Method for optimizing and scheduling interconnection capacity between active power distribution networks containing wind power and photovoltaic power generation

Abstract

The invention discloses an inter-active power distribution network interconnection capacity optimization scheduling method containing wind power and photovoltaic power generation, and the method comprises the steps: judging whether the surplus power of the wind-solar power generation of each active power distribution network has complementary correlation or not based on the correlation between the wind-solar power generation output and the load demand in each active power distribution network and the surplus power of the wind-solar power generation; if the judgment result is that the complementary correlation does not exist, capacity interconnection is not carried out; and if the judgment result is that the complementary correlation exists, establishing an interconnection capacity optimization evaluation model of the multi-active power distribution network in which the complementary correlation exists in the wind-solar power generation residual power, solving the interconnection capacity optimization evaluation model to obtain the size of the interconnection capacity, and realizing capacity interconnection between the active power distribution networks according to the solved interconnection capacity. According to the invention, the resource utilization efficiency is improved, the stability of power supply is ensured, sharing and optimal distribution of power resources can be realized, and the utilization rate of new energy is improved.

Description

Technical Field

[0001] The present invention relates to the field of active distribution network optimization dispatching methods, in particular to an active distribution network interconnection capacity optimization dispatching method including wind power and photovoltaic power generation. Background Art

[0002] Globally, the installed capacity of clean new energy power generation equipment represented by wind power and photovoltaic power generation is growing steadily. However, wind power and photovoltaic power generation are random and uncertain, with uneven distribution in regions and fluctuations in time. Therefore, it is of great significance to interconnect the active distribution networks to achieve grid interconnection, mutual backup, and mutual assistance during peak hours.

[0003] At present, there is a certain foundation for the research on active distribution networks containing new energy (including wind power and photovoltaic power generation). Some studies have proposed to use the dedicated transformers of adjacent buildings to power charging stations in interconnected buildings or across buildings, so as to give full play to the redundant capacity of regional dedicated transformers, power charging stations in the area, and give full play to the mutual assistance capacity between various active distribution networks, but the research did not consider the overall operation and economic benefits of various active distribution networks containing new energy (including wind power and photovoltaic power generation).

[0004] At the same time, a study has established a multi-microgrid system optimization dispatch model, taking into account the hybrid energy sharing site to optimize the operation economy of the multi-microgrid system. However, it only considers the energy interaction process between the local active distribution network and the main grid, and does not fully consider the mutual assistance capacity of the interconnection of each active distribution network.

[0005] In addition, other studies have focused on the modeling and analysis of uncertainty factors in renewable energy output, loads and power transmission lines. The inter-regional mutual assistance capacity is obtained from their respective energy storage and backup power sources, while the interconnection of renewable energy surplus power and load matching between active distribution networks containing renewable energy (including wind power and photovoltaic power generation) has not been studied.

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

[0007] The present invention provides a method for optimizing the dispatching of interconnected capacity between active distribution networks including wind power and photovoltaic power generation, so as to solve the problem that the prior art fails to fully realize the interconnection of the capacity of each active distribution network.

[0008] In order to achieve the above object, the technical solution adopted by the present invention is:

[0009] The method for optimizing the interconnection capacity of active distribution networks including wind power and photovoltaic power generation is as follows:

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

[0011] If the judgment result is that there is no complementary correlation between the surplus power of wind and solar power generation in each source distribution network after the wind and solar power generation output matches the load demand, the capacity interconnection between the active distribution networks will not be carried out;

[0012] If the judgment result is that there is a complementary correlation between the surplus power of wind and solar power generation in each source distribution network after the wind and solar power generation output is matched with the load demand, or there is a complementary correlation between the surplus power of wind and solar power generation in some source distribution networks after the wind and solar power generation output is matched with the load demand, then an interconnection capacity optimization and evaluation model for multiple active distribution networks with complementary correlation in the surplus power of wind and solar power generation is established, and the interconnection capacity optimization and evaluation model is solved to obtain the interconnection capacity size when the operating economic cost of the multiple active distribution networks with complementary correlation in the surplus power of wind and solar power generation is minimized, and according to the interconnection capacity obtained by the solution, the capacity interconnection between the active distribution networks with complementary correlation in the 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 generation output and load demand in each active distribution network, and the Copula function is used to describe the correlation between wind and solar power generation 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 generation 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 generation output and load demand in each active distribution network.

[0015] Furthermore, based on the correlation between wind and solar power generation output and load demand in each active distribution network, and the remaining power of wind and solar power generation after the wind and solar power generation output in each active distribution network matches the load demand, a correlation coefficient is established. The complementary correlation of the remaining power of wind and solar power generation of any two active distribution networks after the wind and solar power generation output matches the load demand is quantified through the correlation coefficient, thereby judging whether there is a complementary correlation in the remaining power of wind and solar power generation of each active distribution network after the wind and solar power generation output matches the load demand.

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

[0017] Furthermore, the overall operating cost of multiple active distribution networks includes the cost of power interaction between each active distribution network, the cost of power interaction between each active distribution network 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 power balance constraints of each active distribution network, wind and solar power generation output constraints and thermal power generation unit operation constraints in each active distribution network, power interaction constraints within each active distribution network, and power interaction constraints between each active distribution network and the external main grid.

[0019] In the present invention, from the perspective of source-load matching, the complementary characteristics of the output of new energy and the load demand in each active distribution network including wind power and photovoltaic power generation, as well as the feasibility of interconnection between the surplus power of the active distribution network are analyzed. In addition, in the present invention, by constructing an interconnection capacity optimization evaluation model, the model aims to minimize the overall operating cost of the active distribution network, and at the same time considers the constraints of the remaining mutual aid capacity after source-load matching and the constraints of power interaction inside and outside the system, and can quantitatively determine the interconnection capacity between active distribution networks including wind power and photovoltaic power generation, so as to fully utilize the potential of interconnection of multiple distribution networks.

[0020] Compared with the prior art, the present invention has the following advantages:

[0021] 1. By optimizing and evaluating the interconnection capacity between active distribution networks, the system's backup capacity requirements can be reduced and resource utilization efficiency can be improved.

[0022] 2. Improve the reliability of system operation and ensure the stability of power supply by optimizing and evaluating the interconnection capacity and interaction between active distribution networks.

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

[0024] 4. Consider the power interaction inside and outside the system at the same time to realize the sharing and optimal allocation of power resources.

[0025] 5. Improve the utilization rate of new energy (including wind power and photovoltaic power generation) and promote the sustainable development of new energy. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a flow chart of a method according to an embodiment of the present invention. DETAILED DESCRIPTION

[0027] The present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0028] like Figure 1 As shown, this embodiment discloses a method for optimizing the interconnected capacity of active distribution networks including wind power and photovoltaic power generation. It utilizes the complementary characteristics of power supply and load peak power consumption between independent active distribution networks to achieve power transmission within the system to realize resource sharing, which is of great significance for the nearby access and consumption of distributed energy. Due to the randomness and volatility of new energy output, as well as 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 the output of wind and solar power generation (i.e. wind power generation and photovoltaic power generation) and load demand in each active distribution network.

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

[0031] Joint distribution H(x 1 , x 2 , ..., x n )'s marginal distribution function F 1 (x 1 ), F 2 (x 2 ), ..., F n (x n ), there exists a Copula function C such that H(x 1 , x 2 , …, x n )=C(F 1 (x 1 ), F 2 (x 2 ), …, F n (x n )). Among them, x 1 , x 2 , ..., x n are n random variables.

[0032] Based on the above-mentioned joint distribution Copula function C, this embodiment establishes a Copula function C that describes the correlation between wind and solar power generation output and load demand in each active distribution network. n (u 1 ,u 2 ,u 3), as shown below:

[0033]

[0034] in:

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

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

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

[0038] X(.) is an indicator function, which means that its value is 1 when the condition in the brackets is met, and 0 when it is not met;

[0039] u 1 ,u 2 ,u 3 They represent the marginal distribution function values ​​of random output of wind power generation, random output of photovoltaic power generation and random power demand of load respectively;

[0040] is the marginal distribution function of the random wind power output of the ith active distribution network at time t;

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

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

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

[0044] In order to ensure the accuracy of the Copula function describing the correlation between wind and solar power generation output and load demand in each active distribution network, the Euclidean distance is used in this embodiment to select the optimal Copula function from the Copula function describing the correlation between wind and solar power generation output and load demand in each active distribution network, and use the optimal Copula function to determine the correlation between wind and solar power generation output and load demand in each active distribution network. The Euclidean distance d is calculated as shown in the following formula:

[0045]

[0046] in:

[0047] It is the empirical Copula function of the correlation between wind and solar power generation output and load power demand;

[0048] It is a Copula function that describes the correlation between wind and solar power generation 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 its wind and solar power generation output and load demand is calculated, and 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 closer the selected Copula function is to the empirical Copula function, then the optimal Copula function is used to describe the correlation between the wind and solar power generation output and load demand in each active distribution network.

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

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

[0052]

[0053] in:

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

[0055] is 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 surplus power of wind and solar power generation after the wind and solar power generation output of any two active distribution networks matches the load demand. The complementary correlation of the surplus power of wind and solar power generation after the wind and solar power generation output of any two active distribution networks matches the load demand is quantified by the correlation coefficient, thereby determining whether there is a complementary correlation between the surplus power of wind and solar power generation of each active distribution network after the wind and solar power generation output matches the load demand.

[0057] For each active distribution network, the calculation formula of the correlation coefficient ρ of the surplus power of wind and solar power generation after the wind and solar power generation output matches the load demand of the i-th active distribution network and the k-th active distribution network is as follows:

[0058]

[0059] in:

[0060] They are the power of the ith and kth active distribution networks after source-load matching (meaning the matching of wind and solar power generation output with load demand) at time t;

[0061] is the marginal distribution function of the power of the i-th active distribution network after source-load matching (meaning that the wind and solar power generation output matches the load demand) at time t;

[0062] is the marginal distribution function of the power of the kth active distribution network after source-load matching (meaning that the wind and solar power generation output matches the load demand) at time t;

[0063] It is the Copula function of the power marginal distribution function of the i-th and k-th active distribution networks after source-load matching (referring to the matching of wind and solar power generation output with load demand) at time t.

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

[0065] If the correlation coefficient ρ<0, the surplus power of wind and solar power generation in the i-th active distribution network and the k-th active distribution network after the wind and solar power generation output matches the load demand is negatively correlated, indicating that the greater the surplus power of a certain active distribution network, the smaller the possibility of surplus power in the other active distribution network, and it is in a state of power shortage. At this time, there is complementarity between the active distribution networks, and the smaller ρ is, the stronger the complementarity between regions is.

[0066] Therefore, by calculating the correlation coefficient of 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, it can be determined whether there is a complementary correlation in 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.

[0067] Step 3: When the judgment result in step 2 is that there is no complementary correlation between the surplus power of wind and solar power generation in each source distribution network after the wind and solar power generation output matches the load demand, the capacity interconnection between the active distribution networks is not performed.

[0068] When the judgment result in step 2 is that there is a complementary correlation between the surplus 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 there is a complementary correlation between the surplus 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 of multiple active distribution networks with complementary correlation in the surplus power of wind and solar power generation is established.

[0069] In this embodiment, the interconnection capacity optimization evaluation model of the multi-active distribution network is established, and the objective function is constructed with the minimum overall operation cost of the multi-active distribution network. The overall operation cost of the multi-active distribution network includes the cost of power interaction between each active distribution network, the power interaction cost between each active distribution network and the external main network, and the operation cost of the thermal power generating unit in each active distribution network. The specific description is as follows:

[0070] (A1) The method of optimizing active distribution networks is to first interconnect the power between active distribution networks including wind and solar power generation, and then interact with the external main grid when multiple active distribution networks have power surplus or shortage as a whole.

[0071] The cost of power interaction between active distribution networks including wind and solar power generation As shown below:

[0072]

[0073] in:

[0074] are the prices of electricity purchased and sold by the i-th active distribution network to the k-th active distribution network at time t;

[0075] is the power delivered by the kth active distribution network to the ith active distribution network at time t;

[0076] is the 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 below:

[0078]

[0079] in:

[0080] are the electricity purchase and sales prices of the ith active distribution network at time t;

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

[0082] is the power output from the i-th active distribution network to the external main grid at time t.

[0083] (A3) Operating costs of thermal power generating units in each active distribution network As shown below:

[0084]

[0085] in:

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

[0087] N G is the total number of thermal power generating units in the multi-active distribution network interconnection system;

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

[0089] are the startup and shutdown costs of the generator set respectively;

[0090] They are 0-1 variables, indicating whether the generator is started or stopped, respectively, to ensure that the generator is not in the started and stopped states at the same time.

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

[0092]

[0093] in:

[0094] The cost of power interaction between active distribution networks including wind and solar power generation;

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

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

[0097] In the coordination of active distribution networks, active distribution networks must not only consider the constraints of conventional power sources such as coal and gas, but also the operating characteristics of random power sources such as wind power, the power interaction between the external power grid and the active distribution network, and the remaining power limit 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 wind and solar power generation output constraints and the operation constraints of thermal power generators 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, which are specifically described as follows:

[0098] (B1) The power balance constraint of each active distribution network is as follows:

[0099]

[0100] in:

[0101] is the power of the ith active distribution network after source-load matching at time t;

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

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

[0104] is the power output from the i-th active distribution network to the external main grid at time t;

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

[0106] is the power output by the i-th active distribution network at time t.

[0107] (B2) The wind and solar power generation output constraints and thermal power generation unit operation constraints in each active distribution network are as follows:

[0108]

[0109] in:

[0110] is a 0-1 variable, indicating 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 stopped.

[0111] They are the upper and lower limits of the ramp rate of thermal power units respectively;

[0112] represents the output of renewable energy (including wind power and photovoltaic power) of the i-th active distribution network at time t;

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

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

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

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

[0117] The remaining power after source-load matching has priority in participating in the internal power interconnection between active distribution networks, and the capacity of internal interconnection should not exceed the remaining power after source-load matching. Since the line is the main transmission channel for realizing the internal power interconnection of the active distribution network, it cannot be in the state of power transmission and power reception at the same time, and needs to meet the range of power transmission restrictions, that is, the internal power interaction of each active distribution network must meet the constraints shown in the following formula:

[0118]

[0119] in:

[0120] is the power imported by the i-th active distribution network from other active distribution networks at time t;

[0121] is the power exported by the i-th active distribution network to other active distribution networks at time t;

[0122] They are the lower limit and upper limit of line power transmission between active distribution networks respectively;

[0123] are variables ranging from 0 to 1, is the power flow state from the kth distribution network to the ith distribution network; is the power flow state from the ith distribution network to the kth distribution network. 0 means there is no power interaction between distribution networks, and 1 means there is power interaction between distribution networks.

[0124] (B4) The power interaction constraint between each active distribution network and the external main grid is as follows:

[0125]

[0126] in:

[0127] is a 0-1 variable, is the power flow state input from the main grid to the i-th distribution network; is the power flow state output from the i-th distribution network to the main grid.

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

[0129] for The upper limit value of

[0130] is the power output from the i-th active distribution network to the external main grid at time t;

[0131] for The upper limit value of .

[0132] Step 4. Use the CPLEX solver in MATLAB to solve the interconnection capacity optimization evaluation model established in step 3, and obtain the interconnection capacity size when the operating economic cost of the multi-active distribution network with complementary correlation between the surplus power of wind and solar power generation is the minimum. The objective function is obtained, including the cost of power interaction between each active distribution network, the power interaction cost between the distribution network and the external main network, and the operating cost parameters of the thermal power generating units in the distribution network. The minimum operating economic cost of the multi-active distribution network and the interconnection capacity at this time are calculated to achieve capacity interconnection optimization between active distribution networks with complementary correlation between the surplus power of wind and solar power generation.

[0133] This embodiment fully considers the complementary characteristics of power supply and load of the active distribution network including wind and solar power, gives priority to the internal mutual assistance between active distribution networks, and then considers the mutual assistance with the external main grid, and constructs an active distribution network mutual assistance capacity optimization and evaluation model that minimizes the overall operating cost.

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

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

[0136] This embodiment takes into account various operating constraints in the active distribution network, and 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.

[0137] This embodiment fully utilizes the interconnection potential between distribution networks, reduces the system backup capacity demand, and improves the reliability of system operation.

[0138] The preferred embodiments of the present invention are described in detail above in conjunction with the accompanying drawings. The embodiments described in the present invention are merely descriptions of the preferred embodiments of the present invention, and do not limit the concept and scope of the present invention. The various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction, and such combinations should also be regarded as the contents disclosed in the present disclosure as long as they do not violate the concept of the present invention. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0139] The present invention is not limited to the specific details of the above-mentioned embodiments. Within the technical concept of the present invention and without departing from the design concept of the present invention, various modifications and improvements made to the technical solution of the present invention by technical personnel in this field should fall within the protection scope of the present invention. The technical contents for which protection is sought in the present invention have been fully recorded in the claims.

Claims

1. A method for optimizing the interconnection capacity of active distribution networks including wind power and photovoltaic power generation, characterized in that: The process is as follows: Based on the correlation between wind and solar power generation output and load demand in each active distribution network, and the remaining power of wind and solar power generation after the wind and solar power generation output and load demand are matched in each active distribution network, it is determined whether there is a complementary correlation between the remaining power of wind and solar power generation in each active distribution network after the wind and solar power generation output and load demand are matched; If the judgment result is that there is no complementary correlation between the surplus power of wind and solar power generation in each source distribution network after the wind and solar power generation output matches the load demand, the capacity interconnection between the active distribution networks will not be carried out; If the judgment result is that there is a complementary correlation between the surplus power of wind and solar power generation in each source distribution network after the wind and solar power generation output is matched with the load demand, or there is a complementary correlation between the surplus power of wind and solar power generation in some source distribution networks after the wind and solar power generation output is matched with the load demand, then an interconnection capacity optimization and evaluation model for multiple active distribution networks with complementary correlation in the surplus power of wind and solar power generation is established, and the interconnection capacity optimization and evaluation model is solved to obtain the interconnection capacity size when the operating economic cost of the multiple active distribution networks with complementary correlation in the surplus power of wind and solar power generation is minimized, and according to the interconnection capacity obtained by the solution, the capacity interconnection between the active distribution networks with complementary correlation in the surplus power of wind and solar power generation is realized.

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

3. The method for optimizing the interconnection capacity of active distribution networks including wind power and photovoltaic power generation according to claim 2 is 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 generation 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 generation output and load demand in each active distribution network.

4. The method for optimizing the interconnection capacity of active distribution networks including wind power and photovoltaic power generation according to claim 1 is characterized in that: Based on the correlation between wind and solar power generation output and load demand in each active distribution network, and the remaining power of wind and solar power generation after the wind and solar power generation output in each active distribution network matches the load demand, a correlation coefficient is established. The complementary correlation of the remaining power of wind and solar power generation of any two active distribution networks after the wind and solar power generation output matches the load demand is quantified through the correlation coefficient, thereby judging whether there is a complementary correlation in the remaining power of wind and solar power generation of each active distribution network after the wind and solar power generation output matches the load demand.

5. The method for optimizing the interconnection capacity of active distribution networks including wind power and photovoltaic power generation according to claim 1 is characterized in that: In the interconnection capacity optimization evaluation model of the multi-active distribution network, the objective function is constructed with the minimum overall operation cost of the multi-active distribution network.

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

7. The method for optimizing the interconnection capacity of active distribution networks including wind power and photovoltaic power generation according to claim 5 is characterized in that: The constraints of the interconnection capacity optimization evaluation model include the power balance constraints of each active distribution network, the wind and solar power generation output constraints and the thermal power generation unit operation constraints 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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