CN116822107A - Distributed new energy power distribution network configuration optimization method and system - Google Patents
Distributed new energy power distribution network configuration optimization method and system Download PDFInfo
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Abstract
The invention discloses a distributed new energy power distribution network configuration optimization method and system, and relates to the field of power distribution networks, wherein the method comprises the following steps: performing supply-demand matching degree identification based on the distributed new energy topological network and the first new energy storage requirement to obtain a first new energy supply-demand matching degree; if the first new energy supply and demand matching degree does not meet the preset supply and demand matching degree, generating a new energy storage scheduling instruction; activating a new energy storage configuration space, performing iterative optimization on Q new energy storage configuration particles in the new energy storage configuration space according to an energy storage configuration fitness function based on a first energy storage time limit identifier to obtain a new energy storage configuration optimization scheme of the power grid, and executing new energy storage configuration optimization based on the new energy storage configuration optimization scheme of the power grid. The novel energy storage configuration method and the novel energy storage configuration device solve the technical problems that in the prior art, the novel energy storage configuration for the distributed novel energy distribution network is low in adaptability and insufficient in accuracy, and the novel energy storage effect of the distributed novel energy distribution network is poor.
Description
Technical Field
The invention relates to the field of power distribution networks, in particular to a distributed new energy power distribution network configuration optimization method and system.
Background
In order to improve the increasingly prominent energy environment problem, the distributed new energy with small pollution and high energy utilization rate is continuously connected into the power distribution network, thereby forming the distributed new energy power distribution network. The distributed new energy power distribution network has the advantages of peak clipping, valley filling, voltage quality improvement and the like. Meanwhile, the fluctuation and intermittence of the distributed new energy bring great influence to the safe and stable operation of the power distribution network, such as frequency modulation difficulty, node voltage out-of-limit and other problems.
In the prior art, the technical problems of poor new energy storage effect of the distributed new energy distribution network caused by low new energy storage configuration adaptability and insufficient accuracy of the distributed new energy distribution network exist.
Disclosure of Invention
The application provides a distributed new energy power distribution network configuration optimization method and system. The novel energy storage configuration method and the novel energy storage configuration device solve the technical problems that in the prior art, the novel energy storage configuration for the distributed novel energy distribution network is low in adaptability and insufficient in accuracy, and the novel energy storage effect of the distributed novel energy distribution network is poor. The novel energy storage control method and the novel energy storage control system achieve the technical effects of achieving targeted energy storage control of the distributed novel energy distribution network according to the novel energy storage requirement, improving the novel energy storage configuration fitness and accuracy of the distributed novel energy distribution network and improving the novel energy storage quality of the distributed novel energy distribution network.
In view of the above problems, the application provides a distributed new energy power distribution network configuration optimization method and system.
In a first aspect, the present application provides a distributed new energy power distribution network configuration optimization method, where the method is applied to a distributed new energy power distribution network configuration optimization system, and the method includes: n distributed new energy sources of the first distributed new energy source distribution network are obtained, wherein N is a positive integer greater than 1; respectively acquiring the real-time residual capacities of the N distributed new energy sources, obtaining an energy residual capacity acquisition result, and identifying the N distributed new energy sources based on the energy residual capacity acquisition result to generate a distributed new energy source topology network, wherein the distributed new energy source topology network comprises N distributed new energy source topology nodes; receiving a first new energy storage demand of the first distributed new energy distribution network, wherein the first new energy storage demand has a first energy storage time limit identifier; performing supply and demand matching degree identification based on the distributed new energy topological network and the first new energy storage demand, obtaining a first new energy supply and demand matching degree, and judging whether the first new energy supply and demand matching degree meets a preset supply and demand matching degree or not; if the first new energy supply and demand matching degree does not meet the preset supply and demand matching degree, generating a new energy storage scheduling instruction; activating a new energy storage configuration space based on the new energy storage scheduling instruction, wherein the new energy storage configuration space comprises Q new energy storage configuration particles, and Q is a positive integer greater than 1; and performing iterative optimization on the Q new energy storage configuration particles according to an energy storage configuration fitness function based on the first energy storage time limit identifier to obtain a new energy configuration optimization scheme of a power grid, and executing new energy storage configuration optimization of the first distributed new energy distribution network based on the new energy configuration optimization scheme of the power grid.
In a second aspect, the present application further provides a distributed new energy power distribution network configuration optimization system, where the system includes: the system comprises a power grid new energy obtaining module, a first power grid new energy distribution network and a second power grid new energy distribution network, wherein the power grid new energy obtaining module is used for obtaining N distributed new energy sources of the first distributed new energy distribution network, and N is a positive integer greater than 1; the topology network generation module is used for respectively acquiring the real-time residual capacities of the N distributed new energy sources, obtaining an energy residual capacity acquisition result, and identifying the N distributed new energy sources based on the energy residual capacity acquisition result to generate a distributed new energy source topology network, wherein the distributed new energy source topology network comprises N distributed new energy source topology nodes; the energy storage demand receiving module is used for receiving a first new energy storage demand of the first distributed new energy distribution network, wherein the first new energy storage demand has a first energy storage time limit identifier; the supply and demand matching degree judging module is used for carrying out supply and demand matching degree identification based on the distributed new energy topological network and the first new energy storage requirement, obtaining first new energy supply and demand matching degree and judging whether the first new energy supply and demand matching degree meets preset supply and demand matching degree or not; the energy storage scheduling instruction generation module is used for generating a new energy storage scheduling instruction if the first new energy supply and demand matching degree does not meet the preset supply and demand matching degree; the space activation module is used for activating a new energy storage configuration space based on the new energy storage scheduling instruction, wherein the new energy storage configuration space comprises Q new energy storage configuration particles, and Q is a positive integer greater than 1; the energy storage configuration optimizing module is used for carrying out iterative optimization on the Q new energy storage configuration particles according to an energy storage configuration fitness function based on the first energy storage time limit identification to obtain a new energy configuration optimizing scheme of the power grid, and executing new energy storage configuration optimization of the first distributed new energy distribution network based on the new energy configuration optimizing scheme of the power grid.
One or more technical schemes provided by the application have at least the following technical effects or advantages:
marking N distributed new energy sources through an energy residual capacity acquisition result to generate a distributed new energy topological network; the method comprises the steps of obtaining a first new energy supply and demand matching degree by carrying out supply and demand matching degree identification on a distributed new energy topological network and a first new energy storage requirement, and judging whether the first new energy supply and demand matching degree meets a preset supply and demand matching degree or not; if the first new energy supply and demand matching degree does not meet the preset supply and demand matching degree, generating a new energy storage scheduling instruction, and activating a new energy storage configuration space according to the new energy storage scheduling instruction; and performing iterative optimization on the activated new energy storage configuration space according to the energy storage configuration fitness function based on the first energy storage time limit identifier to obtain a new energy configuration optimization scheme of the power grid, and executing new energy storage configuration optimization of the first distributed new energy distribution network based on the new energy configuration optimization scheme of the power grid. The novel energy storage control method and the novel energy storage control system achieve the technical effects of achieving targeted energy storage control of the distributed novel energy distribution network according to the novel energy storage requirement, improving the novel energy storage configuration fitness and accuracy of the distributed novel energy distribution network and improving the novel energy storage quality of the distributed novel energy distribution network.
The foregoing description is only an overview of the present application, and is intended to be implemented in accordance with the teachings of the present application in order that the same may be more clearly understood and to make the same and other objects, features and advantages of the present application more readily apparent.
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In order to more clearly illustrate the technical solution of the embodiments of the present application, the following description will briefly explain the drawings of the embodiments of the present application. It is apparent that the figures in the following description relate only to some embodiments of the application and are not limiting of the application.
FIG. 1 is a schematic flow chart of a distributed new energy distribution network configuration optimization method;
fig. 2 is a schematic flow chart of a new energy configuration optimization scheme of a power grid obtained in the configuration optimization method of the distributed new energy power distribution network;
fig. 3 is a schematic structural diagram of a distributed new energy distribution network configuration optimizing system according to the present application.
Reference numerals illustrate: the system comprises a power grid new energy obtaining module 11, a topology network generating module 12, an energy storage demand receiving module 13, a supply and demand matching degree judging module 14, an energy storage scheduling instruction generating module 15, a space activating module 16 and an energy storage configuration optimizing module 17.
Detailed Description
The application provides a distributed new energy power distribution network configuration optimization method and system. The novel energy storage configuration method and the novel energy storage configuration device solve the technical problems that in the prior art, the novel energy storage configuration for the distributed novel energy distribution network is low in adaptability and insufficient in accuracy, and the novel energy storage effect of the distributed novel energy distribution network is poor. The novel energy storage control method and the novel energy storage control system achieve the technical effects of achieving targeted energy storage control of the distributed novel energy distribution network according to the novel energy storage requirement, improving the novel energy storage configuration fitness and accuracy of the distributed novel energy distribution network and improving the novel energy storage quality of the distributed novel energy distribution network.
Example 1
Referring to fig. 1, the application provides a distributed new energy power distribution network configuration optimization method, wherein the method is applied to a distributed new energy power distribution network configuration optimization system, and the method specifically comprises the following steps:
step S100: n distributed new energy sources of the first distributed new energy source distribution network are obtained, wherein N is a positive integer greater than 1;
step S200: respectively acquiring the real-time residual capacities of the N distributed new energy sources, obtaining an energy residual capacity acquisition result, and identifying the N distributed new energy sources based on the energy residual capacity acquisition result to generate a distributed new energy source topology network, wherein the distributed new energy source topology network comprises N distributed new energy source topology nodes;
Specifically, real-time residual capacity collection is performed on N distributed new energy sources of the first distributed new energy source distribution network respectively, an energy residual capacity collection result is obtained, and the N distributed new energy sources are identified according to the energy residual capacity collection result to generate a distributed new energy source topology network. The first distributed new energy distribution network can be any distributed new energy distribution network which uses the distributed new energy distribution network configuration optimizing system to conduct intelligent new energy storage configuration. The first distributed new energy power distribution network comprises N distributed new energy sources. And N is a positive integer greater than 1. The N distributed new energy sources comprise N new energy source power generation modes such as wind power, photovoltaic power generation and the like. And each distributed new energy source is provided with a plurality of new energy power generation equipment. For example, the plurality of new energy power generation devices corresponding to wind power include wind generating sets, box transformers, collector lines, reactive compensation devices, and the like. And the energy residual capacity acquisition result comprises N real-time residual power generation amounts corresponding to the N distributed new energy sources. The distributed new energy topology network comprises N distributed new energy topology nodes. Each distributed new energy topological node comprises a distributed new energy, and a plurality of new energy power generation devices and real-time surplus power generation capacity corresponding to the distributed new energy. The method achieves the technical effects of constructing a distributed new energy topological network and laying a foundation for carrying out energy storage configuration optimization on the first distributed new energy distribution network in the follow-up process.
Step S300: receiving a first new energy storage demand of the first distributed new energy distribution network, wherein the first new energy storage demand has a first energy storage time limit identifier;
step S400: performing supply and demand matching degree identification based on the distributed new energy topological network and the first new energy storage demand, obtaining a first new energy supply and demand matching degree, and judging whether the first new energy supply and demand matching degree meets a preset supply and demand matching degree or not;
step S500: if the first new energy supply and demand matching degree does not meet the preset supply and demand matching degree, generating a new energy storage scheduling instruction;
specifically, the distributed new energy distribution network configuration optimization system is connected to collect the first new energy storage requirement of the first distributed new energy distribution network. The first new energy storage demand includes a demand new energy generation of the first distributed new energy distribution network. And the first new energy storage demand has a first energy storage time limit identifier. The first energy storage time limit identifier comprises a new energy storage time interval corresponding to a first new energy storage requirement.
Further, supply and demand matching degree identification is conducted on the distributed new energy topological network and the first new energy storage requirement, namely N pieces of real-time residual generated energy in the distributed new energy topological network are added, and total residual generated energy of the new energy is obtained. And outputting the ratio between the total residual power generation amount of the new energy and the energy storage requirement of the first new energy as the matching degree of the supply and the demand of the first new energy. And then judging whether the first new energy supply and demand matching degree meets the preset supply and demand matching degree. If the first new energy supply and demand matching degree does not meet the preset supply and demand matching degree, the distributed new energy distribution network configuration optimizing system automatically generates a new energy storage scheduling instruction. The preset supply and demand matching degree comprises a new energy supply and demand matching degree threshold value which is preset and determined by the distributed new energy distribution network configuration optimizing system. The new energy storage scheduling instruction is instruction information for representing that the supply and demand matching degree of the first new energy source does not meet the preset supply and demand matching degree and energy storage management needs to be carried out on the first distributed new energy distribution network. The new energy storage scheduling instruction is adaptively generated according to the first new energy supply and demand matching degree of the distributed new energy topological network and the first new energy storage demand, so that the technical effect of improving the new energy storage configuration adaptability of the distributed new energy distribution network is achieved.
Step S600: activating a new energy storage configuration space based on the new energy storage scheduling instruction, wherein the new energy storage configuration space comprises Q new energy storage configuration particles, and Q is a positive integer greater than 1;
further, the step S600 of the present application further includes:
step S610: obtaining a first retrieval constraint according to the N distributed new energy topological nodes;
step S620: obtaining a second retrieval constraint according to the first new energy storage requirement;
step S630: collecting new energy storage configuration records according to the first retrieval constraint and the second retrieval constraint to obtain a new energy storage configuration record library, wherein the new energy storage configuration record library comprises a plurality of new energy storage configuration records;
step S640: traversing the plurality of new energy storage configuration records to perform confidence analysis to obtain a plurality of energy storage configuration-confidence coefficients;
step S650: and arranging the plurality of new energy storage configuration records in a descending order based on the plurality of energy storage configuration-confidence degrees, setting the first Q new energy storage configuration records as Q new energy storage configuration particles, and adding the Q new energy storage configuration particles into the new energy storage configuration space.
Specifically, setting N distributed new energy topological nodes as first retrieval constraints, setting first new energy storage demands as second retrieval constraints, and collecting new energy storage configuration records according to the first retrieval constraints and the second retrieval constraints to obtain a new energy storage configuration record library. The new energy storage configuration record library comprises a plurality of new energy storage configuration records. Each new energy storage configuration record comprises N historical distributed new energy topological nodes, historical new energy storage demands, and historical energy storage power generation capacity and historical new energy power generation equipment control data of each distributed new energy in N distributed new energy corresponding to the N historical distributed new energy topological nodes and the historical new energy storage demands. The historical new energy power generation equipment control data comprises historical control parameters of a plurality of new energy power generation equipment of the distributed new energy corresponding to the historical energy storage power generation capacity.
Further, traversing the plurality of new energy storage configuration records to perform confidence analysis, namely counting the occurrence frequency of each new energy storage configuration record in the new energy storage configuration record library, and obtaining a plurality of record supporters corresponding to the plurality of new energy storage configuration records. The sum of the plurality of record support degrees is set as the total record support degree. And respectively carrying out ratio calculation on the plurality of record supporters and the total record supporters to obtain a plurality of energy storage configuration-confidence coefficients. Each record support degree comprises the occurrence frequency of each new energy storage configuration record in the new energy storage configuration record library. Each energy storage configuration-confidence level includes a ratio between each record support level and the total record support level.
Further, the plurality of new energy storage configuration records are arranged in descending order according to the plurality of energy storage configuration-confidence degrees, the first Q new energy storage configuration records are set to be Q new energy storage configuration particles, and the Q new energy storage configuration particles are added into the new energy storage configuration space. And then, when the distributed new energy distribution network configuration optimizing system generates a new energy storage scheduling instruction, activating a new energy storage configuration space according to the new energy storage scheduling instruction. The descending order arrangement means that a plurality of new energy storage configuration records are arranged according to the order of the plurality of storage configuration-confidence coefficient from large to small. The new energy storage configuration space comprises Q new energy storage configuration particles, and Q is a positive integer greater than 1. Preferably, the Q value is 2/3 of the number of the plurality of new energy storage configuration records. The new energy storage scheduling instruction is also used for representing the activation instruction information of the new energy storage configuration space.
Step S700: and performing iterative optimization on the Q new energy storage configuration particles according to an energy storage configuration fitness function based on the first energy storage time limit identifier to obtain a new energy configuration optimization scheme of a power grid, and executing new energy storage configuration optimization of the first distributed new energy distribution network based on the new energy configuration optimization scheme of the power grid.
Further, as shown in fig. 2, step S700 of the present application further includes:
step S710: based on the first energy storage time limit identifier, respectively carrying out energy storage fitness analysis on the Q new energy storage configuration particles according to the energy storage configuration fitness function to obtain Q first energy storage fitness;
further, step S710 of the present application further includes:
step S711: obtaining an ith new energy storage configuration particle based on the Q new energy storage configuration particles, wherein i is a positive integer, and i is more than or equal to 1 and less than or equal to Q;
step S712: acquiring a first time limit weather forecast based on the first energy storage time limit identifier;
step S713: connecting a digital twin platform, and modeling the N distributed new energy sources to obtain an energy twin model;
step S714: based on the digital twin platform, performing simulated energy storage operation on the ith new energy storage configuration particle according to the energy twin model and the first time limit weather forecast, and obtaining an ith simulated energy storage operation result;
specifically, Q new energy storage configuration particles in the new energy storage configuration space are traversed to randomly select, and the ith new energy storage configuration particle is obtained. And meanwhile, carrying out weather forecast query on the first energy storage time limit identifier to obtain a first time limit weather forecast. The ith new energy storage configuration particle is each new energy storage configuration particle in the new energy storage configuration space in sequence. i is a positive integer, and i is more than or equal to 1 and less than or equal to Q. The first time limit weather forecast comprises weather forecast information such as wind direction, wind power, precipitation, temperature, sunshine duration, sunshine intensity and the like corresponding to the first energy storage time limit mark.
Further, device parameter collection is carried out on the N distributed new energy sources respectively, and a new energy source device set is obtained. Uploading the new energy equipment set to a digital twin platform, and carrying out simulation modeling on N distributed new energy sources by the digital twin platform according to the new energy equipment set to obtain an energy twin model. And then uploading the first time limit weather forecast and the ith new energy storage configuration particle to a digital twin platform, and carrying out simulated energy storage operation on the energy twin model by the digital twin platform according to the first time limit weather forecast and the ith new energy storage configuration particle to obtain an ith simulated energy storage operation result. The new energy equipment set comprises equipment basic parameters such as equipment material information, equipment size information, equipment structure information and the like corresponding to each new energy power generation equipment of each distributed new energy in N distributed new energy. The digital twin platform is in communication connection with the distributed new energy power distribution network configuration optimizing system. The digital twin platform may be a digital twin system as in the prior art. The digital twin platform has the function of performing simulation modeling and simulated energy storage operation on the input new energy equipment set by utilizing a digital twin technology. The digital twin technology is a technology for realizing mirror image mapping of a new energy device set by carrying out multi-scale simulation on the new energy device set so as to reflect the full life cycle state of the new energy device set. The energy twin model comprises a simulation model corresponding to the new energy equipment set, and the simulation model is completely consistent with the new energy equipment set. The energy twin model is a complete and accurate digital description of the new energy equipment set. The ith simulated energy storage operation result comprises simulated energy storage power generation duration information, simulated energy storage power generation capacity information and simulated energy storage loss electric quantity information of the energy twin model, simulated output power information and simulated output voltage information of the new energy power generation equipment under the condition of the first time limit weather forecast and the ith new energy storage configuration particle. The method achieves the technical effects of performing simulated energy storage operation on the ith new energy storage configuration particles through the digital twin platform, obtaining accurate ith simulated energy storage operation results, and improving the digitization degree and the intelligence of the new energy storage configuration of the distributed new energy distribution network.
Step S715: performing multidimensional index analysis based on the ith simulated energy storage operation result to obtain an ith simulated energy storage analysis result, wherein the ith simulated energy storage analysis result comprises a simulated energy storage loss coefficient, a simulated energy storage efficiency, a simulated energy storage fluctuation coefficient and a simulated energy storage voltage deviation coefficient;
further, step S715 of the present application further includes:
step S715-1: based on big data, acquiring an analog energy storage index analysis record set;
step S715-2: building a simulated energy storage analysis model based on the simulated energy storage index analysis record set, wherein the simulated energy storage analysis model comprises multi-dimensional indexes including a simulated energy storage loss index, a simulated energy storage efficiency index, a simulated energy storage fluctuation index and a simulated energy storage voltage deviation index;
step S715-3: and carrying out multidimensional index analysis on the ith simulated energy storage operation result based on the simulated energy storage analysis model to generate the ith simulated energy storage analysis result.
Specifically, based on big data, a simulated energy storage index analysis record set is acquired. The simulated energy storage index analysis record set comprises a plurality of simulated energy storage index analysis records. Each simulated energy storage index analysis record comprises a historical simulated energy storage analysis result, and a historical simulated energy storage loss coefficient, a historical simulated energy storage efficiency, a historical simulated energy storage fluctuation coefficient and a historical simulated energy storage voltage deviation coefficient corresponding to the historical simulated energy storage analysis result. And then, continuously self-training and learning the simulated energy storage index analysis record set to a convergence state according to the fully-connected neural network to obtain a simulated energy storage analysis model. The fully-connected neural network is also called as a multi-layer perceptron, and is an artificial neural network structure with a simpler connection mode. The fully-connected neural network is a feedforward neural network consisting of an input layer, a hidden layer and an output layer. Also, there may be multiple neurons in the hidden layer. The simulated energy storage analysis model comprises an input layer, a hidden layer and an output layer.
Further, taking the ith simulated energy storage operation result as input information, inputting the input information into a simulated energy storage analysis model, and analyzing the ith simulated energy storage operation result by the simulated energy storage analysis model according to the multi-dimension index to obtain the ith simulated energy storage analysis result. The multi-dimensional index comprises an analog energy storage loss index, an analog energy storage efficiency index, an analog energy storage fluctuation index and an analog energy storage voltage deviation index. The ith simulated energy storage analysis result comprises a simulated energy storage loss coefficient, a simulated energy storage efficiency, a simulated energy storage fluctuation coefficient and a simulated energy storage voltage deviation coefficient. The simulated energy storage loss coefficient is data information for representing the simulated energy storage loss degree of the ith simulated energy storage analysis result. The simulated energy storage efficiency is data information for representing the simulated energy storage efficiency of the ith simulated energy storage analysis result. The simulated energy storage fluctuation coefficient is data information for representing the stability of the simulated output power of the ith simulated energy storage analysis result. The lower the analog output power stability of the ith analog energy storage analysis result is, the higher the corresponding analog energy storage fluctuation coefficient is. The analog energy storage voltage offset coefficient is data information for representing the stability of the analog output voltage of the ith analog energy storage analysis result. The higher the analog output voltage stability of the ith analog energy storage analysis result is, the lower the corresponding analog energy storage voltage offset coefficient is. The method achieves the technical effects that the multi-dimensional index analysis is carried out on the ith simulated energy storage operation result through the simulated energy storage analysis model, and the comprehensive and accurate ith simulated energy storage analysis result is obtained, so that the reliability of iterative optimization of Q new energy storage configuration particles is improved.
Step S716: inputting the ith simulated energy storage analysis result into the energy storage configuration fitness function to obtain an ith simulated energy storage fitness, and adding the ith simulated energy storage fitness to the Q first energy storage fitness.
Step S720: acquiring first optimal energy storage configuration particles corresponding to the first optimal energy storage fitness and the first optimal energy storage fitness based on the Q first energy storage fitness and the Q new energy storage configuration particles;
step S730: judging whether the first optimal energy storage fitness meets a preset energy storage fitness or not;
step S740: and if the first optimal energy storage fitness meets the preset energy storage fitness, outputting the first optimal energy storage configuration particles as the new energy configuration optimization scheme of the power grid.
Specifically, inputting an ith simulated energy storage analysis result into an energy storage configuration fitness function to obtain an ith simulated energy storage fitness, and adding the ith simulated energy storage fitness to the Q first energy storage fitness. The Q first energy storage fitness is the same as the i-th analog energy storage fitness, and is not described herein for brevity of description. The energy storage configuration fitness function is Wherein (1)>For the i-th simulated energy storage fitness corresponding to the output i-th simulated energy storage analysis result,/th simulated energy storage fitness>Representing a fusion coefficient of the simulated energy storage result, representing the simulated energy storage efficiency in the ith simulated energy storage analysis result, and +.>Respectively representing the simulated energy storage loss coefficient, the simulated energy storage fluctuation coefficient and the simulated energy storage voltage deviation coefficient in the ith simulated energy storage analysis result, and (I)>Respectively setting a determined simulated energy storage efficiency weight value, a simulated energy storage loss coefficient weight value, a simulated energy storage fluctuation coefficient weight value and a simulated energy storage voltage deviation coefficient weight value for the distributed new energy distribution network configuration optimizing system in advance, and>. Further, the maximum value of the Q first energy storage fitness is set as a first optimal energy storage fitness, and new energy storage configuration particles corresponding to the first optimal energy storage fitness are recorded as first optimal energy storage configuration particles in the Q new energy storage configuration particles. Then, whether the first optimal energy storage fitness meets the preset energy storageAnd judging the fitness. And if the first optimal energy storage fitness meets the preset energy storage fitness, outputting the first optimal energy storage configuration particles as a new energy configuration optimization scheme of the power grid, and performing new energy storage configuration optimization on the first distributed new energy distribution network according to the new energy configuration optimization scheme of the power grid. Thereby improving the new energy storage effect of the distributed new energy distribution network. The preset energy storage fitness comprises energy storage fitness threshold information preset and determined by the distributed new energy power distribution network configuration optimization system.
Further, step S730 of the present application further includes:
step S731: if the first optimal energy storage fitness does not meet the preset energy storage fitness, constructing a first new energy storage configuration neighborhood space based on the first optimal energy storage configuration particles, wherein the first new energy storage configuration neighborhood space comprises a plurality of new energy storage configuration first neighborhood particles;
step S732: based on the first energy storage time limit identifier, respectively carrying out energy storage fitness analysis on the first neighborhood particles of the plurality of new energy storage configuration according to the energy storage configuration fitness function to obtain a plurality of first neighborhood energy storage fitness;
step S733: configuring first neighborhood particles based on the plurality of first neighborhood energy storage fitness and the plurality of new energy storage fitness to obtain first neighborhood optimal energy storage fitness and optimal first neighborhood particles corresponding to the first neighborhood optimal energy storage fitness;
step S734: judging whether the first neighborhood optimal energy storage fitness meets the preset energy storage fitness or not;
step S735: outputting the optimal first neighborhood particles as the new energy configuration optimization scheme of the power grid if the optimal energy storage fitness of the first neighborhood meets the preset energy storage fitness;
Step S736: and if the optimal energy storage fitness of the first neighborhood does not meet the preset energy storage fitness, performing iterative optimization based on the optimal first neighborhood particles until the new energy configuration optimization scheme of the power grid is obtained.
Specifically, when judging whether the first optimal energy storage fitness meets the preset energy storage fitness or not, if the first optimal energy storage fitness does not meet the preset energy storage fitness, performing Q times of random adjustment on the first optimal energy storage configuration particles to obtain Q first neighborhood particles, and adding the Q first neighborhood particles into a first new energy storage configuration neighborhood space. The first new energy storage configuration neighborhood space includes a plurality of new energy storage configuration first neighborhood particles. The plurality of new energy storage configuration first neighborhood particles comprise first optimal storage configuration particles and Q first neighborhood particles.
Further, based on the first energy storage time limit identification, according to the energy storage configuration fitness function, energy storage fitness analysis is performed on the first neighborhood particles of the plurality of new energy storage configuration to obtain a plurality of first neighborhood energy storage fitness. The plurality of first neighborhood energy storage fitness is the same as the i-th simulated energy storage fitness in the same manner, and for brevity of description, details are not repeated here. And then setting the maximum value of the plurality of first neighborhood energy storage fitness to be the first neighborhood optimal energy storage fitness, and setting the new energy storage configuration first neighborhood particles corresponding to the first neighborhood optimal energy storage fitness to be the optimal first neighborhood particles in the plurality of new energy storage configuration first neighborhood particles.
Further, judging whether the optimal energy storage fitness of the first neighborhood meets the preset energy storage fitness. And if the optimal energy storage fitness of the first neighborhood meets the preset energy storage fitness, outputting the optimal first neighborhood particles as a new energy configuration optimization scheme of the power grid. If the optimal energy storage fitness of the first neighborhood does not meet the preset energy storage fitness, performing iterative optimization on the optimal first neighborhood particles until a new energy configuration optimization scheme of the power grid is obtained. Therefore, comprehensive iterative optimization is realized, and the adaptability of the new energy configuration optimization scheme of the power grid is improved. The method for performing iterative optimization on the optimal first neighborhood particles is the same as the method for obtaining the new energy configuration optimization scheme of the power grid according to the optimal first neighborhood particles. For brevity of the description, the description is not repeated here.
In summary, the distributed new energy power distribution network configuration optimization method provided by the application has the following technical effects:
1. marking N distributed new energy sources through an energy residual capacity acquisition result to generate a distributed new energy topological network; the method comprises the steps of obtaining a first new energy supply and demand matching degree by carrying out supply and demand matching degree identification on a distributed new energy topological network and a first new energy storage requirement, and judging whether the first new energy supply and demand matching degree meets a preset supply and demand matching degree or not; if the first new energy supply and demand matching degree does not meet the preset supply and demand matching degree, generating a new energy storage scheduling instruction, and activating a new energy storage configuration space according to the new energy storage scheduling instruction; and performing iterative optimization on the activated new energy storage configuration space according to the energy storage configuration fitness function based on the first energy storage time limit identifier to obtain a new energy configuration optimization scheme of the power grid, and executing new energy storage configuration optimization of the first distributed new energy distribution network based on the new energy configuration optimization scheme of the power grid. The novel energy storage control method and the novel energy storage control system achieve the technical effects of achieving targeted energy storage control of the distributed novel energy distribution network according to the novel energy storage requirement, improving the novel energy storage configuration fitness and accuracy of the distributed novel energy distribution network and improving the novel energy storage quality of the distributed novel energy distribution network.
2. And the digital twin platform is used for carrying out simulated energy storage operation on the ith new energy storage configuration particles to obtain an accurate ith simulated energy storage operation result, so that the digitization degree and the intelligence of the new energy storage configuration of the distributed new energy distribution network are improved.
Example two
Based on the same inventive concept as the method for optimizing the configuration of the distributed new energy distribution network in the foregoing embodiment, the present invention further provides a system for optimizing the configuration of the distributed new energy distribution network, referring to fig. 3, where the system includes:
the power grid new energy obtaining module 11 is used for obtaining N distributed new energy sources of the first distributed new energy power distribution network, wherein N is a positive integer greater than 1;
the topology network generation module 12 is configured to collect real-time residual capacities of the N distributed new energy sources respectively, obtain an energy residual capacity collection result, and identify the N distributed new energy sources based on the energy residual capacity collection result, so as to generate a distributed new energy topology network, where the distributed new energy topology network includes N distributed new energy topology nodes;
the energy storage demand receiving module 13 is configured to receive a first new energy storage demand of the first distributed new energy distribution network, where the first new energy storage demand has a first energy storage time limit identifier;
The supply-demand matching degree judging module 14, wherein the supply-demand matching degree judging module 14 is configured to identify supply-demand matching degrees based on the distributed new energy topological network and the first new energy storage demand, obtain a first new energy supply-demand matching degree, and judge whether the first new energy supply-demand matching degree meets a preset supply-demand matching degree;
the energy storage scheduling instruction generating module 15 is configured to generate a new energy storage scheduling instruction if the first new energy supply and demand matching degree does not meet the preset supply and demand matching degree;
the space activation module 16 is configured to activate a new energy storage configuration space based on the new energy storage scheduling instruction, where the new energy storage configuration space includes Q new energy storage configuration particles, and Q is a positive integer greater than 1;
the energy storage configuration optimizing module 17 is configured to perform iterative optimization on the Q new energy storage configuration particles according to an energy storage configuration fitness function based on the first energy storage time limit identifier, obtain a new energy configuration optimizing scheme of the power grid, and execute new energy storage configuration optimization of the first distributed new energy distribution network based on the new energy configuration optimizing scheme of the power grid.
Further, the system further comprises:
the first retrieval constraint obtaining module is used for obtaining first retrieval constraints according to the N distributed new energy topological nodes;
the second retrieval constraint obtaining module is used for obtaining a second retrieval constraint according to the first new energy storage requirement;
the energy storage configuration record acquisition module is used for acquiring new energy storage configuration records according to the first retrieval constraint and the second retrieval constraint to obtain a new energy storage configuration record library, wherein the new energy storage configuration record library comprises a plurality of new energy storage configuration records;
the confidence analysis module is used for traversing the plurality of new energy storage configuration records to perform confidence analysis and obtain a plurality of energy storage configuration-confidence coefficients;
the first execution module is used for arranging the plurality of new energy storage configuration records in a descending order based on the plurality of energy storage configuration-confidence degrees, setting the first Q new energy storage configuration records into Q new energy storage configuration particles, and adding the Q new energy storage configuration particles into the new energy storage configuration space.
Further, the system further comprises:
the first energy storage fitness determination module is used for respectively carrying out energy storage fitness analysis on the Q new energy storage configuration particles according to the energy storage configuration fitness function based on the first energy storage time limit identifier to obtain Q first energy storage fitness;
the first optimal energy storage configuration particle determining module is used for obtaining first optimal energy storage configuration particles corresponding to the first optimal energy storage fitness and the first optimal energy storage fitness based on the Q first energy storage fitness and the Q new energy storage configuration particles;
the energy storage fitness judging module is used for judging whether the first optimal energy storage fitness meets preset energy storage fitness or not;
and the second execution module is used for outputting the first optimal energy storage configuration particles to be the new energy configuration optimization scheme of the power grid if the first optimal energy storage fitness meets the preset energy storage fitness.
Further, the system further comprises:
the third execution module is used for obtaining an ith new energy storage configuration particle based on the Q new energy storage configuration particles, wherein i is a positive integer, and i is more than or equal to 1 and less than or equal to Q;
The time limit weather forecast obtaining module is used for obtaining a first time limit weather forecast based on the first energy storage time limit identifier;
the fourth execution module is used for connecting a digital twin platform, modeling the N distributed new energy sources and obtaining an energy twin model;
the simulated energy storage operation module is used for carrying out simulated energy storage operation on the ith new energy storage configuration particle according to the energy twin model and the first time limit weather forecast based on the digital twin platform to obtain an ith simulated energy storage operation result;
the multi-dimensional index analysis module is used for carrying out multi-dimensional index analysis based on the ith simulated energy storage operation result to obtain an ith simulated energy storage analysis result, wherein the ith simulated energy storage analysis result comprises a simulated energy storage loss coefficient, a simulated energy storage efficiency, a simulated energy storage fluctuation coefficient and a simulated energy storage voltage deviation coefficient;
and the fifth execution module is used for inputting the i-th simulated energy storage analysis result into the energy storage configuration fitness function to obtain the i-th simulated energy storage fitness, and adding the i-th simulated energy storage fitness to the Q first energy storage fitness.
Wherein the energy storage configuration fitness function isWherein (1)>Characterizing the i-th analog energy storage fitness, +.>Representing the fusion coefficient of the simulated energy storage result, < >>Characterization of simulated energy storage efficiency, < >>Respectively representing the simulated energy storage loss coefficient, the simulated energy storage fluctuation coefficient and the simulated energy storage voltage deviation coefficient,respectively representing a simulated energy storage efficiency weight value, a simulated energy storage loss coefficient weight value, a simulated energy storage fluctuation coefficient weight value and a simulated energy storage voltage deviation coefficient weight value, and +.>. Further, the system further comprises:
the index analysis record set acquisition module is used for acquiring the simulated energy storage index analysis record set based on big data;
the building module is used for building a simulated energy storage analysis model based on the simulated energy storage index analysis record set, wherein the simulated energy storage analysis model comprises multi-dimensional indexes, and the multi-dimensional indexes comprise a simulated energy storage loss index, a simulated energy storage efficiency index, a simulated energy storage fluctuation index and a simulated energy storage voltage deviation index;
and the sixth execution module is used for carrying out multi-dimensional index analysis on the ith simulated energy storage operation result based on the simulated energy storage analysis model to generate the ith simulated energy storage analysis result.
Further, the system further comprises:
a seventh execution module, configured to construct a first new energy storage configuration neighborhood space based on the first optimal energy storage configuration particle if the first optimal energy storage fitness does not meet the preset energy storage fitness, where the first new energy storage configuration neighborhood space includes a plurality of new energy storage configuration first neighborhood particles;
the first neighborhood energy storage fitness determination module is used for respectively carrying out energy storage fitness analysis on the plurality of new energy storage configuration first neighborhood particles according to the energy storage configuration fitness function based on the first energy storage time limit identification to obtain a plurality of first neighborhood energy storage fitness;
the optimal first neighborhood particle determining module is used for configuring first neighborhood particles based on the plurality of first neighborhood energy storage fitness and the plurality of new energy storage fitness to obtain first neighborhood optimal energy storage fitness and optimal first neighborhood particles corresponding to the first neighborhood optimal energy storage fitness;
the eighth execution module is used for judging whether the first neighborhood optimal energy storage fitness meets the preset energy storage fitness or not;
A ninth execution module, configured to output the optimal first neighborhood particles as the new energy configuration optimization scheme of the power grid if the optimal energy storage fitness of the first neighborhood meets the preset energy storage fitness;
and the particle iterative optimization module is used for carrying out iterative optimization based on the optimal first neighborhood particles until the new energy configuration optimization scheme of the power grid is obtained if the optimal energy storage fitness of the first neighborhood does not meet the preset energy storage fitness.
The distributed new energy power distribution network configuration optimization system provided by the embodiment of the application can execute the distributed new energy power distribution network configuration optimization method provided by any embodiment of the application, and has the corresponding functional modules and beneficial effects of the execution method.
All the included modules are only divided according to the functional logic, but are not limited to the above-mentioned division, so long as the corresponding functions can be realized; in addition, the specific names of the functional modules are only for distinguishing from each other, and are not used for limiting the protection scope of the present application.
The application provides a distributed new energy power distribution network configuration optimization method, wherein the method is applied to a distributed new energy power distribution network configuration optimization system, and the method comprises the following steps: marking N distributed new energy sources through an energy residual capacity acquisition result to generate a distributed new energy topological network; the method comprises the steps of obtaining a first new energy supply and demand matching degree by carrying out supply and demand matching degree identification on a distributed new energy topological network and a first new energy storage requirement, and judging whether the first new energy supply and demand matching degree meets a preset supply and demand matching degree or not; if the first new energy supply and demand matching degree does not meet the preset supply and demand matching degree, generating a new energy storage scheduling instruction, and activating a new energy storage configuration space according to the new energy storage scheduling instruction; and performing iterative optimization on the activated new energy storage configuration space according to the energy storage configuration fitness function based on the first energy storage time limit identifier to obtain a new energy configuration optimization scheme of the power grid, and executing new energy storage configuration optimization of the first distributed new energy distribution network based on the new energy configuration optimization scheme of the power grid. The novel energy storage configuration method and the novel energy storage configuration device solve the technical problems that in the prior art, the novel energy storage configuration for the distributed novel energy distribution network is low in adaptability and insufficient in accuracy, and the novel energy storage effect of the distributed novel energy distribution network is poor. The novel energy storage control method and the novel energy storage control system achieve the technical effects of achieving targeted energy storage control of the distributed novel energy distribution network according to the novel energy storage requirement, improving the novel energy storage configuration fitness and accuracy of the distributed novel energy distribution network and improving the novel energy storage quality of the distributed novel energy distribution network.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
Claims (8)
1. The distributed new energy power distribution network configuration optimization method is characterized by comprising the following steps of:
n distributed new energy sources of the first distributed new energy source distribution network are obtained, wherein N is a positive integer greater than 1;
respectively acquiring the real-time residual capacities of the N distributed new energy sources, obtaining an energy residual capacity acquisition result, and identifying the N distributed new energy sources based on the energy residual capacity acquisition result to generate a distributed new energy source topology network, wherein the distributed new energy source topology network comprises N distributed new energy source topology nodes;
Receiving a first new energy storage demand of the first distributed new energy distribution network, wherein the first new energy storage demand has a first energy storage time limit identifier;
performing supply and demand matching degree identification based on the distributed new energy topological network and the first new energy storage demand, obtaining a first new energy supply and demand matching degree, and judging whether the first new energy supply and demand matching degree meets a preset supply and demand matching degree or not;
if the first new energy supply and demand matching degree does not meet the preset supply and demand matching degree, generating a new energy storage scheduling instruction;
activating a new energy storage configuration space based on the new energy storage scheduling instruction, wherein the new energy storage configuration space comprises Q new energy storage configuration particles, and Q is a positive integer greater than 1;
and performing iterative optimization on the Q new energy storage configuration particles according to an energy storage configuration fitness function based on the first energy storage time limit identifier to obtain a new energy configuration optimization scheme of a power grid, and executing new energy storage configuration optimization of the first distributed new energy distribution network based on the new energy configuration optimization scheme of the power grid.
2. The method of claim 1, wherein the method comprises:
Obtaining a first retrieval constraint according to the N distributed new energy topological nodes;
obtaining a second retrieval constraint according to the first new energy storage requirement;
collecting new energy storage configuration records according to the first retrieval constraint and the second retrieval constraint to obtain a new energy storage configuration record library, wherein the new energy storage configuration record library comprises a plurality of new energy storage configuration records;
traversing the plurality of new energy storage configuration records to perform confidence analysis to obtain a plurality of energy storage configuration-confidence coefficients;
and arranging the plurality of new energy storage configuration records in a descending order based on the plurality of energy storage configuration-confidence degrees, setting the first Q new energy storage configuration records as Q new energy storage configuration particles, and adding the Q new energy storage configuration particles into the new energy storage configuration space.
3. The method of claim 1, wherein iteratively optimizing the Q new energy storage configuration particles according to a storage configuration fitness function based on the first storage time limit identifier to obtain a grid new energy configuration optimization scheme, comprising:
based on the first energy storage time limit identifier, respectively carrying out energy storage fitness analysis on the Q new energy storage configuration particles according to the energy storage configuration fitness function to obtain Q first energy storage fitness;
Acquiring first optimal energy storage configuration particles corresponding to the first optimal energy storage fitness and the first optimal energy storage fitness based on the Q first energy storage fitness and the Q new energy storage configuration particles;
judging whether the first optimal energy storage fitness meets a preset energy storage fitness or not;
and if the first optimal energy storage fitness meets the preset energy storage fitness, outputting the first optimal energy storage configuration particles as the new energy configuration optimization scheme of the power grid.
4. The method of claim 3, wherein based on the first energy storage time limit identifier, respectively performing energy storage fitness analysis on the Q new energy storage configuration particles according to the energy storage configuration fitness function to obtain Q first energy storage fitness values, including:
obtaining an ith new energy storage configuration particle based on the Q new energy storage configuration particles, wherein i is a positive integer, and i is more than or equal to 1 and less than or equal to Q;
acquiring a first time limit weather forecast based on the first energy storage time limit identifier;
connecting a digital twin platform, and modeling the N distributed new energy sources to obtain an energy twin model;
based on the digital twin platform, performing simulated energy storage operation on the ith new energy storage configuration particle according to the energy twin model and the first time limit weather forecast, and obtaining an ith simulated energy storage operation result;
Performing multidimensional index analysis based on the ith simulated energy storage operation result to obtain an ith simulated energy storage analysis result, wherein the ith simulated energy storage analysis result comprises a simulated energy storage loss coefficient, a simulated energy storage efficiency, a simulated energy storage fluctuation coefficient and a simulated energy storage voltage deviation coefficient;
inputting the ith simulated energy storage analysis result into the energy storage configuration fitness function to obtain an ith simulated energy storage fitness, and adding the ith simulated energy storage fitness to the Q first energy storage fitness.
5. The method of claim 4, wherein the stored energy configuration fitness function isWherein (1)>Characterizing the i-th analog energy storage fitness, +.>Representing the fusion coefficient of the simulated energy storage result, < >>Characterization of simulated energy storage efficiency, < >>Respectively representing a simulated energy storage loss coefficient, a simulated energy storage fluctuation coefficient and a simulated energy storage voltage deviation coefficient, +.>Respectively representing an analog energy storage efficiency weight value, an analog energy storage loss coefficient weight value, an analog energy storage fluctuation coefficient weight value and an analog energy storage voltage deviation coefficient weight value,。
6. the method of claim 4, wherein performing a multi-dimensional index analysis based on the ith simulated energy storage operation result comprises:
Based on big data, acquiring an analog energy storage index analysis record set;
building a simulated energy storage analysis model based on the simulated energy storage index analysis record set, wherein the simulated energy storage analysis model comprises multi-dimensional indexes including a simulated energy storage loss index, a simulated energy storage efficiency index, a simulated energy storage fluctuation index and a simulated energy storage voltage deviation index;
and carrying out multidimensional index analysis on the ith simulated energy storage operation result based on the simulated energy storage analysis model to generate the ith simulated energy storage analysis result.
7. The method of claim 3, wherein determining whether the first optimal stored energy fitness meets a preset stored energy fitness comprises:
if the first optimal energy storage fitness does not meet the preset energy storage fitness, constructing a first new energy storage configuration neighborhood space based on the first optimal energy storage configuration particles, wherein the first new energy storage configuration neighborhood space comprises a plurality of new energy storage configuration first neighborhood particles;
based on the first energy storage time limit identifier, respectively carrying out energy storage fitness analysis on the first neighborhood particles of the plurality of new energy storage configuration according to the energy storage configuration fitness function to obtain a plurality of first neighborhood energy storage fitness;
Configuring first neighborhood particles based on the plurality of first neighborhood energy storage fitness and the plurality of new energy storage fitness to obtain first neighborhood optimal energy storage fitness and optimal first neighborhood particles corresponding to the first neighborhood optimal energy storage fitness;
judging whether the first neighborhood optimal energy storage fitness meets the preset energy storage fitness or not;
outputting the optimal first neighborhood particles as the new energy configuration optimization scheme of the power grid if the optimal energy storage fitness of the first neighborhood meets the preset energy storage fitness;
and if the optimal energy storage fitness of the first neighborhood does not meet the preset energy storage fitness, performing iterative optimization based on the optimal first neighborhood particles until the new energy configuration optimization scheme of the power grid is obtained.
8. A distributed new energy distribution network configuration optimization system for performing the method of any one of claims 1 to 7, the system comprising:
the system comprises a power grid new energy obtaining module, a first power grid new energy distribution network and a second power grid new energy distribution network, wherein the power grid new energy obtaining module is used for obtaining N distributed new energy sources of the first distributed new energy distribution network, and N is a positive integer greater than 1;
the topology network generation module is used for respectively acquiring the real-time residual capacities of the N distributed new energy sources, obtaining an energy residual capacity acquisition result, and identifying the N distributed new energy sources based on the energy residual capacity acquisition result to generate a distributed new energy source topology network, wherein the distributed new energy source topology network comprises N distributed new energy source topology nodes;
The energy storage demand receiving module is used for receiving a first new energy storage demand of the first distributed new energy distribution network, wherein the first new energy storage demand has a first energy storage time limit identifier;
the supply and demand matching degree judging module is used for carrying out supply and demand matching degree identification based on the distributed new energy topological network and the first new energy storage requirement, obtaining first new energy supply and demand matching degree and judging whether the first new energy supply and demand matching degree meets preset supply and demand matching degree or not;
the energy storage scheduling instruction generation module is used for generating a new energy storage scheduling instruction if the first new energy supply and demand matching degree does not meet the preset supply and demand matching degree;
the space activation module is used for activating a new energy storage configuration space based on the new energy storage scheduling instruction, wherein the new energy storage configuration space comprises Q new energy storage configuration particles, and Q is a positive integer greater than 1;
the energy storage configuration optimizing module is used for carrying out iterative optimization on the Q new energy storage configuration particles according to an energy storage configuration fitness function based on the first energy storage time limit identification to obtain a new energy configuration optimizing scheme of the power grid, and executing new energy storage configuration optimization of the first distributed new energy distribution network based on the new energy configuration optimizing scheme of the power grid.
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