, a variable cross probability factor, namely C is adopted R And the number of iterations increases from small to large. Is provided with C Rmin To the minimum cross probability, C Rmax If the maximum cross probability is obtained, T is the current iteration number, and T is the maximum iteration number, C R Can be expressed as:
s5.6: evaluating the fitness value of each individual in the new population obtained in the S5.5, wherein the higher the fitness is, the better the individual is; if the new individual obtained in S5.5 is superior to the original individual, the new individual is reserved in the next generation population, otherwise, the original individual is reserved, and the update of the S5.4 new population is completed;
the evaluation of the offspring is one-to-one competition with the parents, and any parent individual will be replaced by a child if its fitness value is worse than that of the offspring, otherwise will remain with the next generation. In the evaluation process, two steps are performed, the first step is one-to-one competition, and the second step is to determine the best individual in the current population, which can be expressed as:
in the formula, argmin represents the inverse function of the minimum value of the objective function to find the minimum optimal individual of the objective function,
representing the optimal solution in the individual offspring.
S5.7: setting a maximum iteration frequency Q, judging whether the maximum iteration frequency Q is reached, and if the maximum iteration frequency Q is reached, outputting an individual corresponding to the optimal objective function value in S5.6 to obtain an optimal network structure and compensation capacity; if not, returning to S5.4 to continue iterative computation.
Example 2:
the embodiment 2 of the present invention provides a power distribution network multi-measure combination loss reduction optimization system considering source network load storage collaborative optimization, including:
a photovoltaic contribution prediction generation module configured to: according to the actually measured meteorological data, photovoltaic output prediction is carried out by utilizing a neural network model, and a photovoltaic output curve is obtained;
a photovoltaic output prediction correction module configured to: according to the acquired historical data of the illumination intensity, a photovoltaic output random model is constructed, and the prediction is corrected by using a classic scene obtained by the photovoltaic output random model to obtain a corrected photovoltaic output curve;
a battery energy storage model generation module configured to: constructing a storage battery energy storage model based on the residual electric quantity level and the charge and discharge power according to the acquired storage battery parameter data;
a multi-measure combination impairment optimization module configured to: and obtaining an optimal network structure and compensation capacity according to the corrected photovoltaic output curve, the storage battery energy storage model, the power distribution network parameter data and the preset source network charge storage and power distribution network collaborative optimization loss reduction model.
The working method of the system is the same as the multi-measure combined loss reduction optimization method for the power distribution network considering source network load storage cooperative optimization provided in embodiment 1, and is not described here again.
Example 3:
embodiment 3 of the present invention provides a computer-readable storage medium, on which a program is stored, where the program, when executed by a processor, implements the steps in the power distribution network multi-measure combination loss reduction optimization method considering source network load and storage cooperative optimization according to embodiment 1 of the present invention.
Example 4:
embodiment 4 of the present invention provides an electronic device, which includes a memory, a processor, and a program stored in the memory and capable of running on the processor, where the processor executes the program to implement the steps in the power distribution network multi-measure combination loss reduction optimization method considering source network load-storage cooperative optimization according to embodiment 1 of the present invention.
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 a hardware embodiment, a 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, 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.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (7)
1. A power distribution network multi-measure combination loss reduction optimization method considering source network load storage collaborative optimization is characterized by comprising the following steps:
the method comprises the following steps:
according to the actually measured meteorological data, photovoltaic output prediction is carried out by utilizing a neural network model, and a photovoltaic output curve is obtained;
according to the acquired historical data of the illumination intensity, a photovoltaic output random model is constructed, a prediction result is corrected by using a classical scene obtained by the photovoltaic output random model, and a corrected photovoltaic output curve is obtained; the method comprises the following specific steps: according to the acquired historical data of the illumination intensity, fitting parameters of a photovoltaic output stochastic model by using a moment estimation method, establishing the photovoltaic output stochastic model, correcting a photovoltaic output curve according to a classical scene obtained by the model, and acquiring the illumination intensity utilization photovoltaic output stochastic model acquired by the corrected photovoltaic output curve;
constructing a storage battery energy storage model based on the residual electric quantity level and the charge and discharge power according to the acquired storage battery parameter data;
obtaining an optimal network structure and compensation capacity according to the corrected photovoltaic output curve, the storage battery energy storage model, the power distribution network parameter data and a preset source network charge storage and power distribution network collaborative optimization loss reduction model; the source network, storage battery and distribution power grid collaborative optimization loss reduction model comprises a power flow constraint, a voltage constraint, a current constraint, a storage battery operation constraint and a photovoltaic operation constraint;
the source network load storage and distribution power grid collaborative optimization loss reduction model comprises the following steps:
the method comprises the steps of constructing a multi-objective optimization model by taking the minimum grid loss, the minimum three-phase unbalanced current and the minimum circuit interconnection switch action times containing photovoltaic and energy storage access as targets, respectively carrying out normalization processing on each quantity, and taking the geometric mean value of each target as a target function;
network loss P containing photovoltaic and energy storage access in target LOSS The method specifically comprises the following steps:
in the formula, N is the total branch number of the power distribution network; k is a radical of formula n，t For the closed state of all the switches on the branch n in the period t, the value of 0 or 1,0 represents that the switches are in the open state, and 1 represents that the switches are in the closed state; r n Resistance for branch n; p n，t Active power, Q, of branch n for period t n，t The reactive power of the branch n is t time period; p PVn，t Photovoltaic active power, Q, for a branch n connected at time t PVn，t The photovoltaic reactive power accessed to the branch n in the t period; mu.s t The charging and discharging condition of the energy storage device in the t time period is 1 or-1, 1 represents that the energy storage device is in a discharging state, and-1 represents that the energy storage device is in a charging state; p is ESn，t 、Q ESn，t Active power and reactive power which are sent out or absorbed by the energy storage device on the branch n at the t time period; i V n，t L is the voltage amplitude of the tail end of the branch n in the period t;
three-phase unbalanced current I in target ub Comprises the following steps:
in the formula,
for each phase current on line n for a period t,
represents the average current on line n over a period t;
number of switching actions in target lambda k Comprises the following steps:
in the formula, x k，t 、x k，t-1 The state of switch k for period t and its preceding period.
2. The method for optimizing the combined loss reduction of the power distribution network by considering the source network load storage cooperative optimization as claimed in claim 1, wherein:
and (4) according to the actually measured meteorological data, utilizing a BP neural network to predict photovoltaic output, and obtaining a photovoltaic output curve.
3. The method for optimizing the combined loss reduction of the power distribution network by considering the source network load storage cooperative optimization as claimed in claim 1, wherein:
a battery energy storage model based on residual power level and charge-discharge power, comprising:
obtaining the residual electric quantity level of the storage battery in the charging process according to the self-discharge rate of the storage battery, the charging power and the charging efficiency of the storage battery, the rated capacity of the storage battery and the sampling interval;
and obtaining the residual electric quantity level of the storage battery in the discharging process according to the self-discharging rate of the storage battery, the discharging power and the discharging efficiency of the storage battery, the rated capacity of the storage battery and the sampling interval.
4. The method for optimizing the combined loss reduction of the power distribution network by considering the source network load storage cooperative optimization as claimed in claim 1, wherein:
solving the source network load storage and distribution power grid collaborative optimization loss reduction model by using an improved mixed integer differential evolution algorithm, wherein the solving comprises the following steps:
initializing parameters of a differential evolution algorithm;
generating an initial population;
carrying out mutation and cross operation;
evaluating the individual fitness value, judging whether a termination condition is met, if so, terminating and outputting a result; otherwise, judging whether to accelerate operation;
when the acceleration operation is required, judging whether the operation is transferred or not after the acceleration is finished, and when the acceleration operation is not required, directly judging whether the operation is transferred or not;
and when the transfer operation is not needed, the step is directly returned to the mutation and crossover operation step.
5. A power distribution network multi-measure combination loss reduction optimization system considering source network load storage collaborative optimization is characterized in that:
the method comprises the following steps:
a photovoltaic contribution prediction generation module configured to: according to the actually measured meteorological data, photovoltaic output prediction is carried out by utilizing a neural network model, and a photovoltaic output curve is obtained;
a photovoltaic output prediction correction module configured to: according to the acquired historical data of the illumination intensity, a photovoltaic output random model is constructed, a prediction result is corrected by using a classical scene obtained by the photovoltaic output random model, and a corrected photovoltaic output curve is obtained; the method comprises the following specific steps: according to the acquired historical data of the illumination intensity, fitting parameters of a photovoltaic output stochastic model by using a moment estimation method, establishing the photovoltaic output stochastic model, correcting a photovoltaic output curve according to a classic scene obtained by the model, and acquiring the illumination intensity acquired by the corrected photovoltaic output curve and utilizing the photovoltaic output stochastic model;
a battery energy storage model generation module configured to: according to the acquired storage battery parameter data, a storage battery energy storage model based on the residual electric quantity level and the charge and discharge power is constructed;
a multi-measure combination impairment optimization module configured to: obtaining an optimal network structure and compensation capacity according to the corrected photovoltaic output curve, the storage battery energy storage model, the power distribution network parameter data and a preset source network, power storage and distribution network collaborative optimization loss reduction model; the source network, storage battery and distribution power grid collaborative optimization loss reduction model comprises a power flow constraint, a voltage constraint, a current constraint, a storage battery operation constraint and a photovoltaic operation constraint;
the source network load storage and distribution power grid collaborative optimization loss reduction model comprises the following steps:
the method comprises the steps of constructing a multi-objective optimization model by taking the minimum grid loss, the minimum three-phase unbalanced current and the minimum circuit interconnection switch action times containing photovoltaic and energy storage access as targets, respectively carrying out normalization processing on each quantity, and taking the geometric mean value of each target as a target function;
network loss P containing photovoltaic and energy storage access in target LOSS The method specifically comprises the following steps:
in the formula, N is the total branch number of the power distribution network; k is a radical of formula n，t For the closed state of all the switches on the branch n in the period t, the value of 0 or 1,0 represents that the switches are in the open state, and 1 represents that the switches are in the closed state; r n Resistance for branch n; p is n，t Active power, Q, of branch n for a period of t n，t The reactive power of the branch n is t time period; p PVn，t Photovoltaic active power, Q, for a branch n connected at time t PVn，t The photovoltaic reactive power accessed to the branch n in the t period; mu.s t The charging and discharging condition of the energy storage device in the t time period is 1 or-1, 1 represents that the energy storage device is in a discharging state, and-1 represents that the energy storage device is in a charging state; p ESn，t 、Q ESn，t Active power and reactive power which are sent out or absorbed by the energy storage device on the branch n at the t time period; | V n，t L is the voltage amplitude of the tail end of the branch n in the period t;
three-phase unbalanced current I in target ub Comprises the following steps:
in the formula,
for each phase current on line n for a period t,
represents the average current on line n over a period t;
number of switching actions in target lambda k Comprises the following steps:
in the formula, x k，t 、x k，t-1 The state of switch k for period t and its preceding period.
6. A computer-readable storage medium, on which a program is stored, which program, when being executed by a processor, carries out the steps of the method for optimizing multi-measure combined loss reduction of a power distribution network in view of source-network load-store co-optimization of any one of claims 1 to 4.
7. An electronic device comprising a memory, a processor and a program stored on the memory and executable on the processor, wherein the processor implements the steps of the method for power distribution network multi-measure combined loss reduction optimization considering source network load-storage cooperative optimization as claimed in any one of claims 1 to 4 when executing the program.
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