A power distribution scheduling method and system based on distributed power supply and energy storage configuration

Abstract

The application provides a power distribution scheduling method and system based on distributed power supply and energy storage configuration, the method comprising determining wind power output, photovoltaic output and load power under different scenarios; constructing a target model, the target model comprising an upper model and a lower model; solving the upper model by using a preset solving algorithm to obtain an upper result, solving the lower model by using the preset solving algorithm to obtain a lower result; correcting the model parameters of the upper model by using the lower result and repeatedly solving, and outputting a power distribution scheduling result. The application can meet the diversity and uncertainty of data, improve the accuracy of subsequent model optimization, effectively reduce power distribution network loss, improve voltage amplitude reduction deviation, and reduce comprehensive cost. The application can reasonably configure DG and ESS capacity, position and quantity, effectively improve system network loss and voltage deviation, and improve the economy and power supply safety of the power distribution network.

Description

Technical Field

[0001] This invention belongs to the technical field of power distribution dispatching, and specifically relates to a power distribution dispatching method and system based on distributed power sources and energy storage configuration. Background Technology

[0002] The deep integration of high-proportion renewable energy sources and energy storage with distribution networks will give rise to the basic characteristics and development patterns of new power systems. The continuous infiltration of massive amounts of distributed renewable energy sources and energy storage into the power system is changing the topology and power flow characteristics of the distribution network, posing new challenges to the operation planning of the distribution system.

[0003] The grid integration of distributed renewable energy sources, such as wind and solar power (DG) and energy storage systems (ESS), has altered traditional distribution network operation planning. Inappropriate DG and ESS configuration planning can directly lead to increased power system losses, decreased distribution network security and stability, and increased economic costs. Furthermore, the strong correlation between DG output and the natural environment exacerbates the uncontrollable factors in distribution system operation, making power system planning and operation decisions extremely complex. In studying DG and energy storage planning, given the high penetration rate, decentralization, and complex uncertainties on both the source and load sides of distributed generation, in order to improve the absorption level of DG in the distribution network and meet the demand response of different load sides, in-depth research on distributed energy dispatch theory and methods is needed.

[0004] Existing distribution scheduling methods for distributed power sources and energy storage mainly focus on the operating status of the distribution network, neglecting the correlation characteristics between clusters and the economic impact of energy storage timing output. This affects the accuracy of the optimization model establishment and solution results, and consequently the final distribution scheduling process. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a power distribution scheduling method and system based on distributed power sources and energy storage configuration, which solves the technical problems in the prior art.

[0006] In a first aspect, the present invention provides the following technical solution: a power distribution dispatching method based on distributed power sources and energy storage configuration, comprising: Obtain wind power data, photovoltaic data, and load data for the area to be planned, and determine the wind power output power, photovoltaic output power, and load power under different scenarios based on the wind power data, the photovoltaic data, and the load data. A target model is constructed based on the wind power output, photovoltaic power output, and load power under different scenarios, wherein the target model includes an upper-level model and a lower-level model; The upper-level model is solved using a preset solution algorithm to obtain the upper-level result. The upper-level result is then transmitted to the lower-level model, and the lower-level model is solved using the preset solution algorithm to obtain the lower-level result. The model parameters of the upper-level model are corrected by the lower-level results, and the process of solving the upper-level model and the lower-level model is repeated iteratively until the iteration stopping condition is met, and the power distribution scheduling result is output.

[0007] Compared with existing technologies, the beneficial effects of this invention are as follows: First, this invention acquires wind power data, photovoltaic data, and load data for the area to be planned. Based on these data, it determines the wind power output, photovoltaic output power, and load power under different scenarios. Then, it constructs a target model based on the wind power output, photovoltaic output power, and load power under different scenarios. This target model includes an upper-level model and a lower-level model. Next, a preset solution algorithm is used to solve the upper-level model to obtain the upper-level result. The upper-level result is then transferred to the lower-level model, and the preset solution algorithm is used to solve the lower-level model to obtain the lower-level result. Finally, the model parameters of the upper-level model are corrected using the lower-level result, and the process of solving the upper-level and lower-level models is repeated iteratively. Until the iteration stopping condition is met, the power distribution scheduling result is output. This invention processes multiple existing data samples through probability distribution sampling and scenario sorting to obtain a large amount of scenario data. Finally, a clustering method is used to reduce source and load scenarios, satisfying the diversity and uncertainty of the data and improving the accuracy of subsequent model optimization. Then, this invention constructs a two-layer model with the overall cost and integrity of the power distribution network as objectives. It has better capabilities in optimizing configuration problems, can effectively reduce power distribution network losses, increase voltage amplitude and reduce deviation, and reduce overall costs. It can reasonably configure the capacity, location and number of DG and ESS, which can effectively improve system network losses and voltage deviation, improve the economy and power supply security of the power distribution network, shorten the power flow calculation time, and avoid falling into the dimensionality curse of the solution.

[0008] Preferably, the step of determining the wind power output, photovoltaic output power, and load power under different scenarios based on the wind power data, the photovoltaic data, and the load data specifically includes: Obtain the variance and expected value of the wind data, the photovoltaic data, and the load data, and determine the wind probability distribution function, the photovoltaic probability distribution function, and the load probability distribution function based on the variance and expected value, respectively: ; , ; ; , ; ; ; In the formula, These are the wind probability distribution function and the photovoltaic probability distribution function, respectively. These are the probability distribution functions of the load active power and reactive power, respectively. These are the scale parameters and shape parameters of the wind model, respectively. These represent the variance and expected value of the wind data, respectively. For wind speed, For the Gamma function, These are light intensity and maximum light intensity, respectively. These are the location and shape parameters of the beta distribution, respectively. These represent the variance and expected value of the photovoltaic data, respectively. These represent the variance and expected value of the load active power, respectively. These represent the variance and expected value of the reactive power of the load, respectively. These are the active power of the load and the reactive power of the load, respectively. The wind probability distribution function, photovoltaic probability distribution function, and load probability distribution function are uniformly divided into several non-overlapping sub-regions on the coordinate axis. For any sub-region, a random number between 0 and 1 is randomly generated, and the sub-region is sampled according to the random number to obtain wind sampling data, photovoltaic sampling data, and load sampling data respectively. Several variables within the sub-regions corresponding to the wind probability distribution function and the photovoltaic probability distribution function are sampled to obtain the wind sampling matrix and the photovoltaic sampling matrix; The wind sampling matrix and the photovoltaic sampling matrix are respectively decomposed to obtain the wind lower triangular matrix and the photovoltaic lower triangular matrix; The wind force lower triangular matrix is ​​multiplied by the wind force sampling data, and the photovoltaic lower triangular matrix is ​​multiplied by the photovoltaic smoke extraction data to obtain the wind force density value and the photovoltaic density value, respectively. The wind density value and the photovoltaic density value are converted into actual distribution data according to the equal probability transformation to obtain the actual distribution value of wind and the actual distribution value of photovoltaic. Based on the actual wind distribution values, determine the wind output power under different scenarios. : ; In the formula, , , , They are respectively The actual wind force distribution, cut-in wind speed, cut-out wind speed, and rated wind speed at any given time. This refers to the rated power of the wind turbine generator set; Based on the actual photovoltaic distribution value, determine the photovoltaic output power under different scenarios. : ; In the formula, for The actual distribution value of photovoltaic power at time t, Rated power of photovoltaic power; Clustering is performed on the load sampling data, wind power output in different scenarios, and photovoltaic power output in different scenarios to obtain wind power output, photovoltaic power output, and load power in different scenarios.

[0009] Preferably, the upper-level model is: ; ; ; ; ; In the formula, , These include the total daily comprehensive cost of the distribution network, the investment and operation and maintenance cost of distributed power sources, the investment and operation and maintenance cost of energy storage systems, electricity purchase cost, and power generation subsidies. These represent the total number of nodes in distributed power generation and energy storage systems, respectively. These are the current subsidy rate and the planned service life, respectively. The first Investment and maintenance costs of each distributed power node for Time of the first The capacity of each distributed power node These are the investment cost per unit capacity of energy storage and the operation and maintenance cost per unit power of energy storage, respectively. The total number of time periods. For the first The energy storage capacity of each energy storage system node They are respectively Time of the first The charging and discharging power of each energy storage system node. for Time-of-use electricity pricing, where electricity is purchased from the upper-level power grid at all times. , , They are respectively Load power at time, the first The wind power output of the first distributed power node, the first Photovoltaic output power of a distributed power node for Time of the first The energy storage output power of each energy storage system node Subsidies for distributed power sources.

[0010] Preferably, the constraints of the upper-level model are: ; ; In the formula, The first The lower and upper limits of the active power capacity of each distributed power node. The first Lower and upper limits of reactive power capacity for each distributed power node , The first Active and reactive power output of each distributed power node To allow for the maximum distributed power penetration rate, The total active load of the distribution network, The first The lower and upper limits of active power for each energy storage system node. The first The lower and upper limits of reactive power for each energy storage system node. The first The active and reactive power output of each energy storage system node.

[0011] Preferably, the lower-level model is: ; ; ; ; In the formula, The first, second, and third weights are respectively. These are distribution network losses, voltage deviations, and grid vulnerability values. For the first a side road Current at any moment For the first The resistance of the branch circuit, For the number of branch roads, For the first Each distribution network node Voltage amplitude at time 10:00 for The voltage rating at any given time. For the number of nodes in the distribution network, These are the equilibrium degree of grid vulnerability and the average vulnerability of the grid, respectively. This represents the total number of time periods.

[0012] Preferably, the constraints of the lower-level model are: ; ; ; In the formula, The first Each distribution network node Active power and reactive power at any given time The first Each distribution network node Voltage amplitude at time 10:00 The first The first distribution network node, the first Branch admittance of each distribution network node For the first The distribution network node and the first Voltage phase angle difference between distribution network nodes For the first The current flowing through each branch, This is the upper limit of the maximum current flowing through the branch. These are the lower and upper limits of the node voltage amplitude, respectively. For the first Voltage amplitude at each distribution network node, For energy storage The charging and discharging power at any given time For charging and discharging efficiency, The first Time, Number The ESS state of charge at time t, For energy storage capacity, These are the lower and upper limits of the ESS (Electronic Power Saving) quota. These are the lower and upper limits of the ESS state of charge, respectively. For the first The state of charge of each energy storage system node.

[0013] Preferably, the preset solution algorithm is the COFA algorithm.

[0014] Secondly, the present invention provides the following technical solution: a power distribution dispatching system based on distributed power sources and energy storage configuration, the system comprising: The determination module is used to acquire wind power data, photovoltaic data, and load data of the area to be planned, and to determine the wind power output power, photovoltaic output power, and load power under different scenarios based on the wind power data, the photovoltaic data, and the load data. A model is constructed to build a target model based on the wind power output power, the photovoltaic output power, and the load power under different scenarios, wherein the target model includes an upper-level model and a lower-level model; The solving module is used to solve the upper-level model using a preset solving algorithm to obtain the upper-level result, and to transmit the upper-level result to the lower-level model and solve the lower-level model using the preset solving algorithm to obtain the lower-level result. The output module is used to correct the model parameters of the upper-level model based on the lower-level results and repeatedly iterate the solution process of the upper-level model and the lower-level model until the iteration stopping condition is met, and output the power distribution scheduling results.

[0015] Thirdly, the present invention provides the following technical solution: a computer, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, it implements the power distribution scheduling method based on distributed power source and energy storage configuration as described above.

[0016] Fourthly, the present invention provides the following technical solution: a storage medium storing a computer program, wherein when the computer program is executed by a processor, it implements the power distribution scheduling method based on distributed power source and energy storage configuration as described above. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A flowchart of a power distribution scheduling method based on distributed power source and energy storage configuration provided in Embodiment 1 of the present invention; Figure 2 This is a structural block diagram of a power distribution dispatching system based on distributed power sources and energy storage configuration provided in Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the hardware structure of a computer provided for another embodiment of the present invention.

[0019] The embodiments of the present invention will be further described below with reference to the accompanying drawings. Detailed Implementation

[0020] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain embodiments of the present invention, and should not be construed as limiting the present invention.

[0021] Example 1 In Embodiment 1 of the present invention, as Figure 1 As shown, a power distribution dispatching method based on distributed power generation and energy storage configuration includes: S1. Obtain wind power data, photovoltaic data, and load data for the area to be planned, and determine the wind power output power, photovoltaic output power, and load power under different scenarios based on the wind power data, the photovoltaic data, and the load data. Step S1 includes: S11. Obtain the variance and expected value of the wind power data, the photovoltaic data, and the load data; determine the wind power probability distribution function, the photovoltaic probability distribution function, and the load probability distribution function based on the variance and expected value, respectively. ; , ; ; , ; ; ; In the formula, These are the wind probability distribution function and the photovoltaic probability distribution function, respectively. These are the probability distribution functions of the load active power and reactive power, respectively. These are the scale parameters and shape parameters of the wind model, respectively. These represent the variance and expected value of the wind data, respectively. For wind speed, For the Gamma function, These are light intensity and maximum light intensity, respectively. These are the location and shape parameters of the beta distribution, respectively. These represent the variance and expected value of the photovoltaic data, respectively. These represent the variance and expected value of the load active power, respectively. These represent the variance and expected value of the reactive power of the load, respectively. These are the active power of the load and the reactive power of the load, respectively. Specifically, the power generated by the wind turbine will change accordingly with the fluctuation of wind speed, and wind speed is greatly affected by the environment. This application uses the distribution model in the above formula to simulate the real-time wind speed, light intensity probability, and load probability distribution.

[0022] S12. Divide the wind probability distribution function, photovoltaic probability distribution function and load probability distribution function into several non-overlapping sub-regions on the coordinate axis. For any sub-region, generate a random number between 0 and 1, and sample the sub-region according to the random number to obtain wind sampling data, photovoltaic sampling data and load sampling data respectively. Specifically, the sampling process here uses the Latin hypercube sampling method.

[0023] S13. Sample several variables in the sub-regions corresponding to the wind probability distribution function and the photovoltaic probability distribution function to obtain the wind sampling matrix and the photovoltaic sampling matrix. S14. Perform matrix decomposition on the wind sampling matrix and the photovoltaic sampling matrix respectively to obtain the wind lower triangular matrix and the photovoltaic lower triangular matrix; Specifically, the matrix decomposition here refers to the Cholesky decomposition method.

[0024] S15. Multiply the wind force lower triangular matrix with the wind force sampling data, and multiply the photovoltaic lower triangular matrix with the photovoltaic smoke extraction data to obtain the wind force density value and the photovoltaic density value, respectively.

[0025] S16. Convert the wind density value and the photovoltaic density value into actual distribution data according to the equal probability conversion to obtain the actual distribution value of wind and the actual distribution value of photovoltaic.

[0026] S17. Determine the wind output power under different scenarios based on the actual wind distribution value. : ; In the formula, , , , They are respectively The actual wind force distribution, cut-in wind speed, cut-out wind speed, and rated wind speed at any given time. This refers to the rated power of the wind turbine.

[0027] S18. Determine the photovoltaic output power under different scenarios based on the actual photovoltaic distribution value. : ; In the formula, for The actual distribution value of photovoltaic power at time t, This refers to the rated power of the photovoltaic system.

[0028] S19. Cluster the load sampling data, wind power output power under different scenarios, and photovoltaic power output power under different scenarios respectively to obtain wind power output power, photovoltaic power output power and load power under different scenarios. Specifically, the clustering process here is as follows: Select a number of samples as initial cluster centers. Calculate the shortest distance between each sample and the current cluster center. Use the probability calculation method in the K-means++ algorithm to calculate the probability of each sample being selected as the next cluster center. Calculate the next cluster center using the roulette wheel method. Repeat the above steps until several cluster centers are selected. Assign the corresponding samples to the nearest cluster center based on the shortest distance. If the number of samples in a cluster center is less than a preset value, discard the cluster center and reassign its samples to other nearest cluster centers. Then, recalculate the cluster centers, the average distance D1 from within a cluster to its cluster center, and the average distance D2 from all samples to their cluster centers using the K-means++ algorithm. Repeat the above steps iteratively, and determine whether to perform a merge / split process based on the following conditions: 1. If the number of iterations reaches the maximum, proceed with the merging process; 2. If the number of cluster centers is less than half the value of K in the K-means++ algorithm, then proceed with the splitting process; 3. If the current iteration number is even or the number of clusters is greater than twice the value of K, then a merging process is performed; otherwise, a splitting process is performed. The merging process is as follows: calculate the distance between all cluster centers, sort the cluster centers with a distance less than the distance threshold in ascending order, merge the clusters with a distance less than the distance threshold and calculate new cluster centers, and check whether the two clusters have been merged starting from the distance after the second sort. If two clusters have not been merged, the merging process is executed until the number of merged clusters reaches a maximum of W pairs. The splitting process is as follows: Calculate the standard deviation vector of the samples in each category. If the largest element of the standard deviation vector of a certain category is greater than the preset value and satisfies that D1 is greater than D2, then split the above category into two cluster centers U+X and UX, where U is the original cluster center of the above category and X is a random number between 0 and 1. At the same time, it is necessary to ensure that the samples before the split are still in the two new sets. After the above steps, the clustering results are output until the classification results no longer change or the maximum number of iterations is reached.

[0029] S2. Construct a target model based on the wind power output, photovoltaic power output, and load power under different scenarios, wherein the target model includes an upper-level model and a lower-level model. The upper-level model is as follows: ; ; ; ; ; In the formula, , These include the total daily comprehensive cost of the distribution network, the investment and operation and maintenance cost of distributed power sources, the investment and operation and maintenance cost of energy storage systems, electricity purchase cost, and power generation subsidies. These represent the total number of nodes in distributed power generation and energy storage systems, respectively. These are the current subsidy rate and the planned service life, respectively. The first Investment and maintenance costs of each distributed power node for Time of the first The capacity of each distributed power node These are the investment cost per unit capacity of energy storage and the operation and maintenance cost per unit power of energy storage, respectively. The total number of time periods. For the first The energy storage capacity of each energy storage system node They are respectively Time of the first The charging and discharging power of each energy storage system node. for Time-of-use electricity pricing, where electricity is purchased from the upper-level power grid at all times. , , They are respectively Load power at time, the first The wind power output of the first distributed power node, the first Photovoltaic output power of a distributed power node for Time of the first The energy storage output power of each energy storage system node Subsidies for distributed power sources.

[0030] The constraints of the upper-level model are as follows: ; ; In the formula, The first The lower and upper limits of the active power capacity of each distributed power node. The first Lower and upper limits of reactive power capacity for each distributed power node , The first Active and reactive power output of each distributed power node To allow for the maximum distributed power penetration rate, The total active load of the distribution network, The first The lower and upper limits of active power for each energy storage system node. The first The lower and upper limits of reactive power for each energy storage system node. The first The active and reactive power output of each energy storage system node.

[0031] The lower-level model is as follows: ; ; ; ; In the formula, The first, second, and third weights are respectively. These are distribution network losses, voltage deviations, and grid vulnerability values. For the first a side road Current at any moment For the first The resistance of the branch circuit, For the number of branch roads, For the first Each distribution network node Voltage amplitude at time 10:00 for The voltage rating at any given time. For the number of nodes in the distribution network, These are the equilibrium degree of grid vulnerability and the average vulnerability of the grid, respectively. This represents the total number of time periods.

[0032] The constraints of the lower-level model are as follows: ; ; ; In the formula, The first Each distribution network node Active power and reactive power at any given time The first Each distribution network node Voltage amplitude at time 10:00 The first The first distribution network node, the first Branch admittance of each distribution network node For the first The distribution network node and the first Voltage phase angle difference between distribution network nodes For the first The current flowing through each branch, This is the upper limit of the maximum current flowing through the branch. These are the lower and upper limits of the node voltage amplitude, respectively. For the first Voltage amplitude at each distribution network node, For energy storage The charging and discharging power at any given time For charging and discharging efficiency, The first Time, Number The ESS state of charge at time t, For energy storage capacity, These are the lower and upper limits of the ESS (Electronic Power Saving) quota. These are the lower and upper limits of the ESS state of charge, respectively. For the first The state of charge of each energy storage system node.

[0033] S3. The upper-level model is solved using a preset solution algorithm to obtain the upper-level result. The upper-level result is then transmitted to the lower-level model, and the lower-level model is solved using the preset solution algorithm to obtain the lower-level result.

[0034] The preset solution algorithm is specifically the COFA algorithm. The CFOA algorithm, inspired by traditional rural fishing methods, proposes a heuristic optimization algorithm based on human behavior. The algorithm mainly consists of two phases: an exploration phase and a development phase. The exploration phase is further divided into two stages: an individual capture phase based on personal experience and intuition, and a group capture phase based on human proficiency in tool use and collaboration. Individual capture is also an independent search phase, learning from the exploration efforts of other "fishermen" (i.e., search agents) in the global search space, and then relying on group capture to conduct a more focused and in-depth exploration of the local area. In the development phase, all fishermen surround the fish school and collectively harvest the remaining fish—a collective capture strategy. The algorithm utilizes collective capture and three-layer cooperative development to refine and converge the optimal solution.

[0035] S4. The model parameters of the upper-level model are corrected based on the lower-level results, and the process of solving the upper-level model and the lower-level model is repeated iteratively until the iteration stopping condition is met, and the power distribution scheduling result is output. The power distribution dispatch results include the installation locations and specific outputs of the DG and ESS.

[0036] The power distribution scheduling method based on distributed power generation and energy storage configuration provided in Embodiment 1 of this invention first acquires wind power data, photovoltaic data, and load data of the area to be planned. Based on the wind power data, photovoltaic data, and load data, the wind power output power, photovoltaic output power, and load power under different scenarios are determined. Then, a target model is constructed based on the wind power output power, photovoltaic output power, and load power under different scenarios. The target model includes an upper-level model and a lower-level model. Then, a preset solution algorithm is used to solve the upper-level model to obtain the upper-level result. The upper-level result is transmitted to the lower-level model, and the preset solution algorithm is used to solve the lower-level model to obtain the lower-level result. Finally, the model parameters of the upper-level model are corrected based on the lower-level result, and the upper-level model and the lower-level model are iterated repeatedly. The solution process continues until the iteration stopping condition is met, outputting the distribution scheduling result. This invention processes multiple existing data samples through probability distribution sampling and scenario sorting to obtain a large amount of scenario data. Finally, a clustering method is used to reduce source-load scenarios, satisfying the diversity and uncertainty of the data and improving the accuracy of subsequent model optimization. Then, this invention constructs a two-layer model with the overall cost and integrity of the distribution network as objectives. It has better capabilities in optimizing configuration problems, effectively reducing distribution network losses, increasing voltage amplitude and reducing deviation, and reducing overall costs. It can reasonably configure the capacity, location and number of DG and ESS, effectively improving system network losses and voltage deviation, improving the economy and power supply security of the distribution network, shortening the power flow calculation time, and avoiding the curse of dimensionality in the solution.

[0037] Example 2 like Figure 2 As shown, in Embodiment 2 of the present invention, a power distribution dispatching system based on distributed power sources and energy storage configuration is provided. The system includes: Module 1 is used to acquire wind power data, photovoltaic data, and load data of the area to be planned, and to determine the wind power output power, photovoltaic output power, and load power under different scenarios based on the wind power data, the photovoltaic data, and the load data. Model 2 is constructed to build a target model based on the wind power output, photovoltaic power output, and load power under different scenarios. The target model includes an upper-level model and a lower-level model. The solving module 3 is used to solve the upper-level model using a preset solving algorithm to obtain the upper-level result, and to transmit the upper-level result to the lower-level model and solve the lower-level model using a preset solving algorithm to obtain the lower-level result. Output module 4 is used to correct the model parameters of the upper-level model based on the lower-level results and repeatedly iterate the solution process of the upper-level model and the lower-level model until the iteration stopping condition is met, and output the power distribution scheduling results.

[0038] The determining module 1 includes: Obtain the variance and expected value of the wind data, the photovoltaic data, and the load data, and determine the wind probability distribution function, the photovoltaic probability distribution function, and the load probability distribution function based on the variance and expected value, respectively: ; , ; ; , ; ; ; In the formula, These are the wind probability distribution function and the photovoltaic probability distribution function, respectively. These are the probability distribution functions of the load active power and reactive power, respectively. These are the scale parameters and shape parameters of the wind model, respectively. These represent the variance and expected value of the wind data, respectively. For wind speed, For the Gamma function, These are light intensity and maximum light intensity, respectively. These are the location and shape parameters of the beta distribution, respectively. These represent the variance and expected value of the photovoltaic data, respectively. These represent the variance and expected value of the load active power, respectively. These represent the variance and expected value of the reactive power of the load, respectively. These are the active power of the load and the reactive power of the load, respectively. The wind probability distribution function, photovoltaic probability distribution function, and load probability distribution function are uniformly divided into several non-overlapping sub-regions on the coordinate axis. For any sub-region, a random number between 0 and 1 is randomly generated, and the sub-region is sampled according to the random number to obtain wind sampling data, photovoltaic sampling data, and load sampling data respectively. Several variables within the sub-regions corresponding to the wind probability distribution function and the photovoltaic probability distribution function are sampled to obtain the wind sampling matrix and the photovoltaic sampling matrix; The wind sampling matrix and the photovoltaic sampling matrix are respectively decomposed to obtain the wind lower triangular matrix and the photovoltaic lower triangular matrix; The wind force lower triangular matrix is ​​multiplied by the wind force sampling data, and the photovoltaic lower triangular matrix is ​​multiplied by the photovoltaic smoke extraction data to obtain the wind force density value and the photovoltaic density value, respectively. The wind density value and the photovoltaic density value are converted into actual distribution data according to the equal probability transformation to obtain the actual distribution value of wind and the actual distribution value of photovoltaic. Based on the actual wind distribution values, determine the wind output power under different scenarios. : ; In the formula, , , , They are respectively The actual wind force distribution, cut-in wind speed, cut-out wind speed, and rated wind speed at any given time. This refers to the rated power of the wind turbine generator set; Based on the actual photovoltaic distribution value, determine the photovoltaic output power under different scenarios. : ; In the formula, for The actual distribution value of photovoltaic power at time t, Rated power of photovoltaic power; Clustering is performed on the load sampling data, wind power output in different scenarios, and photovoltaic power output in different scenarios to obtain wind power output, photovoltaic power output, and load power in different scenarios.

[0039] In other embodiments of the present invention, the present invention provides the following technical solution: a computer, including a memory 102, a processor 101, and a computer program stored in the memory 102 and executable on the processor 101, wherein the processor 101 executes the computer program to implement the power distribution scheduling method based on distributed power source and energy storage configuration as described above.

[0040] Specifically, the processor 101 may include a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of the present invention.

[0041] The memory 102 may include a large-capacity memory for data or instructions. For example, and not limitingly, the memory 102 may include a hard disk drive (HDD), a floppy disk drive, a solid-state drive (SSD), flash memory, an optical disk drive, a magneto-optical disk drive, magnetic tape, or a Universal Serial Bus (USB) drive, or a combination of two or more of these. Where appropriate, the memory 102 may include removable or non-removable (or fixed) media. Where appropriate, the memory 102 may be internal or external to a data processing device. In a particular embodiment, the memory 102 is non-volatile memory. In a particular embodiment, the memory 102 includes read-only memory (ROM) and random access memory (RAM). Where appropriate, the ROM may be a mask-programmed ROM, a programmable read-only memory (PROM), an erasable read-only memory (EPROM), an electrically erasable read-only memory (EEPROM), an electrically alterable read-only memory (EAROM), or flash memory, or a combination of two or more of these. Where appropriate, the RAM can be Static Random-Access Memory (SRAM) or Dynamic Random-Access Memory (DRAM). DRAM can be Fast Page Mode Dynamic Random Access Memory (FPMDRAM), Extended Data Out Dynamic Random Access Memory (EDODRAM), Synchronous Dynamic Random-Access Memory (SDRAM), etc.

[0042] The memory 102 can be used to store or cache various data files that need to be processed and / or used for communication, as well as possible computer program instructions executed by the processor 101.

[0043] The processor 101 reads and executes the computer program instructions stored in the memory 102 to implement the above-mentioned power distribution scheduling method based on distributed power sources and energy storage configuration.

[0044] In some embodiments, the computer may further include a communication interface 103 and a bus 100. For example, Figure 3 As shown, the processor 101, memory 102, and communication interface 103 are connected through bus 100 and complete communication with each other.

[0045] The communication interface 103 is used to enable communication between the various modules, devices, units, and / or equipment in the embodiments of the present invention. The communication interface 103 can also enable data communication with other components such as external devices, image / data acquisition devices, databases, external storage, and image / data processing workstations.

[0046] Bus 100 includes hardware, software, or both, that couples components of a computer device together. Bus 100 includes, but is not limited to, at least one of the following: data bus, address bus, control bus, expansion bus, and local bus. For example, and not as a limitation, bus 100 may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an InfiniBand interconnect, a Low Pin Count (LPC) bus, a memory bus, a Micro Channel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local Bus (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 100 may include one or more buses. Although specific buses are described and illustrated in the embodiments of the present invention, the present invention is contemplated by any suitable bus or interconnect.

[0047] The computer can execute the power distribution scheduling method based on distributed power sources and energy storage configuration of the present invention based on the power distribution scheduling system based on distributed power sources and energy storage configuration, thereby realizing power distribution scheduling based on distributed power sources and energy storage configuration.

[0048] In some further embodiments of the present invention, in conjunction with the above-described power distribution scheduling method based on distributed power sources and energy storage configuration, the present invention provides the following technical solution: a storage medium storing a computer program, wherein the computer program, when executed by a processor, implements the above-described power distribution scheduling method based on distributed power sources and energy storage configuration.

[0049] Those skilled in the art will understand that the logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-included system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can mean any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0050] More specific examples of readable media (a non-exhaustive list) include: electrical connections (electronic devices) with one or more wires, portable computer disk drives (magnetic devices), random access memory (RAM), read-only memory (ROM), erasable and editable read-only memory (EPROM or flash memory), fiber optic devices, and portable optical disc read-only memory (CDROM). Furthermore, computer-readable media can even be paper or other suitable media on which the program can be printed, since the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpreting, or otherwise processing as necessary, and then stored in computer memory.

[0051] It should be understood that various parts of the present invention can be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, it can be implemented using any one or a combination of the following techniques known in the art: discrete logic circuits having logic gates for implementing logical functions on data signals, application-specific integrated circuits (ASICs) having suitable combinational logic gates, programmable gate arrays (PGAs), field-programmable gate arrays (FPGAs), etc.

[0052] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0053] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these all fall within the protection scope of the present invention. Therefore, the protection scope of this invention patent should be determined by the appended claims.

Claims

1. A power distribution dispatching method based on distributed power generation and energy storage configuration, characterized in that, include: Obtain wind power data, photovoltaic data, and load data for the area to be planned, and determine the wind power output power, photovoltaic output power, and load power under different scenarios based on the wind power data, the photovoltaic data, and the load data. A target model is constructed based on the wind power output, photovoltaic power output, and load power under different scenarios, wherein the target model includes an upper-level model and a lower-level model; The upper-level model is solved using a preset solution algorithm to obtain the upper-level result. The upper-level result is then transmitted to the lower-level model, and the lower-level model is solved using the preset solution algorithm to obtain the lower-level result. The model parameters of the upper-level model are corrected by the lower-level results, and the process of solving the upper-level model and the lower-level model is repeated iteratively until the iteration stopping condition is met, and the power distribution scheduling result is output.

2. The power distribution dispatching method based on distributed power source and energy storage configuration according to claim 1, characterized in that, The steps for determining wind power output, photovoltaic output, and load power under different scenarios based on the wind power data, photovoltaic data, and load data specifically include: Obtain the variance and expected value of the wind data, the photovoltaic data, and the load data, and determine the wind probability distribution function, the photovoltaic probability distribution function, and the load probability distribution function based on the variance and expected value, respectively: ； ， ； ； ， ； ； ； In the formula, These are the wind probability distribution function and the photovoltaic probability distribution function, respectively. These are the probability distribution functions of the load active power and reactive power, respectively. These are the scale parameters and shape parameters of the wind model, respectively. These represent the variance and expected value of the wind data, respectively. For wind speed, For the Gamma function, These are light intensity and maximum light intensity, respectively. These are the location and shape parameters of the beta distribution, respectively. These represent the variance and expected value of the photovoltaic data, respectively. These represent the variance and expected value of the load active power, respectively. These represent the variance and expected value of the reactive power of the load, respectively. These are the active power of the load and the reactive power of the load, respectively. The wind probability distribution function, photovoltaic probability distribution function, and load probability distribution function are uniformly divided into several non-overlapping sub-regions on the coordinate axis. For any sub-region, a random number between 0 and 1 is randomly generated, and the sub-region is sampled according to the random number to obtain wind sampling data, photovoltaic sampling data, and load sampling data respectively. Several variables within the sub-regions corresponding to the wind probability distribution function and the photovoltaic probability distribution function are sampled to obtain the wind sampling matrix and the photovoltaic sampling matrix; The wind sampling matrix and the photovoltaic sampling matrix are respectively decomposed to obtain the wind lower triangular matrix and the photovoltaic lower triangular matrix; The wind force lower triangular matrix is ​​multiplied by the wind force sampling data, and the photovoltaic lower triangular matrix is ​​multiplied by the photovoltaic smoke extraction data to obtain the wind force density value and the photovoltaic density value, respectively. The wind density value and the photovoltaic density value are converted into actual distribution data according to the equal probability transformation to obtain the actual distribution value of wind and the actual distribution value of photovoltaic. Based on the actual wind distribution values, determine the wind output power under different scenarios. : ； In the formula, , , , They are respectively The actual wind force distribution, cut-in wind speed, cut-out wind speed, and rated wind speed at any given time. This refers to the rated power of the wind turbine generator set; Based on the actual photovoltaic distribution value, determine the photovoltaic output power under different scenarios. : ； In the formula, for The actual distribution value of photovoltaic power at time t, Rated power of photovoltaic power; Clustering is performed on the load sampling data, wind power output in different scenarios, and photovoltaic power output in different scenarios to obtain wind power output, photovoltaic power output, and load power in different scenarios.

3. The power distribution dispatching method based on distributed power source and energy storage configuration according to claim 1, characterized in that, The upper-level model is: ； ； ； ； ； In the formula, , These include the total daily comprehensive cost of the distribution network, the investment and operation and maintenance cost of distributed power sources, the investment and operation and maintenance cost of energy storage systems, electricity purchase cost, and power generation subsidies. These represent the total number of nodes in distributed power generation and energy storage systems, respectively. These are the current subsidy rate and the planned service life, respectively. The first Investment and maintenance costs of each distributed power node for Time of the first The capacity of each distributed power node These are the investment cost per unit capacity of energy storage and the operation and maintenance cost per unit power of energy storage, respectively. The total number of time periods. For the first The energy storage capacity of each energy storage system node They are respectively Time of the first The charging and discharging power of each energy storage system node. for Time-of-use electricity pricing, where electricity is purchased from the upper-level power grid at all times. , , They are respectively Load power at time, the first The wind power output of the first distributed power node, the first Photovoltaic output power of a distributed power node for Time of the first The energy storage output power of each energy storage system node Subsidies for distributed power sources.

4. The power distribution dispatching method based on distributed power source and energy storage configuration according to claim 3, characterized in that, The constraints of the upper-level model are: ； ； In the formula, The first The lower and upper limits of the active power capacity of each distributed power node. The first Lower and upper limits of reactive power capacity for each distributed power node , The first Active and reactive power output of each distributed power node To allow for the maximum distributed power penetration rate, The total active load of the distribution network, The first The lower and upper limits of active power for each energy storage system node. The first The lower and upper limits of reactive power for each energy storage system node. The first The active and reactive power output of each energy storage system node.

5. The power distribution dispatching method based on distributed power source and energy storage configuration according to claim 1, characterized in that, The lower-level model is: ； ； ； ； In the formula, The first, second, and third weights are respectively. These are distribution network losses, voltage deviations, and grid vulnerability values. For the first a side road Current at any moment For the first The resistance of the branch circuit, For the number of branch roads, For the first Each distribution network node Voltage amplitude at time 10:00 for The voltage rating at any given time. For the number of nodes in the distribution network, These are the equilibrium degree of grid vulnerability and the average vulnerability of the grid, respectively. This represents the total number of time periods.

6. The power distribution dispatching method based on distributed power source and energy storage configuration according to claim 5, characterized in that, The constraints of the lower-level model are: ； ； ； In the formula, The first Each distribution network node Active power and reactive power at any given time The first Each distribution network node Voltage amplitude at time 10:00 The first The first distribution network node, the first Branch admittance of each distribution network node For the first The distribution network node and the first Voltage phase angle difference between distribution network nodes For the first The current flowing through each branch, This is the upper limit of the maximum current flowing through the branch. These are the lower and upper limits of the node voltage amplitude, respectively. For the first Voltage amplitude at each distribution network node, For energy storage The charging and discharging power at any given time For charging and discharging efficiency, The first Time, Number The ESS state of charge at time t, For energy storage capacity, These are the lower and upper limits of the ESS (Electronic Power Saving) quota. These are the lower and upper limits of the ESS state of charge, respectively. For the first The state of charge of each energy storage system node.

7. The power distribution dispatching method based on distributed power source and energy storage configuration according to claim 1, characterized in that, The preset solution algorithm is specifically the COFA algorithm.

8. A power distribution dispatching system based on distributed power sources and energy storage configuration, characterized in that, The system includes: The determination module is used to acquire wind power data, photovoltaic data, and load data of the area to be planned, and to determine the wind power output power, photovoltaic output power, and load power under different scenarios based on the wind power data, the photovoltaic data, and the load data. A model is constructed to build a target model based on the wind power output power, the photovoltaic output power, and the load power under different scenarios, wherein the target model includes an upper-level model and a lower-level model; The solving module is used to solve the upper-level model using a preset solving algorithm to obtain the upper-level result, and to transmit the upper-level result to the lower-level model and solve the lower-level model using the preset solving algorithm to obtain the lower-level result. The output module is used to correct the model parameters of the upper-level model based on the lower-level results and repeatedly iterate the solution process of the upper-level model and the lower-level model until the iteration stopping condition is met, and output the power distribution scheduling results.

9. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the power distribution scheduling method based on distributed power source and energy storage configuration as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the power distribution scheduling method based on distributed power source and energy storage configuration as described in any one of claims 1 to 7.

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