Multi-objective optimization scheduling method and system for active distribution network based on source-grid-load-storage coordination

Abstract

This application relates to the field of power grid optimization dispatching technology, specifically to a multi-objective optimization dispatching method and system for active distribution networks with source-grid-load-storage coordination. The method includes: collecting the energy storage capacity of each renewable energy power station in the active distribution network, and the energy dispatched by each transformer from each renewable energy power station; calculating the priority dispatching characteristics of each renewable energy power station; predicting the future energy demand of each transformer and the future energy storage capacity of the renewable energy power stations supplying them; coordinating the transmission distances between each transformer and the renewable energy power stations supplying them; determining the dispatch matching coefficient between the renewable energy power stations and transformers; based on the dispatch matching coefficient, constructing an objective function with the goal of minimizing the source-grid-load-storage coordinated dispatching cost and maximizing renewable energy consumption; and using a multi-objective optimization algorithm to solve for the optimal solution, which is then used for power dispatching. This improves the rationality of power resource dispatching in the distribution network.

Description

Technical Field

[0001] This application relates to the field of power grid optimization and dispatching technology, specifically to a multi-objective optimization and dispatching method and system for active distribution networks with source-grid-load-storage coordination. Background Technology

[0002] With the increase in urban electricity consumption, new energy power generation has gradually become an important part of the power grid. Due to the indirect and random nature of new energy power generation, an active distribution network is needed to solve this problem. An active distribution network can better cope with the fluctuations of new energy and improve the stability of the power system. However, to achieve the efficient operation of an active distribution network that coordinates the source, grid, load and storage, it is necessary to solve the problem of coordinated scheduling of source, grid, load and storage. While meeting the electricity demand, it is also necessary to consider multiple objectives such as new energy consumption, economy and stability.

[0003] Existing active distribution networks primarily employ single-objective optimization scheduling methods, which often result in poor renewable energy absorption capacity. While the Non-Dominated Sorting Genetic Algorithm (NSGA-II) is a commonly used multi-objective optimization method that can improve renewable energy absorption capacity, it mainly considers the distance of renewable energy dispatching without taking into account the density of renewable energy power plant distribution. This leads to uncoordinated renewable energy dispatching and ultimately uneven Pareto front solutions, resulting in poor multi-objective optimization scheduling performance for active distribution networks with coordinated generation, grid, load, and storage. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a multi-objective optimization scheduling method and system for active distribution networks with source-grid-load-storage coordination. The specific technical solution adopted is as follows: In a first aspect, embodiments of this application provide a multi-objective optimization scheduling method for active distribution networks that coordinates source, grid, load, and storage. This method includes the following steps: Collect the stored power of each new energy power station in the active distribution network, as well as the power dispatched by each transformer area from each new energy power station; Analyze the location distance between each new energy power station and its adjacent new energy power stations to determine the first density characteristics of each new energy power station. Combine the energy storage capacity and rated capacity of each new energy power station to calculate the priority scheduling characteristics of each new energy power station. The future power demand of each transformer area and the future energy storage capacity of each new energy power station supplying it are predicted. The transmission distance between each transformer area and each new energy power station supplying it, as well as the priority scheduling characteristics, are considered to determine the scheduling matching coefficient between the new energy power station and the transformer. Based on the scheduling matching coefficient, an objective function is constructed with the goal of minimizing the cost of coordinated scheduling of power generation, grid, load and storage and maximizing the consumption of new energy. The optimal solution is obtained by using a multi-objective optimization algorithm and is then used for power dispatching.

[0005] In one embodiment, determining the first density feature includes: The location distances between each new energy power station and its preset number of adjacent new energy power stations are positively integrated to obtain the first density characteristics of each new energy power station.

[0006] In one embodiment, calculating the priority scheduling characteristics of each new energy power station includes: The ratio of the energy storage capacity of each new energy power station to its rated capacity is calculated. Combined with the energy storage capacity of each new energy power station and the first density characteristic, the priority scheduling characteristic is obtained. The priority scheduling characteristic is positively correlated with the energy storage capacity of each new energy power station and the ratio, and negatively correlated with the first density characteristic.

[0007] In one embodiment, the prediction of future electricity demand for each transformer substation and future energy storage capacity of each renewable energy power station supplying it includes: The power dispatched by each transformer in the past from the various new energy power plants supplying it is used as the input of the time series prediction algorithm, and the output is the predicted value of the power dispatched by each transformer in the future from the various new energy power plants supplying it, which is used as the power demand. Correspondingly, the future energy storage capacity of each new energy power generation station supplying power to each transformer area will be obtained.

[0008] In one embodiment, determining the scheduling matching coefficient includes: Based on the negative correlation mapping result of the transmission distance, the first distance feature is obtained, and based on the ratio of the future energy storage capacity of each new energy power generation station supplying power to each transformer area to the predicted value, the supply and demand priority feature is obtained. The scheduling matching coefficient is determined by using the first distance feature, the supply and demand priority feature, and the priority scheduling feature.

[0009] In one embodiment, the scheduling matching coefficient is specifically: The first distance feature and the supply-demand priority feature are positively fused, and the result of the positive fusion is weighted and summed with the priority scheduling feature to obtain the scheduling matching coefficient.

[0010] In one embodiment, the objective function corresponding to minimizing the source-grid-load-storage coordinated scheduling cost is... Specifically: In the formula, N represents the number of transformers in the active distribution network. This represents the number of renewable energy power plants supplying power to the j-th transformer in the active distribution network. The dispatch cost is the power resource dispatch from the y-th renewable energy power plant to the j-th transformer substation. The cost of curtailing renewable energy during power resource dispatch from the y-th renewable energy power plant to the j-th transformer substation is specifically the product of the unused electricity volume during dispatch and the unit price of electricity. Let y be the dispatch matching coefficient between the y-th new energy power station and the j-th transformer substation.

[0011] In one embodiment, the objective function corresponding to maximizing the consumption of new energy sources Specifically: In the formula, This refers to the active power output dispatched from the y-th new energy power station to the j-th transformer substation.

[0012] In one embodiment, constraints are defined during multi-objective optimization, specifically as follows: The sum of active power dispatched from the y-th new energy power station to all transformer substations must be greater than or equal to 0 and less than or equal to the maximum active power output of the y-th new energy power station. The sum of the electricity dispatched by all new energy power plants for the j-th transformer area must equal the total new energy electricity demand of the j-th transformer area.

[0013] Secondly, embodiments of this application also provide an active distribution network multi-objective optimization scheduling system for source-grid-load-storage coordination, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0014] This application has at least the following beneficial effects: This application optimizes the power dispatching of new energy power plants by accurately analyzing their energy storage capacity and rated capacity, and calculating priority dispatch characteristics, thereby maximizing the absorption of new energy power while ensuring power supply reliability. Active dispatching reduces the impact of power fluctuations and load changes on the absorption of new energy power generation, promoting the utilization of clean energy. Secondly, by considering the power demand of transformers in each distribution area and the energy storage capacity of new energy power plants, flexible dispatching decisions can be made based on predicted future demand and energy storage capacity, avoiding power supply shortages caused by load fluctuations and insufficient energy storage, thus contributing to a more stable and efficient power system operation. By coordinating the transmission distance and energy storage capacity between transformers in distribution areas and new energy power plants, as well as priority dispatching characteristics, the stability and security of power supply can be ensured during peak demand periods. Optimizing the power supply path reduces voltage fluctuations and power losses caused by excessively long transmission distances, contributing to improved grid safety and preventing power outages or accidents caused by unforeseen circumstances. Based on a multi-objective optimization algorithm using dispatching matching coefficients, the optimal dispatching match between power sources, grid, load, and storage effectively reduces power transmission losses and dispatching costs, improving the grid's economic efficiency. While meeting power demand, it maximizes the use of new energy power generation, avoids unnecessary energy waste, and enhances the overall economic benefits of the power system. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, 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 this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart illustrating the steps of a multi-objective optimization scheduling method for active distribution networks with source-grid-load-storage coordination provided in one embodiment of this application. Detailed Implementation

[0017] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the active distribution network multi-objective optimization scheduling method and system for source-grid-load-storage coordination proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0018] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0019] The following, in conjunction with the accompanying drawings, details the specific scheme of the active distribution network multi-objective optimization scheduling method and system for source-grid-load-storage coordination provided in this application.

[0020] Please see Figure 1 This document illustrates a flowchart of a multi-objective optimization scheduling method for an active distribution network with source-grid-load-storage coordination, provided in an embodiment of this application. The method includes the following steps: S1 collects the stored power of each new energy power station in the active distribution network, as well as the power dispatched by each transformer substation from each new energy power station.

[0021] Smart meters are used to collect data on the energy storage capacity of each renewable energy power station in the active distribution network, such as wind farms and photovoltaic power stations, as well as the amount of electricity dispatched by each transformer substation from various renewable energy power stations in the past 24 hours. In addition, the geographical coordinates of each renewable energy power station and the length of the power grid lines are obtained from the power grid system's GIS geographic information system.

[0022] To avoid missing data collected by smart meters, the `isnull()` function from the Pandas library is used to check for missing values ​​in various types of collected data. If missing values ​​are found, a linear interpolation algorithm is used to fill them in, obtaining the complete energy storage capacity of each renewable energy power station and the energy dispatched by each transformer substation from various renewable energy power stations. The `isnull()` function and the linear interpolation algorithm in the Pandas library are well-known technologies, and the specific process will not be elaborated further.

[0023] S2. Analyze the location distance between each new energy power station and its adjacent new energy power stations to determine the first density characteristics of each new energy power station. Combine the energy storage capacity and rated capacity of each new energy power station to calculate the priority scheduling characteristics of each new energy power station.

[0024] When the distribution network supplies power to each transformer in a distribution area, each transformer can choose to dispatch power resources from multiple surrounding renewable energy power stations. Under normal circumstances, power resource dispatch prioritizes dispatching from the nearest renewable energy power station to reduce power loss during transmission. However, this method only considers the distance between the transformer in the distribution area and the renewable energy power station, without fully considering the distribution density of renewable energy power stations.

[0025] Densely distributed renewable energy power plants may have excess renewable energy due to the limited number of surrounding transformer substations, meaning that the distribution network dispatching capacity for renewable energy absorption is poor. Therefore, in order to ensure that transformer substations can stably dispatch power resources from the nearest sparsely distributed renewable energy power plants in the long term, the dispatching of renewable energy power plants between transformer substations can be adjusted so that they prioritize dispatching power resources from densely distributed renewable energy power plants. This reduces the dispatching pressure on sparse renewable energy power plants, makes the power dispatching of transformer substations more stable, and in the long run, can improve the stability of distribution network dispatching and the renewable energy absorption capacity.

[0026] To analyze the distribution density characteristics of each new energy power generation and obtain the geographical coordinates of all new energy power stations, taking the i-th new energy power station as an example, the Euclidean distance between the i-th new energy power station and all other new energy power stations is calculated. All Euclidean distances are sorted in ascending order, and the Euclidean distances of the top K new energy power stations are selected. The value of K can be set by the implementer without special restrictions; in this embodiment, it is set to 8. If the number of new energy power stations managed by the local distribution network dispatch system is less than K, then K is set to the total number of new energy power stations managed by it. The K Euclidean distances are then forward-fused, and the maximum value of the forward-fused result is normalized as the first density feature of the i-th new energy power station. This reflects the density of the geographical distribution of new energy power plants. The smaller the first density feature, the closer the new energy power plant is to its neighboring power plants, and the denser the geographical distribution of the new energy power plants. Maximum value normalization is a well-known technique, and the specific process will not be elaborated further.

[0027] It should be noted that forward fusion means combining multiple variables in a positive direction. Specifically, it can be calculated using methods such as addition, multiplication, or averaging. In this embodiment, the mean of K Euclidean distances is calculated, and the maximum value is normalized to serve as the first density feature of the i-th renewable energy power station. .

[0028] If a renewable energy power station has a high first density characteristic but low energy storage, its dispatch priority remains low. Therefore, it's necessary to analyze the energy storage characteristics of renewable energy power stations. The more energy a renewable energy power station stores, the better it can meet the demand of the transformer area, and its dispatch priority is higher than that of renewable energy power stations with lower energy storage. Furthermore, the closer the energy storage of a renewable energy power station is to its rated capacity, the more priority it should be in dispatching to avoid the termination of renewable energy generation and the indirect waste of renewable energy resources. Specifically, the maximum value of the energy storage of the i-th renewable energy power station is normalized to serve as the energy storage significance. The significance of energy storage reflects the extent to which the new energy power station stores electricity. The higher the significance of energy storage, the more electricity the new energy power station stores, which is better able to meet the power demand of the transformer in the distribution area. Compared with other new energy power stations with less energy storage, it is more likely to be prioritized for dispatch.

[0029] Furthermore, the ratio of the energy storage capacity to the rated capacity of the i-th renewable energy power station is calculated as a characteristic of the energy storage state. This reflects the ratio of the stored energy to the rated capacity of a new energy power station. The larger the stored energy status characteristic, the closer the stored energy of the new energy power station is to its rated capacity, and the more it needs to dispatch power resources to other regions.

[0030] Based on the above analysis, the priority scheduling characteristic of the i-th renewable energy power station is calculated to characterize the significance of priority scheduling of renewable energy power stations. The specific expression is as follows: In the formula, This represents the priority scheduling characteristic of the i-th renewable energy power station. The normalization method is indicated here. In this embodiment, maximum value normalization is selected, and it is performed based on the calculation results corresponding to all new energy power plants. This represents the first density feature of the i-th renewable energy power station. This represents the significance of energy storage in the i-th renewable energy power station. This represents the energy storage state characteristics of the i-th renewable energy power station. This represents a preset parameter tuning constant greater than 0, used to avoid a denominator of 0. In this embodiment, the value is 0.01, but the implementer can set it as needed.

[0031] First density characteristic The smaller the value, the denser the distribution of the i-th renewable energy power station, the more likely it is to cause a surplus of renewable energy, thus its dispatch priority is relatively high, and the significance of energy storage is relatively low. and energy storage state characteristics The larger the value, the more energy the i-th renewable energy power station has stored and the closer it is to its rated capacity. In this case, the priority scheduling feature is activated. The larger the value, the higher the significance of priority scheduling for the i-th renewable energy power station.

[0032] In another embodiment, the priority scheduling characteristic of the i-th renewable energy power station is calculated as follows: ;in, , All represent weighting coefficients, with values ​​ranging from 0 to 1, and The specific value is not subject to any special restrictions; in this embodiment, the value is 0.5.

[0033] S3, predict the future power demand of each transformer area and the future power storage capacity of each new energy power station supplying it, and determine the scheduling matching coefficient between the new energy power station and the transformer by coordinating the transmission distance between each transformer area and the new energy power station supplying it, as well as the priority scheduling characteristics.

[0034] In the actual process of active power resource dispatching of the distribution network in coordination of source, grid, load and storage, it is necessary not only to determine the dispatch priority of new energy power plants based on their distribution and energy storage conditions, but also to consider the power consumption of transformers in the distribution area and their location relationship with new energy power plants.

[0035] To analyze the locational relationship between transformer substations and renewable energy power stations, taking the j-th transformer substation as an example, all renewable energy power stations that can supply power to the j-th transformer substation are counted. Taking the y-th renewable energy power station supplying power to the j-th transformer substation as another example, the transmission distance between the j-th transformer substation and the y-th renewable energy power station is obtained. Transmission distance refers to the length of the power grid line between the j-th transformer substation and the y-th renewable energy power station. Generally, the closer the transmission distance between the y-th renewable energy power station and the j-th transformer substation, the higher its scheduling priority. Therefore, a negative correlation mapping is performed on the transmission distance between the j-th transformer substation and the y-th renewable energy power station. Based on the results of this negative correlation mapping for all renewable energy power stations supplying power to the j-th transformer substation, the maximum value of the negative correlation mapping result between the j-th transformer substation and the y-th renewable energy power station is normalized, and the normalized result is used as the first distance feature. The shorter the transmission distance, the larger the first distance feature, indicating that the priority of power resource dispatch from the y-th new energy power station to the j-th transformer area is relatively high. In this embodiment, the negative correlation mapping is calculated by taking the reciprocal; in another embodiment, the transmission distance can also be normalized, and the difference between the natural number 1 and the normalized result can be calculated as the first distance feature.

[0036] There may be a situation where the initial distance between a renewable energy power station and a transformer substation is relatively large, but the power storage capacity is insufficient to meet the demand of the transformer substation. In this case, its scheduling priority should be reduced, and power resources should be dispatched to other renewable energy power stations. To obtain the power demand of the j-th transformer substation in the next time period, the power dispatched by the j-th transformer substation from the y-th renewable energy power station in the historical 24 hours and the power storage capacity of the y-th renewable energy power station are used as inputs to the time series prediction algorithm. The output is the power demand that the j-th transformer substation needs to dispatch from the y-th renewable energy power station in the next time period, denoted as the power demand, and the power storage capacity of the y-th renewable energy power station in the next time period. In this embodiment, the ARIMA time series prediction algorithm is selected, but implementers can choose other existing feasible time series prediction algorithms.

[0037] It should be noted that if historical data is less than 24 hours, the current electricity consumption of the transformers in the current distribution area is used as the predicted electricity demand, and the current energy storage capacity of the renewable energy power plants is used as the predicted energy storage capacity. The ratio of the energy storage capacity of the y-th renewable energy power plant at the next time point to the electricity demand of the j-th transformer in the next time point is calculated, and the maximum value is normalized to serve as the supply and demand priority characteristic. This reflects the priority of dispatching new energy power plants in meeting the electricity demand of transformers in the distribution area. The greater the priority of supply and demand, the less the power storage capacity of new energy power plants can meet the power demand of transformers in the distribution area, and the lower the power dispatching pressure. Therefore, its dispatch priority should be higher.

[0038] Based on the above analysis, when scheduling power resources for transformer substations, if the priority scheduling characteristics of new energy power stations are greater, i.e., their distribution density is higher and their energy storage capacity is more significant, then their scheduling priority is higher than that of other new energy power stations. Since there are limitations to prioritizing based solely on the characteristics of new energy power stations, the power consumption of transformer substations and their location relationship with new energy power stations are further considered. That is, the closer the new energy power station is to the transformer substation and the less power scheduling pressure it faces in meeting the power demand of the transformer substation, the higher its scheduling priority.

[0039] Therefore, the dispatch matching coefficient between the renewable energy power plant and the transformer substation is calculated to characterize the degree of dispatch matching between the renewable energy power plant and the transformer substation during power resource dispatch. The specific expression is as follows: In the formula, This represents the dispatch matching coefficient between the y-th renewable energy power station and the j-th transformer substation. This represents the weighting coefficient, which ranges from 0 to 1 without any special restrictions. In this embodiment, a value of 0.5 is preferred. Let represent the priority scheduling characteristics of the y-th renewable energy power station. This represents the first distance characteristic between the j-th transformer and the y-th renewable energy power station. This indicates the supply and demand priority characteristics between the j-th transformer and the y-th renewable energy power station. This represents the positive fusion result of the first distance feature and the supply-demand priority feature, quantifying the correlation between the transformer substation and the renewable energy power station. The closer the renewable energy power station is to the transformer substation and the more it meets the transformer substation's power demand, the higher the dispatch matching degree between the two. If the distance is far or the power demand of the transformer substation is not met, the dispatch matching degree between the two will decrease. The dispatch matching coefficient is... The larger the value, the higher the matching degree between the y-th new energy power station and the j-th distribution transformer during power resource dispatch, and the more priority is given to dispatching power resources from the y-th new energy power station to the j-th distribution transformer.

[0040] S4, based on the scheduling matching coefficient, constructs an objective function with the goal of minimizing the cost of coordinated scheduling of power generation, grid, load and storage and maximizing the consumption of new energy. It uses a multi-objective optimization algorithm to solve for the optimal solution and is used for power dispatching.

[0041] To prioritize scheduling of renewable energy power plants with high scheduling matching coefficients, i.e., minimizing scheduling costs and maximizing renewable energy absorption, an optimization objective function incorporating scheduling matching coefficients is constructed, specifically as follows: In the formula, To minimize the objective function corresponding to the source-grid-load-storage coordinated scheduling cost, min represents finding the minimum value. To maximize the objective function corresponding to renewable energy consumption, max represents finding the maximum value, and N represents the number of transformers in the active distribution network. This represents the number of renewable energy power plants supplying power to the j-th transformer in the active distribution network. The dispatch cost for power resource dispatch from the y-th renewable energy power plant to the j-th transformer substation is specifically the sum of the dispatch power cost (unit price of electricity × dispatched power volume) and the power transmission loss cost (unit price of electricity × power consumption per unit distance × total transmission distance). The term "wasteful renewable energy" refers to the cost of power resource dispatch from the y-th renewable energy power plant to the j-th transformer substation. This cost refers to the ineffective utilization of renewable energy due to insufficient renewable energy absorption capacity in the distribution network, even when the power plant has already generated electricity. Specifically, it is the product of the unused renewable energy volume during power resource dispatch and the unit price of renewable energy. Let be the dispatch matching coefficient between the y-th renewable energy power station and the j-th transformer substation. This refers to the active power output dispatched from the y-th new energy power station to the j-th transformer substation.

[0042] In addition, define the constraints: in, This represents the active power dispatched from the y-th renewable energy power station to the j-th transformer substation. This represents the maximum active power output of the y-th renewable energy power station. This represents the total renewable energy demand of the j-th transformer area.

[0043] Will , Two objective functions are used as inputs. The population size ranges from 50 to 400, the number of iterations ranges from 100 to 1000, and the crossover probability ranges from 0 to 1. The implementer can appropriately increase the population size and the number of iterations according to the complexity of the optimization problem. In this embodiment, the population size is set to 200, the number of iterations is set to 500, and the crossover probability is set to 0.9. The non-dominated sorting genetic algorithm NSGA-II is used for iterative optimization, and finally all Pareto optimal solutions are obtained. The set of all Pareto optimal solutions in the space is the Pareto front. It should be noted that the Pareto front does not directly provide a unique optimal solution, but rather a set of optimal solutions. During distribution network scheduling, the implementer can select the solution based on the target requirements. If the distribution network aims to maximize renewable energy absorption, the solution with the best renewable energy absorption effect in the Pareto front should be selected. If the distribution network aims to minimize scheduling costs, the solution with the lowest scheduling cost in the Pareto front should be selected. If the goal is to achieve a balanced scheduling between minimizing scheduling costs and maximizing renewable energy absorption, then a solution that falls somewhere in between in the Pareto front should be selected, without special restrictions. This embodiment aims to maximize renewable energy absorption. The non-dominated sorting genetic algorithm NSGA-II and the Pareto front are well-known technologies, and their specific implementations will not be described in detail.

[0044] After selecting an optimal solution from the Pareto front, a dispatching instruction is sent to the distribution network dispatching system, for example, dispatching 1 MW of power from the y-th renewable energy power plant to the j-th transformer substation, ultimately completing the dispatching of power resources for all transformer substations. By adopting the multi-objective optimization dispatching method proposed in this application, the dispatching efficiency of the active distribution network with source-grid-load-storage coordination can be effectively improved, and the capacity for renewable energy absorption can be greatly enhanced.

[0045] Based on the same inventive concept as the above methods, this application also provides a multi-objective optimization scheduling system for active distribution networks with source-grid-load-storage coordination, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described active distribution network multi-objective optimization scheduling methods with source-grid-load-storage coordination.

[0046] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, specific embodiments of this specification have been described above. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0047] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0048] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A multi-objective optimization scheduling method for active distribution networks with source-grid-load-storage coordination, characterized in that, The method includes the following steps: Collect the stored power of each new energy power station in the active distribution network, as well as the power dispatched by each transformer area from each new energy power station; Analyze the location distance between each new energy power station and its adjacent new energy power stations to determine the first density characteristics of each new energy power station. Combine the energy storage capacity and rated capacity of each new energy power station to calculate the priority scheduling characteristics of each new energy power station. The future power demand of each transformer area and the future energy storage capacity of each new energy power station supplying it are predicted. The transmission distance between each transformer area and each new energy power station supplying it, as well as the priority scheduling characteristics, are considered to determine the scheduling matching coefficient between the new energy power station and the transformer. Based on the scheduling matching coefficient, an objective function is constructed with the goal of minimizing the cost of coordinated scheduling of power generation, grid, load and storage and maximizing the consumption of new energy. The optimal solution is obtained by using a multi-objective optimization algorithm and is then used for power dispatching.

2. The active distribution network multi-objective optimization scheduling method with source-grid-load-storage coordination as described in claim 1, characterized in that, The determination of the first density feature includes: The location distances between each new energy power station and its preset number of adjacent new energy power stations are positively integrated to obtain the first density characteristics of each new energy power station.

3. The active distribution network multi-objective optimization scheduling method with source-grid-load-storage coordination as described in claim 1, characterized in that, The calculation of the priority scheduling characteristics of each new energy power station includes: The ratio of the energy storage capacity of each new energy power station to its rated capacity is calculated. Combined with the energy storage capacity of each new energy power station and the first density characteristic, the priority scheduling characteristic is obtained. The priority scheduling characteristic is positively correlated with the energy storage capacity of each new energy power station and the ratio, and negatively correlated with the first density characteristic.

4. The active distribution network multi-objective optimization scheduling method with source-grid-load-storage coordination as described in claim 1, characterized in that, The forecasting of future electricity demand for each transformer area and future energy storage capacity of the new energy power stations supplying them includes: The power dispatched by each transformer in the past from the various new energy power plants supplying it is used as the input of the time series prediction algorithm, and the output is the predicted value of the power dispatched by each transformer in the future from the various new energy power plants supplying it, which is used as the power demand. Correspondingly, the future energy storage capacity of each new energy power generation station supplying power to each transformer area will be obtained.

5. The active distribution network multi-objective optimization scheduling method with source-grid-load-storage coordination as described in claim 4, characterized in that, The determination of the scheduling matching coefficient includes: Based on the negative correlation mapping result of the transmission distance, the first distance feature is obtained, and based on the ratio of the future energy storage capacity of each new energy power generation station supplying power to each transformer area to the predicted value, the supply and demand priority feature is obtained. The scheduling matching coefficient is determined by using the first distance feature, the supply and demand priority feature, and the priority scheduling feature.

6. The active distribution network multi-objective optimization scheduling method with source-grid-load-storage coordination as described in claim 5, characterized in that, The scheduling matching coefficient is specifically: The first distance feature and the supply-demand priority feature are positively fused, and the result of the positive fusion is weighted and summed with the priority scheduling feature to obtain the scheduling matching coefficient.

7. The active distribution network multi-objective optimization scheduling method with source-grid-load-storage coordination as described in claim 1, characterized in that, The objective function corresponding to minimizing the source-grid-load-storage coordinated scheduling cost Specifically: In the formula, N represents the number of transformers in the active distribution network. This represents the number of renewable energy power plants supplying power to the j-th transformer in the active distribution network. The dispatch cost is the power resource dispatch from the y-th renewable energy power plant to the j-th transformer substation. The cost of curtailing renewable energy during power resource dispatch from the y-th renewable energy power plant to the j-th transformer substation is specifically the product of the unused electricity volume during dispatch and the unit price of electricity. Let y be the dispatch matching coefficient between the y-th new energy power station and the j-th transformer substation.

8. The active distribution network multi-objective optimization scheduling method with source-grid-load-storage coordination as described in claim 7, characterized in that, The objective function corresponding to maximizing the consumption of new energy Specifically: In the formula, This refers to the active power output dispatched from the y-th new energy power station to the j-th transformer substation.

9. The active distribution network multi-objective optimization scheduling method with source-grid-load-storage coordination as described in claim 8, characterized in that, In multi-objective optimization, constraints are defined as follows: The sum of active power dispatched from the y-th new energy power station to all transformer substations must be greater than or equal to 0 and less than or equal to the maximum active power output of the y-th new energy power station. The sum of the electricity dispatched by all new energy power plants for the j-th transformer area must equal the total new energy electricity demand of the j-th transformer area.

10. A multi-objective optimization scheduling system for an active distribution network with coordinated generation, grid, load, and storage, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-9.

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