Active power distribution network dynamic equilibrium scheduling method based on real-time electricity price signal

Abstract

The invention relates to an active power distribution network dynamic balance scheduling method based on a real-time electricity price signal. The method comprises the steps of obtaining a supply-side supply function and a user-side revenue function; training a balanced scheduling model based on the supply side supply function and the user side revenue function, and obtaining supply side-user side influence data output by the balanced scheduling model; calculating, iterating and converging the supply side-user side influence data by using a distributed iterative algorithm to obtain formula update data; sending the formula update data to a pre-deployed data center, feeding back the formula update data to an intelligent electric meter of a user side by the data center, and forming an updated user side load function curve based on the association between the balanced scheduling model and the user side; according to the updated user side load function curve, performing a user side power distribution process of a corresponding price; the user is guided to adjust the power consumption behavior through the real-time electricity price signal, the dynamic balanced scheduling of the power distribution network is realized, and the economical efficiency, stability and reliability of system operation are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of power distribution networks, and in particular to a method for dynamic balanced dispatching of active power distribution networks based on real-time electricity price signals. Background Art

[0002] With the rapid development of smart grid technology and the widespread application of distributed power sources (such as solar energy and wind energy), the operation mode and dispatching strategy of distribution networks are undergoing profound changes. Traditional distribution network dispatching mainly relies on planned dispatching and manual adjustment, which makes it difficult to respond to the complex and changeable supply and demand relationship in the power system in real time. Especially after the access of distributed power sources, the flow in the distribution network changes from unidirectional flow to bidirectional flow, which puts forward higher requirements on the flexibility and intelligence of the dispatching system.

[0003] In the current power market, real-time electricity prices, as an effective economic means, can guide users to adjust their electricity consumption behavior according to electricity price signals, achieve supply and demand balance, and improve the economy and stability of system operation. However, the existing demand response strategies based on real-time electricity prices are mostly aimed at a single user group (such as large commercial users or residential users), lacking comprehensive consideration of the entire distribution network and dynamic balanced scheduling capabilities. In addition, the intelligent scheduling of distribution networks also faces many challenges such as multi-objective optimization, multi-stage coordination, and information uncertainty. Specifically, the intelligent scheduling of distribution networks needs to consider multiple goals such as power demand, power quality requirements, and scheduling operation requirements at the same time. These goals have different priorities and constraints at different time stages. For example, during peak power consumption periods, it is necessary to prioritize the stability and reliability of power supply; while during non-peak periods, more attention is paid to economy and environmental protection. At the same time, factors such as load demand, power output, and emergencies in the distribution network are highly uncertain, requiring the scheduling system to have strong data processing and real-time decision-making capabilities. Summary of the invention

[0004] The main purpose of the present invention is to provide a method for dynamic balanced scheduling of active distribution networks based on real-time electricity price signals, aiming to guide users to adjust their electricity consumption behavior through real-time electricity price signals, combine advanced optimization algorithms and intelligent scheduling technologies, realize dynamic balanced scheduling of distribution networks, and improve the economy, stability and reliability of system operation.

[0005] To achieve the above object, the present invention provides a method for dynamic balanced scheduling of active distribution network based on real-time electricity price signal, comprising the following steps: Obtaining a supply-side supply function and a user-side revenue function, wherein the supply-side supply function is the power supplier's need to determine the amount of electricity that can be provided at different prices based on its supply function, and the user-side revenue function is a function that queries the user's real-time electricity price information through a smart meter, and each user realizes daily revenue by transferring part of the electricity load; Training a balanced scheduling model based on the supply-side supply function and the user-side benefit function, and obtaining supply-side-user-side impact data output by the balanced scheduling model; A distributed iterative algorithm is used to calculate, iterate and converge the supply-side-user-side impact data to obtain formula update data; The formula update data is sent to a pre-deployed data center, which is fed back to the smart meter on the user side, and based on the association between the balanced scheduling model and the user side, an updated user side load function curve is formed; According to the updated user-side load function curve, the user-side power distribution process with corresponding price is carried out.

[0006] Furthermore, the method for obtaining the supply-side supply function includes: set up For the collection of power suppliers, , Indicates that the user is The total load during the period, is the supply function of electricity supplier j at time t, is the electricity price function; Divide the piecewise linear supply function of the power supplier into h segments, Indicates power supplier The bid amount in time period t is therefore: Each electricity supplier will Sent to the data center as bidding information, All power suppliers The bid amount submitted during the time period; the data center calculates the electricity price based on the bid information submitted by all power suppliers ,therefore ; The combined formulas obtain the supply side supply function: .

[0007] Furthermore, the method for obtaining the user-side benefit function includes: make represents V users, , represents the transferable load of user i. Each user maximizes his profit by transferring part of the load. The profit function is: ;in, is the transferable load of other users, represents the limit load of user i at time t, Indicates the comfort level of user i.

[0008] Furthermore, the step of training a balanced scheduling model based on the supply-side supply function and the user-side benefit function, and obtaining the supply-side-user-side impact data output by the balanced scheduling model includes: The equilibrium scheduling model is trained to derive supply-side impact data, wherein: Prove the correctness of the equilibrium scheduling model when deriving the supply-side impact data, using the definition function: in, Supply-side impact data represented by.

[0009] Furthermore, the step of training a balanced scheduling model based on the supply-side supply function and the user-side benefit function, and obtaining the supply-side-user-side impact data output by the balanced scheduling model includes: Training the balanced scheduling model to derive user-side impact data, in, represents the daily total adjustable load of user i. Based on the model, the user-side impact data can be obtained. .

[0010] Furthermore, the process of calculating, iterating and converging the supply-side-user-side impact data using a distributed iterative algorithm to obtain a step of updating the data using a formula includes: assuming k is the number of iterations, then represents the load of all users i for k iterations, represents the bidding amount of the power supplier iterating k times in time t, represents the total load of k iterations in time t, represents the electricity price iterated k times in time t; the power supplier and the user make corresponding decisions at t=0 to determine the best bid amount and transferable load for the day, and update the bid amount for each time period after k iterations. Each power supplier j updates the electricity price for each time period in the data center ; Users can update the To achieve the update of load curve; (1) Initialization: k = 0, ; (2) Submitting user transferable load to the data center , the total load is calculated by the data center ; (3) Calculated , and then get , after updating ; (4) Updated Iterate (5) converges, and the convergence condition is , repeat steps (2) to (5) until the conditions are met and convergence is completed, and the formula is obtained to update the data.

[0011] Furthermore, the formula update data is sent to a pre-deployed data center, which is fed back to the smart meter on the user side by the data center, and based on the association between the balanced scheduling model and the user side, an updated user side load function curve is formed, including: Sending the formula update data to a pre-deployed data center, and adjusting the current supply-side supply function and user-side revenue function according to the formula update data; Feeding back the adjusted user-side revenue function to the user-side smart meter for conversion to obtain the user-side load function curve; The electricity price balance of the smart meter is updated through the user-side load function curve.

[0012] The present invention proposes an active distribution network dynamic balancing dispatching device based on real-time electricity price signals, comprising: An acquisition unit is used to acquire a supply-side supply function and a user-side revenue function, wherein the supply-side supply function is that the power supplier needs to determine the amount of electricity that can be provided at different prices according to its supply function, and the user-side revenue function is a function that each user realizes daily revenue by transferring part of the electricity load by querying the user's real-time electricity price information through a smart meter; A model unit, used to train a balanced scheduling model based on the supply-side supply function and the user-side benefit function, and obtain supply-side-user-side impact data output by the balanced scheduling model; Iteration unit, a process in which a user uses a distributed iterative algorithm to calculate, iterate and converge the supply-side-user-side impact data to obtain formula update data; An association unit, used to send the formula update data to a pre-deployed data center, which is fed back to the smart meter on the user side by the data center, and form an updated user side load function curve based on the association between the balanced scheduling model and the user side; The electricity price unit is used to carry out the user-side power distribution process with corresponding price according to the updated user-side load function curve.

[0013] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned method for dynamic balanced scheduling of an active distribution network based on real-time electricity price signals are implemented.

[0014] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned method for dynamic balanced scheduling of an active distribution network based on real-time electricity price signals are implemented.

[0015] The present invention provides a method for dynamic balanced dispatching of active distribution networks based on real-time electricity price signals, which has the following beneficial effects: Obtain a supply-side supply function and a user-side revenue function; train a balanced scheduling model based on the supply-side supply function and the user-side revenue function, and obtain the supply-side-user-side impact data output by the balanced scheduling model; use a distributed iterative algorithm to calculate, iterate and converge the supply-side-user-side impact data to obtain formula update data; send the formula update data to a pre-deployed data center, which is fed back to the smart meter on the user side, and based on the association between the balanced scheduling model and the user side, form an updated user-side load function curve; perform a user-side power distribution process with a corresponding price according to the updated user-side load function curve; guide users to adjust their power consumption behavior through real-time electricity price signals, realize dynamic balanced scheduling of the distribution network, and improve the economy, stability and reliability of system operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 It is a flow chart of a method for dynamic balanced dispatching of an active power distribution network based on real-time electricity price signals in one embodiment of the present invention; Figure 2 It is a structural block diagram of an active distribution network dynamic balancing dispatching device based on real-time electricity price signals in one embodiment of the present invention; Figure 3 It is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0017] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0018] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] Reference Figure 1 , which is a flow chart of a method for dynamic balanced dispatching of an active distribution network based on real-time electricity price signals proposed by the present invention, comprising the following steps: S1, obtaining a supply-side supply function and a user-side revenue function, wherein the supply-side supply function is the power supplier's need to determine the amount of electricity that can be provided at different prices based on its supply function, and the user-side revenue function is a function that queries the user's real-time electricity price information through a smart meter, and each user realizes daily revenue by transferring part of the electricity load; S2, training a balanced scheduling model based on the supply-side supply function and the user-side benefit function, and obtaining supply-side-user-side impact data output by the balanced scheduling model; S3, using a distributed iterative algorithm to calculate, iterate and converge the supply-side-user-side impact data to obtain formula update data; S4, sending the formula update data to the pre-deployed data center, which is fed back to the smart meter on the user side, and forming an updated user side load function curve based on the association between the balanced scheduling model and the user side; S5, performing the user-side power distribution process of the corresponding price according to the updated user-side load function curve.

[0020] Specifically, first, we need to obtain the supply function on the supply side and the revenue function on the user side. The supply function on the supply side describes the amount of electricity that the power supplier is willing to provide at different prices, while the revenue function on the user side is the real-time electricity price information of the user queried through the smart meter, as well as the daily revenue achieved by the user by adjusting the electricity consumption. Next, use these two functions to train a balanced scheduling model. The goal of this model is to find the equilibrium point between the supply side and the user side, that is, the best match between price and electricity. After training, the model will output the mutual influence data between the supply side and the user side. Then, the distributed iterative algorithm is used to calculate, iterate and converge these influence data. The goal of this process is to find the optimal scheduling solution, that is, the price and electricity combination that maximizes the overall benefits of the supply side and the user side. After calculation, iteration and convergence, a set of formula update data will be obtained. This set of formula update data will be sent to the pre-deployed data center, and the data center will then feed back these data to the smart meter on the user side. The smart meter will update the user-side load function curve based on these data, which describes the user's electricity consumption at different prices. Finally, according to the updated user-side load function curve, the user-side power distribution process corresponding to the price is carried out.

[0021] In one embodiment, a method for obtaining a supply-side supply function includes: set up For the collection of power suppliers, , Indicates that the user is The total load during the period, is the supply function of electricity supplier j at time t, is the electricity price function; Divide the piecewise linear supply function of the power supplier into h segments, Indicates power supplier The bid amount in time period t is therefore: ;

[0022] Each electricity supplier will Sent to the data center as bidding information, All power suppliers The bid amount submitted during the time period; the data center calculates the electricity price based on the bid information submitted by all power suppliers ,therefore ; The combined formulas obtain the supply side supply function: .

[0023] In one embodiment, a method for obtaining a user-side benefit function includes: make represents V users, , represents the transferable load of user i. Each user maximizes his profit by transferring part of the load. The profit function is: in, is the transferable load of other users, represents the limit load of user i at time t, Indicates the comfort level of user i.

[0024] In one embodiment, the step of training a balanced scheduling model based on the supply-side supply function and the user-side benefit function, and obtaining the supply-side-user-side impact data output by the balanced scheduling model includes: The equilibrium scheduling model is trained to derive supply-side impact data, wherein: Prove the correctness of the equilibrium scheduling model when deriving the supply-side impact data, using the definition function: in, Supply-side impact data represented by.

[0025] In one embodiment, the step of training a balanced scheduling model based on the supply-side supply function and the user-side benefit function, and obtaining the supply-side-user-side impact data output by the balanced scheduling model includes: Training the balanced scheduling model to derive user-side impact data, in, represents the daily total adjustable load of user i. Based on the model, the user-side impact data can be obtained. .

[0026] In one embodiment, the process of calculating, iterating and converging the supply-side-user-side impact data using a distributed iterative algorithm to obtain a step of updating the formula data includes: Let k be the number of iterations, then represents the load of all users i for k iterations, represents the bidding amount of the power supplier iterating k times in time t, represents the total load of k iterations in time t, represents the electricity price iterated k times in time t; the power supplier and the user make corresponding decisions at t=0 to determine the best bid amount and transferable load for the day, and update the bid amount for each time period after k iterations. Each power supplier j updates the electricity price for each time period in the data center ; Users can update the To achieve the update of load curve; (1) Initialization: k = 0, ; (2) Submitting user transferable load to the data center , the total load is calculated by the data center ; (3) Calculated , and then get , after updating ; (4) Updated Iterate (5) converges, and the convergence condition is , repeat steps (2) to (5) until the conditions are met and convergence is completed, and the formula is obtained to update the data.

[0027] In one embodiment, the formula update data is sent to a pre-deployed data center, which is fed back to the smart meter on the user side by the data center, and based on the association between the balanced scheduling model and the user side, an updated user side load function curve is formed, including: Sending the formula update data to a pre-deployed data center, and adjusting the current supply-side supply function and user-side revenue function according to the formula update data; Feeding back the adjusted user-side revenue function to the user-side smart meter for conversion to obtain the user-side load function curve; The electricity price balance of the smart meter is updated through the user-side load function curve.

[0028] Reference Figure 2 The present invention also proposes an active distribution network dynamic balancing dispatching device based on real-time electricity price signals, comprising: An acquisition unit is used to acquire a supply-side supply function and a user-side revenue function, wherein the supply-side supply function is that the power supplier needs to determine the amount of electricity that can be provided at different prices according to its supply function, and the user-side revenue function is a function that each user realizes daily revenue by transferring part of the electricity load by querying the user's real-time electricity price information through a smart meter; A model unit, used to train a balanced scheduling model based on the supply-side supply function and the user-side benefit function, and obtain supply-side-user-side impact data output by the balanced scheduling model; Iteration unit, a process in which a user uses a distributed iterative algorithm to calculate, iterate and converge the supply-side-user-side impact data to obtain formula update data; An association unit, used to send the formula update data to a pre-deployed data center, which is fed back to the smart meter on the user side by the data center, and form an updated user side load function curve based on the association between the balanced scheduling model and the user side; The electricity price unit is used to carry out the user-side power distribution process with corresponding price according to the updated user-side load function curve.

[0029] Reference Figure 3 In an embodiment of the present invention, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 3 As shown. The computer device includes a processor, a memory, a display screen, an input device, a network interface and a database connected through a system bus. Among them, the processor designed by the computer is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the corresponding data in this embodiment. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0030] Those skilled in the art will understand that Figure 3 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device to which the solution of the present invention is applied.

[0031] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, the above method is implemented. It can be understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0032] In summary, the supply-side supply function and the user-side revenue function are obtained; the balanced scheduling model is trained based on the supply-side supply function and the user-side revenue function, and the supply-side-user-side impact data output by the balanced scheduling model is obtained; the supply-side-user-side impact data are calculated, iterated and converged using a distributed iterative algorithm to obtain formula update data; the formula update data is sent to a pre-deployed data center, which is fed back to the smart meter on the user side, and based on the association between the balanced scheduling model and the user side, an updated user-side load function curve is formed; according to the updated user-side load function curve, the user-side power distribution process of the corresponding price is carried out; users are guided to adjust their power consumption behavior through real-time electricity price signals, so as to realize dynamic balanced scheduling of the distribution network and improve the economy, stability and reliability of system operation.

[0033] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided by the present invention and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double-speed data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM.

[0034] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, device, article or method including the element.

[0035] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A method for dynamic balanced dispatching of active distribution network based on real-time electricity price signal, characterized in that: The following steps are involved: Obtaining a supply-side supply function and a user-side revenue function, wherein the supply-side supply function is the power supplier's need to determine the amount of electricity that can be provided at different prices based on its supply function, and the user-side revenue function is a function that queries the user's real-time electricity price information through a smart meter, and each user realizes daily revenue by transferring part of the electricity load; Training a balanced scheduling model based on the supply-side supply function and the user-side benefit function, and obtaining supply-side-user-side impact data output by the balanced scheduling model; A distributed iterative algorithm is used to calculate, iterate and converge the supply-side-user-side impact data to obtain formula update data; The formula update data is sent to a pre-deployed data center, which is fed back to the smart meter on the user side, and based on the association between the balanced scheduling model and the user side, an updated user side load function curve is formed; According to the updated user-side load function curve, the user-side power distribution process with the corresponding price is carried out.

2. The method for dynamic balanced dispatching of active distribution network based on real-time electricity price signal according to claim 1, characterized in that: The method for obtaining the supply-side supply function includes: set up For the collection of power suppliers, , Indicates that the user is The total load during the period, is the supply function of electricity supplier j at time t, is the electricity price function; the piecewise linear supply function of the power supplier is divided into h segments, Indicates power supplier The bid amount in time period t is therefore: Each power supplier will Sent to the data center as bidding information, All power suppliers The bid amount submitted during the time period; the data center calculates the electricity price based on the bid information submitted by all power suppliers ,therefore ; The combined formulas obtain the supply side supply function: .

3. The method for dynamic balanced dispatching of active distribution network based on real-time electricity price signal according to claim 1, characterized in that: The method for obtaining the user side benefit function includes: represents V users, , represents the transferable load of user i. Each user maximizes his profit by transferring part of the load. The profit function is: in, is the transferable load of other users, represents the limit load of user i at time t, represents the comfort level of user i.

4. The method for dynamic balanced dispatching of active distribution network based on real-time electricity price signal according to claim 1, characterized in that: The steps of training a balanced scheduling model based on the supply-side supply function and the user-side benefit function, and obtaining supply-side-user-side impact data output by the balanced scheduling model include: The equilibrium scheduling model is trained to derive supply-side impact data, wherein: Prove the correctness of the equilibrium scheduling model when deriving the supply-side impact data, using the definition function: in, Supply-side impact data represented by.

5. The method for dynamic balanced dispatching of active distribution network based on real-time electricity price signal according to claim 1, characterized in that: The steps of training a balanced scheduling model based on the supply-side supply function and the user-side benefit function, and obtaining supply-side-user-side impact data output by the balanced scheduling model include: Training the balanced scheduling model to derive user-side impact data, in, represents the daily total adjustable load of user i. Based on the model, the user-side impact data can be obtained. .

6. The method for dynamic balanced dispatching of active distribution network based on real-time electricity price signal according to claim 1, characterized in that: The process of calculating, iterating and converging the supply-side-user-side impact data using a distributed iterative algorithm to obtain a formula to update the data includes: let k be the number of iterations, then represents the load of all users i for k iterations, represents the bidding amount of the power supplier iterating k times in time t, represents the total load of k iterations in time t, represents the electricity price iterated k times in time t; the power supplier and the user make corresponding decisions at t=0 to determine the best bid amount and transferable load for the day, and update the bid amount for each time period after k iterations. Each power supplier j updates the electricity price for each time period in the data center ; Users can update the To update the load curve; (1) Initialization: k = 0, ; (2) Submit user transferable load to the data center , the total load is calculated by the data center ; (3) Calculate , and then get , after updating ;(4)After the update Iterate; (5) Converge, the convergence condition is , repeat steps (2) to (5) until the conditions are met and convergence is completed, and the formula is obtained to update the data.

7. The method for dynamic balanced dispatching of active distribution network based on real-time electricity price signal according to claim 1, characterized in that: The formula update data is sent to a pre-deployed data center, which is fed back to the smart meter on the user side by the data center, and based on the association between the balanced scheduling model and the user side, an updated user side load function curve is formed, including: Sending the formula update data to a pre-deployed data center, and adjusting the current supply-side supply function and user-side revenue function according to the formula update data; Feeding back the adjusted user-side revenue function to the user-side smart meter for conversion to obtain the user-side load function curve; The electricity price balance of the smart meter is updated through the user-side load function curve.

8. An active distribution network dynamic balancing dispatching device based on real-time electricity price signals, characterized in that: include: An acquisition unit is used to acquire a supply-side supply function and a user-side revenue function, wherein the supply-side supply function is that the power supplier needs to determine the amount of electricity that can be provided at different prices according to its supply function, and the user-side revenue function is a function that each user realizes daily revenue by transferring part of the electricity load by querying the user's real-time electricity price information through a smart meter; A model unit, used to train a balanced scheduling model based on the supply-side supply function and the user-side benefit function, and obtain supply-side-user-side impact data output by the balanced scheduling model; Iteration unit, a process in which a user uses a distributed iterative algorithm to calculate, iterate and converge the supply-side-user-side impact data to obtain formula update data; An association unit, used to send the formula update data to a pre-deployed data center, which is fed back to the smart meter on the user side by the data center, and form an updated user side load function curve based on the association between the balanced scheduling model and the user side; The electricity price unit is used to carry out the user-side power distribution process with corresponding price according to the updated user-side load function curve.

9. A computer device comprising a memory and a processor, wherein a computer program is stored in the memory, wherein: When the processor executes the computer program, the steps of a method for dynamic balanced scheduling of an active distribution network based on a real-time electricity price signal as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of a method for dynamic balanced scheduling of an active distribution network based on a real-time electricity price signal as described in any one of claims 1 to 7 are implemented.

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