Clustering analysis-based net load multi-scale modeling method

Abstract

The invention provides a net load multi-scale modeling method based on clustering analysis, and the method comprises the steps: collecting historical net load data, including net load values of different time points and different regions and related influence factor data; clustering the net load data by using an improved K-Means clustering algorithm based on a maximum mutual information coefficient, and dividing the net load data into different spatial scales; a corresponding model is established for each spatial scale. According to the payload multi-scale modeling method, the spatial and temporal distribution characteristics of the payload can be described more accurately, the change rule of the payload in different areas is analyzed, and a basis is provided for optimal scheduling of a power system.

Description

Technical Field

[0001] The present invention belongs to the field of power system and distribution system control, and relates to a net load multi-scale modeling method, and more specifically, to a net load multi-scale modeling method based on cluster analysis. Background Art

[0002] In the broad context of today's global energy transformation, with the pursuit of sustainable development and increasing concern about climate change, the proportion of renewable energy in the power system is increasing at an unprecedented rate. However, the output of renewable energy is obviously intermittent and uncertain, which brings great challenges to the stable operation and reasonable planning of the power system.

[0003] Net load is the difference between the actual load in the power system and the power generation from renewable energy. Its fluctuation characteristics have a crucial impact on the stability and reliability of the power system. When renewable energy is connected on a large scale, the changes in net load are more complex and diverse, and traditional modeling methods often have difficulty in accurately describing its dynamic characteristics. Summary of the invention

[0004] Technical problem: The purpose of the present invention is to provide a net load multi-scale modeling method based on cluster analysis, which can effectively capture the multi-spatial scale characteristics of the net load, thereby significantly improving the accuracy and reliability of net load modeling.

[0005] Technical solution: The present invention is implemented by the following technical means:

[0006] A multi-scale modeling method for net load based on cluster analysis, the method comprising the following steps:

[0007] 1) Collect historical net load data, including net load values ​​at different time points and in different regions, as well as relevant influencing factors;

[0008] 2) The improved K-Means clustering algorithm based on the maximum mutual information coefficient is used to cluster the net load data and divide it into different spatial scales;

[0009] 3) Build models for each spatial scale.

[0010] In the step 1), 96 pieces of distribution system load-related data are selected every day, one piece is selected every 15 minutes, and the load data of each time period are selected for analysis.

[0011] In step 2), cluster analysis is performed on the load data, including the following steps:

[0012] 11) According to the load data of the distribution system obtained in step 2), since the load changes over time and there is a close correlation between the load characteristics at different times, clustering based on conventional static data often ignores the temporal correlation. Therefore, the improved K-Means time series clustering method is used to comprehensively and accurately aggregate and analyze loads at different spatial scales. The specific calculation formula is as follows:

[0013] The collected data samples are: P = (x1, x2, ..., x n ) and Q=(y1,y2,…y n ), the specific formula of distance measurement is as follows:

[0014]

[0015] Wherein, when p=1, it represents Manhattan distance; when p=2, it represents Euclidean distance (also known as Euclidean distance).

[0016] 12) Based on the historical data of different load types (working area / commercial area / residential area) in step 11), the clustering model input is constructed as follows:

[0017]

[0018] In the formula, x t1 Represents the first point load data of day t (15min sampling scale, 96 points in total per day)

[0019] By performing time series clustering on the load data of power supply areas at different spatial scales over multiple historical days, and then analyzing the integration of grid topology, adjustable resources, and operating loads based on typical scenarios, an accurate aggregation model is constructed.

[0020] In step 3), load models at different spatial scales are established according to the clustering results, as follows:

[0021] Substation power supply area aggregation model:

[0022] Time series clustering is performed for different types of loads, and the load operation characteristics of the distributed photovoltaic / distributed energy storage nodes are integrated as follows:

[0023] P i =P n +P pv +P es (3)

[0024] Where P n is the original operating load of node n; P pv Output for distributed photovoltaic; P es Contribute to distributed energy storage.

[0025] Through time series clustering analysis, we can obtain the typical load operation scenarios of different types of nodes after aggregation at different times, effectively depict the 96-point change pattern of each type of load in a day, accurately grasp the power consumption pattern, thereby optimizing power dispatching and regulating the operation of the entire power grid.

[0026] Feeder power supply area aggregation model:

[0027] A feeder load equivalent model is established, which includes the objective function and constraints of the model.

[0028] 1) Objective function

[0029]

[0030] The objective function of the feeder load equivalent model includes minimizing and maximizing the export power of the feeder substation at each time, where the subscript t is the time interval index, T is the time interval set, and P SUB,t is the export active power of the feeder substation at time t, Q SUB,t is the export reactive power of the feeder substation at time t.

[0031] 2) Constraints

[0032] The feeder load equivalent model constraints include distribution network operation constraints, distribution network safety constraints, and controllable equipment operation constraints.

[0033] a. Distribution network operation constraints

[0034]

[0035] Subscript ij / jk is the branch index, subscript i is the node index, subscript p(j) / e(j) is the PV power station / energy storage power station index corresponding to node j, subscript e is the energy storage index, N PV is the photovoltaic power station collection, N ESS For energy storage, PL ij,t / PL jk,t is the active power of the first end branch of branch ij or jk at time t, QL ij,t / QL jk,t is the reactive power of the first end branch of branch ij or jk at time t, P j,t is the active load of node j at time t, Q j,t is the reactive load of node j at time t, U i,t is the voltage amplitude of node i at time t, P PV,p(j),t is the active power output of photovoltaic power station p(j) at time t, P ESS,e(j),t is the load of energy storage e(j) at time t, Q PV,p(j),t is the reactive power output of photovoltaic power station p(j) at time t, rij and x ij are the resistance and reactance of the branch from i to j.

[0036] b. Distribution network security constraints

[0037] The security constraints of the distribution network consist of voltage amplitude constraints and transmission capacity constraints. max / U min Indicates the upper / lower limit of the voltage amplitude in the distribution network, S max Indicates the maximum transmission capacity of the distribution network branch.

[0038]

[0039] c. Energy storage operation constraints

[0040] The operating constraints of energy storage include the real-time state of charge equation, the energy balance of energy storage during the optimization process, the upper and lower limit constraints of the state of charge of energy storage, the inability of energy storage to charge and discharge at the same time, the charging and discharging decisions of energy storage are binary variables, and the output power constraints of energy storage. ESS represents the energy storage set, E e,t represents the state of charge of energy storage e at time t, E e,ini represents the initial charge state of the e-th energy storage, represents the capacity of the e-th energy storage, represents the rated power of the e-th energy storage, μ ESS Indicates the charging and discharging efficiency of energy storage, represents the charging decision of the e-th energy storage at time slot t, represents the discharge decision of the e-th energy storage at time slot t, E max / min Indicates the upper / lower limit of the energy storage charge state.

[0041]

[0042] d. Operation constraints of photovoltaic power plants

[0043]

[0044] The subscript p is the index of the photovoltaic power station, P PV,p,t is the active power output of photovoltaic power station p at time t, Q PV,p,t is the reactive power output of photovoltaic power station p(j) at time t, S PV,max Indicates the photovoltaic rated power.

[0045] District power supply area aggregation model:

[0046] A load equivalent model for the partitioned power supply area is established. The difference between the partitioned power supply area aggregation model and the feeder aggregation model is that the partitioned power supply area contains multiple feeders and energy storage equipment on the low-voltage side. The objective function and constraints contained in the partitioned power supply area aggregation model are as follows.

[0047] 1) Objective function

[0048]

[0049] The objective function of the load equivalent model in the power supply area includes minimizing and maximizing the sum of the export power of each feeder substation and the power of the energy storage connected to the bus at each moment, where subscript t is the time interval index, subscript c is the subscript of the energy storage connected to the bus, subscript f is the subscript of the feeder connected to the bus, C is the set of energy storage connected to the bus, T is the time interval set, F is the set of feeders connected to the bus, and P AREA,t is the total active power of the power supply area at time t, Q AREA,t is the total reactive power of the power supply area at time t, P SUB,f,t is the active power at the outlet of the f-th feeder substation at time t, Q SUB,f,t is the reactive power at the outlet of the f-th feeder substation at time t, is the power of the energy storage c connected to the bus at time t.

[0050] 2) Constraints

[0051] The load equivalent model constraints in the power supply area include distribution network operation constraints, distribution network safety constraints, and controllable equipment operation constraints.

[0052] a. Distribution network operation constraints

[0053]

[0054] Subscript ij / jk is the branch index, subscript i is the node index, subscript f is the feeder index, and subscript p(j) / e(j) is the PV power station / energy storage power station index corresponding to node j. is the set of photovoltaic power stations under feeder f, is the energy storage set under feeder f, is the active power of the head end branch of feeder branch ij or jk at time t, is the reactive power of the head end branch of feeder branch ij or jk at time t, is the active load of node j on feeder f at time t, is the reactive load of node j on feeder f at time t, is the voltage amplitude of node i of feeder f at time t, is the active power output of the p(j) photovoltaic power station in the f feeder at time t, is the load of energy storage e(j) in feeder f at time t, is the reactive power output of the p(j) photovoltaic power station in the f feeder at time t, and are the resistance and reactance of the branch from i to j in feeder f.

[0055] b. Distribution network security constraints

[0056] The security constraints of the distribution network consist of voltage amplitude constraints and transmission capacity constraints. max / U min Indicates the upper / lower limit of the voltage amplitude in the distribution network, S max represents the maximum transmission capacity of the distribution network branch, is the active power at the head end of node j of feeder f and node h of feeder f′, is the reactive power at the head end of node j of feeder f and node h of feeder f′.

[0057]

[0058] c. Energy storage operation constraints

[0059] The operating constraints of energy storage include the real-time state of charge equation, the energy balance of energy storage during the optimization process, the upper and lower limit constraints of the state of charge of energy storage, the inability of energy storage to charge and discharge at the same time, the charging and discharging decisions of energy storage are binary variables, and the output power constraints of energy storage. Among them, the subscript e represents the energy storage index, represents the energy storage set, represents the charge state of the energy storage e in the feeder f at time t, represents the initial charge state of the e-th energy storage in feeder f, represents the capacity of the e-th energy storage in feeder f, represents the rated power of the e-th energy storage in feeder f, μ ESS Indicates the charging and discharging efficiency of energy storage, represents the charging decision of the e-th energy storage in feeder f at time slot t, represents the discharge decision of the e-th energy storage in feeder f at time slot t, E max / min Indicates the upper / lower limit of the energy storage charge state.

[0060]

[0061] d. Energy storage operation constraints

[0062]

[0063] The subscript p is the photovoltaic power station index, is the active power output of the photovoltaic power station p in the feeder f at time t, Q PV,p,tis the reactive power output of the photovoltaic power station p in the feeder f at time t, S PV,max Indicates the photovoltaic rated power.

[0064] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0065] The present invention proposes a multi-scale modeling method for net load based on cluster analysis. By dividing the net load data into different spatial scales through cluster analysis, the characteristics and change laws of net load at different scales can be better captured, thereby improving the accuracy of modeling. The essential characteristics of net load can be revealed from different angles, not only short-term load fluctuations can be analyzed, but also long-term trend changes and seasonal laws can be studied, providing more comprehensive information for the planning, operation and management of power systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a schematic flow chart of the method of the present invention.

[0067] Figure 2 This is a feeder topology diagram in an embodiment of the present invention.

[0068] Figure 3 It is a schematic diagram of the work area load clustering result in an embodiment of the present invention.

[0069] Figure 4 It is a schematic diagram of the load clustering result of the commercial area in an embodiment of the present invention.

[0070] Figure 5 It is a schematic diagram of the load clustering result of the residential area in an embodiment of the present invention. DETAILED DESCRIPTION

[0071] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is described in detail below in conjunction with the accompanying drawings and implementation examples. It should be understood that the specific implementation examples described herein are only used to explain the present invention and are not used to limit the invention.

[0072] In one embodiment of the method of the present invention, the specific steps are as follows:

[0073] 1) Collect historical net load data, including net load values ​​at different time points and in different regions, as well as relevant influencing factors;

[0074] 2) The improved K-Means clustering algorithm based on the maximum mutual information coefficient is used to cluster the net load data and divide it into different spatial scales;

[0075] 3) Build models for each spatial scale.

[0076] In step 1), 96 data related to the load of the distribution system are selected every day, one data is selected every 15 minutes, and the load data of each time period is selected for analysis.

[0077] In step 2), cluster analysis is performed on the load data, including the following steps:

[0078] 11) According to the load data of the distribution system obtained in step 2), since the load changes over time and there is a close correlation between the load characteristics at different times, clustering based on conventional static data often ignores the temporal correlation. Therefore, the improved K-Means time series clustering method is used to comprehensively and accurately aggregate and analyze loads at different spatial scales. The specific calculation formula is as follows:

[0079] The collected data samples are: P = (x1, x 2, …,x n ) and Q=(y1,y2,…y n ), the specific formula of distance measurement is as follows:

[0080]

[0081] Wherein, when p=1, it represents Manhattan distance; when p=2, it represents Euclidean distance (also known as Euclidean distance).

[0082] 12) Based on the historical data of different load types (working area / commercial area / residential area) in step 11), the clustering model input is constructed as follows:

[0083]

[0084] In the formula, x t1 Represents the first point load data of day t (15min sampling scale, 96 points in total per day)

[0085] By performing time series clustering on the load data of power supply areas at different spatial scales over multiple historical days, and then analyzing the integration of grid topology, adjustable resources, and operating loads based on typical scenarios, an accurate aggregation model is constructed.

[0086] In step 3), load models at different spatial scales are established based on the clustering results, as follows:

[0087] Substation power supply area aggregation model:

[0088] Time series clustering is performed for different types of loads, and the load operation characteristics of the distributed photovoltaic / distributed energy storage nodes are integrated as follows:

[0089] P i =P n +Ppv +P es

[0090] Where P n is the original operating load of node n; P pv Output for distributed photovoltaic; P es Contribute to distributed energy storage.

[0091] Through time series clustering analysis, we can obtain the typical load operation scenarios of different types of nodes after aggregation at different times, effectively depict the 96-point change pattern of each type of load in a day, accurately grasp the power consumption pattern, thereby optimizing power dispatching and regulating the operation of the entire power grid.

[0092] Feeder power supply area aggregation model:

[0093] A feeder load equivalent model is established, which includes the objective function and constraints of the model.

[0094] 1) Objective function

[0095]

[0096] The objective function of the feeder load equivalent model includes minimizing and maximizing the export power of the feeder substation at each time, where the subscript t is the time interval index, T is the time interval set, and P SUB,t is the export active power of the feeder substation at time t, Q SUB,t is the export reactive power of the feeder substation at time t.

[0097] 2) Constraints

[0098] The feeder load equivalent model constraints include distribution network operation constraints, distribution network safety constraints, and controllable equipment operation constraints.

[0099] a. Distribution network operation constraints

[0100]

[0101] Subscript ij / jk is the branch index, subscript i is the node index, subscript p(j) / e(j) is the PV power station / energy storage power station index corresponding to node j, subscript e is the energy storage index, N PV is the photovoltaic power station collection, N ESS For energy storage, PL ij,t / PL jk,t is the active power of the first end branch of branch ij or jk at time t, QL ij,t / QL jk,t is the reactive power of the first end branch of branch ij or jk at time t, P j,t is the active load of node j at time t, Q j,tis the reactive load of node j at time t, U i,t is the voltage amplitude of node i at time t, P PV,p(j),t is the active power output of photovoltaic power station p(j) at time t, P ESS,e(j),t is the load of energy storage e(j) at time t, Q PV,p(j),t is the reactive power output of photovoltaic power station p(j) at time t.

[0102] b. Distribution network security constraints

[0103] The security constraints of the distribution network consist of voltage amplitude constraints and transmission capacity constraints. max / U min Indicates the upper / lower limit of the voltage amplitude in the distribution network, S max Indicates the maximum transmission capacity of the distribution network branch.

[0104]

[0105] c. Energy storage operation constraints

[0106] The operating constraints of energy storage include the real-time state of charge equation, the energy balance of energy storage during the optimization process, the upper and lower limit constraints of the state of charge of energy storage, the inability of energy storage to charge and discharge at the same time, the charging and discharging decisions of energy storage are binary variables, and the output power constraints of energy storage. ESS represents the energy storage set, E e,t represents the state of charge of energy storage e at time t, E e,ini represents the initial charge state of the e-th energy storage, represents the capacity of the e-th energy storage, represents the rated power of the e-th energy storage, μ ESS Indicates the charging and discharging efficiency of energy storage, represents the charging decision of the e-th energy storage at time slot t, represents the discharge decision of the e-th energy storage at time slot t, E max / min Indicates the upper / lower limit of the energy storage charge state.

[0107]

[0108] d. Operation constraints of photovoltaic power plants

[0109]

[0110] The subscript p is the index of the photovoltaic power station, P PV,p,t is the active power output of photovoltaic power station p at time t, Q PV,p,t is the reactive power output of photovoltaic power station p(j) at time t, S PV,max Indicates the photovoltaic rated power.

[0111] District power supply area aggregation model:

[0112] A load equivalent model for the partitioned power supply area is established. The difference between the partitioned power supply area aggregation model and the feeder aggregation model is that the partitioned power supply area contains multiple feeders and energy storage equipment on the low-voltage side. The objective function and constraints contained in the partitioned power supply area aggregation model are as follows.

[0113] 1) Objective function

[0114]

[0115] The objective function of the load equivalent model in the power supply area includes minimizing and maximizing the sum of the export power of each feeder substation and the power of the energy storage connected to the bus at each moment, where subscript t is the time interval index, subscript c is the subscript of the energy storage connected to the bus, subscript f is the subscript of the feeder connected to the bus, C is the set of energy storage connected to the bus, T is the time interval set, F is the set of feeders connected to the bus, and P AREA,t is the total active power of the power supply area at time t, Q AREA,t is the total reactive power of the power supply area at time t, P SUB,f,t is the active power at the outlet of the f-th feeder substation at time t, Q SUB,f,t is the reactive power at the outlet of the f-th feeder substation at time t, is the power of the energy storage c connected to the bus at time t.

[0116] 2) Constraints

[0117] The load equivalent model constraints in the power supply area include distribution network operation constraints, distribution network safety constraints, and controllable equipment operation constraints.

[0118] a. Distribution network operation constraints

[0119]

[0120] Subscript ij / jk is the branch index, subscript i is the node index, subscript f is the feeder index, and subscript p(j) / e(j) is the PV power station / energy storage power station index corresponding to node j. is the set of photovoltaic power stations under feeder f, is the energy storage set under feeder f, is the active power of the head end branch of feeder branch ij or jk at time t, is the reactive power of the head end branch of feeder branch ij or jk at time t, is the active load of node j on feeder f at time t, is the reactive load of node j on feeder f at time t, is the voltage amplitude of node i of feeder f at time t, is the active power output of the p(j) photovoltaic power station in the f feeder at time t, is the load of energy storage e(j) in feeder f at time t, is the reactive power output of PV power station No. p(j) in feeder f at time t.

[0121] b. Distribution network security constraints

[0122] The security constraints of the distribution network consist of voltage amplitude constraints and transmission capacity constraints. max / U min Indicates the upper / lower limit of the voltage amplitude in the distribution network, S max represents the maximum transmission capacity of the distribution network branch, is the active power at the head end of node j of feeder f and node h of feeder f′, is the reactive power at the head end of node j of feeder f and node h of feeder f′.

[0123]

[0124] c. Energy storage operation constraints

[0125] The operating constraints of energy storage include the real-time state of charge equation, the energy balance of energy storage during the optimization process, the upper and lower limit constraints of the state of charge of energy storage, the inability of energy storage to charge and discharge at the same time, the charging and discharging decisions of energy storage are binary variables, and the output power constraints of energy storage. Among them, the subscript e represents the energy storage index, represents the energy storage set, represents the charge state of the energy storage e in the feeder f at time t, represents the initial charge state of the e-th energy storage in feeder f, represents the capacity of the e-th energy storage in feeder f, represents the rated power of the e-th energy storage in feeder f, μ ESS Indicates the charging and discharging efficiency of energy storage, represents the charging decision of the e-th energy storage in feeder f at time slot t, represents the discharge decision of the e-th energy storage in feeder f at time slot t, E max / min Indicates the upper / lower limit of the energy storage charge state.

[0126]

[0127] d. Energy storage operation constraints

[0128]

[0129] The subscript p is the photovoltaic power station index, is the active power output of the photovoltaic power station p in the feeder f at time t, Q PV,p,tis the reactive power output of the photovoltaic power station p in the feeder f at time t, S PV,max Indicates the photovoltaic rated power.

[0130] Based on the actual power supply area of ​​a province or city, a case analysis is performed. A single feeder in the power supply zone is taken for example. The feeder topology diagram is shown as follows: Figure 2 As shown. The load types in this feeder are divided into residential loads, work area loads and commercial area loads. Distributed energy sources such as photovoltaics and energy storage are also connected. The data taken are the actual operating load data of the power grid between April 1 and December 31, 2021, with a sampling interval of 15 minutes. Time series clustering is performed on the data of each typical load from April to December, that is, the load data of 96 time points in one consecutive day is used as a set of features, for a total of 276 days for 9 consecutive months. The number of each cluster center is iteratively calculated 30 times. The clustering results are shown in the improved K-Means clustering algorithm based on the maximum mutual information coefficient. Figure 3 , Figure 4 and Figure 5 shown.

Claims

1. A multi-scale modeling method for net load based on cluster analysis, characterized in that: The method comprises the following steps: 1) Collect historical net load data, including net load values ​​at different time points and in different regions, as well as relevant influencing factors; 2) The improved K-Means clustering algorithm based on the maximum mutual information coefficient is used to cluster the net load data and divide it into different spatial scales; 3) Build models for each spatial scale.

2. The method for multi-scale modeling of net load based on cluster analysis according to claim 1, characterized in that: In the step 1), 96 pieces of distribution system load-related data are selected every day, one piece is selected every 15 minutes, and the load data of each time period are selected for analysis.

3. The method for multi-scale modeling of net load based on cluster analysis according to claim 1, characterized in that: The cluster analysis of the load data in step 2) includes the following steps: 11) Calculate the maximum mutual information coefficient based on the distribution system load data obtained in step 2), screen the features based on the maximum mutual information coefficient value, retain the features with a high correlation with the spatial scale, reduce the interference of irrelevant features on the clustering results, and improve the accuracy of spatial scale division. 12) According to the results obtained in step 11), representative load data points of different spatial scales in the load data are selected as initial clustering centers, and the K-Means time series clustering method is used to perform aggregation analysis on loads of different spatial scales.

4. The method for multi-scale modeling of net load based on cluster analysis according to claim 3 is characterized in that: In step 12), three data points are selected as the initial cluster centers, and then the distance between the data points of the distribution system load data set and each cluster center is calculated, and it is assigned to the cluster center closest to it. The load data is P = (x1, x2). 2, …,x n ) and Q=(y1,y2,…y n ), the distance calculation formula between the data is as follows: Wherein, when p=1, it represents Manhattan distance; when p=2, it represents Euclidean distance.

5. The method for multi-scale modeling of net load based on cluster analysis according to claim 3, characterized in that: Step 12) The input of the clustering model constructed is as follows: In the formula, x t1 represents the first point load data on day t, x t2 Represents the second point load data on day t, and so on.

6. The method for multi-scale modeling of net load based on cluster analysis according to claim 1, characterized in that: In the step 3), load models at different spatial scales are established according to the clustering results, including: a regional aggregation model for power supply in the substation, a regional aggregation model for power supply in the feeder line, and a regional aggregation model for power supply in the subzone.

7. The method for multi-scale modeling of net load based on cluster analysis according to claim 6, characterized in that: The power supply area aggregation model of the substation is: P i =P n +P pv +P es (3) Where P n is the original operating load of node n; P pv Output for distributed photovoltaic; P es Contribute to distributed energy storage.

8. The method for multi-scale modeling of net load based on cluster analysis according to claim 6, characterized in that: The objective function of the feeder power supply area aggregation model is: The objective function of the feeder load equivalent model includes minimizing and maximizing the export power of the feeder substation at each time, where the subscript t is the time interval index, T is the time interval set, and P SUB,t is the export active power of the feeder substation at time t, Q SUB,t is the export reactive power of the feeder substation at time t; 9. The method for multi-scale modeling of net load based on cluster analysis according to claim 6, characterized in that: The constraints of the feeder power supply regional aggregation model include distribution network operation constraints, distribution network safety constraints, energy storage operation constraints and photovoltaic power station operation constraints. The distribution network operation constraints are specifically: Subscript ij / jk is the branch index, subscript i is the node index, subscript p(j) / e(j) is the PV power station / energy storage power station index corresponding to node j, subscript e is the energy storage index, N PV is the photovoltaic power station collection, N ESS For energy storage, PL ij,t / PL jk,t is the active power of the first end branch of branch ij or jk at time t, QL ij,t / QL jk,t is the reactive power of the first end branch of branch ij or jk at time t, P j,t is the active load of node j at time t, Q j,t is the reactive load of node j at time t, U i,t is the voltage amplitude of node i at time t, P PV,p(j),t is the active power output of photovoltaic power station p(j) at time t, P ESS,e(j),t is the load of energy storage e(j) at time t, Q PV,p(j),t is the reactive power output of photovoltaic power station p(j) at time t, r ij and x ij is the resistance and reactance of the branch from i to j; 10. The method for multi-scale modeling of net load based on cluster analysis according to claim 9, characterized in that: The security constraints of the distribution network consist of voltage amplitude constraints and transmission capacity constraints, specifically: Among them, U max / U min Indicates the upper / lower limit of the voltage amplitude in the distribution network, S max Indicates the maximum transmission capacity of the distribution network branch; 11. The method for multi-scale modeling of net load based on cluster analysis according to claim 9, characterized in that: The energy storage operation constraints are: Wherein, the subscript e represents the energy storage index, N ESS represents the energy storage set, E e,t represents the state of charge of energy storage e at time t, E e,ini represents the initial state of charge of the e-th energy storage, represents the capacity of the e-th energy storage, represents the rated power of the e-th energy storage, μ ESS Indicates the charging and discharging efficiency of energy storage, represents the charging decision of the e-th energy storage at time slot t, represents the discharge decision of the e-th energy storage at time slot t, E max / min Indicates the upper / lower limit of the energy storage charge state; The operation constraints of the photovoltaic power station are: The subscript p is the index of the photovoltaic power station, P PV,p,t is the active power output of photovoltaic power station p at time t, Q PV,p,t is the reactive power output of photovoltaic power station p(j) at time t, S PV,max Indicates the photovoltaic rated power.

12. The method for multi-scale modeling of net load based on cluster analysis according to claim 6, characterized in that: The objective function of the regional power supply aggregation model is: Wherein, subscript t is the time interval index, subscript c is the subscript of the energy storage connected to the bus, subscript f is the subscript of the feeder connected to the bus, C is the set of energy storage connected to the bus, T is the time interval set, F is the set of feeders connected to the bus, P AREA,t is the total active power of the power supply area at time t, Q AREA,t is the total reactive power of the power supply area at time t, P SUB,f,t is the active power at the outlet of the f-th feeder substation at time t, Q SUB,f,t is the reactive power at the outlet of the f-th feeder substation at time t, is the power of the energy storage c connected to the bus at time t; 13. The method for multi-scale modeling of net load based on cluster analysis according to claim 6, characterized in that: The constraints of the regional power supply aggregation model specifically include distribution network operation constraints, distribution network safety constraints and energy storage operation constraints. The distribution network operation constraints are: Subscript ij / jk is the branch index, subscript i is the node index, subscript f is the feeder index, and subscript p(j) / e(j) is the PV power station / energy storage power station index corresponding to node j. is the set of photovoltaic power stations under feeder f, is the energy storage set under feeder f, is the active power of the head end branch of feeder branch ij or jk at time t, is the reactive power of the head end branch of feeder branch ij or jk at time t, is the active load of node j on feeder f at time t, is the reactive load of node j on feeder f at time t, is the voltage amplitude of node i of feeder f at time t, is the active power output of the p(j) photovoltaic power station in the f feeder at time t, is the load of energy storage e(j) in feeder f at time t, is the reactive power output of the p(j) photovoltaic power station in the f feeder at time t, and is the resistance and reactance of the branch from i to j in feeder f; 14. The method for multi-scale modeling of net load based on cluster analysis according to claim 12, characterized in that: The distribution network security constraints consist of voltage amplitude constraints and transmission capacity constraints, specifically: Among them, U max / U min Indicates the upper / lower limit of the voltage amplitude in the distribution network, S max represents the maximum transmission capacity of the distribution network branch, is the active power at the head end of node j of feeder f and node h of feeder f′, is the reactive power at the head end of node j of feeder f and node h of feeder f′; 15. The method for multi-scale modeling of net load based on cluster analysis according to claim 12, characterized in that: The energy storage operation constraints include the real-time state of charge equation, the energy balance of the energy storage during the optimization process, the upper and lower limit constraints of the energy storage state of charge, the energy storage cannot be charged and discharged at the same time, the charging and discharging decisions of the energy storage are binary variables, and the output power constraints of the energy storage, which are as follows: Among them, the subscript e represents the energy storage index, which represents the energy storage set. represents the charge state of the energy storage e in the feeder f at time t, represents the initial charge state of the e-th energy storage in feeder f, represents the capacity of the e-th energy storage in feeder f, represents the rated power of the e-th energy storage in feeder f, μ ESS Indicates the charging and discharging efficiency of energy storage, represents the charging decision of the e-th energy storage in feeder f at time slot t, represents the discharge decision of the e-th energy storage in feeder f at time slot t, E max / min Indicates the upper / lower limit of the energy storage charge state; The subscript p is the photovoltaic power station index, is the active power output of the photovoltaic power station p in the feeder f at time t, Q PV,p,t is the reactive power output of the photovoltaic power station p in the feeder f at time t, S PV,max Indicates the photovoltaic rated power.

Images