Dual-scale regulation and control method and device for power distribution network and electronic equipment

Abstract

The invention discloses a dual-scale regulation and control method and device for a power distribution network and electronic equipment. The method comprises the following steps: obtaining power distribution network parameters corresponding to a power distribution network; determining power prediction data respectively corresponding to a plurality of power variables corresponding to the power distribution network according to the power distribution network parameters; calling a dual-time scale function and a corresponding constraint which take the minimum total network loss in a predetermined rolling prediction period as a target; according to the multiple pieces of power prediction data, solving a dual-time-scale function under corresponding constraints to obtain dual-time-scale scheduling parameters; and controlling regulation and control equipment of the power distribution network to operate with the target action parameter in a first period, setting the line state with the target on-off state parameter in the first period, and operating the photovoltaic inverter with the corresponding target reactive power output parameter in a second period of the first period. According to the invention, the technical problem that economy and real-time performance are difficult to consider when the power distribution network is regulated and controlled in the prior art is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power distribution network, in particular to a dual-scale regulation method and device for power distribution network and electronic equipment. BACKGROUND

[0002] With large-scale access of high-proportion renewable energy (wind power, photovoltaic) to the power distribution network, the randomness and volatility of the output significantly increase, bringing severe challenges to the operation and regulation of the power distribution network. Large-scale grid connection of wind turbines and photovoltaic power stations has become the main trend of current power grid development. Under the background of high-proportion access of renewable distributed power to the power distribution network, the intermittency and randomness of the output, combined with the volatility of multiple types of loads, seriously affect the economic and safe operation of the system. Moreover, the traditional regulation strategy of the power distribution network has a lagging response, which is difficult to cope with the real-time fluctuations of new energy and loads, resulting in a decrease in voltage quality. Therefore, in the related art, when regulating the power distribution network, it is difficult to balance the economy and real-time performance.

[0003] At present, no effective solution has been proposed for the above problems. SUMMARY

[0004] The embodiments of the present application provide a dual-scale regulation method and device for power distribution network and electronic equipment, to at least solve the technical problem that it is difficult to balance the economy and real-time performance when regulating the power distribution network in the related art.

[0005] According to an aspect of the embodiments of the present application, there is provided a method for regulating a power distribution network, comprising: obtaining power distribution network parameters corresponding to the power distribution network; determining power prediction data corresponding to a plurality of power variables of the power distribution network according to the power distribution network parameters, wherein the plurality of power variables comprises a wind turbine power variable, a photovoltaic power variable and a load power variable; calling a double-time-scale function and corresponding constraints with a target of minimizing total network loss in a predetermined rolling prediction period, wherein the double-time-scale function comprises a first-stage function and a second-stage function, the first-stage function is a function of a first time period and a rolling control window with a target of minimizing total network loss in the first time period, the second-stage function is a function of a second time period and a rolling control window with a target of minimizing total network loss in the second time period, the first time period is longer than the second time period; solving the double-time-scale function under the corresponding constraints according to the plurality of power prediction data to obtain double-time-scale scheduling parameters, wherein the double-time-scale scheduling parameters comprise long-time-scale parameters corresponding to the first-stage function and short-time-scale parameters corresponding to the second-stage function, the long-time-scale parameters comprise target action parameters of regulating devices and target open-circuit state parameters of line states, and the short-time-scale parameters comprise target reactive power output parameters of photovoltaic inverters; controlling the regulating devices of the power distribution network to operate with the target action parameters in the first time period, the line states to be set with the target open-circuit state parameters in the first time period, and the photovoltaic inverters to operate with corresponding target reactive power output parameters in the second time period of the first time period.

[0006] Optionally, before the calling the double-time-scale function with the corresponding constraint, the method further comprises: in a case that the corresponding constraint comprises a first-stage constraint corresponding to the first-stage function, the first-stage constraint comprising a distribution network topology constraint, a distribution network power flow constraint, and a regulating device constraint, determining a topology network corresponding to the distribution network and a distribution network power flow DistFlow model corresponding to the distribution network according to the distribution network parameters, wherein the distribution network parameters comprise regulating parameters corresponding to the regulating device; determining the distribution network topology constraint corresponding to the topology network, wherein the distribution network topology constraint is used to ensure that the distribution network is in radial operation and each node of the distribution network is connected by constraining line open state; determining the distribution network power flow constraint corresponding to the distribution network power flow DistFlow model, wherein the distribution network power flow constraint is used to ensure that the distribution network is operated in accordance with electrical physical laws and branch power transmission and node voltage change are matched by constraining node power balance relationship and quantitative association of branch power and node voltage; and determining the regulating device constraint according to the regulating parameters corresponding to the regulating device, wherein the regulating device constraint comprises a transformer tap parameter constraint, a capacitor reactive power sum constraint, and a capacitor switching number constraint.

[0007] Optionally, before the calling the double-time-scale function with the corresponding constraint, the method further comprises: in a case that the corresponding constraint comprises a second-stage constraint corresponding to the second-stage function, the second-stage constraint comprising a photovoltaic reactive power output constraint, determining the photovoltaic reactive power output constraint corresponding to each of a plurality of nodes in the distribution network according to active power and rated apparent power of each of the plurality of nodes.

[0008] Optionally, the solving the double-time-scale function under the corresponding constraint according to the plurality of power prediction data to obtain the double-time-scale scheduling parameter further comprises: determining long-time-scale power prediction data and short-time-scale power prediction data according to the plurality of power prediction data; solving the first-stage function under the corresponding constraint according to the long-time-scale power prediction data to obtain long-time-scale parameters corresponding to the first-stage function; solving the second-stage function under the corresponding constraint according to the short-time-scale power prediction data and the long-time-scale parameters to obtain short-time-scale parameters corresponding to the first-stage function; and determining the double-time-scale scheduling parameter according to the long-time-scale parameters and the short-time-scale parameters.

[0009] Optionally, the double-time-scale function under the corresponding constraints is solved according to the plurality of power prediction data to obtain the double-time-scale scheduling parameter, and the method further comprises: in a case where the long-time-scale parameter is determined, controlling the regulating and controlling device of the power distribution network to operate in the target action parameter in the first period, and the line state is set in the target opening state parameter in the first period; in a case where the short-time-scale parameter is determined according to the real-time data of the power distribution network and the plurality of power prediction data, controlling the photovoltaic inverter to operate in the corresponding target reactive power output parameter in a second period in the first period.

[0010] Optionally, the double-time-scale function under the corresponding constraints is solved according to the plurality of power prediction data to obtain the double-time-scale scheduling parameter, and the method further comprises: in a case where the long-time-scale parameter is determined, controlling the regulating and controlling device of the power distribution network to operate in the target action parameter in the first period, and the line state is set in the target opening state parameter in the first period; in a case where the short-time-scale parameter is determined according to the real-time data of the power distribution network and the plurality of power prediction data, controlling the photovoltaic inverter to operate in the corresponding target reactive power output parameter in a second period in the first period.

[0011] Optionally, the double-time-scale function under the corresponding constraints is solved according to the plurality of power prediction data to obtain the double-time-scale scheduling parameter, and the method further comprises: in a case where the long-time-scale parameter is determined, controlling the regulating and controlling device of the power distribution network to operate in the target action parameter in the first period, and the line state is set in the target opening state parameter in the first period; in a case where the short-time-scale parameter is determined according to the real-time data of the power distribution network and the plurality of power prediction data, controlling the photovoltaic inverter to operate in the corresponding target reactive power output parameter in a second period in the first period.

[0012] According to an aspect of an embodiment of the present application, there is provided a power distribution network regulating device, comprising: an acquisition module configured to acquire power distribution network parameters corresponding to a power distribution network; a first determination module configured to determine, according to the power distribution network parameters, power prediction data corresponding to a plurality of power variables corresponding to the power distribution network, wherein the plurality of power variables comprises a wind turbine power variable, a photovoltaic power variable, and a load power variable; a retrieval module configured to retrieve a double-time-scale function and corresponding constraints with a target of minimizing total network loss in a predetermined rolling prediction period, wherein the double-time-scale function comprises a first-stage function and a second-stage function, the first-stage function is a function of a first time period and a rolling control window with a target of minimizing total network loss in the first time period, the second-stage function is a function of a second time period and a rolling control window with a target of minimizing total network loss in the second time period, and the first time period is longer than the second time period; a solving module configured to solve the double-time-scale function under the corresponding constraints according to the plurality of power prediction data to obtain double-time-scale scheduling parameters, wherein the double-time-scale scheduling parameters comprise long-time-scale parameters corresponding to the first-stage function, and short-time-scale parameters corresponding to the second-stage function, the long-time-scale parameters comprise target action parameters of regulating devices and target open-circuit state parameters of line states, and the short-time-scale parameters comprise target reactive power output parameters of photovoltaic inverters; and a control module configured to control the regulating devices of the power distribution network to operate with the target action parameters in the first time period, the line states to be set with the target open-circuit state parameters in the first time period, and the photovoltaic inverters to operate with corresponding target reactive power output parameters in the second time period in the first time period.

[0013] According to an aspect of an embodiment of the present application, there is provided an electronic device, comprising: a processor; and a memory configured to store instructions executable by the processor; wherein the processor is configured to execute the instructions to implement the power distribution network regulating method as described above.

[0014] According to an aspect of an embodiment of the present application, there is provided a computer-readable storage medium, when instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the power distribution network regulating method as described above.

[0015] In the embodiment of the present application, power grid parameters corresponding to the power grid are acquired; power prediction data corresponding to a plurality of power variables corresponding to the power grid are determined according to the power grid parameters, wherein the plurality of power variables include a fan power variable, a photovoltaic power variable and a load power variable; a double-time-scale function with a corresponding constraint with a minimum total network loss in a predetermined rolling prediction period as a target is called, wherein the double-time-scale function includes a first-stage function and a second-stage function, the first-stage function is a function with a minimum total network loss in a first period as a target, a first time period is a rolling control window, the second-stage function is a function with a minimum total network loss in a second period as a target, a second time period is a rolling control window, and the first period is longer than the second period; the double-time-scale function under the corresponding constraint is solved according to the plurality of power prediction data, to obtain double-time-scale scheduling parameters, wherein the double-time-scale scheduling parameters include long-time-scale parameters corresponding to the first-stage function, and short-time-scale parameters corresponding to the second-stage function, the long-time-scale parameters include target action parameters of a regulating and controlling device, target opening and breaking state parameters of a line state, and the short-time-scale parameters include target reactive power output parameters of a photovoltaic inverter; the regulating and controlling device of the power grid is controlled to operate with the target action parameters in the first period, the line state is set with the target opening and breaking state parameters in the first period, and the photovoltaic inverter is controlled to operate with the corresponding target reactive power output parameters in the second period in the first period. The mode of cooperative optimization of double-time-scale model predictive control and network dynamic reconstruction is adopted, a double-layer decision framework combining long-time-scale optimization and short-time-scale rolling optimization with a unified target of minimum total network loss is established, and heterogeneous regulating and controlling resources such as on-load voltage regulating transformers, capacitor banks, network topology switches and photovoltaic inverters are cooperatively scheduled under the framework, so as to achieve the purpose of optimizing system economy and voltage quality under the premise of ensuring safe operation of the power grid, thereby realizing the organic cooperation of slow regulating and controlling devices and fast regulating and controlling devices in the time and space dimensions, giving full play to the economic advantage of global optimization of traditional devices, and taking advantage of the real-time regulation capacity of power electronic devices, which significantly improves the adaptability to high-proportion renewable energy fluctuation, and further solves the technical problem that economy and real-time performance are difficult to be considered in the related art when regulating and controlling the power grid. BRIEF DESCRIPTION OF DRAWINGS

[0016] The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application. In the drawings:

[0017] Figure 1 is a flowchart of a regulating and controlling method of a power grid according to an embodiment of the present application;

[0018] Figure 2The overall technical roadmap of the application combining network dynamic reconfiguration and power distribution network double time scale reactive power optimization analysis method is shown.

[0019] Figure 3 The IEEE33 node schematic diagram provided by the optional embodiment of the application contains a wind turbine and PV access.

[0020] Figure 4 The power graph of photovoltaic output and load demand prediction provided by the optional embodiment of the application;

[0021] Figure 5 The PV and wind turbine grid-connected point voltage operation condition diagram without control in the modified IEEE-33 node active power distribution network of the test example provided by the optional embodiment of the application;

[0022] Figure 6 The slow regulation device OLTC and capacitor group action state of the test example provided by the optional embodiment of the application;

[0023] Figure 7 The PV and wind turbine grid-connected point voltage operation condition diagram after the control of the slow regulation device of the test example provided by the optional embodiment of the application;

[0024] Figure 8 The PV and wind turbine grid-connected point voltage operation condition diagram under the control of the double time scale method of the test example provided by the optional embodiment of the application;

[0025] Figure 9 The structural block diagram of the regulation and control device of the power distribution network according to the embodiment of the application. DETAILED DESCRIPTION

[0026] In order for those skilled in the art to better understand the application scheme, the technical solutions in the embodiments of the application will be described clearly and completely in conjunction with the drawings in the embodiments of the application. Obviously, the described embodiments are only a part of the embodiments of the application, not all the embodiments. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the application.

[0027] It is to be understood that the terms first, second, etc. used in the description and the claims and the above drawings are used to distinguish like objects and are not necessarily to be understood as describing a particular sequential or chronological order. It is to be understood that the use of data herein is not per se a limitation, but that one or more embodiments of the application can make use of such data in any manner and are therefore not limited in their usage. Moreover, the terms including and comprising, as well as their conjugations, are intended to cover a non-exclusive inclusion, such that processes, methods, systems, products, or apparatuses that comprise, include, or are including a list of steps or elements can include those not expressly listed or inherent to such processes, methods, products, or apparatuses.

[0028] Embodiment 1

[0029] According to an embodiment of the present application, an embodiment of a power distribution network regulation method is provided, which can also be referred to as a double-scale regulation method for a power distribution network. 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 herein can be executed in an order different from that shown herein.

[0030] Figure 1 is a flowchart of a power distribution network regulation method according to an embodiment of the present application, as shown in Figure 1 The method comprises the following steps:

[0031] Step S102, obtaining power distribution network parameters corresponding to the power distribution network;

[0032] In the step S102 provided in the present application, the power distribution network parameters such as topology parameters, equipment parameters, operation parameters, and load and power parameters corresponding to the power distribution network are obtained through the data acquisition device, which provides input for subsequent power prediction, optimization modeling, and control execution.

[0033] Among them, the power distribution network refers to the network part in the power system that undertakes the task of power distribution and directly or indirectly supplies power to users, usually with lower voltage level. It is the receiver and distribution channel of electric energy, and its operation state will be directly affected by the access of photovoltaic system.

[0034] Among them, the power distribution network parameters refer to various data describing the structure, equipment characteristics, operation state, and electrical relationship of the power distribution network.

[0035] Through this step, obtaining accurate power distribution network parameters is the premise of establishing an effective optimization model, ensuring that the model can truly reflect the system operation state and avoiding control deviation or optimization failure caused by inaccurate parameters.

[0036] In step S104, power prediction data corresponding to a plurality of power variables of the power distribution network is determined according to the parameters of the power distribution network, wherein the plurality of power variables include a wind turbine power variable, a photovoltaic power variable and a load power variable.

[0037] In step S104, the power prediction data is generated by predicting the wind turbine output, the photovoltaic output and the load demand in the future time period based on the parameters of the power distribution network, so as to provide a basis for the subsequent optimization model and all subsequent optimization decisions.

[0038] The power variable refers to the power that fluctuates with time in the power distribution network.

[0039] The power prediction data refers to the data sequence about the wind turbine, the photovoltaic and the load power change in the future time period generated by the prediction model.

[0040] The wind turbine power variable refers to the active power generated by the wind turbine. The prediction data can be obtained based on the Weibull distribution model of the wind speed and the function relationship between the wind turbine output and the wind speed.

[0041] The photovoltaic power variable refers to the active power generated by the photovoltaic cell array. The prediction data can be obtained based on the Beta distribution model of the light intensity and the linear proportional relationship between the photovoltaic output and the light intensity.

[0042] The load power variable refers to the active power consumed by the power user in the power distribution network. The prediction data can be obtained based on the normal distribution model of the load fluctuation.

[0043] Through the step, the generated power prediction data enables the optimization model to predict the future change trend of the wind turbine, the photovoltaic and the load, so as to develop a control strategy in advance instead of passive response. The probabilistic method handles uncertainty and provides a more realistic data basis for subsequent optimization. This enables the optimization result to cope with various fluctuation scenarios with a certain confidence level in theory, thereby enhancing the effectiveness and practicability of the method.

[0044] In step S106, a double-time-scale function with the minimum total network loss in a predetermined rolling prediction period as the target and corresponding constraints are called, wherein the double-time-scale function includes a first-stage function and a second-stage function, the first-stage function is a function with the minimum total network loss in a first period as the target, the first time period is a rolling control window, the second-stage function is a function with the minimum total network loss in a second period as the target, the second time period is a rolling control window, and the first period is longer than the second period.

[0045] In step S106 provided in the present application, a hierarchical model predictive control optimization model is designed, and an optimization model with the characteristic of minimizing the total network loss in a rolling prediction period is called from a stored model library or algorithm.

[0046] Among them, the double time scale function is involved, which is an integrated optimization mathematical model, and contains two optimization stages (first stage function and second stage function) in time granularity, which together constitute a complete optimization problem.

[0047] Among them, the first stage function is involved, which refers to the part corresponding to the long time scale optimization in the double time scale function. Its optimization goal is to minimize the total network loss in a longer period (first period), and to take a longer time interval (first time period) as the rolling control window.

[0048] Among them, the second stage function is involved, which refers to the part corresponding to the short time scale optimization in the double time scale function. Its optimization goal is to minimize the total network loss in a shorter period (second period), and to take a shorter time interval (second time period) as the rolling control window.

[0049] Among them, the total network loss is involved, which can be set as the goal of minimizing the total network loss in the embodiment, and the total network loss minimization refers to the single objective function of the optimization model, aiming to minimize the total active power loss on all branches of the distribution network due to resistance.

[0050] Among them, the rolling prediction period is involved, which refers to the future time range looked ahead in each optimization in model predictive control.

[0051] Among them, the rolling control window is involved, which refers to the planned time step actually executed after each optimization in model predictive control.

[0052] By this step, by calling the double-time-scale function, the method sets up an optimization decision layer matched with the response speed for slow-acting devices (such as OLTC, capacitor bank) and fast-acting devices (such as photovoltaic inverter) respectively. The first-stage function is responsible for formulating a long-term, relatively stable action plan for slow devices, pursuing overall economy; the second-stage function then finely adjusts the fast devices under fast-changing conditions, achieving rapid suppression of fluctuations. This structure guarantees the effectiveness of coordination from the model level. Taking the minimum total network loss as a unified target throughout the double-time-scale makes the long-time-scale global planning and short-time-scale real-time adjustment serve the same economy index. This avoids the decision-making conflicts that may be brought by multi-objective optimization, and makes the loss reduction effect of the whole control process measurable and traceable. By calling the function with a rolling prediction period and a rolling control window as the core, periodic and rolling optimization is carried out, which can timely correct the control deviation caused by prediction error, so that the system has sustained adaptability to uncertainty.

[0053] In step S108, the double-time-scale function under corresponding constraints is solved according to the plurality of power prediction data, to obtain double-time-scale scheduling parameters, wherein the double-time-scale scheduling parameters include long-time-scale parameters corresponding to the first-stage function and short-time-scale parameters corresponding to the second-stage function, the long-time-scale parameters include target action parameters corresponding to the regulating device and target opening state parameters corresponding to the line state, and the short-time-scale parameters include target reactive power output parameters corresponding to the photovoltaic inverter.

[0054] In step S108 provided in the present application, the plurality of power prediction data generated in S104 is brought into the double-time-scale function under corresponding constraints called in S106 for numerical solution, and a mathematical optimization algorithm is used to calculate the double-time-scale scheduling parameters (long-time-scale parameters corresponding to the first-stage function and short-time-scale parameters corresponding to the second-stage function) for directly guiding the operation of the distribution network.

[0055] Among them, the double-time-scale scheduling parameters are the optimal solution set obtained by solving the double-time-scale function, which is the action plan of all controlled devices within a future execution period, and includes long-time-scale parameters corresponding to the first-stage function and short-time-scale parameters corresponding to the second-stage function, which together constitute a complete coordinated control strategy.

[0056] The long time scale parameter is an optimization result obtained by solving the first stage function, is a scheduling plan for a slow response regulating device, and specifically includes a target action parameter of the regulating device, such as a target tap position of an on-load tap-changing transformer (OLTC) and a target switching number of a shunt capacitor bank (CB), and a target open-circuit state parameter of a line state, that is, a target topology scheme of network dynamic reconstruction by controlling the opening and closing states of a tie switch and a section switch.

[0057] The short time scale parameter is an optimization result obtained by solving the second stage function, is a scheduling plan for a fast response photovoltaic inverter, and specifically includes a target reactive power output parameter of the photovoltaic inverter at each moment in the second period in the first period, for realizing real-time fine suppression of voltage fluctuation.

[0058] By sequentially solving the double time scale function, the global basic operation mode of the network topology and the slow device (the long time scale parameter) is first determined, and then the local fine adjustment strategy of the fast device (the short time scale parameter) is determined on this basis, realizing decoupling and coordination of the control strategy in the time and space dimensions. The solved scheduling parameters ensure that, when responding to renewable energy and load fluctuations, the slow device action is stable to ensure the overall economy and device life of the system, the fast device responds agilely to suppress local voltage mutations, and the economy and adaptability of the system operation are ensured as a whole; accurate and executable decision-making basis is provided for finally realizing safe, economic and reliable voltage optimization control.

[0059] In step S110, the regulating devices of the power distribution network are controlled to operate with the target action parameters in the first period, the line states are set with the target open-circuit state parameters in the first period, and the photovoltaic inverters operate with the corresponding target reactive power output parameters in the second period in the first period.

[0060] In step S110 provided in the present application, the double time scale scheduling parameters solved in step S108 are issued to the corresponding execution units of the power distribution network, to realize coordinated control of different types of regulating devices. The on-load tap-changing transformer (OLTC) and the shunt capacitor bank (CB) are controlled to perform tap adjustment and switching operation according to the target action parameters in the first period (such as the next 1 hour); the tie switch and the section switch are controlled to change the topology structure of the power distribution network according to the target open-circuit state parameters, to complete network reconstruction; at the same time, the photovoltaic inverters at each node are controlled to adjust their reactive power output in real time according to the corresponding target reactive power output parameters in the second period (such as the next 5 minutes) in the first period.

[0061] Among them, the regulating equipment refers to the traditional equipment in power grid for adjusting voltage and reactive power, which can include on-load tap-changing transformer (OLTC) and shunt capacitor bank (CB). These devices have slow response speed and limited action times, and are suitable for adjustment on a long time scale.

[0062] Among them, the line state refers to the on-off state of each branch in the power grid, which is changed by controlling the opening and closing of the tie switch and sectionalizer, i.e. realizing network dynamic reconstruction. Reconstruction can fundamentally change the distribution of power flow and is an effective means of regulating voltage.

[0063] Through this step, the theoretical parameters obtained by optimization calculation are converted into actual control actions, and the control strategy is executed by time scale to ensure that the actions of slow and fast devices are coordinated in time, avoiding frequent device actions and ensuring the real-time of control. First, network reconstruction and slow device adjustment are performed to optimize the global operation state of the system, and then local fine compensation is performed through photovoltaic inverter reactive power regulation, realizing the combination of network dynamic reconstruction as one of the core control methods with reactive power optimization and forming a complete closed-loop control process, which improves the adaptability of the distribution network to high proportion of renewable energy access.

[0064] By the above steps S102-S110, the power distribution network parameters corresponding to the power distribution network are obtained; according to the power distribution network parameters, power prediction data corresponding to a plurality of power variables corresponding to the power distribution network are determined, wherein the plurality of power variables include fan power variables, photovoltaic power variables and load power variables; a double-time scale function with a minimum total network loss in a predetermined rolling prediction period as the target and corresponding constraints are called, wherein the double-time scale function includes a first stage function and a second stage function, the first stage function is a function with the minimum total network loss in the first period as the target, the first time period is the rolling control window, the second stage function is a function with the minimum total network loss in the second period as the target, the second time period is the rolling control window, and the first period is longer than the second period; according to the plurality of power prediction data, the double-time scale function under the corresponding constraints is solved to obtain double-time scale scheduling parameters, wherein the double-time scale scheduling parameters include long-time scale parameters corresponding to the first stage function and short-time scale parameters corresponding to the second stage function, the long-time scale parameters include target action parameters corresponding to the regulating and controlling device, target opening and closing state parameters corresponding to the line state, and the short-time scale parameters include target reactive power output parameters corresponding to the photovoltaic inverter; the regulating and controlling device of the power distribution network is controlled to operate with the target action parameters in the first period, the line state is set with the target opening and closing state parameters in the first period, and the photovoltaic inverter operates with the corresponding target reactive power output parameters in the second period in the first period. By using the double-time scale model prediction control method, the power distribution network parameters are obtained and the power prediction data is generated, the double-time scale optimization model with the minimum total network loss as the target is constructed, the scheduling parameters for different response characteristic devices are obtained by solving, and the control is executed by time scale. The purpose of voltage optimization control in the power distribution network with high proportion of renewable energy is achieved, the slow regulating and controlling device and the fast regulating and controlling device are coordinated, the system economy is ensured, the voltage fluctuation is effectively suppressed, and the technical problem that the economy and the real-time performance cannot be considered in the related art when the power distribution network is regulated and controlled is solved.

[0065] As an optional embodiment, before the double time scale function aiming at minimizing the total network loss in the predetermined rolling prediction period is called and the target constraint is included, it further includes: in the case that the corresponding constraint includes the first stage constraint corresponding to the first stage function, the first stage constraint includes the distribution network topology constraint, the distribution network flow constraint and the regulation device constraint, determining the topology network corresponding to the distribution network and the distribution network flow DistFlow model corresponding to the distribution network according to the distribution network parameters, wherein the distribution network parameters include the regulation parameters corresponding to the regulation device; determining the distribution network topology constraint corresponding to the topology network, wherein the distribution network topology constraint is used to ensure the radial operation of the distribution network and the connectivity of each node of the distribution network by constraining the line break state; determining the distribution network flow constraint corresponding to the distribution network flow DistFlow model, wherein the distribution network flow constraint ensures that the distribution network operation conforms to the electrical physical law and the branch power transmission and the node voltage change are matched by constraining the quantitative correlation of the node power balance relationship, the branch power and the node voltage; determining the regulation device constraint according to the regulation parameters corresponding to the regulation device, wherein the regulation device constraint includes the transformer tap parameter constraint, the capacitor reactive power sum constraint and the capacitor switching number constraint.

[0066] In this embodiment, the specific process of constructing the constraints required by the first stage function is described.

[0067] Among them, the distribution network topology constraint is referred to, which is a mathematical condition set to ensure the radial structure of the closed-loop design and open-loop operation of the distribution network. By constraining the line break state, the predetermined condition can be met, such as ensuring that the optimized network structure meets the requirement that the number of branches is equal to the number of nodes minus 1 and the whole network is connected.

[0068] Among them, the distribution network flow constraint is referred to, which is a set of electrical quantity relationship equations established based on the DistFlow flow model. By constraining the quantitative correlation of the node power balance, the branch power and the node voltage, it is ensured that the optimization result conforms to the basic physical law of the circuit.

[0069] Among them, the regulation device constraint is referred to, which is a condition set for the operation limit of slow regulation devices such as on-load voltage regulating transformers and shunt capacitor banks, including the transformer tap position range, the capacitor bank switching number and the reactive power output range, etc.

[0070] Among them, the topology network is referred to, which is a graph theory model describing the connection relationship between nodes in the distribution network through branches, and is the basis for establishing the distribution network topology constraint.

[0071] The power distribution network flow DistFlow model is involved, which is a linearized power flow calculation method suitable for radial power distribution networks, and provides solvable power flow constraints for optimization problems by establishing a linear relationship between node injection power, branch power and node voltage.

[0072] In this step, first, a topological network describing the network connection relationship and a power flow calculation model based on the DistFlow method are constructed according to the parameters of the power distribution network; then, topological constraints are established based on the topological network to ensure the network radiation and connectivity, power flow constraints are established based on the DistFlow model to meet the electrical physical law, and device operation constraints are established according to the characteristic parameters of the regulation and control equipment; finally, these constraints are integrated to form the first stage constraints, providing complete limiting conditions for subsequent long time scale optimization.

[0073] In this way, strict topological constraints are established to ensure that the network reconstruction scheme decided by optimization always meets the basic requirement of radial operation of the power distribution network, avoiding the generation of infeasible network structure; the DistFlow power flow constraint ensures that all electrical quantities (power, voltage, current) in the optimization process meet the circuit physical law and safe operation range, preventing the optimization results from violating Kirchhoff's law or device overload; by converting the complex power distribution network operation constraints into explicit mathematical expressions, a clear feasible region boundary is provided for the optimization algorithm, avoiding invalid search of the algorithm in the infeasible solution space, and improving the overall solving speed and stability; the first stage constraint system covers three dimensions of network structure, electrical physics and device capacity, providing a comprehensive and accurate mathematical description for long time scale optimization, ensuring that the optimization results meet the economic target and all technical requirements of actual operation.

[0074] As an optional embodiment, before calling the double time scale function with the target of minimizing the total network loss in the predetermined rolling prediction period and the target constraint, it further includes: in the case that the corresponding constraint includes a second stage constraint corresponding to the second stage function, and the second stage constraint includes a photovoltaic reactive power output constraint, determining the photovoltaic reactive power output constraint corresponding to each node in the power distribution network according to the active power and the rated apparent power of each node.

[0075] In this embodiment, the specific process of constructing the core constraint condition required for the second stage function is explained.

[0076] The photovoltaic reactive power output constraint is involved, which is a mathematical condition for limiting the range of photovoltaic inverter reactive power output, and the upper and lower limits are determined by the rated apparent capacity and real-time active power of the inverter, ensuring that the inverter does not exceed its rated capacity during operation.

[0077] This involves nodal active power, which refers to the actual active power output of the photovoltaic power generation unit at the node connected to the photovoltaic inverter in the distribution network. This value is obtained based on power prediction data and is a key parameter for calculating the photovoltaic reactive power regulation capability at the current moment.

[0078] This involves rated apparent power, which refers to the maximum apparent power capacity allowed by the design of a photovoltaic inverter. It represents the maximum load-carrying capacity of the inverter and is the benchmark parameter for calculating the photovoltaic reactive power output limit.

[0079] In this step, the real-time active power prediction value of each photovoltaic node in the distribution network and the rated apparent power parameters of its inverter are first obtained; then, based on the active power and rated apparent power of each node, the maximum reactive power that the photovoltaic inverter of that node can generate and the minimum reactive power that can absorb are calculated at the current operating point according to the power triangle relationship; finally, these upper and lower limits of reactive power output are used as constraints to construct a photovoltaic reactive power output constraint set for short-timescale optimization.

[0080] This approach considers changes in photovoltaic (PV) active power output in real time and dynamically calculates the potential of its remaining capacity for reactive power regulation. This avoids the capacity waste or equipment overload caused by simply setting reactive power output limits to fixed values, achieving refined modeling of the PV inverter's reactive power regulation capability. Using the inverter's apparent power capacity as a hard constraint ensures that the optimization algorithm will not issue reactive power output commands exceeding the PV inverter's capacity when solving for short-timescale parameters. This effectively prevents equipment overheating or damage due to over-regulation, improving system operational safety. By establishing precise PV reactive power output constraints, a clear and reasonable adjustment space is provided for the optimization algorithm, enabling the PV inverter to participate in system voltage regulation to the maximum extent while ensuring active power output. This demonstrates the full utilization of the active support function of distributed power sources and enhances the distribution network's ability to absorb high proportions of renewable energy. It provides precise constraints that conform to the actual operating characteristics of the equipment for short-term optimization, ensuring that the optimized photovoltaic reactive power output parameters are both effective and executable. This provides a reliable control method for real-time voltage fluctuation mitigation and improves the accuracy and reliability of the final voltage control effect.

[0081] As an optional embodiment, based on multiple power prediction data, a dual-timescale function under corresponding constraints is solved to obtain dual-timescale scheduling parameters. This further includes: determining long-timescale power prediction data and short-timescale power prediction data based on the multiple power prediction data; solving a first-stage function under corresponding constraints based on the long-timescale power prediction data to obtain long-timescale parameters corresponding to the first-stage function; solving a second-stage function under corresponding constraints based on the short-timescale power prediction data and the long-timescale parameters to obtain short-timescale parameters corresponding to the first-stage function; and determining dual-timescale scheduling parameters based on the long-timescale parameters and the short-timescale parameters.

[0082] This embodiment illustrates the specific execution flow for solving the dual-time-scale function to obtain the complete scheduling parameters.

[0083] This involves long-term power prediction data, which refers to the power prediction sequence used for the first-stage function optimization solution. It has a coarse time resolution and a long prediction period.

[0084] This includes short-timescale power prediction data, which refers to the power prediction sequence used for the second-stage function optimization solution. It has a finer time resolution and a shorter prediction period.

[0085] In this step, the obtained power prediction data are first separated into prediction data serving long-term optimization and prediction data serving short-term optimization according to time resolution. Then, based on the long-term power prediction data, the first-stage function considering topology, power flow, and equipment constraints is solved to determine the action plan of slow-speed control equipment and network reconfiguration scheme, i.e., the long-term parameters. Next, based on the system operation defined by the determined long-term parameters, the second-stage function considering photovoltaic reactive power output constraints is solved based on the short-term power prediction data to determine the reactive power output plan of the photovoltaic inverter, i.e., the short-term parameters. Finally, the obtained long-term and short-term parameters are integrated to form a complete dual-time-scale scheduling parameter.

[0086] This approach enables ordered and coordinated optimization solutions. First, the long-term problem is solved to determine the backbone framework of the system. Then, within this framework, short-term problems are solved for fine-tuning in localized areas. This ensures that the control strategies for slow and fast devices are spatiotemporally matched at the decision-making level, avoiding conflicts between control commands. When solving for short-term parameters, the boundary conditions of the optimization problem (such as network topology and OLTC gear positions) are fixed by the long-term parameters. This provides a stable and known system operating environment for real-time optimization of fast devices, making the solution results more practically meaningful. By decomposing the complex multi-timescale coupled optimization problem into two relatively independent and sequentially connected sub-problems, the dimensionality and complexity of a single optimization are reduced, facilitating the acquisition of high-quality solutions within limited computation time, which meets the application requirements of online rolling model predictive control.

[0087] As an optional embodiment, based on multiple power prediction data, a dual-time-scale function under corresponding constraints is solved to obtain dual-time-scale scheduling parameters. This also includes: when the long-time-scale parameters are determined, controlling the distribution network's control equipment to operate with target action parameters in the first cycle, and setting the line status with target on / off state parameters in the first cycle; when the short-time-scale parameters are determined based on real-time data from the distribution network and multiple power prediction data, controlling the photovoltaic inverter to operate with the corresponding target reactive power output parameters in the second cycle of the first cycle.

[0088] This embodiment illustrates the process of transforming the dual-time-scale scheduling parameters obtained from the optimization solution into actual control actions in stages and at different time scales.

[0089] This involves real-time data, which refers to the measured values ​​collected by the power distribution network monitoring system that reflect the latest operating status of the system, such as node voltage, branch power, and actual photovoltaic output. These values ​​are used for feedback correction of the short-time-scale optimization model to ensure that control commands are more in line with actual operating conditions.

[0090] In this step, firstly, after completing the first-stage function solution and determining the long-time scale parameters, the parameters are sent to the corresponding execution unit to control the on-load tap changer (OLTC) and parallel capacitor bank (CB) to operate according to the optimized target action parameters throughout the first cycle (e.g., the next hour). At the same time, the corresponding switching actions are controlled to set the line state to the target interruption state parameters, thus completing the network reconfiguration. Then, real-time data of the distribution network is acquired, which is used to assist in completing the second-stage function solution. After determining the short-time scale parameters, these parameters are sent to each photovoltaic inverter to control it to operate according to the corresponding target reactive power output parameters in each second cycle (e.g., the next 5 minutes) within the first cycle.

[0091] This approach establishes an optimized and relatively stable basic operating state (including voltage level and network structure) for the distribution network by executing long-term parameters at the beginning of the first cycle. This ensures the global effectiveness of slow-moving equipment, providing an effective support platform for subsequent rapid adjustments throughout the first cycle. Based on this operating platform established by the slow-moving equipment, and combined with the latest real-time data, short-term parameters are executed, enabling photovoltaic inverters to respond quickly and accurately to minute-level fluctuations in renewable energy and loads, achieving real-time, refined voltage smoothing. By first executing slow, global control and then fast, local control, the collaborative logic of dual-timescale optimization is reproduced at the operational level, allowing control equipment with different characteristics to coordinate in an orderly manner over time, jointly ensuring the safe, stable, and economical operation of the system.

[0092] As an optional embodiment, after the control equipment for the distribution network operates with target action parameters in the first cycle, the line status is set with target on / off state parameters in the first cycle, and the photovoltaic inverter operates with corresponding target reactive power output parameters in the second cycle of the first cycle, the method further includes: obtaining a first predicted total network loss corresponding to the long-term time-scale parameters and a second predicted total network loss corresponding to the short-term time-scale parameters; determining a first actual total network loss in the first cycle corresponding to the distribution network and a second actual total network loss in the second cycle; if the difference between the first actual total network loss and the first predicted total network loss exceeds a first threshold, determining a first correction parameter to correct the first stage function, and if the difference between the second actual total network loss and the second predicted total network loss exceeds a second threshold, determining a second correction parameter to correct the second stage function.

[0093] In this embodiment, the process of feedback correction of the optimization model is described by comparing the deviation between the predicted value and the actual value after the control command is executed.

[0094] This involves the first predicted total network loss, which refers to the total predicted active power loss of the distribution network during the entire first cycle, calculated by the optimization model when solving the first-stage function to obtain the long-time scale parameters. It reflects the optimization model's expectation of the economic effect of the slow equipment control strategy.

[0095] This involves the second predicted total network loss, which refers to the total predicted active power loss of the distribution network within a single second cycle calculated by the optimization model when solving the second-stage function to obtain the short-time scale parameters. It reflects the optimization model's expectation of the economic effect of the fast equipment control strategy.

[0096] This involves the first actual total network loss, which refers to the total actual active power loss of the distribution network during the period calculated from the data actually collected by the distribution network measurement system after the end of the first period.

[0097] This involves the second actual total network loss, which refers to the total actual active power loss of the distribution network during the period calculated from the data actually collected by the distribution network measurement system after the end of a single second cycle.

[0098] This involves a first threshold, which is a critical deviation value used to determine whether the prediction accuracy of the long-term optimization model is acceptable. When the difference between the first actual total network loss and the first predicted total network loss exceeds this threshold, the model is considered to have a large deviation, and the first-stage function needs to be corrected.

[0099] This involves a second threshold, which is a critical deviation value used to judge whether the prediction accuracy of the short-time-scale optimization model is acceptable. When the difference between the second actual total network loss and the second predicted total network loss exceeds this threshold, the model is considered to have a large deviation, and the second-stage function needs to be corrected.

[0100] This involves the first correction parameter, which refers to the adjustment coefficient or parameter set used to correct the function in the first stage. Its purpose is to reduce the gap between the predicted value and the actual value in future optimization cycles, such as to compensate for the error of the prediction model or adjust the weight of the objective function.

[0101] This involves a second correction parameter, which refers to the adjustment coefficients or parameter set used to correct the second-stage function. Its purpose is to enable the short-timescale optimization model to track changes in the actual state of the system more quickly and improve the accuracy of control.

[0102] In this step, firstly, after control execution, the first and second predicted total network losses corresponding to the executed strategy are obtained from the optimization model; simultaneously, actual operating data of the distribution network are collected through the measurement system to calculate the first and second actual total network losses; then, the actual total network losses are compared with the corresponding predicted total network losses. If the difference between the first actual total network loss and the first predicted total network loss exceeds a preset first threshold, a correction mechanism is triggered to determine the first correction parameter used to correct the first stage function; if the difference between the second actual total network loss and the second predicted total network loss exceeds a preset second threshold, a correction mechanism is triggered to determine the second correction parameter used to correct the second stage function.

[0103] This approach enables a complete closed-loop model predictive control. After control execution, actual operating data is used to verify and correct the optimized model, allowing it to adaptively approximate the system's true dynamic characteristics and thus improve the accuracy of subsequent optimization decisions. It enhances the long-term adaptability of the optimized model, gradually correcting optimization performance degradation caused by systematic biases in the predictive model or inaccurate network parameters, ensuring the continued effectiveness of the global optimization strategy. Rapid calibration of the model on short timescales allows for timely responses to short-term changes in system operating characteristics (such as minor variations in equipment characteristics or unmodeled dynamics), making the reactive power regulation commands of the photovoltaic inverter more aligned with actual needs and improving voltage smoothing accuracy. The feedback correction mechanism transforms the voltage optimization control system from a simple open-loop command execution system into an intelligent system capable of sensing its own control effects and adjusting accordingly, significantly improving the overall intelligence level of the distribution network's operation and control.

[0104] As an optional embodiment, based on the distribution network parameters, power prediction data corresponding to multiple power variables corresponding to the distribution network are determined, including: constructing power prediction models corresponding to multiple power variables, wherein the multiple power prediction models include a wind power generation model, a photovoltaic power generation model, and a load power model; and determining power prediction data corresponding to multiple power variables based on the distribution network parameters and the multiple power prediction models.

[0105] This embodiment illustrates the specific method used to generate power prediction data.

[0106] This involves power prediction models, which are mathematical models used to describe and predict the time-varying patterns of power variables such as wind turbines, photovoltaics, and loads. Their construction takes into account the stochastic characteristics of each power variable and is used to generate power prediction sequences for specific future periods.

[0107] This involves a wind power generation model, which is a mathematical model used to describe the relationship between the active power output of a wind turbine and wind speed and to predict its changes. This model can be based on the statistical characteristics that wind speed follows a Weibull distribution, and wind speed prediction can be converted into active power prediction through the wind turbine power-wind speed characteristic curve.

[0108] This involves a photovoltaic power generation model, which is a mathematical model used to describe the relationship between photovoltaic active power output and irradiance and to predict its changes. This model can be based on the statistical characteristic that irradiance follows a Beta distribution and can convert irradiance prediction into active power prediction through the linear conversion relationship of photovoltaic cells.

[0109] This involves a load power model, which is a mathematical model used to describe and predict fluctuations in load active power demand. This model can be used to generate a power prediction sequence that conforms to the random variation law of load fluctuations based on the statistical property that load fluctuations follow a normal distribution.

[0110] In this step, firstly, stochastic power prediction models are constructed for the three types of power variables: wind turbines, photovoltaics, and loads, namely, wind power generation power model, photovoltaic power generation power model, and load power model. Then, the models are parameterized based on distribution network parameters (such as the cut-in, rated, and cut-out wind speeds of wind turbines, relevant efficiency parameters of photovoltaic panels, and the expected value and standard deviation of loads). Finally, the parameterized models are used to generate power prediction data sequences corresponding to multiple power variables and conforming to their respective stochastic characteristics.

[0111] This approach establishes dedicated prediction models for three distinct power variables—wind turbines, photovoltaics, and loads—that conform to their physical laws and statistical characteristics. Compared to using a single general model, this method can more accurately describe and predict their dynamic behavior, providing more reliable input for subsequent optimization. By establishing stochastic models based on probability distributions (Weibull, Beta, and normal distributions), the uncertainties of energy output and load demand are quantified as probabilistic sequences, laying the foundation for subsequent stochastic optimization methods such as chance-constrained programming, enabling optimization decisions to account for the impact of uncertainties. The constructed prediction models can generate prediction data at different time resolutions (e.g., hourly and minute-level), matching the input requirements of long-term and short-term optimization, ensuring the effective operation of the dual-time-scale coordinated control framework. This enhances the adaptability of the overall control scheme to real-world environments; by adopting stochastic prediction models that better reflect actual operating conditions, the entire technology chain from prediction to optimization more closely mirrors the real-world operation of the power distribution network.

[0112] Based on the above embodiments and optional embodiments, an optional implementation method is provided, which is described in detail below.

[0113] New energy sources have experienced rapid development in recent years, with large-scale grid connection of wind turbines and photovoltaic power plants becoming a major trend in current power grid development. With a high proportion of renewable distributed power sources integrated into the distribution network, the intermittent and random nature of their output, coupled with the volatility of various load types, easily leads to node voltage exceeding limits, severely impacting the economic and safe operation of the system. Photovoltaic inverters can perform active / reactive power decoupling control and quickly track and respond to dynamic reactive power compensation, providing an effective means of system regulation. Therefore, how to coordinate distributed power sources with traditional voltage regulation equipment such as on-load tap-changing transformers and parallel capacitor banks to fully tap the reactive power regulation potential of distributed power sources, improve voltage distribution, and reduce network losses is an urgent problem to be solved under the new situation of large-scale integration of multiple heterogeneous distributed power sources into the distribution network.

[0114] Due to the spatial distribution characteristics of voltage and the unsuitability of long-distance, large-scale reactive power transmission, reactive power and voltage regulation in power systems must adhere to the basic principles of hierarchical and zonal control and local reactive power balancing. The distribution network, located at the end of the system and directly connected to the load side, is closely related to users' power supply needs. It is characterized by numerous points, wide coverage, complex structure, and massive data volume, leading to high-dimensional centralized control and complex solutions. Its response speed struggles to match rapid changes in operating conditions, easily causing deviations between the global optimization scheme and local actual operating conditions. Furthermore, the distribution system contains multiple random factors, and prediction errors often cause actual operating results to deviate from the planned scheme. Model predictive control, through rolling optimization and feedback correction of the predictive model, progressively refines and enhances predictive information layer by layer, effectively reducing the deviation between future outputs and reference trajectories. It demonstrates good applicability to power system optimization control problems involving uncertain disturbances.

[0115] Network reconfiguration, as a crucial means of distribution network operation and control, is widely used not only for fault isolation and load transfer during fault conditions but also for energy saving, loss reduction, and load balancing during normal operation by altering feeder power flow. With the large-scale integration of remote-controlled switches, the ability to adjust the distribution network structure has become faster and more flexible. Against this backdrop, network reconfiguration is increasingly becoming an important technical means of voltage regulation. It directly affects voltage levels by changing the topology of the distribution network and adjusting power distribution, thereby achieving proactive voltage management and optimization. Combined with a model predictive control framework, network reconfiguration can respond to real-time voltage deviations, coordinate with reactive power compensation equipment, jointly improve voltage quality, and enhance the system's adaptability to uncertainties. It is an effective way to achieve local reactive power balance and hierarchical voltage regulation.

[0116] In view of this, an optional embodiment of the present invention provides a method that combines network dynamic reconfiguration and reactive power optimization analysis of the distribution network with dual time scales. Figure 2 This invention illustrates the overall technical roadmap of a method combining network dynamic reconfiguration and dual-time-scale reactive power optimization analysis of distribution networks. Figure 3A schematic diagram of an IEEE 33-node system including wind turbines and PV access is provided as an optional embodiment of the present invention. Figure 4 A power diagram for photovoltaic output and load demand forecasting provided as an optional embodiment of the present invention; Figure 5 The test case provided for optional implementation of the present invention is a modified IEEE-33 node active distribution network PV and wind turbine grid connection point voltage uncontrolled operation diagram. Figure 6 The test examples provided for optional embodiments of the present invention include the slow-regulation device OLTC and the operating status of the capacitor bank. Figure 7 The test case provided for optional implementation of the present invention is a diagram showing the operating conditions of the PV and wind turbine grid connection point voltages after being controlled by a slow-regulation device. Figure 8 The following is a description of the operating condition diagram of the PV and wind turbine grid connection point voltage under the control of the dual time scale method provided in the test example of the optional implementation of the present invention.

[0117] This invention proposes a real-time voltage regulation model for distribution networks under network reconfiguration by combining topology reconfiguration and dual-time-scale reactive power optimization of the distribution network (similar to the dual-time-scale function mentioned above that aims to minimize total network loss within a predetermined rolling prediction period). Both time scales employ model power prediction control for rolling optimization. The long time scale uses a 1-hour rolling control window, with a 4-hour rolling prediction period (similar to the first-stage function mentioned above that aims to minimize total network loss within the first period, with the first time period as the rolling control window). This determines the operating positions and switching quantities of on-load tap-changing transformers and capacitor banks, while incorporating topology constraints to optimize the automatic switching actions of distribution network interconnection switches (similar to the aforementioned long-time scale parameter). The parameters include the target action parameters corresponding to the control equipment and the target on / off state parameters corresponding to the line status. Within each rolling control window on a long time scale of 1 hour, the model predictive control method is also integrated. The rolling control window is 5 minutes, and the entire rolling prediction cycle is 20 minutes (similar to the second stage function mentioned above, which is a function with the goal of minimizing the total network loss within the second cycle and the second time period as the rolling control window, and the first cycle is longer than the second cycle). Rolling optimization is performed to determine the reactive power output of the photovoltaic inverter (similar to the short time scale parameters mentioned above, including the target reactive power output parameters corresponding to the photovoltaic inverter). This achieves the smoothing of voltage fluctuations under the access of new energy sources in the distribution network and provides effective support for the safe and stable control of the power grid.

[0118] To achieve the above-mentioned objectives, this invention proposes a method combining network dynamic reconfiguration and dual-time-scale reactive power optimization analysis of distribution networks. The improvement lies in that the method includes the following steps:

[0119] A. Perform load and power prediction for each distributed power source, and perform data preprocessing (similar to the above, based on the distribution network parameters, determine the power prediction data corresponding to multiple power variables corresponding to the distribution network, including wind turbine power variables, photovoltaic power variables and load power variables).

[0120] B. Construct a time-scale optimization model, embed model predictive control, and simultaneously consider the distribution network topology constraints to make decisions on the output and action of slow-control equipment and the opening and closing status of distribution lines to achieve network reconfiguration (same as the above dual-time-scale function and corresponding constraints with the goal of minimizing total network loss within a predetermined rolling prediction period).

[0121] C. Call the real-time voltage control model of the distribution network and perform rolling optimization in a shorter cycle to make decisions on the reactive power output of distributed power sources in a short time scale, thereby smoothing out voltage fluctuations.

[0122] Step A specifically includes the following steps:

[0123] A1. Load and power prediction serialization of each distributed power source;

[0124] Considering multiple random variables such as wind turbine output, photovoltaic output, and load within a microgrid, a microgrid economic dispatch model based on chance-constrained programming is established, which better reflects actual operating conditions. Using the requirement of reserve availability at a certain confidence level as a reliability probability constraint, sequence operation theory is applied to generate a probabilistic sequence from the probability distribution of random variables. Through operations such as convolution and difference, an equivalent load probabilistic sequence is generated, allowing direct calculation of the probability of satisfying the constraint.

[0125] A2. Establishment of stochastic active power output models for wind and solar power in power distribution networks;

[0126] Stochastic model of wind power generation (same as the wind power generation model above): Extensive statistical data analysis shows that the probability density of average wind speed follows a Weibull distribution:

[0127]

[0128] In the formula, v This refers to the actual wind speed; c This is a scale factor, reflecting the average wind speed over a certain period of time; k This is the shape factor.

[0129] wind turbine output P WT With wind speed v The relationship is described as follows:

[0130]

[0131] In the formula, P r ,v ci , v r and v co These are the fan's rated output, cut-in velocity, rated velocity, and cut-out velocity, respectively.

[0132] Photovoltaic power generation stochastic model (same as the photovoltaic power generation model above): Over a certain period of time, solar irradiance can be approximated by a Beta distribution:

[0133]

[0134] In the formula, r and r max These are the actual light intensity and the maximum light intensity during that time period, respectively. δ , All are shape parameters of the Beta distribution, and their relationship is as follows: = δ(1-u) / u , u This represents the average light intensity. The relationship between the output power of the photovoltaic array and the light intensity is as follows:

[0135]

[0136] In the formula, ξ Solar radiation; θ Angle of incidence; η m For maximum power point tracking efficiency; A P This represents the total area of ​​the photovoltaic panel array. η P This refers to the photoelectric conversion efficiency of a photovoltaic cell.

[0137] As shown in equation (4), the output power of the photovoltaic cell is linearly proportional to the light intensity and also follows a Beta distribution, with the probability density function being:

[0138]

[0139] In the formula, P PV,max This represents the maximum output power of the battery array.

[0140] Stochastic load power model (same as the load power model above): Load fluctuations in a distribution network over a certain period of time are generally considered to follow a normal distribution. δ L ( t )~ N ( U L , σ L 2( t )), U L The expected value of the load. σ L ( t ) is the standard deviation of load fluctuation.

[0141] Step B specifically includes the following steps:

[0142] B1. Introduction to Model Predictive Control Principles;

[0143] Distributed power generation is characterized by randomness, intermittency, and volatility, and its prediction accuracy is low, with prediction errors gradually increasing over time. This poses a significant challenge to grid dispatch and control. To better address the uncertainty of renewable energy output, active distribution networks urgently require more efficient control methods. In this context, Model Predictive Control (MPC) has emerged as an effective solution. The dual-scale coordinated control strategy proposed in this invention includes hourly and minute-level calculations, both solved using a rolling optimization method. When setting the rolling period and rolling window for each time scale, the spatiotemporal differences of various control resources within the distribution network must be comprehensively considered. This helps to achieve effective matching between different control resources and each optimization stage, thereby ensuring the rationality, coordination, and operational efficiency of the active distribution network's optimization and control process.

[0144] On an hourly timescale, with Δ T =1h is the time interval, at the start of each rolling... t 0, for the future M Δ T The renewable energy output and load information for each time period are predicted. Based on the prediction results, the output plan of the control resources for that time period is obtained through optimization calculations. However, only the output of each control resource at the first moment is executed, and the output at the next moment is not executed. t 0 + Δ T Then, the time window is shifted forward by one time interval, and the above process is repeated until the actions per hour are calculated.

[0145] B2. Construct a global collaborative regulation voltage optimization model that considers model predictive control;

[0146] Due to the uncertainty of renewable energy output, predicted data often have certain deviations, which may lead to voltage exceeding limits in the distribution network during actual operation. A global collaborative voltage optimization model considering model predictive control is constructed, using a 1-hour rolling window and a 4-hour rolling period.

[0147] Objective function (same as the first-stage function above): taking into account the economic efficiency of distribution network operation, with the goal of minimizing the total network loss in each rolling forecast period.

[0148]

[0149] In the formula, T is the total duration of one rolling cycle, and the rolling prediction cycle is 4 hours on a long time scale. ε It is the set of all branches in the distribution network; for ij Power loss of the line; During the rolling cycle ij The current transmitted through the line; r ij for ij The resistance of the branch circuit.

[0150] Taking into account operational constraints such as the power flow (DistFlow) of the distribution network branches and voltage safety, the optimal power flow model is established as follows (the power flow constraints corresponding to the power flow (DistFlow) model of the distribution network are determined above, wherein the power flow constraints of the distribution network ensure that the operation of the distribution network conforms to the laws of electrical physics and that the power transmission of branches matches the changes in node voltage by constraining the power balance relationship of nodes and the quantitative correlation between branch power and node voltage).

[0151]

[0152] In the formula: power is defined as flowing from the starting node to the ending node as positive, where the starting node is called the parent node and the ending node is called the child node. , , , These are the time periods in the current rolling cycle. j The sum of active power injected into nodes, the sum of active power outflow, the sum of predicted active power output of all DG, and the sum of predicted active power output of all wind turbines; , , , In the current rolling period t Time period j The total reactive power injected into the node, the total reactive power outflowed, the reactive power provided by the CB, and the reactive power provided by the wind turbine; and They are respectively j The set of all parent and child nodes of a node; , In the current rolling period t Time period ij The sum of active and reactive line losses; represent tTime period i The squared term of the node voltage; express t Time period ij The square term of the transmission current of the line; M It is a very large positive number. Equations (10)-(11) are the reactive power output constraints of the wind turbine, where, P m The mechanical power input to the wind turbine is determined by the amount of wind energy captured. Q S Inject reactive power to the stator side; L S , L m These are the stator inductance and the magnetizing inductance, respectively. I S , where are the effective values ​​of the stator and rotor currents; I S,max , I r,max They are respectively I S and I r The maximum value; s For slippage, , , These are the synchronous rotational angular velocity and the rotor rotational angular velocity, respectively. U S The stator voltage is the effective value; Equation (12) is the grid-side reactive power limit, which is limited by the converter capacity. Q WT This refers to the reactive power input from the grid to the grid-side converter; S C,max Equation (13) is the capacity constraint for the grid-side converter, Equation (14) is the current constraint for the distribution network branch, and Equation (15) is the voltage constraint for the distribution network node. U min , U max These are the lower and upper limits of the node voltage.

[0153] To understand the relationship between voltage, current, and power, the second-order constraint needs to be relaxed using a second-order cone. The deformed standard second-order cone form is shown in the following equation.

[0154]

[0155] In the formula: || ||2 represents the 2-norm.

[0156] B3. Establish long-term scale control equipment constraints (same as above, determine the control equipment constraints based on the control parameters corresponding to the control equipment, wherein the control equipment constraints include constraints on transformer tap parameters, total reactive power constraints of capacitors, and constraints on the number of capacitors switched on and off).

[0157]

[0158] In the formula: tap max This refers to the location of the OLTC's maximum tap. tap t It is the tap position of the OLTC at time t during the current rolling cycle; It is a set of 0-1 variables used to represent the current gear state of the OLTC; Equation (19) indicates that the OLTC is in only one gear state at each moment.

[0159]

[0160] In the formula, In the current rolling cycle t Time period j The number of capacitors connected to the node; The maximum number of capacitors in each capacitor bank; Q C The reactive power of each capacitor.

[0161] Since it is voltage regulation, and the primary task is to prevent the voltage from exceeding the limit, the equipment involved in voltage regulation must make certain concessions. Therefore, there are no restrictions on the number of operations and switching of the voltage regulating equipment OLTC and CB. It is assumed that any operation can be performed in each rolling cycle during the entire voltage regulation phase, provided that the above constraints are met.

[0162] B4. Analysis of Network Reconfiguration Mechanism;

[0163] Power system network reconfiguration specifically refers to the operation of altering the network topology by changing the opening and closing states of sectionalizing switches and tie switches in the distribution network, thereby achieving specific operational objectives. Under the premise of satisfying all safe operation constraints, the optimal radial network structure is selected by deciding on switch operations to achieve loss reduction and voltage regulation goals.

[0164] B5. Construct distribution network topology constraints to determine the on / off state of distribution lines; (same as above for determining the distribution network topology constraints corresponding to the topology network, wherein the distribution network topology constraints are used to ensure the radial operation of the distribution network and the connectivity of each node of the distribution network by constraining the on / off state of the lines).

[0165] To ensure the closed-loop design and open-loop operation of the distribution network, based on graph theory principles, the following requirements must be met: 1) The number of closed branches equals the total number of nodes minus 1; 2) The entire distribution network must be interconnected. The virtual network constraints involved are as follows:

[0166]

[0167] In the formula In virtual networks t time, ij The virtual power flowing through the branch, Represents the set of nodes that are not the root node; M It is a very large positive number; This represents the total number of nodes; it is essential to ensure that all nodes connected to the root node remain connected.

[0168] Step C specifically includes the following steps:

[0169] Within each of the aforementioned hourly rolling windows, on a minute-level time scale, Δ t =5min is the time interval, at the start of each scrolling... t 0, for the future K Δ t More refined forecasts of renewable energy output and load for different time periods are obtained. Based on these forecasts, optimized calculations are performed to determine the photovoltaic reactive power output plan for that time period. However, only the photovoltaic output at the first moment is executed, and the output at the next moment is not recorded. t 0 + Δ t Then, the time window is moved forward by one time interval, and the above process is repeated until the action within each cycle is determined.

[0170] A short-timescale control model that takes into account model predictions is constructed to regulate distributed power sources.

[0171] Objective function (same as the second-stage function above): taking into account the economic efficiency of distribution network operation, with the goal of minimizing the total network loss in each rolling forecast period.

[0172]

[0173] In the formula, T in The total duration of a rolling cycle is 20 minutes in a short timescale. ε It is the set of all branches in the distribution network; for ij Power loss of the line; During the rolling cycle ij The current transmitted through the line; r ij forij The resistance of the branch circuit.

[0174] The power flow operation constraints are the same as those in the long-term optimization model described above, but the power at each moment is now based on minute-level predicted data. Photovoltaic active power output uses real-time data and predicted data for the subsequent rolling cycle. Reactive power output is used as a control variable, and the photovoltaic reactive power output is solved in a rolling manner to help smooth voltage fluctuations. Wind turbine reactive power output is not used as a control variable. The photovoltaic reactive power output constraints are established as follows (similar to the above, where the corresponding constraints include the second-stage constraints corresponding to the second-stage function, and the second-stage constraints include photovoltaic reactive power output constraints), based on the node active power and rated apparent power corresponding to each of the multiple nodes in the distribution network, to determine the photovoltaic reactive power output constraints corresponding to each of the multiple nodes.

[0175]

[0176] This expression represents the node. j The reactive power output range of PV is given by the formula: for j The rated apparent capacity of a node PV.

[0177] Alternatively, the following steps may also be implemented:

[0178] Figure 2 This is a technical roadmap for the present invention, which combines network dynamic reconfiguration and a dual-time-scale reactive power optimization analysis method for distribution networks.

[0179] The method provided by this invention includes the following steps:

[0180] Step 1: Perform load and power prediction for each distributed power source to achieve data preprocessing;

[0181] Using an IEEE 33-node distribution system with distributed photovoltaic access as an example, the effectiveness of the proposed method and strategy is verified. In the modified IEEE 33-node active distribution network, the line parameters and topology of the original system remain unchanged. Nodes 6 and 16, and 21 and 32 are connected to distributed photovoltaic and wind turbines, respectively. The modified system network topology is shown below. Figure 3 As shown, the installed capacities of distributed PV and wind turbines are 1.40, 1.10, 1.00 and 1.40 MVA, respectively.

[0182] The data preprocessing process in this invention mainly consists of the following two sub-steps:

[0183] Step 11: Load and distributed power prediction serialization; Considering multiple random variables such as wind turbine output, photovoltaic output, and load within the microgrid, a microgrid economic dispatch model based on chance-constrained programming is established, which better reflects the actual operating conditions. With the requirement of reserve at a certain confidence level as a reliability probability constraint, sequence operation theory is applied to generate a probabilistic sequence from the probability distribution of random variables. Through convolution and difference operations, an equivalent load probabilistic sequence is generated, allowing direct calculation of the probability of satisfying the constraint. Step 12: In this example, PV and load power fluctuation data are set based on power data from a certain region on December 13, 2024, forming a preliminary distribution based on wind speed and solar irradiance in that region. Based on this power fluctuation data, as follows... Figure 4 As shown, the operating conditions of the system PV and wind turbine grid connection point voltages under uncontrolled conditions are also plotted, as follows: Figure 5 As shown.

[0184] Step 2: Construct a long-term time-scale optimization model, embed model power prediction and control, and consider the topological constraints of the distribution network to make decisions on the output and action of slow-control equipment and the opening and closing status of distribution lines to achieve network reconfiguration;

[0185] Step 2 specifically includes the following steps: Step 21: Analyze the principle of model predictive control, clarify the advantages of the optimized control model embedded with this method, and briefly describe the setting principles of rolling windows and rolling periods at each time scale; Step 22: Construct a global collaborative regulation voltage optimization model considering model predictive control, including power flow constraints, operation constraints, and reactive power output constraints of wind turbines; Step 23: Establish long-term time scale regulation equipment constraints, sequentially determine the OLTC tap position and the number of capacitor banks switched on and off, and the optimization results are as follows: Figure 6 As shown. Step 24: Briefly analyze the network reconfiguration mechanism and clarify the purpose and necessity of introducing network reconfiguration; Step 25: Construct distribution network topology constraints to determine the on / off state of distribution lines. During the optimization process, the radiation and connectivity of the distribution network operation must be guaranteed. Figure 7 The voltage curves of photovoltaic / wind turbine nodes are shown when only slow time-scale control is used. The results show that there is still a significant voltage over-limit problem during periods of high photovoltaic output, so real-time fine control is necessary.

[0186] Step 3: Establish a real-time voltage control model for the distribution network, perform rolling optimization in shorter cycles, and make decisions on the reactive power output of distributed power sources in a short time scale to smooth voltage fluctuations.

[0187] Step 3 specifically includes the following steps:

[0188] Considering the uncertainty of photovoltaic power prediction, minute-level fluctuation data is generated based on the interpolation method of the prediction data to simulate real-time power fluctuations. A short-term global coordinated regulation voltage optimization model considering variable-period model predictive control is constructed. Photovoltaic reactive power output constraints are established, and photovoltaic active power output is predicted using model predictive control. Reactive power output is used as a regulation variable to smooth voltage fluctuations. Finally, fast and slow time-scale control are combined and embedded with model predictive control to establish a real-time voltage regulation model for the distribution network. This model determines the actions of fast and slow regulation equipment and reactive power output, and plots the voltage operation diagram of the distributed PV grid-connected points under this method, such as... Figure 8 As shown.

[0189] The above optional implementation methods can achieve at least the following beneficial effects:

[0190] (1) This invention achieves organic coordination between slow-time-scale and fast-time-scale control equipment by combining a dual-time-scale framework of long-time-scale global optimization and real-time model predictive control. This method overcomes the shortcomings of traditional single-time-scale optimization, which cannot simultaneously take into account both economy and real-time performance. It can perform global economic planning and respond quickly to fluctuations in renewable energy and load, thereby improving the overall economy and adaptability of the system operation.

[0191] (2) Traditional optimization methods have limited ability to cope with severe fluctuations. This invention uses network dynamic reconfiguration as one of the core control methods and integrates it deeply with reactive power optimization. By deciding the actions of segmented switches and tie switches, the network topology and power flow distribution are actively changed, fundamentally altering the electrical structure of the system, effectively suppressing voltage over-limits and fluctuations, and significantly improving the stability and pass rate of the power supply voltage;

[0192] (3) This invention incorporates a model predictive control model. The core advantage of model predictive control lies in its rolling optimization and feedback correction mechanism. It utilizes the latest ultra-short-term forecast data to continuously optimize the control strategy within a limited future time window. This makes the control decision not only based on the current state but also has a forward-looking perspective on the future situation, enabling measures to be taken in advance to deal with predicted fluctuations, thereby reducing drastic changes in control quantities and making the system operation more stable and robust.

[0193] (4) This scheme forms a complete closed-loop control process: starting from the prediction in step A, the operation state of the slow-regulating equipment and the opening and closing of the switch are optimized in step B, and finally the reactive power output of the fast-regulating equipment is obtained in step C. The system state after execution is used as feedback to enter the next round of prediction and optimization cycle, which greatly improves the system's intelligence level in dealing with uncertainty.

[0194] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, because according to the present invention, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0195] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods according to the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.

[0196] Example 2

[0197] According to embodiments of the present invention, an apparatus for implementing the above-described control method for a power distribution network is also provided. Figure 9 This is a structural block diagram of a distribution network control device according to an embodiment of the present invention, such as... Figure 9 As shown, the device includes: an acquisition module 902, a first determination module 904, a retrieval module 906, a solution module 908, and a control module 910. The device will be described in detail below.

[0198] The module 902 is used to acquire distribution network parameters corresponding to the distribution network. The first determining module 904, connected to the acquisition module 902, is used to determine power prediction data corresponding to multiple power variables corresponding to the distribution network based on the distribution network parameters. These multiple power variables include wind turbine power variables, photovoltaic power variables, and load power variables. The retrieving module 906, connected to the first determining module 904, is used to retrieve a dual-timescale function and corresponding constraints with the objective of minimizing total network loss within a predetermined rolling prediction period. The dual-timescale function includes a first-stage function and a second-stage function. The first-stage function aims to minimize total network loss within a first period, with a first time period as the rolling control window. The second-stage function aims to minimize total network loss within a second period, with a second time period as the rolling control window. The first period is longer than the second period. The solution module... 908, connected to the aforementioned retrieval module 906, is used to solve the dual-time-scale function under the corresponding constraints based on multiple power prediction data to obtain dual-time-scale scheduling parameters. The dual-time-scale scheduling parameters include long-time-scale parameters corresponding to the first-stage function and short-time-scale parameters corresponding to the second-stage function. The long-time-scale parameters include target action parameters corresponding to the control equipment and target on / off state parameters corresponding to the line status. The short-time-scale parameters include target reactive power output parameters corresponding to the photovoltaic inverter. Control module 910, connected to the aforementioned solving module 908, is used to control the control equipment of the distribution network to operate with the target action parameters within the first cycle, the line status to be set with the target on / off state parameters within the first cycle, and the photovoltaic inverter to operate with the corresponding target reactive power output parameters in the second cycle within the first cycle.

[0199] It should be noted that the above-mentioned acquisition module 902, first determination module 904, retrieval module 906, solution module 908 and control module 910 correspond to steps S102 to S110 in the method for implementing the control of the power distribution network. The multiple modules and the corresponding steps are the same in terms of implementation examples and application scenarios, but are not limited to the content disclosed in the above embodiment 1.

[0200] Example 3

[0201] According to another aspect of the present invention, an electronic device is also provided, comprising: a processor; and a memory for storing processor-executable instructions, wherein the processor is configured to execute instructions to implement the power distribution network control method of any of the above embodiments.

[0202] Example 4

[0203] According to another aspect of the present invention, a computer-readable storage medium is also provided, which, when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, enables the electronic device to perform the power distribution network control method described above.

[0204] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0205] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0206] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0207] The units described as separate components may or may not be physically separate. 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 can be selected to achieve the purpose of this embodiment according to actual needs.

[0208] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0209] If the integrated unit is implemented as 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 invention, in essence, 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. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0210] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for controlling a power distribution network, characterized in that, include: Obtain the distribution network parameters corresponding to the distribution network; Based on the power distribution network parameters, power prediction data corresponding to multiple power variables corresponding to the power distribution network are determined, wherein the multiple power variables include wind turbine power variables, photovoltaic power variables, and load power variables; The dual-time-scale function and corresponding constraints are retrieved with the objective of minimizing the total network loss within a predetermined rolling prediction period. The dual-time-scale function includes a first-stage function and a second-stage function. The first-stage function is a function with the objective of minimizing the total network loss within a first period and a first time period as the rolling control window. The second-stage function is a function with the objective of minimizing the total network loss within a second period and a second time period as the rolling control window. The first period is longer than the second period. Based on multiple power prediction data, the dual-time-scale function under the corresponding constraints is solved to obtain dual-time-scale scheduling parameters. The dual-time-scale scheduling parameters include long-time-scale parameters corresponding to the first-stage function and short-time-scale parameters corresponding to the second-stage function. The long-time-scale parameters include target action parameters corresponding to the control equipment and target on / off state parameters corresponding to the line status. The short-time-scale parameters include target reactive power output parameters corresponding to the photovoltaic inverter. The control equipment controlling the power distribution network operates with the target action parameters in the first cycle, the line status is set with the target on / off status parameters in the first cycle, and the photovoltaic inverter operates with the corresponding target reactive power output parameters in the second cycle of the first cycle.

2. The method according to claim 1, characterized in that, Before retrieving the dual-timescale function and objective constraints aimed at minimizing total network loss within a predetermined rolling forecast period, the following steps are also included: When the corresponding constraints include the first stage constraints corresponding to the first stage function, and the first stage constraints include distribution network topology constraints, distribution network power flow constraints, and control equipment constraints, the topology network corresponding to the distribution network and the distribution network power flow DistFlow model corresponding to the distribution network are determined based on the distribution network parameters, wherein the distribution network parameters include the control parameters corresponding to the control equipment. Determine the distribution network topology constraints corresponding to the topology network, wherein the distribution network topology constraints are used to ensure the radial operation of the distribution network and the connectivity of each node of the distribution network by constraining the line opening and closing states; Determine the power flow constraints of the distribution network corresponding to the DistFlow model, wherein the power flow constraints of the distribution network ensure that the operation of the distribution network conforms to the laws of electrical physics and that the power transmission of branches matches the changes in node voltage by constraining the power balance relationship of nodes and the quantitative correlation between branch power and node voltage. Based on the control parameters corresponding to the control equipment, the constraints of the control equipment are determined, wherein the constraints of the control equipment include constraints on transformer tap parameters, constraints on the total reactive power of capacitors, and constraints on the number of capacitors switched on and off.

3. The method according to claim 1, characterized in that, Before retrieving the dual-timescale function and objective constraints aimed at minimizing total network loss within a predetermined rolling forecast period, the following steps are also included: When the corresponding constraint includes the second stage constraint corresponding to the second stage function, and the second stage constraint includes photovoltaic reactive power output constraint, the photovoltaic reactive power output constraint corresponding to each of the multiple nodes is determined based on the active power and rated apparent power of the nodes respectively corresponding to the multiple nodes in the distribution network.

4. The method according to claim 1, characterized in that, Based on multiple power prediction data, the dual-time-scale function under the corresponding constraints is solved to obtain the dual-time-scale scheduling parameters, which also includes: Based on multiple power prediction data, the long-term power prediction data and the short-term power prediction data are determined; Based on the long-time scale power prediction data, the first-stage function under the corresponding constraints is solved to obtain the long-time scale parameters corresponding to the first-stage function; Based on the short-timescale power prediction data and the long-timescale parameters, the second-stage function under the corresponding constraints is solved to obtain the short-timescale parameters corresponding to the first-stage function; The dual-time-scale scheduling parameters are determined based on the long-time-scale parameters and the short-time-scale parameters.

5. The method according to claim 1, characterized in that, Based on multiple power prediction data, the dual-time-scale function under the corresponding constraints is solved to obtain the dual-time-scale scheduling parameters, which also includes: Given the obtained long-time scale parameters, the control equipment of the distribution network is controlled to operate with the target action parameters within the first cycle, and the line status is set with the target disconnection status parameters within the first cycle. Based on the real-time data of the power distribution network and multiple power prediction data, the short-time scale parameters are determined, and the photovoltaic inverter is controlled to operate with the corresponding target reactive power output parameters in the second cycle within the first cycle.

6. The method according to claim 1, characterized in that, After the control equipment controlling the distribution network operates with the target action parameters in the first cycle, the line status is set with the target on / off status parameters in the first cycle, and the photovoltaic inverter operates with the corresponding target reactive power output parameters in the second cycle of the first cycle, the system further includes: Obtain the first predicted total network loss corresponding to the long time scale parameter, and the second predicted total network loss corresponding to the short time scale parameter; Determine the first actual total network loss within the first cycle corresponding to the distribution network, and the second actual total network loss within the second cycle; If the difference between the first actual total network loss and the first predicted total network loss exceeds a first threshold, a first correction parameter is determined to correct the first stage function, and if the difference between the second actual total network loss and the second predicted total network loss exceeds a second threshold, a second correction parameter is determined to correct the second stage function.

7. The method according to any one of claims 1 to 6, characterized in that, Based on the power distribution network parameters, power prediction data corresponding to multiple power variables associated with the power distribution network are determined, including: Construct power prediction models corresponding to the multiple power variables, wherein the multiple power prediction models include a wind power generation model, a photovoltaic power generation model, and a load power model; Based on the power distribution network parameters and multiple power prediction models, power prediction data corresponding to the multiple power variables are determined.

8. A control device for a power distribution network, characterized in that, include: The acquisition module is used to acquire the distribution network parameters corresponding to the distribution network. The first determining module is used to determine, based on the power distribution network parameters, power prediction data corresponding to multiple power variables corresponding to the power distribution network, wherein the multiple power variables include wind turbine power variables, photovoltaic power variables and load power variables; The retrieval module is used to retrieve a dual-timescale function and corresponding constraints with the objective of minimizing the total network loss within a predetermined rolling prediction period. The dual-timescale function includes a first-stage function and a second-stage function. The first-stage function is a function with the objective of minimizing the total network loss within a first period and a first time period as the rolling control window. The second-stage function is a function with the objective of minimizing the total network loss within a second period and a second time period as the rolling control window. The first period is longer than the second period. The solution module is used to solve the dual-time-scale function under the corresponding constraints based on multiple power prediction data to obtain dual-time-scale scheduling parameters. The dual-time-scale scheduling parameters include long-time-scale parameters corresponding to the first-stage function and short-time-scale parameters corresponding to the second-stage function. The long-time-scale parameters include target action parameters corresponding to the control equipment and target on / off state parameters corresponding to the line status. The short-time-scale parameters include target reactive power output parameters corresponding to the photovoltaic inverter. The control module is used to control the regulating equipment of the power distribution network to operate with the target action parameters in the first cycle, the line status to be set with the target on / off status parameters in the first cycle, and the photovoltaic inverter to operate with the corresponding target reactive power output parameters in the second cycle of the first cycle.

9. An electronic device, characterized in that, include: processor; Memory used to store the processor's executable instructions; The processor is configured to execute the instructions to implement the control method for the power distribution network as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, When the instructions in the computer-readable storage medium are executed by the processor of the electronic device, the electronic device is able to perform the control method of the power distribution network as described in any one of claims 1 to 7.

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