Optimization method considering dynamic reconfigurable model of multi-element cluster energy storage

Abstract

The present invention belongs to the technical field of research on power system model aggregation methods, and specifically provides an optimization method for a dynamic reconfigurable model of multi-element cluster energy storage, including: obtaining the net load fluctuation curve of each node in the system based on an improved conditional generation adversarial network and flow simulation calculation; performing variational modal decomposition on the net load fluctuation curve, and smoothing the high-frequency and low-frequency components of each load curve; using Minkowski summation to obtain an aggregation model of cluster energy storage, with the optimization goal of minimizing the smoothing effect of net load fluctuations in each region at the minimum economic cost; performing output analysis on each energy storage output within the obtained cluster energy storage group, readjusting and aggregating the cluster division results, and optimizing the obtained model again to determine the degree of matching between each energy storage to be utilized and the corresponding region at the provincial level. This method provides a resource optimization and model basis for the system by analyzing the cross-temporal and spatial characteristics of energy storage resources and evaluating the matching of various types of resources.

Description

Technical Field

[0001] The present invention belongs to the technical field of research on power system model aggregation methods, and specifically relates to an optimization method considering a dynamic reconfigurable model of multi-clusters of energy storage. Background Art

[0002] The scale and diversification of energy storage resources provide more technical means for flexible power system operation and smoothing fluctuations in wind and solar power. The diverse output characteristics of energy storage complement each other at different times. However, energy storage resources in different regions have not yet been fully utilized due to electrical distance limitations. Furthermore, due to the integration of energy storage resources by aggregators, the cross-temporal and spatial utilization of energy storage resources in different regions can only be handled through global analysis at the provincial level.

[0003] Therefore, how to achieve cross-temporal and spatial utilization of energy storage resources at the provincial level and take into account the matching of various types of resources in the process of breaking through the model aggregation barriers urgently requires the establishment of a set of model matching and aggregation methods that can effectively target distributed energy storage. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide an optimization method for a dynamic reconfigurable model of multi-clusters of energy storage, which provides a resource optimization and model basis for the system by analyzing the cross-temporal and spatial characteristics of energy storage resources and evaluating the matching of various types of resources.

[0005] To solve the above technical problems, the technical solution adopted by the present invention is: an optimization method considering a multi-element cluster energy storage dynamic reconfigurable model, comprising the following steps:

[0006] S1. Based on the historical output data of wind power and photovoltaic power in each region, an improved conditional generative adversarial network is used to generate the time-series output scenarios of wind power and photovoltaic power in each region of the province. The synchronous back-substitution reduction method is used to reduce the typical wind and solar power output scenarios in each region. Considering the controllable resources and load forecast data of each region in the province, the Newton-Raphson method is used to perform power flow simulation calculations to comprehensively obtain the net load fluctuation curve of each node in the system.

[0007] S2. Perform variational modal decomposition on the net load fluctuation curves of each region. Based on the degree of fluctuation of the curves in each frequency band, perform temporal and spatial matching for power-type and capacity-type energy storage, respectively. Consider using power-type and capacity-type energy storage to smooth the high-frequency and low-frequency components of each load curve, respectively.

[0008] S3. Use Minkowski summation to obtain the aggregation model of cluster energy storage in various time and space, and consider the minimum effect of smoothing net load fluctuations in each region at the minimum economic cost as the optimization goal;

[0009] S4. Analyze the output of each energy storage within the obtained cluster energy storage group, readjust and aggregate the cluster division results, and optimize the obtained model again until the net load balancing effect of each cluster energy storage on each region is balanced, and finally determine the matching degree between each energy storage to be utilized and the corresponding region at the provincial level.

[0010] In the preferred solution, in step S1, the wind and solar historical data are trained based on the improved conditional generative adversarial network to complete the generation of wind power and photovoltaic output scenarios. The improved conditional generative network consists of a generator and a discriminator. A gradient descent layer is added after the convolution layer inside the discriminator to encourage the generator to generate more diverse samples. The specific formula is:

[0011] (1);

[0012] Where, x and y Represent the samples of input discriminator D respectively x and the internal conditions of the discriminator y , represents the neural network output of the discriminator, represents the internal activation function, It is the feature calculated by the Minibatch Discrimination layer, which can process the entire minibatch data to generate a feature that is consistent with the current sample. x Additional information about.

[0013] In a preferred solution, in step S1, the generated scenery scene is reduced using a synchronous back-substitution reduction method: first, the data of the generated scene is used as the initial scene, and the loop is iterated, each time the closest scene is eliminated, and the Kantorovich distance is defined as the distance indicator, which is expressed as:

[0014] (2)

[0015] Where, Represents a scene collection With scene collection The distance between the scenes, the indicator indicates that the sum of the distances between the scenes in the scene set is the smallest; N Indicates the number of scenes in the collection, and Represents a collection of scenes and Specific scenes inside; and Indicates separation and The probability of the scenario occurring; express and Euclidean distance of the scene.

[0016] In a preferred solution, in step S2, variational mode decomposition is used to decompose the net load fluctuation of nodes in each region into frequency bands, and the following variational problem is expanded by minimizing:

[0017] (3);

[0018] Where, K Indicates the decomposed mode The number of modes, each mode corresponds to a center frequency , Indicates time t The derivative of is the Dirac function, j represents the imaginary unit, represents the complex exponential function that shifts the mode to baseband, represents the modal components to be decomposed.

[0019] In the preferred solution, in step S2, the multi-element energy storage in each region is divided into power type and capacity type, and formulas (4) and (5) are used as indicators for evaluating power type and capacity type. A hierarchical analysis method is combined with an entropy weight method to form a subjective and objective hierarchical analysis method to obtain a comprehensive evaluation result. Then, the evaluation results are aggregated using the K-means algorithm to obtain a preliminary cluster division for each energy storage to determine the number of modes to be decomposed.

[0020] Different frequency bands correspond to corresponding types of energy storage output. Frequency bands with larger net load amplitudes correspond to power-type energy storage, while frequency bands with smaller amplitudes but greater volatility correspond to power-type energy storage.

[0021] The degree of matching between energy storage and net load curve is measured from the perspectives of power support and duration. The expression is as follows:

[0022] (4);

[0023] (5);

[0024] Where, Indicates the maximum net load of each node; Indicates the maximum charge and discharge value of stored energy; and Respectively represent the net load values ​​at peak and valley states. Indicates the maximum available capacity of energy storage, and Indicates the discharge and charging efficiency of the corresponding energy storage, and They respectively represent the power support capability of each node energy storage for the corresponding net load and the relative sustainable working time under the corresponding charging and discharging states.

[0025] In a preferred solution, in step S3, the optimization goal is to achieve maximum suppression of power fluctuations while minimizing the economic operation cost of the entire system energy storage. The objective function is:

[0026] (5);

[0027] Where, and Represents energy storage S exist t Charging and discharging power at all times; F (·) represents the optimization objective function; The net load curve of the system and the energy storage output cost are two influencing factors. The net load curve shows the overall output effect of the energy storage, and the energy storage output cost shows the minimum overall operating cost of the energy storage system. Represents the sub-function with the minimum energy storage operation cost, and The energy storage charging and discharging output is the independent variable.

[0028] In the preferred solution, in step S4, through simulation optimization, based on the smoothing effect of each energy storage within the cluster on the local net load fluctuation, the energy storage units that need to be re-divided and aggregated are determined, and step S3 is repeatedly executed, and iteration is performed based on the Nash equilibrium principle: the energy storage under each node of the system is used as a participant, the overall minimum net load fluctuation in each region of the system is used as the Nash equilibrium point, and the division results between different energy storage are used as the strategy set. By looping the step S3 process, each energy storage cluster division is adjusted until the output status of all energy storage is approximately consistent, and the system Nash equilibrium is achieved.

[0029] The present invention provides an optimization method for a multi-element cluster energy storage dynamic reconfigurable model, which has the following beneficial effects:

[0030] 1. In step S1 of the present invention, compared with the traditional application of generating various wind and solar scenarios, this method also combines the system's power flow simulation calculation. Based on the refined analysis of nodes in each region, the synchronous back-substitution reduction method is used to reduce the output scenario of each node, establishing a data analysis foundation for the subsequent cross-temporal and spatial complementary analysis of energy storage.

[0031] 2. In step S2 of the present invention, variational modal decomposition is used to decompose the net load curves of each region. Compared with traditional temporal complementarity analysis and evaluation of resources, the impact of energy storage output on the electrical system of the local and adjacent regions in each time period, namely spatial complementarity, is also analyzed. This overcomes the limitation that the aggregation model, which only integrates resources in the local region, does not fully utilize internal resources.

[0032] 3. In step S3 of the present invention, a two-layer optimization model is established that takes into account both the energy storage operating cost and the operating effect. The traditional Minkowski sum model aggregation method is used as the basis for model aggregation. The focus is still on whether the resource regulation and utilization are sufficient. By comparing the two, the adjustment space for energy storage resources in the aggregation process is analyzed.

[0033] 4. In step S4 of the present invention, the resource division and aggregation process is a process of overall planning. Compared with the previous consideration of resource division and utilization only from a certain region, the provincial level has a larger spatial span and a wide variety of resources, so multiple iterative analyses are required to determine the resource matching effect. Therefore, the Nash equilibrium idea is taken as the main line, and the division results of each cluster are used as the iteration direction in the iterative process until the output effects of each cluster tend to be consistent to achieve balanced use of energy storage resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The present invention will be further described below with reference to the accompanying drawings and examples:

[0035] Figure 1 This is a flow chart of the dynamic division and aggregation of distributed energy storage resources of the present invention;

[0036] Figure 2 A reference diagram for cross-space adjustment of energy storage resources according to the present invention;

[0037] Figure 3 A reference diagram for adjusting energy storage resources across time as described in the present invention;

[0038] Figure 4 This is a typical output scenario diagram for wind power and photovoltaic power;

[0039] Figure 5 Schematic diagram for comparative analysis of regulation optimization and aggregation models. DETAILED DESCRIPTION

[0040] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0041] Combine Figures 1 to 5 The specific embodiments of the present invention are described in further detail.

[0042] In response to the problems mentioned in the above background technology, this embodiment mainly focuses on the cross-temporal and spatial division and model aggregation method of cluster energy storage under the decentralized layout of multi-heterogeneous energy storage at the provincial level. It mainly involves four steps: generating typical cross-temporal and spatial scenarios of wind and solar power, analyzing the cross-temporal and spatial output conditions of multi-heterogeneous energy storage, model optimization aggregation, and cluster Nash equilibrium. Typical spatiotemporal scenarios for wind and solar power are generated primarily through conditional generative adversarial networks and synchronous back-substitution reduction methods, ultimately yielding net load fluctuation curves for each region through power flow simulation. The spatiotemporal output condition analysis of multi-heterogeneous energy storage primarily utilizes variational mode decomposition to determine the output periods for each region. The effective utilization periods for each cluster's energy storage are determined at both the spatial and temporal rolling levels, and further comprehensive considerations yield the spatiotemporal and temporal available output space for energy storage. Since energy storage is a controllable resource, the aggregation process can consider the "barrel effect" to aggregate the model. Therefore, the Minkowski summation is first used to perform a preliminary aggregation of the energy storage model. The calculated available output space for energy storage is then reaggregated internally, with the goal of smoothing electricity fluctuations at the lowest economic cost. Furthermore, to ensure consistent operating conditions for each cluster's energy storage, and considering the possibility that commands within the aggregation model cannot be fully issued, the underutilized energy storage within each cluster is repartitioned and aggregated based on the Nash equilibrium concept until each cluster achieves optimal output.

[0043] like Figure 1 As shown in FIG, an optimization method considering a dynamic reconfigurable model of multi-element cluster energy storage includes the following steps:

[0044] S1. Based on the improved Conditional Generative Adversarial Network (CGAN), historical wind and solar data are trained to generate wind power and photovoltaic output scenarios. The CGAN mainly consists of a generator and a discriminator. This method mainly adds a gradient descent layer after the convolution layer within the discriminator to encourage the generator to generate more diverse samples. The specific formula is:

[0045] (1);

[0046] Where, x and y Represent the samples of input discriminator D respectively x and the internal conditions of the discriminator y , represents the neural network output of the discriminator, represents the internal activation function, It is the feature calculated by the Minibatch Discrimination layer, which can process the entire minibatch data to generate a feature that is consistent with the current sample. x The purpose of this layer is to enable the discriminator to make decisions based on the similarity of samples within a small batch, helping it better distinguish between generated and real samples, thereby improving the diversity of the generated model. Next, a synchronous back-substitution reduction method is used to obtain typical wind and solar power output scenarios, determine the uncertainty representation of wind and solar power output in various regions, and utilize power flow simulation and maximum output calculations for controllable resources other than energy storage to obtain the overall net load fluctuation curve for each node in the system.

[0047] Use the synchronous back-substitution reduction method to reduce the generated scenery scene: first, use the data of the generated scene as the initial scene, iterate in a loop, and remove the closest scene each time, defining the Kantorovich distance as the distance indicator:

[0048] (2)

[0049] Where, Represents a scene collection With scene collection the distance between them; N Indicates the number of scenes in the collection, and Represents a collection of scenes and Specific scenes inside; and Indicates separation and The probability of the scenario occurring; express and The Euclidean distance of the scene. The indicator indicates that the sum of the distances between the scenes in the scene set is the smallest. The scene with the smallest distance is eliminated and the probability of the retained scene is updated to obtain the typical wind and solar output scene, as shown in the following example. Figure 4 Afterwards, the Newton-Raphson method is used to perform power flow analysis on the system.

[0050] S2. Use variational mode decomposition to decompose the net load fluctuation of nodes in each region into frequency bands, mainly by minimizing the following variational problem:

[0051] (3);

[0052] Where, K Indicates the decomposed mode The number of modes, each mode corresponds to a center frequency , Indicates timet The derivative of is the Dirac function, j represents the imaginary unit, represents the complex exponential function that shifts the mode to baseband, represents the modal components to be decomposed.

[0053] The time scale smoothing effect of each energy storage in different scenarios can be referred to Figure 3 As shown. By classifying the multi-energy storage in each region into power type and capacity type, formulas (4) and (5) are used as indicators for evaluating power type and capacity type; the hierarchical analysis method is combined with the entropy weight method to form a subjective and objective hierarchical analysis method to obtain a comprehensive evaluation result, and then the K-means algorithm is used to aggregate the evaluation results to obtain a preliminary cluster division of each energy storage to determine the number of modes to be decomposed;

[0054] Different frequency bands correspond to corresponding types of energy storage output. Frequency bands with larger net load amplitudes correspond to power-type energy storage, and frequency bands with smaller amplitudes but stronger volatility correspond to power-type energy storage. Specifically, the amplitudes (the difference between the peak and the trough) are arranged from small to large, and correspond one-to-one with the divided energy storage. Frequency bands with large amplitudes correspond to cluster energy storage with large power support scores, and frequency bands with smaller amplitudes but stronger volatility correspond to energy storage with large support assessment scores. Overall, they correspond one-to-one.

[0055] The degree of matching between energy storage and net load curve is measured from the perspectives of power support and duration. The expression is as follows:

[0056] (4);

[0057] (5);

[0058] Where, Indicates the maximum net load of each node; Indicates the maximum charge and discharge value of stored energy; and They represent the moment values ​​of net load in peak and valley states respectively. Indicates the maximum available capacity of energy storage, and Indicates the discharge and charging efficiency of the corresponding energy storage. and They respectively represent the power support capability of each node energy storage for the corresponding net load and the relative sustainable working time under the corresponding charging and discharging states.

[0059] In different regions, the effect of energy storage output on the node is mainly based on the optimal state of energy storage and the consideration of electrical distance. Figure 2As shown in the figure, the optimal energy state for all energy storage is defined as 50% of its capacity. The maximum output during peak and valley periods is selected, and system power flow simulation is performed for each energy storage unit. The point at which the energy storage output satisfies the net load fluctuation is set to zero. The standard deviation is used to calculate the fluctuation of power consumption at each node before and after the energy storage unit is integrated.

[0060] S3. Use Minkowski summation to aggregate the operation models of energy storage in the same cluster and establish an optimization model. The optimization goal is to achieve maximum smoothing of power fluctuations while minimizing the economic operation cost of the entire system energy storage. The objective function is:

[0061] (5);

[0062] Where, and Represents energy storage S The charge and discharge power at time t, F (·) represents the optimization objective function; The net load curve of the system and the energy storage output cost are two influencing factors. The net load curve shows the overall output effect of the energy storage, and the energy storage output cost shows the minimum overall operating cost of the energy storage system. Represents the sub-function with the minimum energy storage operation cost, and The energy storage charging and discharging output is the independent variable.

[0063] The operation of other controllable units in the system is not considered. The energy storage operating constraints are the same as traditional operating constraints. Only the effect of energy storage on smoothing net load fluctuations is considered. By calculating the aggregated energy storage model and comparing it with the original aggregate model, the difference between the output of each cluster and the original model boundary is compared to determine the energy storage within the cluster to be adjusted.

[0064] S4. Through simulation optimization, according to the effect of each energy storage within the cluster on smoothing the local net load fluctuation, determine the energy storage units that need to be re-divided and aggregated. The energy storage output effect in one iteration is as follows: Figure 5 As shown, the above step S3 is performed again.

[0065] Iteration is performed based on the Nash equilibrium principle: the energy storage at each node of the system is considered as a participant, the overall minimum net load fluctuation in each region of the system is taken as the Nash equilibrium point, and the division results between different energy storage are used as the strategy set. Through the loop step S3 process, each energy storage cluster division is adjusted until the output status of all energy storage is approximately consistent, thus achieving the system Nash equilibrium.

[0066] like Figure 5As shown in the figure on the right, the boundaries of the aggregated energy storage model are characterized to determine the boundaries of the energy storage parameters at the power and energy levels. The boundaries of the aggregated model are incorporated into the scheduling process to obtain the day-ahead time-series output curve of the energy storage. It can be seen that the upper and lower boundaries of the model's power and capacity effectively limit the energy storage output, indicating that the division and aggregation of clustered energy storage can effectively participate in system scheduling.

[0067] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An optimization method considering a multi-element cluster energy storage dynamic reconfigurable model, characterized in that: The following steps are involved: S1. Based on the historical output data of wind power and photovoltaic power in each region, an improved conditional generative adversarial network is used to generate the time-series output scenarios of wind power and photovoltaic power in each region of the province. The synchronous back-substitution reduction method is used to reduce the typical wind and solar power output scenarios in each region. Considering the controllable resources and load forecast data of each region in the province, the Newton-Raphson method is used to perform power flow simulation calculations to comprehensively obtain the net load fluctuation curve of each node in the system. S2. Perform variational modal decomposition on the net load fluctuation curves of each region. Based on the degree of fluctuation of the curves in each frequency band, perform temporal and spatial matching for power-type and capacity-type energy storage, respectively. Consider using power-type and capacity-type energy storage to smooth the high-frequency and low-frequency components of each load curve, respectively. S3. Use Minkowski summation to obtain the aggregation model of cluster energy storage in various time and space conditions. Considering the best effect of smoothing net load fluctuations in each region at the minimum economic cost as the optimization goal, the optimization goal is to achieve the maximum smoothing of power fluctuations while minimizing the overall economic operation cost of the system energy storage. The objective function is: (5)； Where, and Represents energy storage S exist t Charging and discharging power at all times; F (·) represents the optimization objective function; The net load curve of the system and the energy storage output cost are two influencing factors. The net load curve shows the overall output effect of the energy storage, and the energy storage output cost shows the minimum overall operating cost of the energy storage system. Represents the sub-function with the minimum energy storage operation cost, and Energy storage charging and discharging output is the independent variable; S4. Analyze the output of each energy storage within the obtained cluster energy storage group, readjust and aggregate the cluster division results, and optimize the obtained model again until the net load balancing effect of each cluster energy storage on each region is balanced, and finally determine the matching degree between each energy storage to be utilized and the corresponding region at the provincial level.

2. The optimization method according to claim 1 considering a multi-element cluster energy storage dynamic reconfigurable model is characterized in that: In step S1, the wind and solar historical data are trained based on the improved conditional generative adversarial network to complete the generation of wind power and photovoltaic output scenarios. The conditional generative adversarial network consists of a generator and a discriminator. A gradient descent layer is added after the convolution layer inside the discriminator to encourage the generator to generate more diverse samples. The specific formula is: (1)； Where, x and y Represent the samples of input discriminator D respectively x and the internal conditions of the discriminator y , represents the neural network output of the discriminator, represents the internal activation function, It is the feature calculated by the Minibatch Discrimination layer, which can process the entire minibatch data to generate a feature that is consistent with the current sample. x Additional information about.

3. The optimization method according to claim 1 considering a multi-element cluster energy storage dynamic reconfigurable model, characterized in that: In step S1, the generated scenery scene is reduced using a synchronous back-substitution reduction method: first, the data of the generated scene is used as the initial scene, and the loop is iterated, each time the closest scene is eliminated, and the Kantorovich distance is defined as the distance indicator, which is expressed as: (2) Where, Represents a scene collection With scene collection The distance between the scenes, the indicator indicates that the sum of the distances between the scenes in the scene set is the smallest; N i Indicates the number of scenes in the collection, and Represents a collection of scenes and Specific scenes inside; and Indicates separation and The probability of the scenario occurring; express and Euclidean distance of the scene.

4. The optimization method according to claim 1, wherein: In step S2, variational mode decomposition is used to decompose the net load fluctuation of nodes in each region into frequency bands, and the following variational problem is expanded by minimizing: (3)； Where, K Indicates the decomposed mode The number of modes, each mode corresponds to a center frequency , Indicates time t The derivative of is the Dirac function, j represents the imaginary unit, represents the complex exponential function that shifts the mode to baseband, represents the modal components to be decomposed.

5. The optimization method according to claim 1 considering a multi-element cluster energy storage dynamic reconfigurable model, characterized in that: In step S2, the multi-element energy storage in each region is divided into power type and capacity type, and formulas (4) and (5) are used as indicators for evaluating power type and capacity type. A hierarchical analysis method is combined with an entropy weight method to form a subjective and objective hierarchical analysis method to obtain a comprehensive evaluation result. Then, the evaluation results are aggregated using the K-means algorithm to obtain a preliminary cluster division for each energy storage to determine the number of modes to be decomposed. Different frequency bands correspond to corresponding types of energy storage output. Frequency bands with larger net load amplitudes correspond to power-type energy storage, while frequency bands with smaller amplitudes but greater volatility correspond to power-type energy storage. The degree of matching between energy storage and net load curve is measured from the perspectives of power support and duration. The expression is as follows: (4)； (5)； Where, Indicates the maximum net load of each node; Indicates the maximum charge and discharge value of stored energy; and Respectively represent the net load values ​​at peak and valley states. Indicates the maximum available capacity of energy storage, and Indicates the discharge and charging efficiency of the corresponding energy storage, and They respectively represent the power support capability of each node energy storage for the corresponding net load and the relative sustainable working time under the corresponding charging and discharging states.

6. The optimization method according to claim 1 considering a multi-element cluster energy storage dynamic reconfigurable model, characterized in that: In step S4, through simulation optimization, based on the effect of each energy storage within the cluster on smoothing local net load fluctuations, the energy storage units that need to be re-divided and aggregated are determined, and step S3 is repeatedly executed, and iteration is performed based on the Nash equilibrium principle: the energy storage at each node of the system is regarded as a participant, the overall minimum net load fluctuation in each region of the system is regarded as the Nash equilibrium point, and the division results between different energy storage are regarded as the strategy set. By looping step S3, each energy storage cluster division is adjusted until the output status of all energy storage is approximately consistent, thus achieving system Nash equilibrium.

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