Active power distribution network dynamic scheduling method and device based on full-distributed algorithm

Abstract

The application discloses a kind of active power distribution network dynamic scheduling method and device based on full distribution algorithm, it is related to power distribution network technical field.Therein, the method includes: obtaining the power generation cost and solar energy abandoned power of power distribution network;According to the constraint condition, power generation cost and solar energy abandoned power of power distribution network, active power distribution network scheduling model is constructed;According to the tie line in power distribution network, power distribution network is divided into N line areas;By full distribution algorithm and active power distribution network scheduling model, the target parameter of each line area in N line areas is calculated respectively;According to the target parameter of each line area, power distribution network is dynamically scheduled.The application solves the technical problem that communication pressure is too large due to too many distributed power sources when dynamically scheduling power distribution network in prior art.

Description

Technical Field

[0001] The present application relates to the technical field of distribution network technology, and in particular to a method and device for dynamic scheduling of active distribution networks based on a fully distributed algorithm. Background Art

[0002] The new power system with new energy as the main body has become the development direction of the contemporary power system. Distributed power sources such as distributed photovoltaics are widely connected to the distribution network, and are coupled with traditional micro gas turbines, flexible loads, distributed energy storage systems, etc., which makes the energy flow of the distribution network complicated and has a certain adverse impact on the economic dispatch of the distribution network.

[0003] In addition, the distribution network contains subsystems divided by geographical or administrative regions. The market players of various types of distributed power sources are not the same. Due to the need for confidentiality of commercial information, the information exchange between each player is limited. Moreover, the large number of distributed power sources in the distribution network will bring heavy communication and data burdens to traditional centralized optimization scheduling.

[0004] To address the above-mentioned problems, no effective solutions have been proposed so far. Summary of the Invention

[0005] The embodiments of the present application provide a method and device for dynamic scheduling of an active distribution network based on a fully distributed algorithm, so as to at least solve the technical problem in the prior art of excessive communication pressure caused by an excessive number of distributed power sources when dynamically scheduling the distribution network.

[0006] According to one aspect of an embodiment of the present application, a method for dynamic scheduling of an active distribution network based on a fully distributed algorithm is provided, comprising: obtaining the power generation cost and solar energy curtailment of the distribution network, wherein the solar energy curtailment represents the amount of electricity that is discarded because the solar power generation cannot be fully absorbed by the distribution network; constructing an active distribution network scheduling model based on the constraints of the distribution network, the power generation cost, and the solar energy curtailment, wherein the active distribution network scheduling model is used to reduce the power generation cost and the solar energy curtailment by adjusting the parameters of different line areas of the distribution network while satisfying the constraints, and the constraints are used to ensure the normal operation of the distribution network; dividing the distribution network into N line areas according to the interconnection lines in the distribution network, wherein N is an integer greater than 1; calculating the target parameters of each of the N line areas using the fully distributed algorithm and the active distribution network scheduling model, wherein the target parameters of each line area are used to reduce the power generation cost and the solar energy curtailment of the line area; and dynamically scheduling the distribution network according to the target parameters of each line area.

[0007] Optionally, in the active distribution network dynamic scheduling method based on a fully distributed algorithm provided in the present application, the constraints of the distribution network include at least the following conditions: a first condition, used to constrain the power change rate of the distributed power source of the distribution network to be between a first value and a second value, the first value being the maximum value of the output change rate of the distributed power source of the distribution network, and the second value being the opposite of the first value; a second condition, used to constrain the power generation capacity of the generator of the distribution network; a third condition, used to constrain the power generation power of each branch of the distribution network; a fourth condition, used to constrain the flow balance information of the distribution network, wherein the flow balance information represents the balance information between the power generation and power consumption of the distribution network.

[0008] Optionally, in the active distribution network dynamic scheduling method based on a fully distributed algorithm provided in the present application, before obtaining the power generation cost and solar energy curtailment of the distribution network, a first function and a second function are constructed based on the active power and predicted power of the distributed power sources of the distribution network, wherein the first function is used to calculate the solar energy curtailment and the second function is used to calculate the power generation cost of the distributed power sources of the distribution network; a third function is constructed based on the second function and the number of distributed power sources in the distribution network, wherein the third function is used to calculate the power generation cost of the distribution network.

[0009] Optionally, in the active distribution network dynamic scheduling method based on a fully distributed algorithm provided in the present application, an active distribution network scheduling model is constructed according to the constraints of the distribution network, the power generation cost and the amount of solar energy curtailment, including: determining a fourth function according to the third function and the first function, wherein the fourth function is used to calculate the minimum value of the weighted sum of the power generation cost of the distribution network and the amount of solar energy curtailment; expressing the constraints through the fifth function, and by introducing a branch node matrix, representing the balance information between the power generation and power consumption of the distribution network through an augmented matrix; and constructing an active distribution network scheduling model according to the fourth function, the fifth function and the augmented matrix.

[0010] Optionally, in the active distribution network dynamic scheduling method based on a fully distributed algorithm provided in the present application, the target parameters of each of the N line areas are calculated respectively through the fully distributed algorithm and the active distribution network scheduling model, including: splitting N augmented correlation matrices according to the augmented matrix, wherein the sum of the N augmented correlation matrices is equal to the augmented matrix, and the N augmented correlation matrices correspond one-to-one to the N line areas; calculating the target parameters of the Jth line area according to the augmented correlation matrix corresponding to the Jth line area and the active distribution network scheduling model, wherein J is a positive integer less than or equal to N.

[0011] Optionally, in the active distribution network dynamic scheduling method based on a fully distributed algorithm provided in the present application, the target parameters of the Jth line area are calculated according to the augmented correlation matrix corresponding to the Jth line area and the active distribution network scheduling model, including: converting the active distribution network scheduling model into a target model, wherein the target model is used to convert the fourth function, the fifth function and the augmented matrix into an inequality constraint function based on the Lagrange principle; using the augmented correlation matrix corresponding to the Jth line area to replace the augmented matrix in the target model, solving the target model, and obtaining the positive solution of the unknowns in the target model; and using the positive solution of the unknowns in the target model as the target parameter of the Jth line area.

[0012] Optionally, in the active distribution network dynamic scheduling method based on a fully distributed algorithm provided in the present application, the distribution network is dynamically scheduled according to the target parameters of each line area, including: adjusting the active power generation power and reactive power generation power of each line area according to the target parameters of the line area, wherein the target parameters of different line areas are transmitted independently; by adjusting the active power generation power and reactive power generation power of each line area, the power supply load of each line area is dynamically scheduled.

[0013] According to another aspect of the present application, an active distribution network dynamic scheduling device based on a fully distributed algorithm is also provided, comprising: a first processing unit, configured to obtain the power generation cost and solar energy curtailment of the distribution network, wherein the solar energy curtailment represents the amount of electricity that is discarded because the solar power generation cannot be fully absorbed by the distribution network; a second processing unit, configured to construct an active distribution network scheduling model based on the constraints of the distribution network, the power generation cost, and the solar energy curtailment, wherein the active distribution network scheduling model is configured to reduce the power generation cost and the solar energy curtailment by adjusting the parameters of different line areas of the distribution network while satisfying the constraints, and the constraints are configured to ensure the normal operation of the distribution network; a third processing unit, configured to divide the distribution network into N line areas according to the tie lines in the distribution network, wherein N is an integer greater than 1; a fourth processing unit, configured to calculate the target parameters of each of the N line areas using the fully distributed algorithm and the active distribution network scheduling model, wherein the target parameters of each line area are used to reduce the power generation cost and solar energy curtailment of the line area; and a fifth processing unit, configured to dynamically schedule the distribution network according to the target parameters of each line area.

[0014] According to another aspect of the present application, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer-readable storage medium. When the computer program is running, the device where the computer-readable storage medium is located executes the above-mentioned active distribution network dynamic scheduling method based on the fully distributed algorithm.

[0015] According to another aspect of the present application, an electronic device is also provided, wherein the electronic device includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by one or more processors, the one or more processors execute the above-mentioned active distribution network dynamic scheduling method based on a fully distributed algorithm.

[0016] In the present application, the power generation cost and solar energy curtailment of the distribution network are first obtained, wherein the solar energy curtailment represents the amount of electricity that is discarded because the solar power generation cannot be fully absorbed by the distribution network. Then, based on the constraints of the distribution network, the power generation cost and the solar energy curtailment, an active distribution network scheduling model is constructed, wherein the active distribution network scheduling model is used to reduce the power generation cost and the solar energy curtailment by adjusting the parameters of different line areas of the distribution network under the condition of satisfying the constraints, and the constraints are used to ensure the normal operation of the distribution network. Subsequently, the distribution network is divided into N line areas according to the interconnection lines in the distribution network, wherein N is an integer greater than 1; the target parameters of each line area in the N line areas are calculated respectively by using a fully distributed algorithm and the active distribution network scheduling model, wherein the target parameters of each line area are used to reduce the power generation cost and solar energy curtailment of the line area. Finally, the distribution network is dynamically scheduled according to the target parameters of each line area.

[0017] From the above content, it can be seen that the present application first constructs an active distribution network scheduling model based on the constraints of the distribution network, the cost of power generation and the amount of solar energy curtailment, and then divides the distribution network into N line areas according to the interconnecting lines in the distribution network. Finally, the target parameters of each line area in the N line areas are calculated respectively through the fully distributed algorithm and the active distribution network scheduling model; the distribution network is dynamically scheduled according to the target parameters of each line area. When the technical solution of the present application is used for distribution network scheduling, each line area only needs to exchange limited information with the adjacent area to optimize the output power of the distributed power source. Not only can information security be guaranteed, but the solution also does not require the use of a central coordinator for data processing. Each line area independently calculates the target parameters, thereby effectively reducing the overall communication pressure, thereby solving the technical problem in the prior art of excessive communication pressure caused by the excessive number of distributed power sources when dynamically scheduling the distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0019] Figure 1is a flow chart of an optional method for dynamic scheduling of an active distribution network based on a fully distributed algorithm according to an embodiment of the present application;

[0020] Figure 2 is a schematic diagram of an optional three-area distribution network structure according to an embodiment of the present application;

[0021] Figure 3 This is a schematic diagram of an optional simulation circuit according to an embodiment of the present application.

[0022] Figure 4 This is a schematic diagram of an active distribution network dynamic scheduling device based on a fully distributed algorithm according to an embodiment of the present application. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0024] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0025] It should also be noted that the information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) collected by this application are information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of relevant data comply with the relevant laws, regulations and standards of the relevant regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse. For example, an interface is set up between this system and relevant users or institutions. Before obtaining relevant information, it is necessary to send an acquisition request to the aforementioned user or institution through the interface, and obtain relevant information after receiving the consent information fed back by the aforementioned user or institution.

[0026] According to an embodiment of the present application, an embodiment of a method for dynamic scheduling of an active distribution network based on a fully distributed algorithm is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0027] Figure 1 is a flow chart of an optional active distribution network dynamic scheduling method based on a fully distributed algorithm according to an embodiment of the present application, such as Figure 1 As shown, the method includes the following steps:

[0028] Step S101: Obtain the power generation cost and solar power curtailment amount of the distribution network.

[0029] In step S101 , the amount of solar energy curtailment represents the amount of electricity that is discarded because the solar power generation cannot be fully absorbed by the distribution network.

[0030] Optionally, the goal of active economic dispatch of the distribution network is to minimize the power generation cost F1 of all traditional distributed power sources and minimize the amount of solar energy curtailment F2.

[0031] Step S102: constructing an active distribution network scheduling model based on the constraints of the distribution network, the power generation cost, and the amount of solar power curtailment.

[0032] Optionally, the active distribution network scheduling model is used to reduce the power generation cost and the amount of solar power curtailment by adjusting parameters of different line areas of the distribution network while satisfying constraint conditions, and the constraint conditions are used to ensure the normal operation of the distribution network.

[0033] Optionally, there may be multiple constraints, which are used to constrain the dispatching process of the distribution network from multiple dimensions, ensuring that the distribution network always operates normally during the dispatching process without abnormal conditions.

[0034] Step S103: Divide the distribution network into N line areas according to the tie lines in the distribution network.

[0035] In step S103 , N is an integer greater than 1.

[0036] Optionally, there are fewer tie lines between different line areas, so dynamic scheduling can be performed for each line area separately. For example, the distribution network is divided into N line areas, based on the principle that the number of tie lines between any two line areas is less than a preset number, where the preset number can be customized. For example, if the preset number is set to 3, if the number of tie lines between areas Z1 and Z2 of the distribution network is less than 3, the two areas are determined to be two areas of the N line areas; if the number of tie lines between areas Z1 and Z2 of the distribution network is greater than 3, the two areas are merged into one area.

[0037] Step S104 , calculating target parameters of each of the N line areas respectively by using a fully distributed algorithm and an active distribution network scheduling model.

[0038] In step S104 , the target parameters of each line area are used to reduce the power generation cost and solar energy curtailment amount in the line area.

[0039] Optionally, the target parameter of each line area is a parameter that can determine the output power of the line area. The fully distributed algorithm in the present application may be a fully distributed PDIP (Parallel and Decentralized Inverse Power) algorithm.

[0040] Alternatively, the fully distributed PDIP algorithm is an algorithm for distributed optimization and network synchronization. It is primarily used to solve the problem of distributed systems where individual nodes need to work together to achieve a common goal. This algorithm has applications in many fields, such as power systems, communication networks, and multi-agent systems. The core idea of ​​the PDIP algorithm is to utilize the local information of each node and iteratively update the node status to ultimately synchronize or optimize the entire network. The main steps of the algorithm are as follows:

[0041] 1. Initialization: Each node is initialized according to its own initial state and goal.

[0042] 2. Iterative Update: In each iteration, each node updates its state based on the current state of its neighbors and its own current state. This process can be seen as a local decision-making process based on neighbor information.

[0043] 3. Convergence judgment: After each iteration, the algorithm checks whether the convergence condition is met. If so, the algorithm stops iterating; otherwise, it continues to the next iteration.

[0044] 4. Result output: When the algorithm converges, the final state of each node is output. These states together constitute the synchronization or optimization result of the entire network.

[0045] The advantages of the PDIP algorithm include:

[0046] 1. Distributed: The algorithm does not require a centralized control center. Each node only needs to make decisions based on its own local information and the information of its neighbors.

[0047] 2. Scalability: The algorithm can be easily expanded to large-scale networks because the processing process of each node is independent.

[0048] 3. Robustness: The algorithm is robust to changes in the network topology and node status.

[0049] 4. Convergence: When certain conditions are met, the PDIP algorithm can be guaranteed to converge to the global optimal solution.

[0050] Step S105 : Dynamically dispatching the distribution network according to the target parameters of each line area.

[0051] Optionally, the dispatching system can adjust the active power generation and reactive power generation of each line area according to the target parameters of each line area, wherein the target parameters of different line areas are transmitted independently; by adjusting the active power generation and reactive power generation of each line area, the power supply load of each line area is dynamically dispatched.

[0052] Based on the contents of the above steps S101 to S105, it can be seen that in this application, the power generation cost and solar energy curtailment of the distribution network are first obtained, wherein the solar energy curtailment represents the amount of electricity that is discarded because the solar power generation cannot be fully absorbed by the distribution network. Then, based on the constraints of the distribution network, the power generation cost and the solar energy curtailment, an active distribution network scheduling model is constructed, wherein the active distribution network scheduling model is used to reduce the power generation cost and the solar energy curtailment by adjusting the parameters of different line areas of the distribution network under the condition that the constraints are met, and the constraints are used to ensure the normal operation of the distribution network. Subsequently, the distribution network is divided into N line areas according to the interconnection lines in the distribution network, wherein N is an integer greater than 1; the target parameters of each line area in the N line areas are calculated respectively by using a fully distributed algorithm and an active distribution network scheduling model, wherein the target parameters of each line area are used to reduce the power generation cost and solar energy curtailment of the line area. Finally, the distribution network is dynamically scheduled according to the target parameters of each line area.

[0053] From the above content, it can be seen that the present application first constructs an active distribution network scheduling model based on the constraints of the distribution network, the cost of power generation and the amount of solar energy curtailment, and then divides the distribution network into N line areas according to the interconnecting lines in the distribution network. Finally, the target parameters of each line area in the N line areas are calculated respectively through the fully distributed algorithm and the active distribution network scheduling model; the distribution network is dynamically scheduled according to the target parameters of each line area. When the technical solution of the present application is used for distribution network scheduling, each line area only needs to exchange limited information with the adjacent area to optimize the output power of the distributed power source. Not only can information security be guaranteed, but the solution also does not require the use of a central coordinator for data processing. Each line area independently calculates the target parameters, thereby effectively reducing the overall communication pressure, thereby solving the technical problem in the prior art of excessive communication pressure caused by the excessive number of distributed power sources when dynamically scheduling the distribution network.

[0054] In an optional embodiment, before obtaining the distribution network's generation cost and solar curtailment, a first function and a second function are constructed based on the active power and predicted power of the distributed generation units in the distribution network. The first function is used to calculate the solar curtailment, and the second function is used to calculate the generation cost of the distributed generation units in the distribution network. A third function is then constructed based on the second function and the number of distributed generation units in the distribution network. The third function is used to calculate the generation cost of the distribution network.

[0055] Optionally, a fourth function may be determined based on the third function and the first function, wherein the fourth function is used to calculate the minimum value of a weighted sum of the power generation cost of the distribution network and the amount of solar energy curtailment.

[0056] Alternatively, the goal of the active distribution network economic dispatch is to minimize the generation cost F1 of all traditional distributed power sources and the solar power curtailment F2, which can be expressed as the following formula (1):

[0057] minF=ω1F1+ω2F2

[0058]

[0059] Wherein, the above formula (1) corresponds to the fourth function mentioned above, ω1 and ω2 are two weight coefficients respectively; Ω T ,Ω S They are respectively a collection of traditional distributed power sources and a collection of photovoltaic power stations; are the active power and predicted power of distributed generation j respectively; is the power generation cost function of traditional distributed power sources (i.e., the second function).

[0060] It is easy to notice that formula (1) includes a third function for calculating the power generation cost F1 of the distribution network and a second function for calculating the solar power curtailment F2 of the distribution network.

[0061] in addition, The calculation formula is as follows:

[0062]

[0063] Alternatively, a in formula (2) i 、b i 、c i represent the coefficients of the quadratic equation respectively.

[0064] In an optional embodiment, the constraints of the distribution network include at least the following conditions:

[0065] The first condition is used to constrain the power change rate of the distributed power generation of the distribution network to be between a first value and a second value, where the first value is the maximum value of the output change rate of the distributed power generation of the distribution network, and the second value is the opposite of the first value;

[0066] The second condition is used to constrain the power generation capacity of the generators in the distribution network;

[0067] The third condition is used to constrain the power generation of each branch of the distribution network;

[0068] The fourth condition is used to constrain the power flow balance information of the distribution network, wherein the power flow balance information represents the balance information between the power generation and power consumption of the distribution network.

[0069] Optionally, the constraints for economic dispatch of the distribution network mainly include the ramp limit of distributed power sources (corresponding to the first condition mentioned above), the generator power generation capacity constraint (corresponding to the second condition mentioned above), the branch power constraint (corresponding to the third condition mentioned above) and the flow balance constraint (corresponding to the fourth condition mentioned above).

[0070] Optionally, for the ramp limit of distributed power sources, the power change rate of traditional distributed power sources (such as micro gas turbines) should meet certain limits. In this application, the first condition can be set as formula (3):

[0071]

[0072] in, is the output change rate limit of distributed power source i.

[0073] Alternatively, for the generator capacity constraint and branch power constraint, the capacity constraint of the traditional distributed power source can be expressed as formula (4):

[0074]

[0075] The capacity constraint of photovoltaic power source can be expressed as formula (5):

[0076]

[0077] The branch power constraint can be expressed as formula (6):

[0078]

[0079] in, is the active power of branch ij.

[0080] For the power flow balance constraint, for node i, the power flow balance equation can be written as formula (7):

[0081]

[0082] Here, u:u→i indicates that node u is associated with node i, and power flows to node i.

[0083] In an optional embodiment, an active distribution network scheduling model is constructed based on the constraints of the distribution network, the power generation cost, and the amount of solar energy curtailment, including: determining a fourth function based on the third function and the first function, wherein the fourth function is used to calculate the minimum value of a weighted sum of the power generation cost of the distribution network and the amount of solar energy curtailment; expressing the constraints through a fifth function, and characterizing the balance information between the power generation and power consumption of the distribution network through an augmented matrix by introducing a branch node matrix; and constructing the active distribution network scheduling model based on the fourth function, the fifth function, and the augmented matrix.

[0084] Optionally, for the convenience of expression, the branch node matrix B is introduced to rewrite formula (7) to obtain formula (8):

[0085]

[0086] Alternatively, in formula (8): the elements in B are ij , which means:

[0087]

[0088] in, It can be written as formula (9):

[0089]

[0090] in, is the load of node i.

[0091] Optionally, in the model solution, In order to solve the variables, it is necessary to transform formula (8) for the convenience of solution. First, determine formula (10):

[0092]

[0093] Then substitute formula (10) into formula (8) to obtain formula (11):

[0094]

[0095] make Formula (11) can be simplified to formula (12):

[0096] B s x=b (12)

[0097] Among them, B s It is called the augmented matrix.

[0098] Finally, the above formulas (1), (3)-(6), and (12) constitute the active distribution network scheduling model. Among them, formulas (3)-(6) are collectively referred to as the fifth function.

[0099] In an optional embodiment, target parameters of each of the N line areas are calculated separately using a fully distributed algorithm and an active distribution network scheduling model, including: splitting N augmented correlation matrices according to the augmented matrix, wherein the sum of the N augmented correlation matrices is equal to the augmented matrix, and the N augmented correlation matrices correspond one-to-one to the N line areas; and calculating the target parameters of the J-th line area according to the augmented correlation matrix corresponding to the J-th line area and the active distribution network scheduling model, wherein J is a positive integer less than or equal to N.

[0100] Optionally, since the distribution network mostly operates in a radial manner and there are fewer connecting lines connecting adjacent areas, its augmented association matrix is ​​very sparse, and it is the only factor reflecting regional interconnection. Therefore, in order to simplify the communication process and reduce the overall communication pressure of the distribution network, this application splits N augmented association matrices according to the augmented matrix, and uses the augmented association matrix corresponding to each line area to schedule each line area.

[0101] In an optional embodiment, the target parameters of the J-th line area are calculated based on the augmented correlation matrix corresponding to the J-th line area and the active distribution network scheduling model, including: converting the active distribution network scheduling model into a target model, wherein the target model is used to convert the fourth function, the fifth function and the augmented matrix into an inequality constraint function based on the Lagrange principle; using the augmented correlation matrix corresponding to the J-th line area to replace the augmented matrix in the target model, solving the target model, and obtaining positive solutions to unknowns in the target model; and using the positive solutions to the unknowns in the target model as the target parameters of the J-th line area.

[0102] Optionally, in order to facilitate the solution, the distribution network economic dispatch model is first converted into a standard target model, such as formula (13):

[0103] minF

[0104] st

[0105]

[0106] Alternatively, the PDIP method can be used to effectively solve the optimization problem. The traditional centralized PDIP method requires writing the Karush-Kuhn-Tucker (KKT) equation of formula (14) as follows:

[0107]

[0108] in,

[0109] Let y = (x, λ, v), Δy = (Δx, Δλ, Δv), and give the linearized approximate equation for the Newton method search direction (Equation 15):

[0110]

[0111] Combining equations (14) and (15), the search direction of the decision variable can be obtained as equation (16):

[0112]

[0113] in:

[0114]

[0115] However, the centralized solution method has high requirements for data communication, and when applied to distribution network scheduling, the matrix dimension is too high and the computational complexity is very large. Therefore, this application proposes a fully distributed solution method.

[0116] Alternatively, assuming that the distribution network is divided into N line areas, the variables x, λ, and v are also divided into N areas, expressed as y i =(x i ,λ i , v i ), i = 1, ..., N, for the convenience of analysis, Figure 2 A schematic diagram of an optional three-area distribution network structure is shown, including areas A, B, and C.

[0117] Alternatively, since the distribution network mostly operates in a radial manner and there are fewer tie lines connecting adjacent areas, its augmented correlation matrix is ​​very sparse, and it is the only factor reflecting regional interconnection. As can be seen from formula (14), it involves rdual 、r pri , Δv and Δx, the other calculation steps can be solved locally in each region.

[0118] Optionally, r dual The distributed computing method is as follows:

[0119] According to formula (14), any region, for example, the r dual The calculation formula is updated to formula (18):

[0120]

[0121] in, is the augmented correlation matrix of the Nth region. This matrix ignores the connection lines between regions. Since there are few connection lines between regions, B s Highly sparse, B s and The relationship between them approximately satisfies formula (19), which is also the theoretical basis of the fully distributed PDIP algorithm.

[0122]

[0123] In formula (18), M r dual N To make up for The additional terms introduced by ignoring the tie line are assigned values ​​only to the nodes at one end of the area N in the tie line, and the rest of the nodes are all 0. The calculation of the elements is as follows:

[0124]

[0125] Optionally, r pr i The distributed computing method is as follows:

[0126] Similarly, according to formula (14), the r of the Nth region is pri The calculation formula is updated to:

[0127]

[0128] Among them, M r priN is a column vector that is almost zero, and its element calculation is as follows:

[0129]

[0130] Optionally, a distributed calculation method for Δv is as follows:

[0131] From formula (16), we can know that the calculation design of Δv is Y -1 ,because is a diagonal matrix, is an n×n matrix reflecting the network topology. The structure of Y can be as shown in formula (21):

[0132]

[0133] Since there are few connections between different areas in the distribution network, Y is almost a block diagonal matrix. Therefore, the inverse matrix of Y can be expressed as formula (22):

[0134]

[0135] From formula (22), we can know that each region can also be calculated separately, so we can get formula (23):

[0136]

[0137] Optionally, a distributed calculation method for Δx is as follows:

[0138] Optionally, Δx of the Nth region can be calculated according to formula (16) and formula (22): N The calculation formula is formula (24):

[0139]

[0140] in:

[0141]

[0142] In summary, this application takes advantage of the fact that there are fewer interconnection lines between different areas of the distribution network. s Due to its highly sparse characteristics, the centralized iterative steps are improved to obtain its distributed computing form. Each region performs independent calculations and has limited information exchange between them. This not only improves computing efficiency but also protects each region's privacy. Compared with centralized algorithms, it has greater advantages in the economic dispatch of distribution networks.

[0143] In an optional embodiment, the distribution network is dynamically scheduled according to the target parameters of each line area, including: adjusting the active power generation power and reactive power generation power of each line area according to the target parameters of the line area, wherein the target parameters of different line areas are transmitted independently; by adjusting the active power generation power and reactive power generation power of each line area, the power supply load of each line area is dynamically scheduled.

[0144] Alternatively, an improved IEEE33 node system can be used for simulation analysis, and its network structure and partitioning are as follows: Figure 3 As shown, it is divided into three areas, where area A contains three distributed power sources, and area B and area C each contain two distributed power sources.

[0145] Optionally, Figure 3 The 7 distributed power sources are all micro gas turbines, and their cost coefficient is set to a i ~U(0.01,1),b i ~U(20,20.5),c i ~U(600,1530), the scheduling results based on the fully distributed PDIP algorithm of this application are shown in Table 1.

[0146] Table 1

[0147]

[0148]

[0149] As can be seen from Table 1, each region can achieve self-optimization and coordinate with adjacent regions to ultimately obtain a complete solution.

[0150] According to another aspect of the embodiment of the present application, there is also provided an active distribution network dynamic scheduling device based on a fully distributed algorithm, wherein: Figure 4 is a schematic diagram of an active distribution network dynamic scheduling device based on a fully distributed algorithm according to an embodiment of the present application, such as Figure 4 As shown, the active distribution network dynamic scheduling device based on the fully distributed algorithm includes: a first processing unit 401, a second processing unit 402, a third processing unit 403, a fourth processing unit 404, and a fifth processing unit 405.

[0151] Optionally, a first processing unit 401 is configured to obtain the power generation cost and solar energy curtailment of the distribution network, where the solar energy curtailment represents the amount of electricity that is discarded because the solar power generation cannot be fully absorbed by the distribution network; a second processing unit 402 is configured to construct an active distribution network scheduling model based on the constraints, power generation cost, and solar energy curtailment of the distribution network, where the active distribution network scheduling model is configured to reduce the power generation cost and solar energy curtailment by adjusting parameters of different line areas of the distribution network while satisfying the constraints, and the constraints are configured to ensure the normal operation of the distribution network; a third processing unit 403 is configured to divide the distribution network into N line areas based on the tie lines in the distribution network, where N is an integer greater than 1; a fourth processing unit 404 is configured to calculate target parameters for each of the N line areas using a fully distributed algorithm and the active distribution network scheduling model, where the target parameters of each line area are used to reduce the power generation cost and solar energy curtailment of the line area; and a fifth processing unit 405 is configured to dynamically schedule the distribution network based on the target parameters of each line area.

[0152] Optionally, the constraints of the distribution network include at least the following conditions:

[0153] The first condition is used to constrain the power change rate of the distributed power generation of the distribution network to be between a first value and a second value, where the first value is the maximum value of the output change rate of the distributed power generation of the distribution network, and the second value is the opposite of the first value;

[0154] The second condition is used to constrain the power generation capacity of the generators in the distribution network;

[0155] The third condition is used to constrain the power generation of each branch of the distribution network;

[0156] The fourth condition is used to constrain the power flow balance information of the distribution network, wherein the power flow balance information represents the balance information between the power generation and power consumption of the distribution network.

[0157] Optionally, an active distribution network dynamic scheduling device based on a fully distributed algorithm includes: a sixth processing unit and a seventh processing unit. The sixth processing unit is configured to construct a first function and a second function based on the active power and predicted power of the distributed power sources in the distribution network, wherein the first function is used to calculate the amount of solar power curtailment and the second function is used to calculate the power generation cost of the distributed power sources in the distribution network; and the seventh processing unit is configured to construct a third function based on the second function and the number of distributed power sources in the distribution network, wherein the third function is used to calculate the power generation cost of the distribution network.

[0158] Optionally, the second processing unit 402 includes: a first determining subunit, a first processing subunit, and a second processing subunit. The first determining subunit is configured to determine a fourth function based on the third function and the first function, wherein the fourth function is used to calculate the minimum value of a weighted sum of the power generation cost of the distribution network and the amount of solar energy curtailment; the first processing subunit is configured to represent the constraint condition using a fifth function, and to represent the balance information between the power generation and power consumption of the distribution network using an augmented matrix by introducing a branch node matrix; and the second processing subunit is configured to construct an active distribution network scheduling model based on the fourth function, the fifth function, and the augmented matrix.

[0159] Optionally, the fourth processing unit 404 includes: a third processing subunit and a fourth processing subunit. The third processing subunit is configured to split the augmented matrix into N augmented incidence matrices, wherein the sum of the N augmented incidence matrices is equal to the augmented matrix, and the N augmented incidence matrices correspond one-to-one to the N line areas; and the fourth processing subunit is configured to calculate the target parameters of the J-th line area based on the augmented incidence matrix corresponding to the J-th line area and the active distribution network scheduling model, wherein J is a positive integer less than or equal to N.

[0160] Optionally, the fourth processing subunit includes: a first processing module, a second processing module, and a third processing module. The first processing module is used to convert the active distribution network scheduling model into a target model, wherein the target model is used to convert the fourth function, the fifth function, and the augmented matrix into an inequality constraint function based on the Lagrange principle; the second processing module is used to replace the augmented matrix in the target model with the augmented correlation matrix corresponding to the J-th line area, solve the target model, and obtain the positive solution of the unknown number in the target model; the third processing module is used to use the positive solution of the unknown number in the target model as the target parameter of the J-th line area.

[0161] Optionally, the fifth processing unit 405 includes an adjustment subunit and a scheduling subunit. The adjustment subunit is configured to adjust the active power generation and reactive power generation of each line area according to the target parameters of the line area, wherein the target parameters of different line areas are transmitted independently. The scheduling subunit is configured to dynamically schedule the power supply load of each line area by adjusting the active power generation and reactive power generation of each line area.

[0162] According to another aspect of the present application, a computer-readable storage medium is also provided, wherein a computer program is stored in the computer-readable storage medium. When the computer program is running, the device where the computer-readable storage medium is located executes the above-mentioned active distribution network dynamic scheduling method based on the fully distributed algorithm.

[0163] According to another aspect of the present application, an electronic device is also provided, wherein the electronic device includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by one or more processors, the one or more processors execute the above-mentioned active distribution network dynamic scheduling method based on a fully distributed algorithm.

[0164] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0165] In the above embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0166] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only exemplary. For example, the division of the units can be a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0167] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0168] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0169] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0170] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for dynamic dispatching of active distribution networks based on a fully distributed algorithm, characterized in that: include: Obtaining the power generation cost and solar energy curtailment of the distribution network, wherein the solar energy curtailment represents the amount of electricity that is discarded because the solar power generation cannot be fully absorbed by the distribution network; An active distribution network scheduling model is constructed based on the constraints of the distribution network, the power generation cost, and the amount of solar energy curtailment, wherein the active distribution network scheduling model is used to reduce the power generation cost and the amount of solar energy curtailment by adjusting parameters of different line areas of the distribution network while satisfying the constraints, and the constraints are used to ensure the normal operation of the distribution network; Dividing the distribution network into N line areas according to the tie lines in the distribution network, where N is an integer greater than 1; Calculating target parameters for each of the N line areas using a fully distributed algorithm and the active distribution network scheduling model, wherein the target parameters for each line area are used to reduce power generation costs and solar energy curtailment in the line area; Dynamically dispatching the distribution network according to the target parameters of each line area; Before obtaining the power generation cost and the amount of solar energy curtailment of the distribution network, a first function and a second function are constructed based on the active power and predicted power of the distributed power sources of the distribution network, wherein the first function is used to calculate the amount of solar energy curtailment, and the second function is used to calculate the power generation cost of the distributed power sources of the distribution network; a third function is constructed based on the second function and the number of distributed power sources in the distribution network, wherein the third function is used to calculate the power generation cost of the distribution network; An active distribution network scheduling model is constructed based on the constraints of the distribution network, the power generation cost, and the amount of solar energy curtailment, including: determining a fourth function based on the third function and the first function, wherein the fourth function is used to calculate the minimum value of a weighted sum of the power generation cost of the distribution network and the amount of solar energy curtailment; expressing the constraints through a fifth function, and characterizing the balance information between the power generation and power consumption of the distribution network through an augmented matrix by introducing a branch node matrix; and constructing the active distribution network scheduling model based on the fourth function, the fifth function, and the augmented matrix.

2. The method for dynamic dispatching of active distribution network based on fully distributed algorithm according to claim 1, characterized in that: The constraints of the distribution network include at least the following conditions: A first condition is used to constrain the power change rate of the distributed power sources of the distribution network to be between a first value and a second value, where the first value is the maximum value of the power change rate of the distributed power sources of the distribution network, and the second value is the inverse of the first value; A second condition is used to constrain the power generation capacity of the generators of the distribution network; A third condition is used to constrain the power generation of each branch of the distribution network; The fourth condition is used to constrain the power flow balance information of the distribution network, wherein the power flow balance information represents the balance information between the power generation and power consumption of the distribution network.

3. The method for dynamic dispatching of active distribution network based on fully distributed algorithm according to claim 1, characterized in that: Calculating target parameters for each of the N line areas using a fully distributed algorithm and the active distribution network scheduling model, including: Splitting the augmented matrix into N augmented correlation matrices, wherein a sum of the N augmented correlation matrices is equal to the augmented matrix, and the N augmented correlation matrices correspond one-to-one to the N line areas; Calculate target parameters of the J-th line area according to the augmented incidence matrix corresponding to the J-th line area and the active distribution network scheduling model, where J is a positive integer less than or equal to N.

4. The method for dynamic dispatching of active distribution network based on fully distributed algorithm according to claim 3, characterized in that: Calculating target parameters of the J-th line area according to the augmented correlation matrix corresponding to the J-th line area and the active distribution network scheduling model includes: Converting the active distribution network scheduling model into a target model, wherein the target model is used to convert the fourth function, the fifth function and the augmented matrix into an inequality constraint function based on the Lagrange principle; replacing the augmented matrix in the target model with the augmented incidence matrix corresponding to the J-th line region, solving the target model, and obtaining a positive solution to the unknown number in the target model; The positive solutions of the unknowns in the target model are used as the target parameters of the J-th line area.

5. The method for dynamic dispatching of active distribution network based on fully distributed algorithm according to claim 1, characterized in that: Dynamically dispatching the distribution network according to the target parameters of each line area includes: adjusting the active power generation and reactive power generation of each line area according to the target parameters of each line area, wherein the target parameters of different line areas are transmitted independently; By adjusting the active power generation power and reactive power generation power of each line area, the power supply load of each line area is dynamically scheduled.

6. An active distribution network dynamic dispatching device based on a fully distributed algorithm, used to implement the active distribution network dynamic dispatching method based on a fully distributed algorithm according to any one of claims 1 to 5, characterized in that: include: a first processing unit configured to obtain a power generation cost and an amount of solar energy curtailment of a distribution network, wherein the amount of solar energy curtailment represents the amount of electricity that is discarded because the solar power generation cannot be fully absorbed by the distribution network; a second processing unit, configured to construct an active distribution network scheduling model based on the constraints of the distribution network, the power generation cost, and the amount of solar energy curtailment, wherein the active distribution network scheduling model is configured to reduce the power generation cost and the amount of solar energy curtailment by adjusting parameters of different line areas of the distribution network while satisfying the constraints, and the constraints are configured to ensure the normal operation of the distribution network; a third processing unit, configured to divide the distribution network into N line areas according to the tie lines in the distribution network, where N is an integer greater than 1; a fourth processing unit, configured to calculate target parameters for each of the N line areas using a fully distributed algorithm and the active distribution network scheduling model, wherein the target parameters for each line area are used to reduce power generation costs and solar energy curtailment in the line area; A fifth processing unit is configured to dynamically dispatch the distribution network according to the target parameters of each line area.

7. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, wherein when the computer program is executed, the device where the computer-readable storage medium is located executes the active distribution network dynamic scheduling method based on a fully distributed algorithm as described in any one of claims 1 to 5.

8. An electronic device, characterized in that: It includes one or more processors and a memory, the memory is used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors execute the active distribution network dynamic scheduling method based on a fully distributed algorithm as described in any one of claims 1 to 5.

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