Distributed energy storage configuration method and system containing distributed photovoltaic power distribution network

Abstract

The invention belongs to the field of distributed energy storage, and particularly relates to a distributed energy storage configuration method and system containing a distributed photovoltaic power distribution network. The method comprises the steps of obtaining photovoltaic output data and performing noise reduction processing, clustering the data after noise reduction to obtain each typical scene of photovoltaic output, constructing a distributed energy storage configuration double-layer model, determining constraint conditions of the model, performing solving in each typical scene to obtain a configuration result, and finally determining optimal energy storage configuration. The double-layer model comprises an upper-layer model and a lower-layer model, optimization objectives of the upper-layer model comprise minimum annual investment, operation and maintenance total cost, maximum energy storage annual average arbitrage income, minimum average voltage fluctuation and minimum daily average network loss of the power distribution network, and optimization objectives of the lower-layer model comprise minimum operation network loss and minimum abandoned light of the power distribution network. By reasonably configuring the energy storage system, the fluctuation of photovoltaic output can be smoothed, the photovoltaic consumption capability is improved, and safe and stable operation of the power distribution network is ensured.

Description

Technical Field

[0001] The present invention belongs to the field of distributed energy storage, and in particular relates to a distributed energy storage configuration method and system including a distributed photovoltaic distribution network. Background Art

[0002] With the increasing attention paid to renewable energy around the world, the access volume of distributed photovoltaic (DPV) as a clean and renewable energy form in the distribution network continues to grow. The growth of distributed photovoltaic has brought about a profound change in the power supply mode of the distribution network, gradually changing from the traditional main grid centralized power supply to a diversified power supply mode where the main grid power supply and DPV power supply coexist. However, the decentralization and output uncertainty of DPV have brought many challenges to the operation of the distribution network. The high penetration rate of photovoltaic access has made the local power balance of the distribution network particularly complex. During periods of sufficient sunlight, excess photovoltaic output may cause local voltage over-limit problems, threatening the safe and stable operation of the distribution network, and also becoming a key factor restricting the absorption of photovoltaics. In order to effectively respond to this challenge, it is particularly important to reasonably configure the energy storage system.

[0003] Energy storage systems can smooth out the volatility of photovoltaic output and achieve the space-time transfer of energy through charging and discharging strategies, thereby improving the photovoltaic absorption capacity while ensuring the safe and stable operation of the distribution network. However, the configuration of energy storage systems involves many complex factors, including the location, capacity, and charging and discharging strategies of energy storage. These factors are interrelated and influence each other, making energy storage configuration a very challenging task. Summary of the invention

[0004] The object of the present invention is to provide a distributed energy storage configuration method and system including a distributed photovoltaic distribution network, so as to solve the configuration problem of the energy storage system in the prior art.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides a distributed energy storage configuration method including a distributed photovoltaic distribution network, comprising: Acquire photovoltaic output data, and use an extended Kalman filter algorithm to perform noise reduction processing on the photovoltaic output data to obtain noise-reduced photovoltaic output data; Cluster the denoised photovoltaic output data to obtain typical photovoltaic output scenarios; the number of clusters is determined by the Davidson Paulding index; Construct a two-layer model for distributed energy storage configuration; the two-layer model for distributed energy storage configuration includes an upper model and a lower model; the optimization objectives of the upper model include minimizing the total annual investment and operation and maintenance cost, maximizing the annual average arbitrage income of energy storage, minimizing the average voltage fluctuation, and minimizing the daily average network loss of the distribution network. The decision variables of the upper model include the location and capacity of the distributed energy storage; the optimization objectives of the lower model include minimizing the network loss and abandoned light in the distribution network, and the decision variables of the lower model are the optimal sequential charging and discharging power of each distributed energy storage; Determine the constraints of the two-layer model for distributed energy storage configuration; Based on the constraint conditions, the distributed energy storage configuration double-layer model is solved in each typical scenario to obtain the configuration results corresponding to each typical scenario; The optimal configuration result of distributed energy storage is determined based on the configuration results corresponding to each typical scenario.

[0006] Furthermore, the upper model specifically includes: The objective function of minimizing the total annual investment and maintenance cost is: ; The objective function for maximizing the annual average arbitrage profit of energy storage is: ; Objective function for minimizing average voltage fluctuation: ; The objective function of minimizing the average daily network loss of the distribution network is: ; In the above formula, and They are the annual investment cost and average annual operation and maintenance cost of energy storage; , is the daily arbitrage income, For the revenue from electricity sales, The cost of purchasing electricity, for The opposite of For a typical scenario, Typical scenes of the year The number of days, For a typical scenario A collection of; is the total number of distribution network nodes, For distribution network node i in a typical scenario The average voltage during the next t period, For distribution network node i in a typical scenario The average voltage over the entire period, T Indicates the length of the time series; lb For branch roads, is the total number of branches, For a typical scenario Next t period branch lbline loss.

[0007] Furthermore, the annual investment cost of energy storage and annual operation and maintenance costs The calculation formulas are:

[0008]

[0009] in, is the bank rate; y The estimated useful life of the energy storage; is the total amount of distributed energy storage; They are the unit power cost and unit capacity cost of energy storage respectively; are the rated power and rated capacity of the kth energy storage respectively; For typical scenarios Next k The power of a storage unit in period t; It is the operation and maintenance cost per unit power of energy storage.

[0010] Furthermore, the revenue from electricity sales , Power purchase cost The calculation method is as follows:

[0011]

[0012] in, is the number of distributed energy storage, The time-of-use electricity price for purchasing electricity from the main grid. are the charging power and discharging power of the kth energy storage, respectively. The length of each period.

[0013] Furthermore, the constraints of the upper model include energy storage rated capacity constraints, energy storage rated power constraints and regional energy storage quantity constraints.

[0014] Furthermore, the lower-level model specifically includes: The objective function of the distribution network operation with minimum network loss and abandoned light is: ;

[0015]

[0016] in, For a typical scenario The network loss under For a typical scenario The amount of abandoned light; TIndicates the length of the time series; m、n Represents photovoltaic nodes m and photovoltaic nodes n ; and are the branch current and branch resistance respectively, and They are The maximum active output and actual active output of each PV node.

[0017] Furthermore, the constraints of the lower-level model include upper and lower limits of the main grid exchange power, distribution network flow constraints, node voltage constraints, branch current constraints, energy storage charging and discharging power constraints, energy storage charge state constraints and photovoltaic output constraints, as well as the energy storage location and capacity transmitted by the upper-level model.

[0018] In a second aspect, the present invention provides a distributed energy storage configuration device including a distributed photovoltaic distribution network, comprising: A data preprocessing module is used to obtain photovoltaic output data, and use an extended Kalman filter algorithm to perform noise reduction on the photovoltaic output data to obtain noise-reduced photovoltaic output data; The clustering module is used to cluster the denoised photovoltaic output data to obtain typical scenarios of photovoltaic output; the number of clusters is determined by the Davidson Paulding index; A model building module is used to build a two-layer model for distributed energy storage configuration; the two-layer model for distributed energy storage configuration includes an upper model and a lower model; the optimization objectives of the upper model include minimizing the total annual investment and operation and maintenance cost, maximizing the annual average arbitrage income of energy storage, minimizing the average voltage fluctuation, and minimizing the daily average network loss of the distribution network. The decision variables of the upper model include the location and capacity of the distributed energy storage; the optimization objectives of the lower model include minimizing the network loss and abandoned light in the distribution network, and the decision variables of the lower model are the optimal sequential charging and discharging power of each distributed energy storage; A constraint building module is used to determine the constraints of the distributed energy storage configuration double-layer model; A model solving module, used to solve the distributed energy storage configuration double-layer model in each typical scenario based on constraint conditions to obtain configuration results corresponding to each typical scenario; The result determination module is used to determine the optimal result of distributed energy storage configuration according to the configuration results corresponding to each typical scenario.

[0019] In a third aspect of the present invention, an electronic device is provided, comprising a processor and a memory, wherein the processor is configured to execute a computer program stored in the memory to implement the above-mentioned distributed energy storage configuration method including a distributed photovoltaic distribution network.

[0020] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by a processor, the distributed energy storage configuration method including a distributed photovoltaic distribution network as described above is implemented.

[0021] Compared with the prior art, the beneficial effects of the present invention are as follows: This scheme uses extended Kalman filtering to reduce the noise of photovoltaic output data, and uses clustering algorithm to reduce photovoltaic output scenarios, extract typical scenarios from them, and select the optimal number of clusters according to the DBI index. The obtained energy storage configuration scheme can be widely applied to various situations. A two-layer model for distributed energy storage configuration is established. The upper model takes the minimum annual investment and operation and maintenance total cost, the maximum annual average arbitrage income of energy storage, the minimum average voltage fluctuation and the minimum daily average network loss of the distribution network as the total objective function. The optimization objectives of the lower model include the minimum network loss and abandoned light in the distribution network. Considering the operation of energy storage in typical scenarios, the multi-objective problem is optimized and solved, so that the configured energy storage can efficiently absorb photovoltaics and flexibly and friendly grid connection, and improve the reliability and economy of distribution network operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1 This is a flow chart of a distributed energy storage configuration method including a distributed photovoltaic distribution network according to an embodiment of the present invention; Figure 2 This is a flow chart of the NSGA-II algorithm in an embodiment of the present invention; Figure 3 A diagram showing the steps for solving a double-layer model for distributed energy storage configuration in an embodiment of the present invention; Figure 4 This is a structural block diagram of a distributed energy storage configuration device including a distributed photovoltaic distribution network according to an embodiment of the present invention; Figure 5 The present invention is a structural block diagram of an electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0023] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present application can be combined with each other without conflict.

[0024] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present application belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit exemplary embodiments according to the present invention.

[0025] Example 1 like Figure 1 As shown, a distributed energy storage configuration method including a distributed photovoltaic distribution network includes: S1. Acquire photovoltaic output data, and use an extended Kalman filter algorithm to perform noise reduction processing on the photovoltaic output data to obtain noise-reduced photovoltaic output data.

[0026] In one embodiment, photovoltaic output data is collected by sensors. Since data loss and noise interference may occur during the sensor collection process, this solution performs missing data cleaning and time series data noise reduction on the photovoltaic output data of 365 days a year.

[0027] More specifically, missing data cleaning includes: eliminating dates with more missing data within a day, and using interpolation to complete the data for dates with less missing data within a day.

[0028] More specifically, the time series data denoising includes: using the Extended Kalman Filter (EKF) algorithm to model the photovoltaic output as a discrete time-varying system, and performing state prediction and filtering correction of the photovoltaic output data based on the discrete time-varying system to obtain the photovoltaic output data after denoising. The specific steps include: First, the photovoltaic output is modeled as the following discrete time-varying system:

[0029] in, They are Photovoltaic output at all times; is the state transfer matrix; is the time-varying system noise.

[0030] Then make state prediction:

[0031] In the formula, for k +1 photovoltaic output forecast value at time; for k Estimated photovoltaic output at the moment; They are k +1 with kThe error covariance matrix between the predicted and estimated values ​​of the state vector at each moment; is the noise vector covariance matrix, which reflects the degree of change of the system state at adjacent moments and can be selected based on prior information. is the transpose of the state transfer matrix. is the state transfer matrix; is the time-varying system noise; After the state prediction, the predicted value is filtered and corrected:

[0032] in, for k +1 moment photovoltaic output estimate; for k Kalman gain matrix at time +1; for The measured value at the moment; is the Jacobian matrix of the quantity measurement to the state quantity; for k +1 The covariance matrix of the measurement error at time. is the transpose of the Jacobian matrix of the state quantity measured; for k +1 The covariance matrix of the predicted and estimated values ​​of the state vector after the update; is the observation function; I is a matrix whose elements are all 1.

[0033] S2. Clustering the denoised photovoltaic output data to obtain typical scenarios of photovoltaic output; wherein the number of clusters is determined by the Davidson Paulding index.

[0034] In one embodiment, the Kmeans algorithm is used for clustering. The Kmeans algorithm uses distance as the criterion for dividing clusters, and classifies photovoltaic nodes with similar distances into the same cluster. The number of clusters of the Kmeans algorithm has an important influence on the division result. The present invention uses the Davies-Bouldin Index (DBI) to determine the number of clusters. The formula of DBI is as follows:

[0035] In the formula, is the number of clusters, , Respectively Clusters and The Euclidean distance from each data point in a cluster to its respective cluster center, For the Clusters and The Euclidean distance between the cluster centers of the clusters. In this scheme, the photovoltaic output curve of one day is a data point.

[0036] The smaller the DBI index, the larger the distance between classes and the denser the data within the class, which means the better the clustering effect. The optimal number of clusters can be found by calculating the DBI under the value. After completing the cluster division, the cluster center is used as the typical output curve of the cluster for the following calculations.

[0037] S3. Construct a two-layer model for distributed energy storage configuration; the two-layer model for distributed energy storage configuration includes an upper model and a lower model; the optimization objectives of the upper model include minimizing the total annual investment and operation and maintenance cost, maximizing the annual average arbitrage income of energy storage, minimizing the average voltage fluctuation and minimizing the daily average network loss of the distribution network, and the decision variables of the upper model include the location and capacity of the distributed energy storage; the optimization objectives of the lower model include minimizing the operating network loss and abandoned light of the distribution network, and the decision variables of the lower model are the optimal sequential charging and discharging power of each distributed energy storage.

[0038] In one embodiment, the upper model is a multi-objective optimization problem, and each objective function is: (1) Objective function for minimizing total annual investment and maintenance cost: ; and They are the annual investment cost and average annual operation and maintenance cost of energy storage; More specifically, the annual investment cost of energy storage and annual operation and maintenance costs The calculation formulas are:

[0039]

[0040] in, is the bank rate; y The estimated useful life of the energy storage; is the total amount of distributed energy storage, T Indicates the length of the time series; They are the unit power cost and unit capacity cost of energy storage respectively; are the rated power and rated capacity of the kth energy storage respectively; For typical scenarios Next k The power of a storage unit in period t; It is the operation and maintenance cost per unit power of energy storage.

[0041] (2) Objective function for maximizing the average annual arbitrage profit of energy storage: ; It is the daily arbitrage income; More specifically, the daily operating arbitrage profit of energy storage is calculated as follows:

[0042] in, For the revenue from electricity sales, The cost of electricity purchase; the income from electricity sales , Power purchase cost The calculation method is as follows:

[0043]

[0044] in, is the number of distributed energy storage, The time-of-use electricity price for purchasing electricity from the main grid. are the charging power and discharging power of the kth energy storage, respectively. The length of each period.

[0045] It should be noted that for The opposite number of; the average annual arbitrage income of energy storage is a very large indicator, so this scheme uses its opposite number in the objective function .

[0046] (3) Objective function for minimizing average voltage fluctuation: ; in, For a typical scenario, Typical scenes of the year The number of days, For a typical scenario A collection of; is the total number of distribution network nodes, For distribution network node i in a typical scenario The average voltage during the next t period, For distribution network node i in a typical scenario The average voltage over the entire period.

[0047] (4) Objective function for minimizing the average daily network loss of the distribution network: ; in, For branch roads, is the total number of branches, For a typical scenario Next t period branch line loss.

[0048] In one embodiment, the lower model is used to simulate the daily operation of energy storage after it is put into operation, and to verify the effect of energy storage in alleviating voltage over-limit and promoting photovoltaic consumption. The lower layer accepts the energy storage location and capacity transmitted by the upper layer as part of the constraint conditions, takes the distribution network operation loss and the minimum abandoned light as the goal, uses the second-order cone relaxation method to solve the optimal time sequence charging and discharging power of each energy storage device, and returns the optimization solution to the upper layer for the calculation of the upper layer goal.

[0049] The lower model is a single-objective optimization problem, which includes: The objective function of the distribution network operation with minimum network loss and abandoned light is: ;

[0050]

[0051] in, For a typical scenario The network loss under For a typical scenario The amount of abandoned light; T Indicates the length of the time series; m、n Represents photovoltaic nodes m and photovoltaic nodes n ; and are the branch current and branch resistance respectively, and They are The maximum active output and actual active output of each PV node.

[0052] S4. Determine the constraints of the two-layer model for distributed energy storage configuration.

[0053] In one embodiment, the constraints of the upper model include energy storage rated capacity constraints, energy storage rated power constraints, and regional energy storage quantity constraints.

[0054] More specifically, the constraints of the upper model include: (1) Energy storage rated capacity constraints:

[0055] in, is the rated capacity of the energy storage, are the minimum and maximum rated capacities of the energy storage, respectively.

[0056] (2) Rated power constraints Assume that the rated capacity and rated power of each configured energy storage satisfy a uniform proportional relationship:

[0057] in, is the rated power, is the proportionality coefficient, take .

[0058] (3) Regional energy storage quantity constraints

[0059] in, is the number of distribution network nodes in region g, is a 0-1 variable indicating whether energy storage is built at the ith distribution network node in region g. is the expected total amount of energy storage to be invested in region g.

[0060] In one embodiment, the constraints of the lower-level model include upper and lower limit constraints on the main grid exchange power, distribution network flow constraints, node voltage constraints, branch current constraints, energy storage charging and discharging power constraints, energy storage charge state constraints and photovoltaic output constraints, as well as the energy storage location and capacity transmitted by the upper-level model.

[0061] More specifically, the constraints of the lower-level model include: (1) Upper and lower limits of main network switching power:

[0062]

[0063] in, Typical scenarios Down The active and reactive power of the distribution network and the main grid tie line at all times, are the minimum and maximum values ​​of the exchanged active power between the distribution network and the main network, They are the minimum and maximum values ​​of the exchanged reactive power between the distribution network and the main network respectively.

[0064] (2) Distribution network flow constraints The power factor of photovoltaic and energy storage is approximately 1, and the constraints are as follows:

[0065] in, Typical scenarios Down The node voltage amplitude and branch current amplitude at the moment, Typical scenarios Down The square value of the node voltage and branch current amplitude at the moment; Branch The resistance and reactance, Typical scenarios Down Time Branch From the distribution network node Flow to distribution network nodes Active power and reactive power; , Typical scenarios Down Distribution network node at all times The active and reactive power injected from the main grid; It is a typical scene Down Distribution network node at all times The active power generated by photovoltaic power plant; Typical scenarios Down Distribution network node at all times Active load and reactive load; Typical scenarios Down Distribution network node at all times The discharge power and charging power of the energy storage at the node. For the distribution network node without energy storage installed, these two items are 0. It is a typical scene s Down t Distribution network node at all times n The square value of the voltage amplitude; It is a branch road mn The square value of the upper current amplitude, It is a distribution network node m The square of the voltage amplitude.

[0066] (3) Node voltage constraints

[0067] in, are the upper and lower limits of the node voltage respectively.

[0068] (4) Branch current constraints

[0069] in, The maximum current allowed to pass through the branch.

[0070] (5) Energy storage charging and discharging power constraints

[0071] in, It represents a typical scenario Down Distribution network node at all times A 0-1 variable indicating the energy storage charge and discharge state, Indicates charging. Indicates discharge; It is a distribution network node The rated power of the energy storage device.

[0072] (6) Energy storage state of charge constraints

[0073] in, Typical scenarios Down Moment and Distribution network node at all times The state of charge for energy storage; Represents the distribution network nodes The charging and discharging efficiency of energy storage; is the length of a period; are the upper and lower limits of the energy storage charge state at the distribution network node m.

[0074] (7) Photovoltaic output constraints

[0075] in, It is a typical scene Down The maximum photovoltaic output value at that moment.

[0076] S5. Based on the constraint conditions, the distributed energy storage configuration double-layer model is solved in each typical scenario to obtain the configuration results corresponding to each typical scenario.

[0077] In one embodiment, if Figure 3 As shown in the figure, the solution steps of the distributed energy storage configuration double-layer model include: According to the objective functions and corresponding constraints of the upper model, the NSGA-II algorithm is used to solve the energy storage location and capacity, which are passed to the lower model as parameters; the lower model accepts the parameters passed by the upper model, takes the parameters as part of the constraints, and solves the lower model to obtain the optimal timing charging and discharging power of each distributed energy storage. Then, the optimal timing charging and discharging power and the flow number of each distributed energy storage are returned to the upper layer, and the upper model is solved by NSGA-II; the iterative calculation is performed in this way. After reaching the preset number of iterations, the solution of the distributed energy storage configuration double-layer model is completed.

[0078] In one embodiment, the upper model is a mixed integer nonlinear multi-objective optimization problem, and the present invention uses the NSGA-II algorithm to solve the upper model.

[0079] like Figure 2As shown in the figure, the NSGA-II process is as follows: first, an initial population is generated, in which each individual represents a storage configuration scheme (position and capacity), the number of iterations is set to 1, and then the individuals are prioritized according to rank and congestion, and then the binary tournament method is used to select parent individuals according to the priority, and the position code and capacity code are crossovered and mutated to generate offspring, and then the parents and offspring are merged and reordered, and iterate until the maximum number of iterations is reached.

[0080] Since the decision variables of the upper model are the location and capacity of the energy storage, and there are both integer variables and non-integer variables, a hybrid encoding method is used to encode the chromosomes. The specific method is as follows: Before the chromosome n -1 bit ( n is the total number of distribution network nodes) is a 0-1 variable, which is used to indicate whether each distribution network node except node 0 in the distribution network is equipped with energy storage. If energy storage is installed, the corresponding position variable is 1, otherwise it is 0. The bit is a positive real number variable, which represents the rated capacity of each energy storage. In the algorithm, it is necessary to perform cross-mutation operations on the position code and capacity code respectively. The specific steps are as follows: (1) Use the binary tournament method to select two individuals from the population as parents. The binary tournament method is an operation for selecting individuals in a genetic algorithm. This method selects two individuals from the population each time to compete and selects the dominant individual as the parent of the next generation.

[0081] (2) For the two selected parents The position vector and capacity vector are respectively expressed as the crossover probability The position crossover method is as follows: randomly select region g and exchange the energy storage position codes in the region of the two parents. The energy storage position in The energy storage position in , then after the cross The energy storage position in , The energy storage position in The capacity crossover adopts the arithmetic crossover method, as follows: Assume that the capacity solution vectors of the two parents are:

[0082] in, Parents The capacity solution vector of ; Parents The capacity solution vector m A quantity.

[0083] generate mRandom numbers , and generate the descendant vector as follows:

[0084] in, They are The descendant vector of ; are the descendant vectors No. m A quantity.

[0085]

[0086] in, are the descendant vectors No. i Quantity; Parents The capacity solution vector i A quantity.

[0087] This crossover method essentially obtains offspring by modifying certain components of the parents, which can obtain many possible results, fully realize the information exchange between the two parent individuals, and is conducive to searching for global extreme values.

[0088] (3) The offspring individuals obtained by crossover The mutation probability The position vector and capacity vector are mutated. The position vector is mutated by randomly selecting a certain area, removing its original energy storage, and regenerating the energy storage position. The capacity is mutated by randomly selecting the energy storage in an area and adding a random deviation within the allowed capacity range.

[0089] An adaptive crossover probability is used in the iteration, and the crossover probability formula is as follows:

[0090] in, is the crossover probability; are the maximum and minimum values ​​of the crossover probability respectively; is the number of iterations, The preset maximum number of iterations.

[0091] Since it deals with multi-objective optimization problems, NSGA-II cannot judge the quality of individuals by a simple single objective value. It uses a combination of fast non-dominated sorting and crowding sorting to sort the quality of individuals. After fast non-dominated sorting according to rank values, the individuals in each rank are arranged in order from small to large crowding to complete the priority sorting of all individuals. The higher the priority of the individual, the easier it is to be selected and retained.

[0092] S6. Determine the optimal configuration result of distributed energy storage based on the configuration results corresponding to each typical scenario.

[0093] In one embodiment, the configuration results corresponding to each typical scenario are taken together as a Pareto optimal set, and then a suitable configuration scheme can be selected from the Pareto optimal set according to preference or by using methods such as TOPSIS as the optimal result of distributed energy storage configuration.

[0094] In a preferred embodiment, when running the distributed energy storage configuration double-layer model, the regional division of the distribution network and the total capacity and quantity range of the energy storage to be configured in each region can be set as needed. For example, if the region is not considered and the energy storage is directly configured in the entire distribution network, it is only necessary to set the number of regions to 1 and set the upper and lower limits of the number and capacity of the energy storage; if you want to configure energy storage in certain specific areas, or not configure energy storage in certain areas, it is only necessary to input the distribution network node division of each area into the distributed energy storage configuration double-layer model, and set the upper and lower limits of the number and capacity of energy storage in each area, so that the configuration scheme can be generated under the given requirements. After running the distributed energy storage configuration double-layer model, the distributed energy storage configuration double-layer model will automatically extract several typical scenarios from the photovoltaic output curve of one year, optimize the configuration of the energy storage, and make the operation of the energy storage in typical scenarios reach the non-dominated optimum of multiple indicators. The optimal solution set of the energy storage configuration scheme is given, so that the appropriate configuration scheme can be selected from it according to preference or comprehensive evaluation method.

[0095] Example 2 like Figure 4 As shown, based on the same inventive concept as the above embodiment, the present invention also provides a distributed energy storage configuration device including a distributed photovoltaic distribution network, comprising: A data preprocessing module is used to obtain photovoltaic output data, and use an extended Kalman filter algorithm to perform noise reduction on the photovoltaic output data to obtain noise-reduced photovoltaic output data; The clustering module is used to cluster the denoised photovoltaic output data to obtain typical scenarios of photovoltaic output; the number of clusters is determined by the Davidson Paulding index; A model building module is used to build a two-layer model for distributed energy storage configuration; the two-layer model for distributed energy storage configuration includes an upper model and a lower model; the optimization objectives of the upper model include minimizing the total annual investment and operation and maintenance cost, maximizing the annual average arbitrage income of energy storage, minimizing the average voltage fluctuation, and minimizing the daily average network loss of the distribution network. The decision variables of the upper model include the location and capacity of the distributed energy storage; the optimization objectives of the lower model include minimizing the network loss and abandoned light in the distribution network, and the decision variables of the lower model are the optimal sequential charging and discharging power of each distributed energy storage; A constraint building module is used to determine the constraints of the distributed energy storage configuration double-layer model; A model solving module, used to solve the distributed energy storage configuration double-layer model in each typical scenario based on constraint conditions to obtain configuration results corresponding to each typical scenario; The result determination module is used to determine the optimal result of distributed energy storage configuration according to the configuration results corresponding to each typical scenario.

[0096] Example 3 like Figure 5 As shown, the present invention also provides an electronic device 100 for implementing a distributed energy storage configuration method including a distributed photovoltaic distribution network; The electronic device 100 includes a memory 101 , at least one processor 102 , a computer program 103 stored in the memory 101 and executable on the at least one processor 102 , and at least one communication bus 104 .

[0097] The memory 101 can be used to store a computer program 103. The processor 102 implements the steps of a distributed energy storage configuration method for a distributed photovoltaic distribution network in Example 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101.

[0098] The memory 101 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area may store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 may include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.

[0099] At least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.

[0100] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a distributed energy storage configuration method including a distributed photovoltaic distribution network, and the processor 102 can execute the plurality of instructions to implement: Acquire photovoltaic output data, and use an extended Kalman filter algorithm to perform noise reduction processing on the photovoltaic output data to obtain noise-reduced photovoltaic output data; Cluster the denoised photovoltaic output data to obtain typical photovoltaic output scenarios; the number of clusters is determined by the Davidson Paulding index; Construct a two-layer model for distributed energy storage configuration; the two-layer model for distributed energy storage configuration includes an upper model and a lower model; the optimization objectives of the upper model include minimizing the total annual investment and operation and maintenance cost, maximizing the annual average arbitrage income of energy storage, minimizing the average voltage fluctuation, and minimizing the daily average network loss of the distribution network. The decision variables of the upper model include the location and capacity of the distributed energy storage; the optimization objectives of the lower model include minimizing the network loss and abandoned light in the distribution network, and the decision variables of the lower model are the optimal sequential charging and discharging power of each distributed energy storage; Determine the constraints of the two-layer model for distributed energy storage configuration; Based on the constraint conditions, the distributed energy storage configuration double-layer model is solved in each typical scenario to obtain the configuration results corresponding to each typical scenario; The optimal configuration result of distributed energy storage is determined based on the configuration results corresponding to each typical scenario.

[0101] Example 4 If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. Computer-readable media may include: any entity or device capable of carrying computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).

[0102] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0103] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0104] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0105] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0106] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0107] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A distributed energy storage configuration method including a distributed photovoltaic distribution network, characterized in that: include: Acquire photovoltaic output data, and use an extended Kalman filter algorithm to perform noise reduction processing on the photovoltaic output data to obtain noise-reduced photovoltaic output data; Cluster the denoised photovoltaic output data to obtain typical photovoltaic output scenarios; the number of clusters is determined by the Davidson Paulding index; Construct a two-layer model for distributed energy storage configuration; the two-layer model for distributed energy storage configuration includes an upper model and a lower model; the optimization objectives of the upper model include minimizing the total annual investment and operation and maintenance cost, maximizing the annual average arbitrage income of energy storage, minimizing the average voltage fluctuation, and minimizing the daily average network loss of the distribution network. The decision variables of the upper model include the location and capacity of the distributed energy storage; the optimization objectives of the lower model include minimizing the network loss and abandoned light in the distribution network, and the decision variables of the lower model are the optimal sequential charging and discharging power of each distributed energy storage; Determine the constraints of the two-layer model for distributed energy storage configuration; Based on the constraint conditions, the distributed energy storage configuration double-layer model is solved in each typical scenario to obtain the configuration results corresponding to each typical scenario; The optimal configuration result of distributed energy storage is determined based on the configuration results corresponding to each typical scenario.

2. The distributed energy storage configuration method including a distributed photovoltaic distribution network according to claim 1, characterized in that: The upper model specifically includes: The objective function of minimizing the total annual investment and maintenance cost is: ; The objective function for maximizing the annual average arbitrage profit of energy storage is: ; Objective function for minimizing average voltage fluctuation: ; The objective function of minimizing the average daily network loss of the distribution network is: ; in, and They are the annual investment cost and average annual operation and maintenance cost of energy storage; , is the daily arbitrage income, For the revenue from electricity sales, The cost of purchasing electricity, for The opposite of For a typical scenario, Typical scenes of the year The number of days, For a typical scenario A collection of; is the total number of distribution network nodes, For distribution network node i in a typical scenario The average voltage during the next t period, For distribution network node i in a typical scenario The average voltage over the entire period, T Indicates the length of the time series; For branch roads, is the total number of branches, For a typical scenario Next t period branch line loss.

3. The distributed energy storage configuration method including a distributed photovoltaic distribution network according to claim 2, characterized in that: Annual investment cost of energy storage and annual operation and maintenance costs The calculation formulas are: in, is the bank rate; y The estimated useful life of the energy storage; is the total amount of distributed energy storage; They are the unit power cost and unit capacity cost of energy storage respectively; are the rated power and rated capacity of the kth energy storage respectively; For typical scenarios Next k The power of a storage unit in period t; It is the operation and maintenance cost per unit power of energy storage.

4. The distributed energy storage configuration method including a distributed photovoltaic distribution network according to claim 2, characterized in that: Revenue from electricity sales , Power purchase cost The calculation method is as follows: in, is the number of distributed energy storage, The time-of-use electricity price for purchasing electricity from the main grid. are the charging power and discharging power of the kth energy storage, respectively. The length of each period.

5. The distributed energy storage configuration method including a distributed photovoltaic distribution network according to claim 1, characterized in that: The constraints of the upper model include energy storage rated capacity constraints, energy storage rated power constraints and regional energy storage quantity constraints.

6. The distributed energy storage configuration method including a distributed photovoltaic distribution network according to claim 1, characterized in that: The lower-level model specifically includes: The objective function of the distribution network operation with minimum network loss and abandoned light is: ; in, For a typical scenario The network loss under For a typical scenario The amount of abandoned light; T Indicates the length of the time series; m、n Represents photovoltaic nodes m and photovoltaic nodes n ; and are the branch current and branch resistance respectively, and They are The maximum active output and actual active output of each PV node.

7. The distributed energy storage configuration method including a distributed photovoltaic distribution network according to claim 1, characterized in that: The constraints of the lower-level model include the upper and lower limits of the main grid exchange power, the distribution network flow constraints, the node voltage constraints, the branch current constraints, the energy storage charging and discharging power constraints, the energy storage charge state constraints and the photovoltaic output constraints, as well as the energy storage location and capacity transmitted by the upper-level model.

8. A distributed energy storage configuration device including a distributed photovoltaic distribution network, characterized in that: include: A data preprocessing module is used to obtain photovoltaic output data, and use an extended Kalman filter algorithm to perform noise reduction on the photovoltaic output data to obtain noise-reduced photovoltaic output data; The clustering module is used to cluster the denoised photovoltaic output data to obtain typical scenarios of photovoltaic output; the number of clusters is determined by the Davidson Paulding index; A model building module is used to build a two-layer model for distributed energy storage configuration; the two-layer model for distributed energy storage configuration includes an upper model and a lower model; the optimization objectives of the upper model include minimizing the total annual investment and operation and maintenance cost, maximizing the annual average arbitrage income of energy storage, minimizing the average voltage fluctuation, and minimizing the daily average network loss of the distribution network. The decision variables of the upper model include the location and capacity of the distributed energy storage; the optimization objectives of the lower model include minimizing the network loss and abandoned light in the distribution network, and the decision variables of the lower model are the optimal sequential charging and discharging power of each distributed energy storage; A constraint building module is used to determine the constraints of the distributed energy storage configuration double-layer model; A model solving module, used to solve the distributed energy storage configuration double-layer model in each typical scenario based on constraint conditions to obtain configuration results corresponding to each typical scenario; The result determination module is used to determine the optimal result of distributed energy storage configuration according to the configuration results corresponding to each typical scenario.

9. An electronic device, characterized in that: It comprises a processor and a memory, wherein the processor is used to execute a computer program stored in the memory to implement a distributed energy storage configuration method for a distributed photovoltaic distribution network as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, and when the at least one instruction is executed by the processor, the distributed energy storage configuration method including a distributed photovoltaic distribution network as described in any one of claims 1 to 7 is implemented.

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