Interconnection capacity optimization scheduling method between active distribution network containing wind power and photovoltaic power generation

Abstract

The application discloses a kind of active distribution network interconnection capacity optimization scheduling methods containing wind power and photovoltaic power generation, based on the correlation of wind and light power generation output and load demand in each active distribution network, wind and light power generation, judge whether the complementary correlation of wind and light power generation residual power of each active distribution network exists;If the judgment result is that there is no complementary correlation, no capacity interconnection is carried out;If the judgment result is that there is complementary correlation, the interconnection capacity optimization evaluation model of the multiple active distribution network of wind and light power generation residual power complementary correlation is established, and the interconnection capacity optimization evaluation model is solved, the interconnection capacity size is obtained, and the capacity interconnection between active distribution network is realized according to the interconnection capacity obtained by solving.The application improves resource utilization efficiency, ensures the stability of power supply, can realize the sharing and optimal allocation of power resources, and improves the utilization rate of new energy.

Description

Technical Field

[0001] This invention relates to the field of active distribution network optimization scheduling methods, specifically a method for optimizing the interconnection capacity of active distribution networks including wind and photovoltaic power generation. Background Technology

[0002] Globally, the installed capacity of clean renewable energy power generation equipment, represented by wind and solar power, is steadily increasing. However, wind and solar power generation is characterized by randomness and uncertainty, with uneven regional distribution and temporal fluctuations. Therefore, utilizing the interconnection between various active distribution networks to achieve grid interconnection, mutual backup, and peak-hour mutual assistance is of great significance.

[0003] Currently, there is a certain foundation for research on active distribution networks incorporating new energy sources (including wind and solar power). Some studies have proposed utilizing dedicated transformers in adjacent buildings to supply power to charging stations in interconnected or cross-building structures, thereby fully utilizing the redundant capacity of regional dedicated transformers to power charging stations in the region and maximizing the mutual support capacity between various active distribution networks. However, these studies have not considered the overall operation and economic benefits of each active distribution network incorporating new energy sources (including wind and solar power).

[0004] Meanwhile, some studies have established an optimal scheduling model for multi-microgrid systems, considering hybrid energy sharing sites to optimize the operational economy of multi-microgrid systems. However, this model only considers the energy interaction process between local active distribution networks and the main grid, without fully considering the mutual support capacity of interconnected active distribution networks.

[0005] In addition, other studies have focused on modeling and analyzing the uncertainties in renewable energy output, load, and power transmission lines. Inter-regional mutual assistance capacity is derived from their respective energy storage and backup power sources. However, the interconnection of renewable energy surplus power and load matching between active distribution networks containing renewable energy (including wind and solar power) has not been studied.

[0006] In summary, existing methods still have shortcomings in considering the overall operation and economic benefits of each active distribution network, the mutual support capacity between each active distribution network, and the matching of surplus power from new energy sources (including wind and photovoltaic power generation) with loads in each active distribution network. Summary of the Invention

[0007] This invention provides a method for optimizing the scheduling of interconnected capacity between active distribution networks that include wind and photovoltaic power generation, in order to solve the problem that existing technologies have not fully realized the interconnection of the capacity of various active distribution networks.

[0008] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0009] The method for optimizing the scheduling of interconnected capacity in active power distribution networks that include wind and solar power generation is as follows:

[0010] Based on the correlation between wind and solar power output and load demand in each active distribution network, and the remaining power of wind and solar power after matching wind and solar power output and load demand in each active distribution network, it is determined whether there is a complementary correlation between the remaining power of wind and solar power after matching wind and solar power output and load demand in each active distribution network.

[0011] If the judgment result is that there is no complementary correlation between the remaining power of wind and solar power generation after the wind and solar power output is matched with the load demand, then no capacity interconnection will be carried out between the active distribution networks.

[0012] If the judgment result indicates that there is a complementary correlation between the surplus power of wind and solar power generation in each source distribution network after matching the output of wind and solar power generation with the load demand, or if there is a complementary correlation between the surplus power of wind and solar power generation in some source distribution networks after matching the output of wind and solar power generation with the load demand, then an interconnection capacity optimization evaluation model for multiple active distribution networks with complementary surplus power of wind and solar power generation is established, and the interconnection capacity optimization evaluation model is solved to obtain the interconnection capacity size when the economic cost of operating multiple active distribution networks with complementary surplus power of wind and solar power generation is minimized. Based on the solved interconnection capacity, capacity interconnection between active distribution networks with complementary surplus power of wind and solar power generation is realized.

[0013] Furthermore, a Copula function is established to describe the correlation between wind and solar power output and load demand in each active distribution network. The Copula function is then used to describe the correlation between wind and solar power output and load demand in each active distribution network.

[0014] Furthermore, based on the Euclidean distance, the optimal Copula function is selected from the Copula functions that describe the correlation between wind and solar power output and load demand in each active distribution network, and the optimal Copula function is used to describe the correlation between wind and solar power output and load demand in each active distribution network.

[0015] Furthermore, based on the correlation between wind and solar power output and load demand in each active distribution network, and the remaining power of wind and solar power after matching wind and solar power output and load demand in each active distribution network, a correlation coefficient is established. The correlation coefficient is used to quantify the complementary correlation of the remaining power of wind and solar power in any two active distribution networks after matching wind and solar power output and load demand, thereby determining whether there is a complementary correlation of the remaining power of wind and solar power in each active distribution network after matching wind and solar power output and load demand.

[0016] Furthermore, in the interconnection capacity optimization evaluation model of the multi-active distribution network, the objective function is constructed with minimizing the overall operating cost of the multi-active distribution network.

[0017] Furthermore, the overall operating cost of the multi-active distribution network includes the cost of power interaction between the various active distribution networks, the cost of power interaction between the various active distribution networks and the external main grid, and the operating cost of thermal power generating units in each active distribution network.

[0018] Furthermore, the constraints of the interconnection capacity optimization evaluation model include the power balance constraints of each active distribution network, the power output constraints of wind and solar power generation and the operation constraints of thermal power generating units in each active distribution network, the power interaction constraints within each active distribution network, and the power interaction constraints between each active distribution network and the external main grid.

[0019] This invention analyzes the complementary characteristics of renewable energy output and load demand in each active distribution network containing wind and solar power from the perspective of source-load matching, as well as the feasibility of interconnecting surplus power between active distribution networks. Furthermore, this invention constructs an interconnection capacity optimization evaluation model. This model aims to minimize the overall operating cost of the active distribution network while simultaneously considering constraints on the remaining mutual support capacity after source-load matching and constraints on power interaction within and outside the system. This allows for the quantification of interconnection capacity between active distribution networks containing wind and solar power, thus fully utilizing the potential for interconnecting multiple distribution networks.

[0020] Compared with the prior art, the advantages of the present invention are:

[0021] 1. By optimizing the assessment of interconnection capacity between active distribution networks, the system's reserve capacity requirements can be reduced, thereby improving resource utilization efficiency.

[0022] 2. By optimizing the assessment of interconnection capacity and interaction between active distribution networks, the reliability of system operation can be improved, ensuring the stability of power supply.

[0023] 3. With the goal of minimizing the overall operating cost of the active distribution network, an optimization model is constructed to achieve cost-effectiveness for the system.

[0024] 4. Simultaneously consider the interaction between internal and external power systems to achieve the sharing and optimized allocation of power resources.

[0025] 5. Improve the utilization rate of new energy sources (including wind and solar power) and promote the sustainable development of new energy sources. Attached Figure Description

[0026] Figure 1 This is a flowchart of the method according to an embodiment of the present invention. Detailed Implementation

[0027] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0028] like Figure 1 As shown, this embodiment discloses a capacity optimization scheduling method for active distribution networks including wind and photovoltaic power generation. It utilizes the complementary power supply characteristics and load peak-shifting power consumption characteristics between independent active distribution networks to achieve power transmission within the system for resource sharing, which is of great significance for the local access and consumption of distributed energy. Due to the randomness and volatility of new energy output, and the differences in load demand time in active distribution networks in different regions, if there are complementary characteristics between the new energy and load of each active distribution network, capacity mutual assistance between distribution networks can be achieved. The process of this embodiment includes the following steps:

[0029] Step 1: Use the Copula function to describe the correlation between wind and solar power (i.e., wind power and photovoltaic power) output and load demand in each active distribution network.

[0030] In this embodiment, active power distribution network and line parameters are obtained, including power output data such as wind and solar power, load power demand, generator parameters, and electricity prices in the active power distribution network. Then, the correlation between wind and solar power output and load demand in each active power distribution network is described based on the Copula function of the joint distribution H.

[0031] Joint distribution H(x1, x2, ..., x n The marginal distribution functions F1(x1), F2(x2), ..., F2(x2) are: n (x n There exists a copula function C such that H(x1, x2, ..., x...) n )=C(F1(x1), F2(x2),…,F n (x n Where x1, x2, ..., x n There are n random variables.

[0032] Based on the aforementioned joint distribution Copula function C, this embodiment establishes a Copula function C describing the correlation between wind and solar power generation output and load demand in each active distribution network. n (u1, u2, u3), as shown in the following formula:

[0033]

[0034] in:

[0035] Let be the random wind power output of the i-th active distribution network at time t;

[0036] Let be the random photovoltaic power output of the i-th active distribution network at time t;

[0037] For the random power demand of the active distribution network load;

[0038] X(.) is an indicator function that has a value of 1 when the condition inside the parentheses is met and 0 when it is not met.

[0039] u1, u2, and u3 represent the marginal distribution function values ​​of random power output from wind power generation, random power output from photovoltaic power generation, and random power demand from load, respectively.

[0040] Let be the marginal distribution function of the random wind power output of the i-th active distribution network at time t;

[0041] Let be the marginal distribution function of the random photovoltaic power output of the i-th active distribution network at time t;

[0042] It is the marginal distribution function of the stochastic power demand of the active distribution network load.

[0043] Therefore, this embodiment can determine the correlation between wind and solar power output and load demand in each active distribution network based on the Copula function.

[0044] To ensure the accuracy of the Copula function describing the correlation between wind and solar power output and load demand in each active distribution network, this embodiment uses the Euclidean distance. The optimal Copula function is selected from those describing the correlation between wind and solar power output and load demand in each active distribution network, and then used to determine the correlation between wind and solar power output and load demand in each active distribution network. The Euclidean distance d is calculated as follows:

[0045]

[0046] in:

[0047] An empirical Copula function relating wind and solar power output to load power demand;

[0048] Copula is a function used to describe the correlation between wind and solar power output and load demand.

[0049] In this embodiment, for each active distribution network, the Euclidean distance d of the optimal Copula function is selected from the Copula functions that describe the correlation between wind and solar power output and load demand. The Copula function with the smallest Euclidean distance d is selected as the optimal Copula function. That is, when the Euclidean distance d is the smallest, the selected Copula function is closer to the empirical Copula function. Therefore, this optimal Copula function is used to describe the correlation between wind and solar power output and load demand in each active distribution network.

[0050] Step 2: Based on the remaining power of wind and solar power generation after matching the output of wind and solar power generation with the load demand in each active distribution network, and the optimal Copula function obtained in Step 1 describing the correlation between the output of wind and solar power generation and the load demand in each active distribution network, use the correlation coefficient to determine whether there is a complementary correlation between the remaining power of wind and solar power generation in each active distribution network after matching the output of wind and solar power generation with the load demand.

[0051] In this embodiment, the formula for calculating the remaining power of wind and solar power generation after matching the output of wind and solar power generation with the load demand in the i-th active distribution network is as follows:

[0052]

[0053] in:

[0054] The output of new energy sources (including wind power and photovoltaic power) of the i-th active distribution network at time t;

[0055] Let be the load power consumption of the i-th active distribution network at time t.

[0056] In this embodiment, in order to analyze the correlation between different distribution networks after source-load matching, a correlation coefficient is established based on the remaining power of wind and solar power generation after matching the output of wind and solar power generation with the load demand of any two active distribution networks. The correlation coefficient is used to quantify the complementary correlation of the remaining power of wind and solar power generation after matching the output of wind and solar power generation with the load demand of any two active distribution networks, thereby determining whether there is a complementary correlation between the remaining power of wind and solar power generation after matching the output of wind and solar power generation with the load demand of each active distribution network.

[0057] For each active distribution network, the correlation coefficient ρ of the surplus power generated by wind and solar power after matching the output of wind and solar power generation with the load demand is calculated using the following formula:

[0058]

[0059] in:

[0060] These are the power outputs of the i-th and k-th active distribution networks after source-load matching (referring to the matching of wind and solar power output with load demand) at time t, respectively.

[0061] Let be the edge distribution function of the power of the i-th active distribution network after source-load matching (referring to the matching of wind and solar power output with load demand) at time t;

[0062] Let be the edge distribution function of the power in the k-th active distribution network after source-load matching (referring to the matching of wind and solar power output with load demand) at time t;

[0063] Copula functions are used for the power edge distribution functions of the i-th and k-th active distribution networks at time t after source-load matching (referring to the matching of wind and solar power output with load demand).

[0064] After calculating the correlation coefficient ρ, if the correlation coefficient ρ>0, then the residual power of wind and solar power generation between the i-th active distribution network and the k-th active distribution network after matching the output of wind and solar power generation with the load demand is positively correlated, indicating that these two active distribution networks have a large amount of "residual power" and there is no need for capacity complementarity between distribution networks.

[0065] If the correlation coefficient ρ < 0, then the surplus power generated by wind and solar power in the i-th and k-th active distribution networks after matching wind and solar power output with load demand is negatively correlated. This indicates that the larger the surplus power of one active distribution network, the smaller the probability of surplus power in the other active distribution network, suggesting a power shortage. In this case, there is complementarity between the active distribution networks, and the smaller ρ is, the stronger the complementarity between regions.

[0066] Therefore, by calculating the correlation coefficient of the remaining power generated by wind and solar power after the output of wind and solar power generation matches the load demand of each active distribution network, it can be determined whether there is a complementary correlation between the remaining power generated by wind and solar power after the output of wind and solar power generation matches the load demand of each active distribution network.

[0067] Step 3: If the judgment result in Step 2 is that there is no complementary correlation between the remaining power of wind and solar power generation after the wind and solar power generation output matches the load demand, then no capacity interconnection will be performed between the active distribution networks.

[0068] If the judgment result in step 2 is that there is a complementary correlation between the remaining power of wind and solar power generation in each source distribution network after the wind and solar power generation output matches the load demand, or if there is a complementary correlation between the remaining power of wind and solar power generation in some source distribution networks after the wind and solar power generation output matches the load demand, then an interconnection capacity optimization evaluation model for multiple source distribution networks with complementary correlation between the remaining power of wind and solar power generation is established.

[0069] In this embodiment, the established interconnection capacity optimization evaluation model for multi-active distribution networks uses the minimization of the overall operating cost of the multi-active distribution network as the objective function. The overall operating cost of the multi-active distribution network includes the cost of power interaction between various active distribution networks, the cost of power interaction between various active distribution networks and the external main grid, and the operating cost of thermal power generating units in each active distribution network. The specific explanation is as follows:

[0070] (A1) The first step in optimizing the active distribution network is to interconnect the active distribution networks that include wind and solar power generation. Then, when multiple active distribution networks experience power surplus or shortage, they can interact with the external main grid.

[0071] The cost of power exchange between various active distribution networks including wind and solar power generation As shown in the following formula:

[0072]

[0073] in:

[0074] These represent the prices at which the i-th active distribution network purchases and sells electricity to the k-th active distribution network at time t, respectively.

[0075] The amount of electricity delivered from the k-th active distribution network to the i-th active distribution network at time t;

[0076] The amount of power transmitted from the i-th active distribution network to the k-th active distribution network at time t.

[0077] (A2) Power interaction costs between each active distribution network and the external main grid As shown in the following formula:

[0078]

[0079] in:

[0080] These are the purchase and sale prices of electricity for the i-th active distribution network at time t, respectively.

[0081] Let be the power input from the external main grid to the i-th active distribution network at time t;

[0082] Let be the power output of the i-th active distribution network to the external main network at time t.

[0083] (A3) Operating costs of thermal power generating units in various active distribution networks As shown in the following formula:

[0084]

[0085] in:

[0086] a i ,b i ,c i All of these are the power generation cost coefficients of thermal power generating units in the i-th active distribution network;

[0087] N G This refers to the total number of thermal power generating units in a multi-active power distribution network interconnection system.

[0088] Let z be the output of the z-th thermal power generating unit in the i-th active distribution network at time t;

[0089] These are the start-up and shutdown costs of the generator set, respectively.

[0090] These are variables of 0 and 1, representing whether the generator is started or stopped, ensuring that the generator is not in both starting and stopping states at the same time.

[0091] (A4) Finally, in the interconnection capacity optimization evaluation model of the multi-active distribution network in this embodiment, the objective function constructed with the goal of minimizing the overall operating cost of the multi-active distribution network is as follows:

[0092]

[0093] in:

[0094] The cost of power exchange between various active distribution networks that include wind and solar power generation;

[0095] The operating costs of thermal power generating units in each active power distribution network;

[0096] The operating cost of thermal power generating units in each active power distribution network.

[0097] In the coordination of active distribution networks, constraints must be considered not only for conventional power sources such as coal and gas, but also for stochastic power sources such as wind power, the power interaction between the external power grid and the active distribution network, and the remaining power limitations of the active distribution network after source-load matching. Therefore, in this embodiment, the constraints established for the interconnection capacity optimization evaluation model of multiple active distribution networks include the power balance constraints of each active distribution network, the power output constraints of wind and solar power generation and the operating constraints of thermal power generating units in each active distribution network, the power interaction constraints within each active distribution network, and the power interaction constraints between each active distribution network and the external main grid, as detailed below:

[0098] (B1) The power balance constraints for each active distribution network are shown in the following formula:

[0099]

[0100] in:

[0101] The power of the i-th active distribution network after source-load matching at time t;

[0102] The output of the thermal power generating unit in the i-th active distribution network at time t;

[0103] Let be the power input from the external main grid to the i-th active distribution network at time t;

[0104] The power output of the i-th active distribution network to the external main network at time t;

[0105] The power input to the i-th active distribution network at time t;

[0106] Let be the power output of the i-th active distribution network at time t.

[0107] (B2) The power output constraints of wind and solar power generation and the operating constraints of thermal power generating units in each active distribution network are shown in the following formula:

[0108]

[0109] in:

[0110] The variable is 0-1, representing the operating state of the controllable unit at time t. A value of 1 indicates that the generator is running, and a value of 0 indicates that the generator is not running.

[0111] These are the upper and lower limits of the ramp rate for thermal power units, respectively.

[0112] This represents the output of new energy sources (including wind power and solar power) in the i-th active distribution network at time t;

[0113] express The lower limit value; express The upper limit;

[0114] This represents the output of the thermal power generating unit in the i-th active distribution network at time t;

[0115] express The lower limit value; express The upper limit.

[0116] (B3) Internal power interaction constraints of each active distribution network

[0117] The surplus power after source-load matching should preferentially participate in the internal power interconnection between active distribution networks, and the capacity of internal interconnection should not exceed the surplus power after source-load matching. Since lines are the main transmission channels for realizing internal power interconnection within active distribution networks, they cannot be in both power-transmitting and power-receiving states simultaneously. Therefore, they must meet the power transmission limitations, meaning that the internal power interaction within each active distribution network must satisfy the constraints shown in the following formula:

[0118]

[0119] in:

[0120] Let be the power input from other active distribution networks to the i-th active distribution network at time t;

[0121] Let be the power output of the i-th active distribution network to other active distribution networks at time t;

[0122] These are the lower and upper limits for power transmission between active distribution networks, respectively.

[0123] Variables are 0 and 1 respectively. This represents the power flow state from the k-th distribution network to the i-th distribution network; This represents the power flow state from the i-th distribution network to the k-th distribution network. 0 indicates no power interaction between distribution networks, and 1 indicates power interaction between distribution networks.

[0124] (B4) The power interaction constraints between each active distribution network and the external main grid are shown in the following formula:

[0125]

[0126] in:

[0127] For 0-1 variables, This represents the power flow state input from the main grid to the i-th distribution network; This represents the power flow state output from the i-th distribution network to the main network.

[0128] Let be the power input from the external main grid to the i-th active distribution network at time t;

[0129] for The upper limit;

[0130] The power output of the i-th active distribution network to the external main network at time t;

[0131] for The upper limit.

[0132] Step 4: Use the CPLEX solver in MATLAB to solve the interconnection capacity optimization evaluation model established in Step 3. Obtain the interconnection capacity when the economic cost of operating multiple active distribution networks with complementary correlation of surplus wind and solar power is minimized. Obtain the parameters of the objective function: the cost of power interaction between each active distribution network, the cost of power interaction between the distribution network and the external main grid, and the operating cost of thermal power generating units in the distribution network. Calculate the minimum economic cost of operating multiple active distribution networks and the interconnection capacity at this time, thus achieving capacity interconnection optimization among active distribution networks with complementary correlation of surplus wind and solar power.

[0133] This embodiment fully considers the complementary characteristics of power supply and load in active distribution networks containing wind and solar power, prioritizes internal mutual assistance among active distribution networks, and then considers mutual assistance with the external main grid, thus constructing an active distribution network mutual assistance capacity optimization and evaluation model that minimizes overall operating costs.

[0134] This embodiment quantifies the feasibility of capacity mutual assistance in active distribution networks from the perspective of source-load matching.

[0135] This embodiment adopts the principle of prioritizing power exchange within the region and then trading surplus or insufficient power with the external power grid, which effectively improves the economy and flexibility of active distribution network operation.

[0136] This embodiment takes into account various operational constraints in active distribution networks. The obtained interconnection capacity can reflect the value of power interaction between regional distribution networks in different time periods, providing a basis for subsequent regulation and control.

[0137] This embodiment fully utilizes the interconnection potential between distribution networks, reduces the system's reserve capacity requirements, and improves the reliability of system operation.

[0138] The preferred embodiments of the present invention have been described in detail above with reference to the accompanying drawings. These embodiments are merely descriptions of preferred embodiments and are not intended to limit the scope or concept of the invention. The specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. Such combinations, as long as they do not violate the spirit of the present invention, should also be considered as part of this disclosure. To avoid unnecessary repetition, the present invention will not further describe the various possible combinations.

[0139] This invention is not limited to the specific details of the above embodiments. Within the scope of the technical concept of this invention and without departing from the design idea of ​​this invention, all modifications and improvements made by those skilled in the art to the technical solutions of this invention should fall within the protection scope of this invention. The technical content for which protection is sought in this invention has been fully described in the claims.

Claims

1. A method for optimized scheduling of interconnection capacity between active distribution networks including wind and photovoltaic power generation, characterized in that, The process is as follows: Based on the correlation between wind and solar power output and load demand in each active distribution network, and the remaining power of wind and solar power after matching wind and solar power output and load demand in each active distribution network, a correlation coefficient is established. This correlation coefficient quantifies the complementary correlation of the remaining power of wind and solar power between any two active distribution networks after matching wind and solar power output and load demand. When the correlation coefficient ρ < 0, complementary correlation is determined to exist; when the correlation coefficient ρ ≥ 0, complementary correlation is determined to not exist. If the determination result is that there is no complementary correlation between the remaining power of wind and solar power in each active distribution network after matching wind and solar power output and load demand, then no capacity exchange is performed between the active distribution networks. If the judgment result is that there is a complementary correlation between the surplus power of wind and solar power generation in each active distribution network after the wind and solar power generation output matches the load demand, or if there is a complementary correlation between the surplus power of wind and solar power generation in some active distribution networks after the wind and solar power generation output matches the load demand, then an interconnection capacity optimization evaluation model for multiple active distribution networks with complementary surplus power of wind and solar power generation is established, and the interconnection capacity optimization evaluation model is solved to obtain the interconnection capacity size when the operating economic cost of multiple active distribution networks with complementary surplus power of wind and solar power generation is minimized. Based on the solved interconnection capacity, capacity interconnection between active distribution networks with complementary surplus power of wind and solar power generation is realized. For each active distribution network, the correlation coefficient ρ of the surplus power generated by wind and solar power after matching the output of wind and solar power generation with the load demand is calculated using the following formula: ； in: , These represent the power of the i-th and k-th active distribution networks after source-load matching at time t, respectively. Let be the edge distribution function of the power of the i-th active distribution network after source-load matching at time t; Let be the edge distribution function of the power of the k-th active distribution network after source-load matching at time t; Let be the Copula function of the power edge distribution function of the i-th and k-th active distribution networks after source-load matching at time t.

2. The method for optimized scheduling of interconnected capacity in active distribution networks including wind and photovoltaic power generation according to claim 1, characterized in that, A Copula function is established to describe the correlation between wind and solar power output and load demand in each active distribution network. The Copula function is then used to describe the correlation between wind and solar power output and load demand in each active distribution network.

3. The method for optimized scheduling of interconnected capacity in active distribution networks including wind and photovoltaic power generation according to claim 2, characterized in that, Based on the Euclidean distance, the optimal Copula function is selected from the Copula functions that describe the correlation between wind and solar power output and load demand in each active distribution network. The optimal Copula function is then used to describe the correlation between wind and solar power output and load demand in each active distribution network.

4. The method for optimized scheduling of interconnected capacity in active distribution networks including wind and photovoltaic power generation according to claim 1, characterized in that, In the interconnection capacity optimization evaluation model of the multi-active distribution network, the objective function is constructed with minimizing the overall operating cost of the multi-active distribution network.

5. The method for optimized scheduling of interconnected capacity in active distribution networks including wind and photovoltaic power generation according to claim 4, characterized in that, The overall operating cost of the multi-active distribution network includes the cost of power interaction between the various active distribution networks, the cost of power interaction between the various active distribution networks and the external main grid, and the operating cost of thermal power generating units in each active distribution network.

6. The method for optimized scheduling of interconnected capacity in active distribution networks including wind and photovoltaic power generation according to claim 4, characterized in that, The constraints of the interconnection capacity optimization evaluation model include the power balance constraints of each active distribution network, the power output constraints of wind and solar power generation and the operation constraints of thermal power generating units in each active distribution network, the power interaction constraints within each active distribution network, and the power interaction constraints between each active distribution network and the external main grid.

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