Urban active power distribution network energy scheduling control method and system based on probability balance model

Abstract

The invention discloses an urban active power distribution network energy scheduling control method and system based on a probability balance model, and relates to the technical field of active power distribution network scheduling control, and the method comprises the following steps: dividing an active power distribution network into a three-layer control structure of a region layer, a grid layer and a unit layer; constructing a target function by taking balance of energy of each layer as a target; the target function is solved based on a pre-obtained hierarchical probability constraint to obtain a global control parameter, the active power distribution network energy is controlled, the hierarchical probability constraint is obtained based on a probability control parameter, and the probability control parameter is obtained by initializing based on an FPGA competition strategy according to a pre-obtained probability scene set and updating based on an FPGA risk strategy. The method is used for solving the problem that the energy dispatching control of the urban active power distribution network is unstable under the high-uncertainty condition due to the fact that the probability control parameters are manually set.

Description

Technical Field

[0001] The present invention relates to the technical field of active distribution network dispatching control, and more specifically, to an urban active distribution network energy dispatching control method and system based on a probabilistic balance model. Background Art

[0002] With the large-scale integration of uncertain resources such as new energy (wind power, photovoltaics) and electric vehicles into distribution networks, traditional dispatch strategies based on "deterministic balance" face severe challenges. Due to the randomness and volatility of the output of these resources, the system frequently faces power mismatches, energy gaps, and overload risks during operation. To this end, researchers introduced a probabilistic balance model. By introducing random variables, scenario simulation, confidence intervals, and other methods, they expanded the power balance relationship from a "rigid constraint" to a "probabilistic satisfaction condition," enabling the dispatch model to achieve risk-controlled and robust operation in uncertain environments.

[0003] Active distribution networks are smart grids that integrate distributed power sources, energy storage systems, adjustable loads, and automated control equipment, building upon traditional distribution networks. These networks offer local optimization, bidirectional power flow, and autonomous response capabilities. With the advancement of intelligent sensing, communication, and control technologies, dispatch control objectives have expanded from single-objective power supply reliability to multi-objective coordination. In this context, dispatch control methods have gradually shifted from static fixed-value control to dynamic optimal dispatch, hierarchical control, and coordinated control. In particular, multi-timescale adaptive control strategies that consider the coordinated operation of sources, loads, and storage have become the core enabling technology for efficient operation and flexible dispatch of distribution networks.

[0004] For example, the invention patent publication number CN102169342B discloses a coordinated control system and control method for a distribution network that takes distributed power sources into account. The system includes a master controller, a simulation display screen, 13 transformers, 9 distributed switch control terminals, 2 distributed power generation device control terminals, 2 distributed energy storage device control terminals, 2 distributed power generation devices, 2 three-phase inverters, 2 energy storage devices, and 2 charging / inverter devices. This invention enables the distribution system to coordinate the operating status of distributed power sources within the network, thereby maximizing the utilization of clean energy within the network. It coordinates and optimizes the operating status of distributed power sources, achieving a clean energy load tracking rate of over 70%, while also preventing the impact of random grid connection or disconnection of distributed power sources on the distribution network. When an extreme event occurs in the distribution network, i.e., when the distribution network experiences "islanding" operation, the system coordinates the maximum power operation of the distributed power sources and energy storage devices within the network to ensure normal power supply to critical loads within the network, thus resolving the power supply issues faced by the distribution network during extreme events.

[0005] For example, the invention patent announcement with the publication number CN104516323B discloses a power grid dispatching system. The system includes: multiple data acquisition devices, each of which is connected to one or more sensors, wherein the data acquisition device is used to acquire sensor signals output by the connected sensors and output a first numerical signal converted from the sensor signals; a switching device connected to the multiple data acquisition devices; and a display device connected to the switching device, wherein the display device includes a screen for displaying the current value corresponding to the first numerical signal received from the switching device. The present invention solves the technical problem in the prior art of the lack of integrity in the display of dispatching information of the power grid dispatching system on the human-machine interface of the system due to the dispersion of display devices.

[0006] The above disclosed technical solutions have at least the following technical problems: In existing energy dispatching methods for urban active distribution networks, probabilistic control parameters are usually manually set based on expert experience or offline simulations. This is not only time-consuming and labor-intensive, but also difficult to accurately adapt to the time-varying uncertainty of distributed renewable energy output and loads. Once the parameter settings deviate, the robustness of the dispatching strategy decreases, energy balance becomes unbalanced in extreme cases, and even over-charging and discharging of energy storage or cross-layer exchange bottlenecks occur, seriously restricting the stability and energy efficiency improvement of the distribution network.

[0007] In view of the above problems, the present invention proposes a solution. Summary of the Invention

[0008] In order to overcome the above-mentioned defects of the prior art, the embodiments of the present invention provide an energy dispatching and control method and system for an urban active distribution network based on a probabilistic balance model. By introducing an FPGA-based competition and risk strategy to dynamically initialize and update the probabilistic control parameters, the problem of unstable energy dispatching and control of the urban active distribution network under high uncertainty conditions caused by artificially set probabilistic control parameters is solved.

[0009] To achieve the above object, the present invention provides the following technical solutions: An energy dispatching and control method for an urban active distribution network based on a probabilistic balance model comprises the following steps: dividing the active distribution network into a three-layer control structure comprising a regional layer, a grid layer, and a unit layer; constructing an objective function with the goal of balancing the energy of each layer; solving the objective function based on pre-acquired hierarchical probabilistic constraints to obtain global control parameters, and controlling the energy of the active distribution network. The hierarchical probabilistic constraints are obtained based on probabilistic control parameters, which are initialized based on an FPGA competition strategy according to a pre-acquired probabilistic scenario set and updated based on an FPGA risk strategy.

[0010] In a preferred embodiment, the global control parameters include cross-layer transmission capacity, spare coefficient and self-sufficiency parameter; the probabilistic control parameters include a probability confidence set.

[0011] In a preferred embodiment, the method for obtaining the probability scenario set specifically includes: obtaining an output prediction sequence, wherein the output prediction sequence includes a photovoltaic output prediction sequence and a wind power output prediction sequence; according to the output prediction sequence, based on the photovoltaic output obeying the Beta distribution and the wind power output obeying the Weibull distribution, obtaining the probability scenario set through Monte Carlo sampling.

[0012] In a preferred embodiment, the FPGA-based competition strategy initialization is specifically as follows: based on the probability scenario set and the FPGA competition strategy, a three-layer parallel architecture is constructed according to the three-layer control structure; in the three-layer parallel architecture, the balance loss of each scenario in each layer is calculated based on Monte Carlo simulation to obtain the balance loss data of each layer; based on the balance loss data of each layer, the initial probability control parameters are obtained based on the quantile statistical algorithm.

[0013] In a preferred embodiment, the FPGA-based risk strategy update is specifically as follows: based on the initial probability control parameters and the obtained distributed power output historical data, a speculative architecture and an extreme scenario set are constructed based on the FPGA risk strategy; based on the speculative architecture and the extreme scenario set, Monte Carlo simulation is performed to obtain extreme loss data; based on the extreme loss data, the inter-layer influence amount is calculated through the inter-layer influence matrix, and the inter-layer influence matrix is obtained based on the balanced loss data of each layer; based on the inter-layer influence amount and the extreme loss data, the initial probability control parameters are updated based on the dynamic arbitration fallback algorithm.

[0014] In a preferred embodiment, the method for obtaining the global control parameters specifically includes: constructing a hierarchical probability constraint, and using the updated probability control parameters as the confidence of the hierarchical probability constraint, the hierarchical probability constraint including the regional layer constraint, the grid layer constraint and the unit layer constraint; according to the energy balance principle, constructing an objective function with the goal of balancing the energy of each layer; based on the hierarchical probability constraint, solving the objective function to obtain the global control parameters.

[0015] In a preferred embodiment, the control of the active distribution network energy is specifically as follows: obtaining load forecast data, the load forecast data including regional load forecast, grid load forecast and unit load forecast; controlling the inter-regional exchange power in each time period based on the cross-layer transmission capacity, combined with the load forecast data and the output forecast sequence; controlling the energy storage charging and discharging power of the grid layer in each time period based on the reserve coefficient, combined with the load forecast data; and controlling the energy storage charging and discharging power and the adjustable load power of the unit layer in each time period based on the self-sufficiency parameters.

[0016] In a preferred embodiment, the method for obtaining the inter-layer influence matrix is specifically as follows: according to the balance loss data of each layer, the inter-layer influence coefficient is obtained based on the covariance and variance; and according to the inter-layer influence coefficient, the inter-layer influence matrix is constructed.

[0017] An urban active distribution network energy dispatching and control system based on a probabilistic balance model includes: a hierarchical module for dividing the active distribution network into a three-layer control structure: a regional layer, a grid layer, and a unit layer; a parameter optimization module for initializing probabilistic control parameters based on an FPGA competition strategy according to a pre-acquired set of probabilistic scenarios, and updating the probabilistic control parameters based on an FPGA risk strategy; and a global control module for constructing an objective function with the goal of balancing the energy of each layer, and solving the objective function based on pre-acquired hierarchical probabilistic constraints to obtain global control parameters, thereby controlling the energy of the active distribution network. The hierarchical probabilistic constraints are obtained based on the probabilistic control parameters.

[0018] The technical effects and advantages of the urban active distribution network energy dispatching control method and system based on the probabilistic balance model of the present invention are as follows: The present invention obtains a set of probabilistic scenarios of distributed power output, constructs a multi-level structure of regional layer, grid layer and unit layer, and combines hierarchical probabilistic constraints to achieve global energy balance scheduling control. A significant technical feature is the introduction of FPGA-based competition and risk strategies, which are used to initialize and dynamically update probabilistic control parameters, respectively, significantly improving the adaptability and robustness of the control strategy under uncertainty conditions. Through Monte Carlo simulation, quantile statistics and dynamic arbitration mechanisms, the control parameters can better cope with extreme output fluctuations, thereby enhancing the stability and energy efficiency of the distribution network, and effectively solving the problem of unstable energy scheduling and control of urban active distribution networks under high uncertainty conditions due to artificially set probabilistic control parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 A schematic flow chart of an urban active distribution network energy dispatching and control method based on a probabilistic balance model provided in an embodiment of the present invention.

[0020] Figure 2 A schematic structural diagram of an urban active distribution network energy dispatching and control system based on a probabilistic balance model provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0022] Example 1, Figure 1 The present invention provides an energy dispatching and control method for an urban active distribution network based on a probabilistic balance model, which includes the following steps: The active distribution network is divided into three-layer control structure: regional layer, grid layer and unit layer; Construct an objective function with the goal of balancing the energy of each layer; The energy of the active distribution network is controlled by solving the objective function based on pre-acquired hierarchical probabilistic constraints to obtain global control parameters. The hierarchical probabilistic constraints are obtained based on probabilistic control parameters. The probabilistic control parameters are initialized based on an FPGA competition strategy according to a pre-acquired set of probabilistic scenarios and updated based on an FPGA risk strategy.

[0023] This embodiment obtains a set of probabilistic scenarios of distributed power output, constructs a multi-level structure of regional layer, grid layer and unit layer, and combines hierarchical probabilistic constraints to achieve global energy balance scheduling control. A significant technical feature is the introduction of FPGA-based competition and risk-taking strategies, which are used to initialize and dynamically update probabilistic control parameters, respectively, significantly improving the adaptability and robustness of the control strategy under uncertainty conditions. Through Monte Carlo simulation, quantile statistics and dynamic arbitration mechanisms, the control parameters can better cope with extreme output fluctuations, thereby enhancing the stability and energy efficiency of the distribution network, and effectively solving the problem of unstable energy scheduling and control of urban active distribution networks under high uncertainty conditions due to artificially set probabilistic control parameters.

[0024] In this embodiment, the method for obtaining the probability scenario set specifically includes: Obtaining an output prediction sequence, wherein the output prediction sequence includes a photovoltaic output prediction sequence and a wind power output prediction sequence; According to the output forecast sequence, based on the assumption that photovoltaic output follows Beta distribution and wind power output follows Weibull distribution, a probability scenario set is obtained through Monte Carlo sampling.

[0025] It should be noted that photovoltaic power output obeys the Beta probability distribution, and wind power output obeys the Weibull probability distribution.

[0026] In this embodiment, the global control parameters include cross-layer transmission capacity, spare coefficient and self-sufficiency parameter; The probability control parameters include a probability confidence set.

[0027] In this embodiment, the FPGA-based competition strategy initialization is specifically as follows: According to the set of probabilistic scenarios, a three-layer parallel architecture is constructed based on the FPGA competition strategy according to the three-layer control structure; In the three-layer parallel architecture, the balance loss of each scenario at each layer is calculated based on Monte Carlo simulation to obtain the balance loss data of each layer; According to the balance loss data of each layer, the initial probability control parameters are obtained based on the quantile statistics algorithm.

[0028] In this embodiment, the balance loss data of each layer is calculated using the following formula:

[0029]

[0030]

[0031] Where, is the regional level balance loss set, is the total transmission capacity of the regional layer to the transmission grid, is the transmission capacity of the kth grid to the regional layer, is the grid-level balanced loss set, is the equilibrium ratio of the lth unit layer, is the maximum load of the lth unit layer, is the transmission capacity from the grid layer to the regional layer, To store energy in the grid layer, is the reserve coefficient, is the maximum total load of the unit layer in the grid layer, is the unit-level balanced loss set, is the maximum load in the unit layer, To contribute to the new energy in the unit layer, is a self-sufficient parameter, It is the energy storage output in the unit layer.

[0032] In this embodiment, the specific calculation formula of the initial probability control parameter is:

[0033]

[0034]

[0035] Where, 、 and are the initial probability control parameters of the regional layer, the initial probability control parameters of the grid layer and the initial probability control parameters of the unit layer, respectively. Loss set middle The loss value of the quantile position, 、 and They are the regional quantile, grid quantile and cell quantile respectively.

[0036] It should be noted that the regional layer quantile is greater than the grid layer quantile, and the grid layer quantile is greater than the unit layer quantile.

[0037] In this embodiment, the FPGA-based risk strategy update is specifically as follows: Based on the initial probabilistic control parameters and the acquired historical output data of distributed power sources, a speculative architecture and an extreme scenario set are constructed based on the FPGA risk-taking strategy; Based on the speculative architecture and extreme scenario set, Monte Carlo simulation is performed to obtain extreme loss data; According to the extreme loss data, the inter-layer influence amount is calculated by using the inter-layer influence matrix, wherein the inter-layer influence matrix is obtained based on the balanced loss data of each layer; According to the inter-layer influence and extreme loss data, the initial probability control parameters are updated based on the dynamic arbitration fallback algorithm.

[0038] In this embodiment, the method for obtaining the inter-layer influence matrix is specifically as follows: According to the balanced loss data of each layer, the inter-layer influence coefficient is obtained based on the covariance and variance; According to the inter-layer influence coefficient, the inter-layer influence matrix is constructed.

[0039] In this embodiment, the method for constructing an extreme scenario set includes: Based on the historical output data of distributed power generation, the daily photovoltaic and wind power output series are extracted based on the sliding window; According to the statistical distribution method, the lowest output quantile and the maximum volatility quantile are obtained; Based on the joint distribution method, scenarios that meet both low output and high volatility are screened out and constructed into an extreme scenario set.

[0040] In this embodiment, the extreme loss data is calculated using the following formula:

[0041] Where, For extreme losses, 、 and are the regional layer loss, grid layer loss and unit layer loss in extreme scenarios, 、 and is the weight coefficient.

[0042] In this embodiment, the inter-layer influence matrix is specifically formulated as follows:

[0043] Where, is the inter-layer influence matrix, is the influence of the mth layer on the nth layer, is the covariance function, is the variance function.

[0044] In this embodiment, the inter-layer influence amount is specifically expressed as follows:

[0045] Where, 、 and is the inter-layer influence of each layer.

[0046] In this embodiment, the initial probability control parameter is updated, and the specific calculation formula of the updated probability control parameter is:

[0047] Where, 、 and is the updated probability control parameter, 、 and is the risk sensitivity coefficient, 、 and is the correction factor for interlayer effects, 、 and The preset physical upper limit.

[0048] In this embodiment, the method for obtaining the risk sensitivity coefficient is specifically as follows: Based on the historical balance loss data of each layer, the standard deviation is calculated as a volatility indicator; Based on the upper deviation amplitude of each layer loss in extreme scenarios, the average deviation level is extracted; The standard deviation and the upper deviation amplitude are normalized, and the risk sensitivity coefficient is constructed based on the weighted average method.

[0049] In this embodiment, the layered probability constraint is specifically formulated as follows: Regional layer constraints:

[0050] Where, is the number of grid layers in the regional layer, is the transmission capacity of the kth grid to the regional layer, is the total transmission capacity of the regional layer to the transmission grid, is the transmission capacity margin, is the confidence level of the regional layer; Grid layer constraints:

[0051] Where, is the number of unit layers in the grid layer, is the equilibrium ratio of the lth unit layer, is the maximum load of the lth unit layer, is the transmission capacity from the grid layer to the regional layer, To store energy in the grid layer, is the reserve coefficient, is the maximum total load of the unit layer in the grid layer, is the confidence of the grid layer; Cell level constraints:

[0052] Where, To contribute to the new energy in the unit layer, For the energy storage output in the unit layer, is a self-sufficient parameter, is the maximum load in the unit layer, is the confidence level of the unit layer.

[0053] In this embodiment, the objective function is specifically calculated as follows:

[0054]

[0055]

[0056]

[0057] Where, 、 and They are regional-level cross-layer transmission balance, grid-level reserve capacity balance, and unit-level self-sufficiency balance; is the total number of regional layers, is the current regional layer number, is the total number of grid layers, is the current grid layer number, is the total number of unit layers, The current grid layer number.

[0058] In this embodiment, the method for obtaining the global control parameters specifically includes: Constructing a hierarchical probability constraint and using the updated probability control parameter as the confidence of the hierarchical probability constraint, wherein the hierarchical probability constraint includes a regional layer constraint, a grid layer constraint, and a cell layer constraint; According to the energy balance principle, the objective function is constructed with the goal of balancing the energy of each layer; Based on the hierarchical probability constraints, the objective function is solved to obtain the global control parameters.

[0059] In this embodiment, the control of the active power distribution network energy is specifically as follows: Obtaining load forecast data, the load forecast data including regional load forecast, grid load forecast, and unit load forecast; Based on the cross-layer transmission capacity, combined with load forecast data and output forecast sequence, the inter-regional exchange power in each time period is controlled; According to the reserve coefficient and combined with the load forecast data, the energy storage charging and discharging power of the grid layer in each period is controlled; According to the self-sufficiency parameters, the energy storage charging and discharging power and the adjustable load power of the unit layer in each time period are controlled.

[0060] It should be noted that the cross-layer transmission capacity refers to the maximum energy limit allowed for transmission between different levels of power grids. It is used to ensure that the power interaction between layers does not exceed the carrying capacity of equipment or lines; the reserve coefficient refers to the ratio of the reserve capacity reserved for responding to power grid emergencies to the maximum load of the system; the self-sufficiency parameter refers to the proportion of the unit layer that can cover its maximum load through local distributed new energy and energy storage systems. It reflects the ability of local resources to support load demand.

[0061] FPGA competition and risk-taking strategies are two important yet distinct approaches in FPGA design optimization. FPGA competition strategies primarily focus on enabling multiple functional units or tasks to "compete" for logic resources in time or space, within limited hardware resources, through efficient scheduling and resource reuse. This type of strategy is typically used to improve resource utilization and system throughput, and is common in embedded systems with frequent task switching or shared computing resources. Its core lies in designing a rational scheduling mechanism and arbitration logic to balance resource allocation with system response time. Loss rankings are simultaneously obtained within a batch computation to re-initialize confidence levels, avoiding the overly conservative or risky nature of traditional manually preset confidence levels. FPGA risk-taking strategies, on the other hand, focus on pushing performance limits. They often employ aggressive optimization techniques to maximize design potential, such as manually breaking timing constraints, overclocking, and reducing redundant logic. They may even temporarily violate design specifications in exchange for higher clock frequencies or smaller area. This strategy is particularly common in high-performance computing and high-speed signal processing. Through the risk mechanism, the system can remain sensitive to extremely low-probability events; once the deviation is too large, the speculative calculation path can quickly fall back to ensure overall consistency.

[0062] Example 2, Figure 2The present invention provides an urban active distribution network energy dispatching and control system based on a probabilistic balance model, comprising: A hierarchical module is used to divide the active distribution network into a three-layer control structure: regional layer, grid layer and unit layer; A parameter optimization module is used to initialize the probability control parameters based on the FPGA competition strategy according to the pre-acquired probability scenario set, and to update the probability control parameters based on the FPGA risk strategy; The global control module is used to construct an objective function with the goal of balancing the energy of each layer, and solve the objective function based on the pre-acquired hierarchical probability constraints to obtain global control parameters to control the energy of the active distribution network. The hierarchical probability constraints are obtained based on the probabilistic control parameters.

[0063] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0064] The above embodiments may be implemented in whole or in part through software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product.

[0065] Those skilled in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0066] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0067] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0068] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. An energy dispatching and control method for an urban active distribution network based on a probabilistic balance model is characterized in that: The following steps are involved: The active distribution network is divided into three-layer control structure: regional layer, grid layer and unit layer; Construct an objective function with the goal of balancing the energy of each layer; The energy of the active distribution network is controlled by solving the objective function based on pre-acquired hierarchical probabilistic constraints to obtain global control parameters. The hierarchical probabilistic constraints are obtained based on probabilistic control parameters. The probabilistic control parameters are initialized based on an FPGA competition strategy according to a pre-acquired set of probabilistic scenarios and updated based on an FPGA risk strategy.

2. The urban active distribution network energy dispatching control method based on the probabilistic balance model according to claim 1 is characterized in that: The global control parameters include cross-layer transmission capacity, spare coefficient and self-sufficiency parameter; the probability control parameters include probability confidence set.

3. The urban active distribution network energy dispatching control method based on the probabilistic balance model according to claim 2 is characterized in that: The method for obtaining the probability scenario set specifically includes: Obtaining an output prediction sequence, wherein the output prediction sequence includes a photovoltaic output prediction sequence and a wind power output prediction sequence; According to the output forecast sequence, based on the assumption that photovoltaic output follows Beta distribution and wind power output follows Weibull distribution, a probability scenario set is obtained through Monte Carlo sampling.

4. The urban active distribution network energy dispatching control method based on the probabilistic balance model according to claim 3 is characterized in that: The FPGA-based competition strategy initialization is specifically as follows: According to the set of probabilistic scenarios, a three-layer parallel architecture is constructed based on the FPGA competition strategy according to the three-layer control structure; In the three-layer parallel architecture, the balance loss of each scenario at each layer is calculated based on Monte Carlo simulation to obtain the balance loss data of each layer; According to the balance loss data of each layer, the initial probability control parameters are obtained based on the quantile statistics algorithm.

5. The urban active distribution network energy dispatching control method based on the probabilistic balance model according to claim 4 is characterized in that: The FPGA-based risk strategy update is specifically as follows: Based on the initial probabilistic control parameters and the acquired historical output data of distributed power sources, a speculative architecture and an extreme scenario set are constructed based on the FPGA risk-taking strategy; Based on the speculative architecture and extreme scenario set, Monte Carlo simulation is performed to obtain extreme loss data; According to the extreme loss data, the inter-layer influence amount is calculated by using the inter-layer influence matrix, wherein the inter-layer influence matrix is obtained based on the balanced loss data of each layer; According to the inter-layer influence and extreme loss data, the initial probability control parameters are updated based on the dynamic arbitration fallback algorithm.

6. The urban active distribution network energy dispatching control method based on the probabilistic balance model according to claim 5 is characterized in that: The method for obtaining the global control parameters specifically includes: Constructing a hierarchical probability constraint and using the updated probability control parameter as the confidence of the hierarchical probability constraint, wherein the hierarchical probability constraint includes a regional layer constraint, a grid layer constraint, and a cell layer constraint; According to the energy balance principle, the objective function is constructed with the goal of balancing the energy of each layer; Based on the hierarchical probability constraints, the objective function is solved to obtain the global control parameters.

7. The urban active distribution network energy dispatching control method based on the probabilistic balance model according to claim 6 is characterized in that: The control of the active power distribution network energy is specifically as follows: Obtaining load forecast data, wherein the load forecast data includes regional load forecast, grid load forecast, and unit load forecast; Based on the cross-layer transmission capacity, combined with load forecast data and output forecast sequence, the inter-regional exchange power in each time period is controlled; According to the reserve coefficient and combined with the load forecast data, the energy storage charging and discharging power of the grid layer in each period is controlled; According to the self-sufficiency parameters, the energy storage charging and discharging power and the adjustable load power of the unit layer in each time period are controlled.

8. The urban active distribution network energy dispatching control method based on the probabilistic balance model according to claim 7 is characterized in that: The method for obtaining the inter-layer influence matrix is specifically as follows: According to the balanced loss data of each layer, the inter-layer influence coefficient is obtained based on the covariance and variance; According to the inter-layer influence coefficient, the inter-layer influence matrix is constructed.

9. A system using the urban active distribution network energy dispatch control method based on a probabilistic balance model according to any one of claims 1 to 8, comprising: A hierarchical module is used to divide the active distribution network into a three-layer control structure: regional layer, grid layer and unit layer; A parameter optimization module is used to initialize the probability control parameters based on the FPGA competition strategy according to the pre-acquired probability scenario set, and to update the probability control parameters based on the FPGA risk strategy; The global control module is used to construct an objective function with the goal of balancing the energy of each layer, and solve the objective function based on the pre-acquired hierarchical probability constraints to obtain global control parameters to control the energy of the active distribution network. The hierarchical probability constraints are obtained based on the probabilistic control parameters.

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