Power distribution network multi-time scale scheduling method and device considering wind power intermittency and IMPC

Abstract

The invention relates to the technical field of energy storage, in particular to a power distribution network multi-time-scale scheduling method and device considering wind power intermittency and IMPC, and the device comprises a framework construction unit which constructs a multi-time-scale active power distribution network active and reactive coordinated scheduling framework; the objective function construction unit is used for constructing objective functions of a day-ahead scheduling stage, an intra-day scheduling stage and a real-time scheduling stage in the active and reactive coordinated scheduling framework of the multi-time-scale active power distribution network; the constraint condition design unit is used for designing constraint conditions of multi-time-scale active and reactive coordinated optimization of the active power distribution network; according to the invention, the target function corresponding to each time scale is constructed, and the target function of each time scale is solved based on the constraint condition, so that the scheduling schemes of different time scales are obtained, and the adjustment of the corresponding time scale is realized according to the scheduling schemes. The prediction precision selection optimization method under multiple time scales and different time scales has the advantage that the output volatility and randomness of renewable energy sources are improved.

Description

Technical Field

[0001] This invention relates to the field of energy storage technology, and in particular to a method and apparatus for multi-timescale dispatching of distribution networks that takes into account the intermittency of wind power and IMPC. Background Technology

[0002] As renewable energy sources are increasingly integrated into the distribution network, the dispatch and control of the distribution network are becoming increasingly complex. Traditional distribution networks are gradually evolving into active distribution networks with numerous controllable resources. The biggest problem with renewable energy lies in the uncertainty and randomness of its output, especially wind and solar power, which has a significant impact on the operation of the distribution network. Furthermore, when there are large fluctuations in the output of large-scale renewable energy sources, the power fluctuations at the interface between the distribution and transmission networks are significant, affecting the operation of the transmission network and causing a major shock to the operation of the distribution network.

[0003] To address these issues, a multi-period day-ahead optimization scheduling method is adopted. This method controls the operational status of various distributed resources in the distribution network according to time periods, and makes more detailed estimates of renewable energy output by subdividing the time periods. However, the results obtained from a single day-ahead offline optimization have too large an error compared to the actual situation.

[0004] Based on the characteristic that the accuracy of renewable energy power prediction continuously improves as the prediction time scale decreases, a multi-time scale optimization scheduling method is adopted. By continuously providing the latest prediction data to the optimization program at short time scales, the error between the scheduling results and the actual situation is greatly reduced, further solving the uncertainty and volatility problems of photovoltaic power output and load demand.

[0005] To effectively address the uncertainties of renewable energy in active distribution networks, traditional deterministic optimization models are becoming increasingly inapplicable, making uncertain optimization models a focus of attention for scholars both domestically and internationally. Among these, stochastic optimization is the most widely used. Stochastic optimization typically employs scenario-based methods to simulate uncertain variables across multiple scenarios. To reduce computational burden, scenarios are reduced to typical scenarios, which are then optimized and scheduled. Finally, expected values ​​are calculated for decision-making. Scholars have used scenario-based methods to represent the uncertainty of wind power output, enabling the scheduling of generator output and the configuration of energy storage capacity.

[0006] Unlike transmission networks, distribution networks have similar line resistance and reactance values, and active and reactive power are more strongly coupled. Therefore, unilateral active / reactive power optimization of distribution networks is neither complete nor comprehensive. Summary of the Invention

[0007] This invention provides a method and apparatus for multi-timescale scheduling of distribution networks that considers the intermittency of wind power and IMPC, overcoming the shortcomings of the prior art. It can effectively solve the problems of high operating costs and large power fluctuations between the distribution network and the transmission network in existing multi-timescale scheduling methods.

[0008] To address the above problems, one of the technical solutions of this invention is achieved through the following method: a multi-timescale scheduling method for distribution networks considering wind power intermittency and IMPC, comprising the following steps:

[0009] A multi-timescale active distribution network active and reactive power coordinated scheduling framework is constructed, which includes day-ahead scheduling, intraday scheduling, and real-time scheduling.

[0010] Construct objective functions for the day-ahead scheduling phase, intraday scheduling phase, and real-time scheduling phase in a multi-timescale active distribution network active and reactive power coordinated scheduling framework.

[0011] Design constraints for active and reactive power coordination optimization across multiple time scales in an active distribution network;

[0012] Based on the constraints, the objective functions of the day-ahead scheduling stage, intraday scheduling stage, and real-time scheduling stage in the active distribution network active scheduling framework of multiple time scales are solved to obtain the scheduling schemes for the day-ahead scheduling stage, intraday scheduling stage, and real-time scheduling stage.

[0013] The scheduling is implemented according to the scheduling schemes of the day-ahead scheduling phase, intraday scheduling phase, and real-time scheduling phase to achieve the corresponding time scale.

[0014] The above-mentioned solution to the objective function of the day-ahead scheduling stage in the multi-time-scale active and reactive power coordinated scheduling framework for distribution networks based on constraints includes: obtaining the parameter information of the day-ahead scheduling stage, using a first time interval as the resolution, and using the parameter information of the day-ahead scheduling stage to solve the objective function of the day-ahead scheduling stage in the multi-time-scale active and reactive power coordinated scheduling framework for distribution networks based on constraints.

[0015] The parameter information for the day-ahead scheduling phase includes typical scenarios of wind power output and corresponding load forecast data for each typical scenario. The scheduling scheme for the day-ahead scheduling phase includes the optimal scheduling scheme for each typical scenario. The typical scenarios of wind power output and their load forecast data are obtained by analyzing the correlation of wind power output in the distribution network using copula theory.

[0016] The above-mentioned solution to the objective function of the intraday scheduling phase in the multi-time-scale active and reactive power coordinated scheduling framework for the distribution network, based on constraints, includes: obtaining the parameter information of the intraday scheduling phase; using the second time interval as the resolution; and solving the objective function of the intraday scheduling phase in the multi-time-scale active and reactive power coordinated scheduling framework based on constraints.

[0017] The parameter information for the intraday scheduling phase includes typical scenarios for wind power output. The scheduling scheme for the intraday scheduling phase includes optimizing the distribution network based on these typical scenarios and predicting typical scenarios for wind power output in the next 4 hours. The typical scenarios for wind power output are obtained by analyzing the correlation of wind power output in the distribution network using copula theory. The above-mentioned solution to the objective function of the real-time scheduling phase in the multi-timescale active distribution network active and reactive power coordinated scheduling framework, based on constraints, includes: obtaining the parameter information for the real-time scheduling phase; using a third time interval as the resolution; and solving the objective function of the real-time scheduling phase in the multi-timescale active distribution network active and reactive power coordinated scheduling framework based on constraints.

[0018] The parameter information in the real-time scheduling phase includes wind power output data for the next hour and corresponding load forecast data. The scheduling scheme in the real-time scheduling phase includes scheduling using a deterministic optimization approach, wherein the deterministic optimization adopts rolling optimization, that is, in each optimization process, only the decision value of the first cycle is executed, and the optimal control sequence is retrieved at the next optimization time. The wind power output data and its load forecast data are obtained by analyzing the correlation of wind power output in the distribution network through copula theory.

[0019] The objective functions for each time scale in the above-mentioned framework for constructing active and reactive power coordinated scheduling of multi-time-scale distribution networks include:

[0020] 1) Construct the objective functions for the day-ahead scheduling phase and the intraday scheduling phase.

[0021] The objective function for both the day-ahead and intraday scheduling phases is to minimize the operating cost of the distribution network. The objective functions for the day-ahead and intraday scheduling phases are as follows:

[0022]

[0023] The superscript DA indicates the day-ahead scheduling phase or the intraday scheduling phase, and ID indicates the day-ahead scheduling phase or the intraday scheduling phase. It is the cost for the distribution network to obtain electrical energy from the transmission network in the t-th time period and the s-th scenario; It is the power generation cost of the i-th controllable DG in the t-th time period and the s-th scenario; p is the operating cost of the i-th energy storage device in the t-th time period and the s-th scenario. s Let be the probability of the s-th scenario, n be the number of scenarios, M be the number of controllable DGs, N be the number of energy storage devices, and T be the number of time periods.

[0024] 2) Construct the objective function for the real-time scheduling phase.

[0025] The objective function in the real-time scheduling phase is to minimize power fluctuations at the transmission and distribution network interfaces. The objective function for the real-time scheduling phase is...

[0026]

[0027] The above RT represents the real-time scheduling phase. and This is the power exchange value promised between the transmission and distribution networks during the intraday dispatch phase. and α is the optimization variable in the real-time scheduling phase, representing the power value on the transmission and distribution network interconnects in time period t, where T is the number of time periods, and α is the power value on the interconnects. t The weights represent the power fluctuations during time period t.

[0028] The above constraints include power flow constraints, branch capacity constraints, node voltage constraints, renewable energy generation operation constraints, controllable distributed generation operation constraints, on-load tap-changing transformer operation constraints, static var compensator operation constraints, capacitor bank operation constraints, and energy storage device operation constraints: among which,

[0029] 1) The equation for power flow constraints is,

[0030]

[0031] Where α(j) is the set of the first nodes of the branch with node j as the last node; β(j) is the set of the last nodes of the branch with node j as the first node; P ij,t and Q ij,t These represent the active and reactive power at the beginning of branch ij, respectively; P j,t and Q j,t These represent the active and reactive power injection values ​​at node j, respectively; r ij +jx ij I is the impedance of branch ij; ij,t k is the square of the current amplitude flowing through branch ij; ij,t U represents the turns ratio of the on-load tap-changing transformer at branch ij; i,t The square of the voltage magnitude at node i;

[0032] 2) Branch capacity constraints are

[0033]

[0034] In the formula: I ij,t Let be the current in branch ij at time t. This represents the upper limit of the current amplitude in branch ij;

[0035] 3) Node voltage constraints are

[0036]

[0037] In the formula, U i,t Let be the current value at node t. and These are the upper and lower limits of the node voltage amplitude, respectively.

[0038] 4) Renewable energy power generation operation constraints are

[0039]

[0040]

[0041] In the formula, P is the predicted value of active power output from renewable energy sources. RES,i,t Q represents the active power output of renewable energy. RES,i,t Reactive power output of renewable energy, S i The capacity of the inverter for renewable energy power generation devices;

[0042] 5) Controllable distributed generation operation constraints are

[0043]

[0044] In the formula, P CDG,i,t and Q CDG,i,t P represents the active and reactive power output of controllable distributed generation; CDG,min P CDG,max Q CDG,min and Q CDG,max S represents the upper and lower limits of active and reactive power output of controllable distributed generation; CDG,i,t The installed capacity for controllable distributed generation; Indicates the upper and lower limits of ramp rate for controllable distributed generation;

[0045] 6) Operating constraints for on-load tap-changing transformers are as follows:

[0046]

[0047] In the formula, k0 is the standard turns ratio of an on-load tap-changing transformer; Δk ij To adjust the step size; K ij,t , These refer to the adjustable tap positions of the on-load tap-changing transformer and their upper and lower limits;

[0048] 7) Operating constraints of the static var compensator are:

[0049]

[0050] Where, and These are the adjustable upper and lower limits of reactive power output of the static var compensator;

[0051] 8) The operating constraints of the compensation capacitor bank are as follows:

[0052]

[0053] Where: H i,t Adjustable gears; ΔQ c,i,t To adjust the step size; H max To compensate for the maximum adjustable range of the capacitor bank;

[0054] 9) Energy storage device operating constraints are

[0055]

[0056] In the formula: P ch,i,t and P dis,i,t These represent the charging and discharging power of the energy storage device, respectively; η ch and η dis These are the charging and discharging efficiencies of the energy storage device, respectively. and These represent the maximum charging and discharging power of the energy storage device, respectively; D ch,i,t and D dis,i,t It is a 0-1 variable, representing the charging and discharging state of the energy storage device; This refers to the capacity of the energy storage device.

[0057] The second technical solution of this invention is achieved through the following means: a multi-timescale dispatching device for a distribution network that considers the intermittency of wind power and IMPC, comprising,

[0058] The framework construction unit constructs a multi-timescale active distribution network active and reactive power coordinated scheduling framework, which includes day-ahead scheduling, intraday scheduling, and real-time scheduling.

[0059] Objective function construction unit: Constructs objective functions for the day-ahead scheduling stage, intraday scheduling stage, and real-time scheduling stage in the multi-timescale active distribution network active and reactive power coordinated scheduling framework;

[0060] The constraint design unit designs the constraints for active and reactive power coordination optimization across multiple time scales in an active distribution network.

[0061] The scheduling scheme acquisition unit solves the objective functions of the day-ahead scheduling stage, intraday scheduling stage, and real-time scheduling stage in the multi-time-scale active distribution network active and reactive power coordinated scheduling framework based on constraints, and obtains the scheduling schemes for the day-ahead scheduling stage, intraday scheduling stage, and real-time scheduling stage.

[0062] The scheduling implementation unit implements scheduling at the corresponding time scale according to the scheduling schemes of the day-ahead scheduling phase, intraday scheduling phase, and real-time scheduling phase.

[0063] The above includes a day-ahead scheduling module, an intraday scheduling module, and a real-time scheduling module, wherein:

[0064] The day-ahead scheduling module solves the objective function of the day-ahead scheduling stage in the multi-time-scale active and reactive power coordinated scheduling framework for distribution networks based on constraints. This includes: acquiring parameter information for the day-ahead scheduling stage; using a first time interval as resolution; and solving the objective function of the day-ahead scheduling stage based on the parameter information and constraints.

[0065] The parameter information of the day-ahead scheduling phase includes typical scenarios of wind power output and the corresponding load forecast data for each typical scenario. The scheduling scheme of the day-ahead scheduling phase includes the optimal scheduling scheme for each typical scenario. The typical scenarios of wind power output and their load forecast data are obtained by analyzing the correlation of wind power output in the distribution network using copula theory.

[0066] The intraday scheduling module solves the objective function of the intraday scheduling phase in the multi-timescale active and reactive power coordinated scheduling framework for distribution networks based on constraints. This includes: acquiring parameter information for the intraday scheduling phase; using a second time interval as the resolution; and solving the objective function of the intraday scheduling phase based on the parameter information and constraints.

[0067] The parameter information for the intraday scheduling phase includes various typical scenarios of wind power output. The scheduling scheme for the intraday scheduling phase includes optimizing the scheduling of the distribution network based on typical scenarios and predicting typical scenarios of wind power output in the next 4 hours. The typical scenarios of wind power output are obtained by analyzing the correlation of wind power output in the distribution network using copula theory.

[0068] The real-time scheduling module solves the objective function of the real-time scheduling stage in the multi-time-scale active and reactive power coordinated scheduling framework for distribution networks based on constraints. This includes: acquiring parameter information for the real-time scheduling stage; using a third time interval as the resolution; and solving the objective function of the real-time scheduling stage based on the parameter information and constraints.

[0069] The parameter information in the real-time scheduling phase includes wind power output data for the next hour and corresponding load forecast data. The scheduling scheme in the real-time scheduling phase includes scheduling using a deterministic optimization approach, wherein the deterministic optimization adopts rolling optimization, that is, in each optimization process, only the decision value of the first cycle is executed, and the optimal control sequence is retrieved at the next optimization time. The wind power output data and its load forecast data are obtained by analyzing the correlation of wind power output in the distribution network through copula theory.

[0070] This invention constructs objective functions for each time scale and solves these functions based on constraints to obtain scheduling schemes for day-ahead, intraday, and real-time scheduling phases. Adjustments are then made at the corresponding time scales according to these scheduling schemes. This invention realizes the theory of selecting optimization methods based on prediction accuracy at different time scales. Specifically, stochastic optimization methods are used in the day-ahead and intraday scheduling phases with lower prediction accuracy, while deterministic optimization methods are used in the real-time scheduling phase with higher prediction accuracy. Selecting optimization methods based on prediction accuracy at multiple and different time scales improves the advantages of renewable energy output volatility and stochasticity. This invention also has low operating costs and can reduce power fluctuations between the distribution network and the transmission network.

[0071] Furthermore, this invention proposes an IMPC method, which executes the decision scheme for the first cycle only in each optimization process, and repeats the control strategy in the next time. This method further limits the fluctuation of the exchange power between the transmission and distribution networks by reasonably setting the target weights for each time period in the rolling optimization process. Attached Figure Description

[0072] The specific embodiments of the present invention will be described in further detail below with reference to the accompanying drawings.

[0073] Figure 1 This is a flowchart of the method in Embodiment 1 of the present invention.

[0074] Figure 2 This is a diagram of the multi-timescale scheduling framework in Embodiment 1 of the present invention.

[0075] Figure 3 This is a structural block diagram of Embodiment 2 of the present invention.

[0076] Figure 4 This is a structural block diagram of the framework building unit in Embodiment 2 of the present invention.

[0077] Figure 5 This is a diagram of the IEEE 33-node improved system in Embodiment 3 of the present invention.

[0078] Figure 6 This is a diagram illustrating the effect of the method of the present invention and the comparative method in limiting grid voltage in Embodiment 3 of the present invention.

[0079] Figure 7 This is a diagram illustrating the effect of the method of the present invention and the comparative method in limiting active power fluctuations between power transmission and distribution networks in Embodiment 3 of the present invention.

[0080] Figure 8 This is a diagram illustrating the effect of the method of the present invention and the comparative method in limiting reactive power fluctuations between power transmission and distribution networks in Embodiment 3 of the present invention. Detailed Implementation

[0081] The present invention is not limited to the following embodiments, and the specific implementation can be determined according to the technical solution of the present invention and the actual situation.

[0082] Example 1: Figure 1 As shown in the figure, this invention discloses a multi-time-scale scheduling method for distribution networks that considers wind power intermittency and IMPC, including the following steps:

[0083] S101, Construct a multi-timescale active distribution network active and reactive power coordinated scheduling framework, which includes day-ahead scheduling stage, intraday scheduling stage and real-time scheduling stage;

[0084] S102, Construct the objective functions for the day-ahead scheduling stage, intraday scheduling stage, and real-time scheduling stage in the multi-time-scale active distribution network active and reactive power coordinated scheduling framework;

[0085] S103, Design constraints for active and reactive power coordination optimization of active distribution networks at multiple time scales;

[0086] S104, based on constraints, solve the objective functions of the day-ahead scheduling stage, intraday scheduling stage and real-time scheduling stage in the active and reactive power coordination scheduling framework of multi-time-scale active distribution network, and obtain the scheduling schemes of the day-ahead scheduling stage, intraday scheduling stage and real-time scheduling stage.

[0087] S105, implements scheduling at the corresponding time scale according to the scheduling schemes of the day-ahead scheduling phase, intraday scheduling phase, and real-time scheduling phase.

[0088] In step S101 above, such as Figure 2As shown, the active distribution network dispatching framework is divided into three stages: day-ahead dispatching, intraday dispatching, and real-time dispatching. Each stage optimizes the system with different objectives at different time scales, specifically:

[0089] 1. Day-ahead scheduling phase

[0090] Solving the objective function of the day-ahead scheduling stage in the multi-time-scale active and reactive power coordinated scheduling framework for distribution networks based on constraints includes: obtaining parameter information of the day-ahead scheduling stage; using a first time interval as the resolution; and using the parameter information of the day-ahead scheduling stage to solve the objective function of the day-ahead scheduling stage in the multi-time-scale active and reactive power coordinated scheduling framework based on constraints.

[0091] The parameter information for the day-ahead scheduling phase includes typical scenarios of wind power output and corresponding load forecast data for each typical scenario. The scheduling scheme for the day-ahead scheduling phase includes the optimal scheduling scheme for each typical scenario. The typical scenarios of wind power output and their load forecast data are obtained by analyzing the correlation of wind power output in the distribution network using copula theory.

[0092] The first time interval is selected as 1 hour. The optimization objective in the day-ahead scheduling phase is to minimize the system's 24-hour operating cost. In the day-ahead scheduling phase, the action status and action amount of slow-dynamic devices in the system on the next day are determined, namely the OLTC and the compensation capacitor. The optimization results will also be used as reference values ​​in the intraday scheduling phase.

[0093] 2. Intraday scheduling phase

[0094] Solving the objective function of the intraday scheduling phase in the multi-timescale active and reactive power coordinated scheduling framework for a distribution network based on constraints includes: obtaining parameter information for the intraday scheduling phase; using a second time interval as the resolution; and solving the objective function of the intraday scheduling phase based on the parameter information and constraints in the multi-timescale active and reactive power coordinated scheduling framework for a distribution network.

[0095] The parameter information for the intraday scheduling phase includes various typical scenarios of wind power output. The scheduling scheme for the intraday scheduling phase includes optimizing the scheduling of the distribution network based on the typical scenarios and predicting the typical scenarios of wind power output in the next 4 hours. The typical scenarios of wind power output are obtained by analyzing the correlation of wind power output in the distribution network using copula theory.

[0096] The second time interval is selected as 15 minutes. The purpose of the intraday scheduling phase is to minimize the system's operating cost. The optimization variables are the outputs of the controllable DG, ESS, and SVC. The optimization results will be used as reference values ​​for the real-time scheduling phase.

[0097] 3. Real-time scheduling phase

[0098] Solving the objective function of the real-time scheduling stage in the multi-time-scale active and reactive power coordinated scheduling framework for a distribution network based on constraints includes: obtaining parameter information of the real-time scheduling stage, using a third time interval as the resolution, and solving the objective function of the real-time scheduling stage in the multi-time-scale active and reactive power coordinated scheduling framework based on constraints.

[0099] The parameter information in the real-time scheduling phase includes wind power output data for the next hour and corresponding load forecast data. The scheduling scheme in the real-time scheduling phase includes scheduling using a deterministic optimization approach, wherein the deterministic optimization adopts rolling optimization, that is, in each optimization process, only the decision value of the first cycle is executed, and the optimal control sequence is retrieved at the next optimization time. The wind power output data and its load forecast data are obtained by analyzing the correlation of wind power output in the distribution network through copula theory.

[0100] The third time interval is 5 minutes. The optimization objective of the real-time scheduling phase is to minimize the fluctuation between the power exchanged between the transmission and distribution networks and the reference value. The optimization variables are the active power output of the controllable DG and ESS, and the reactive power output of the SVC. By reasonably setting the target weights for each time period, the fluctuation of the power exchanged between the transmission and distribution networks is further limited.

[0101] In step S102 above, the objective function for each time scale in the multi-time-scale active and reactive power coordinated scheduling framework of the distribution network is constructed, including:

[0102] 1) Construct the objective functions for the day-ahead scheduling phase and the intraday scheduling phase.

[0103] To ensure the economic efficiency of system operation, the optimization objective in the objective functions of both the day-ahead and intraday scheduling phases is to minimize the operating cost of the distribution network. The objective functions for the day-ahead and intraday scheduling phases are as follows:

[0104]

[0105] The superscript DA indicates the day-ahead scheduling phase or the intraday scheduling phase, and ID indicates the day-ahead scheduling phase or the intraday scheduling phase. It is the cost for the distribution network to obtain electrical energy from the transmission network in the t-th time period and the s-th scenario; It is the power generation cost of the i-th controllable DG in the t-th time period and the s-th scenario; p is the operating cost of the i-th energy storage device in the t-th time period and the s-th scenario. s Let be the probability of the s-th scenario, n be the number of scenarios, M be the number of controllable DGs, N be the number of energy storage devices, and T be the number of time periods.

[0106] in, It represents the cost for the distribution network to obtain electricity from the transmission network in the t-th time period and s-th scenario. To better reflect reality, this paper adopts peak-valley electricity pricing.

[0107] 2) Construct the objective function for the real-time scheduling phase.

[0108] To reduce the impact of renewable energy uncertainties on grid operation, the optimization objective in the real-time dispatch phase is to minimize power fluctuations at the transmission and distribution network interfaces. The objective function for the real-time dispatch phase is:

[0109]

[0110] The above RT represents the real-time scheduling phase. and This is the power exchange value promised between the transmission and distribution networks during the intraday dispatch phase. and α is the optimization variable in the real-time scheduling phase, representing the power value on the transmission and distribution network interconnects in time period t, where T is the number of time periods, and α is the power value on the interconnects. t The weights represent the power fluctuations during time period t.

[0111] Since the real-time scheduling phase adopts rolling optimization, that is, in the optimization period, only the scheduling decision of the first period is executed, and the optimization scheduling will be re-executed in the next period, this invention proposes to reasonably allocate the weight of the optimization objectives in each period, so as to ensure the realization of the optimization objectives in the rolling optimization and better cope with the uncertainty and volatility of renewable energy output.

[0112] In step S103 above, the constraints include:

[0113] 1) The equation for power flow constraints is,

[0114]

[0115] Where α(j) is the set of the first nodes of the branch with node j as the last node; β(j) is the set of the last nodes of the branch with node j as the first node; P ij,t and Q ij,t These represent the active and reactive power at the beginning of branch ij, respectively; P j,t and Q j,t These represent the active and reactive power injection values ​​at node j, respectively; r ij +jx ij I is the impedance of branch ij; ij,t k is the square of the current amplitude flowing through branch ij; ij,t U represents the turns ratio of the on-load tap-changing transformer at branch ij;i,t Let be the square of the voltage amplitude at node i.

[0116] Based on the characteristics of power distribution networks, this invention employs power flow constraint equations, which can ensure the accuracy of the results while being easy to solve.

[0117] 2) Branch capacity constraints are

[0118]

[0119] In the formula: I ij,t Let be the current in branch ij at time t. This represents the upper limit of the current amplitude in branch ij;

[0120] 3) Node voltage constraints are

[0121]

[0122] In the formula, U i,t Let be the current value at node t. and These are the upper and lower limits of the node voltage amplitude, respectively.

[0123] 4) Renewable energy power generation operation constraints are

[0124]

[0125]

[0126] In the formula, P is the predicted value of active power output from renewable energy sources. RES,i,t Q represents the active power output of renewable energy. RES,i,t Reactive power output of renewable energy, S i The capacity of the inverter for renewable energy power generation devices;

[0127] Among them, the use of inverters from renewable energy power generation devices to provide reactive power support for the distribution network is widely adopted. When the active power generated by the renewable energy power generation device is less than the rated capacity of its inverter, the reactive power output of the inverter can be adjusted to improve the voltage distribution of the distribution network.

[0128] Furthermore, since the cost of renewable energy generation is lower than the cost of controllable distributed generation (DG) and the electricity price on the grid, the active power output of renewable energy under this constraint will approach the predicted value of renewable energy active power output as closely as possible, so as to maximize the absorption of renewable energy.

[0129] 5) Controllable distributed generation operation constraints are

[0130]

[0131] In the formula, P CDG,i,t and Q CDG,i,t P represents the active and reactive power output of controllable distributed generation; CDG,min P CDG,max Q CDG,min and Q CDG,max S represents the upper and lower limits of active and reactive power output of controllable distributed generation; CDG,i,t The installed capacity for controllable distributed generation; Indicates the upper and lower limits of ramp rate for controllable distributed generation;

[0132] 6) Operating constraints for on-load tap-changing transformers are as follows:

[0133] An accurate linearized model of the on-load tap-changing transformer is performed as follows:

[0134]

[0135] In the formula, k0 is the standard turns ratio of an on-load tap-changing transformer; Δk ij To adjust the step size; K ij,t , These refer to the adjustable tap positions of the on-load tap-changing transformer and their upper and lower limits;

[0136] 7) Operating constraints of the static var compensator are:

[0137]

[0138] In the formula, and These are the adjustable upper and lower limits of reactive power output of the static var compensator;

[0139] 8) The operating constraints of the compensation capacitor bank are as follows:

[0140]

[0141] In the formula: H i,t Adjustable gears; ΔQ c,i,t To adjust the step size; H max To compensate for the maximum adjustable range of the capacitor bank;

[0142] 9) Energy storage device operating constraints are

[0143]

[0144] In the formula: P ch,i,t and P dis,i,t These represent the charging and discharging power of the energy storage device, respectively; η ch and η dis These are the charging and discharging efficiencies of the energy storage device, respectively. and These represent the maximum charging and discharging power of the energy storage device, respectively; D ch,i,t and D dis,i,t It is a 0-1 variable, representing the charging and discharging state of the energy storage device; This refers to the capacity of the energy storage device.

[0145] Among them, D ch,i,t and D dis,i,t It is a 0-1 variable, representing the charging and discharging state of the energy storage device, to ensure that charging and discharging of the energy storage device do not occur simultaneously; Considering the lifespan of the energy storage device, its usage range is limited to 20%-80% based on the capacity of the energy storage device.

[0146] In summary, this invention is based on the theory of selecting optimization methods for prediction accuracy at different time scales. Specifically, it adopts a stochastic optimization method in the day-ahead and intraday scheduling stages with lower prediction accuracy, and a deterministic optimization method in the real-time scheduling stage with higher prediction accuracy. The optimization method for prediction accuracy at multiple time scales and different time scales improves the advantages of renewable energy output volatility and stochasticity. This invention has low operating costs and can reduce power fluctuations between the distribution network and the transmission network.

[0147] Furthermore, this invention proposes an IMPC (improved Model Predictive Control) method, which executes the decision scheme of the first cycle only in each optimization process, and repeats the control strategy in the next time. This method further limits the fluctuation of the exchange power between the transmission and distribution networks by reasonably setting the target weights of each time period in the rolling optimization process.

[0148] Example 2: Figure 3 As shown, this embodiment of the invention discloses a multi-timescale dispatching device for a distribution network that considers the intermittency of wind power and IMPC, comprising,

[0149] The framework construction unit constructs a multi-timescale active distribution network active and reactive power coordinated scheduling framework, which includes day-ahead scheduling, intraday scheduling, and real-time scheduling.

[0150] Objective function construction unit: Constructs objective functions for the day-ahead scheduling stage, intraday scheduling stage, and real-time scheduling stage in the multi-timescale active distribution network active and reactive power coordinated scheduling framework;

[0151] The constraint design unit designs the constraints for active and reactive power coordination optimization across multiple time scales in an active distribution network.

[0152] The scheduling scheme acquisition unit solves the objective functions of the day-ahead scheduling stage, intraday scheduling stage, and real-time scheduling stage in the multi-time-scale active distribution network active and reactive power coordinated scheduling framework based on constraints, and obtains the scheduling schemes for the day-ahead scheduling stage, intraday scheduling stage, and real-time scheduling stage.

[0153] The scheduling implementation unit implements scheduling at the corresponding time scale according to the scheduling schemes of the day-ahead scheduling phase, intraday scheduling phase, and real-time scheduling phase.

[0154] Among them, such as Figure 4 As shown, a multi-timescale dispatching device for distribution networks that considers wind power intermittency and IMPC further includes a day-ahead dispatching module, an intraday dispatching module, and a real-time dispatching module, wherein:

[0155] The day-ahead scheduling module solves the objective function of the day-ahead scheduling stage in the multi-time-scale active and reactive power coordinated scheduling framework for distribution networks based on constraints. This includes: acquiring parameter information for the day-ahead scheduling stage; using a first time interval as resolution; and solving the objective function of the day-ahead scheduling stage based on the parameter information and constraints.

[0156] The parameter information for the day-ahead scheduling phase includes typical wind power output scenarios and corresponding load forecast data for each typical scenario. The scheduling scheme for the day-ahead scheduling phase includes the optimal scheduling scheme for each typical scenario. The typical wind power output scenarios and their load forecast data are obtained by analyzing the correlation of wind power output in the distribution network using copula theory.

[0157] The intraday scheduling module solves the objective function of the intraday scheduling phase in the multi-timescale active and reactive power coordinated scheduling framework for distribution networks based on constraints. This includes: acquiring parameter information for the intraday scheduling phase; using a second time interval as the resolution; and solving the objective function of the intraday scheduling phase based on the parameter information and constraints.

[0158] The parameter information for the intraday scheduling phase includes various typical scenarios of wind power output. The scheduling scheme for the intraday scheduling phase includes optimizing the scheduling of the distribution network based on typical scenarios and predicting typical scenarios of wind power output in the next 4 hours. The typical scenarios of wind power output are obtained by analyzing the correlation of wind power output in the distribution network using copula theory.

[0159] The real-time scheduling module solves the objective function of the real-time scheduling stage in the multi-time-scale active and reactive power coordinated scheduling framework for distribution networks based on constraints. This includes: acquiring parameter information for the real-time scheduling stage; using a third time interval as the resolution; and solving the objective function of the real-time scheduling stage based on the parameter information and constraints.

[0160] The parameter information in the real-time scheduling phase includes wind power output data for the next hour and corresponding load forecast data. The scheduling scheme in the real-time scheduling phase includes scheduling using a deterministic optimization approach, wherein the deterministic optimization adopts rolling optimization, that is, in each optimization process, only the decision value of the first cycle is executed, and the optimal control sequence is retrieved at the next optimization time. The wind power output data and its load forecast data are obtained by analyzing the correlation of wind power output in the distribution network through copula theory.

[0161] Example 3: As Figure 5 As shown, this embodiment of the invention performs simulation calculations based on an improvement to the IEEE 33-node system. The simulation example is performed in the MATLAB R2019a compilation environment, using the Yalmip optimization tool for modeling and Gurobi for solving. The computer configuration for compiling the algorithm is an Intel(R) Core(TM) i7-5500 2.40GHz with 8GB of RAM. The specific implementation process is as follows:

[0162] 1) Analysis of operating costs and network losses

[0163] To verify the effectiveness of the proposed method in reducing operating costs and network losses, it is compared with single active power optimization and single reactive power optimization. Single active power optimization refers to optimizing variables that only include the active power output of distributed generation sources and the active power output of energy storage devices; single reactive power optimization refers to optimizing variables that only include the reactive power output of distributed generation sources, OLTC settings, capacitor bank switching settings, and SVC reactive power output. Table 1 shows the operating costs of the three methods.

[0164] By comparison, the operating cost and network loss of active and reactive power coordinated optimization are lower than the other two methods. This is because the resistance and reactance values ​​in the distribution network are close, and the coupling between active and reactive power is strong. Therefore, single active and reactive power optimization scheduling is no longer applicable, so active and reactive power coordinated scheduling of the distribution network is required.

[0165] 2) Analysis of volatility and uncertainty in renewable energy output

[0166] The integration of renewable energy into multiple points in the distribution network is becoming increasingly common. Due to the volatility and uncertainty of renewable energy output, the voltage at renewable energy access points in the distribution network fluctuates significantly and is prone to exceeding limits, which may cause overvoltage and even limit the grid connection capacity of renewable energy.

[0167] This invention employs a multi-time-scale scheduling method to address the impact of the volatility and uncertainty of renewable energy output on the voltage at the point of access. In contrast, a single-time-scale scheduling method is selected as a comparative method. The effectiveness of the method in this invention and the comparative method in limiting overvoltage is compared below.

[0168] like Figure 6 As shown in the comparison, the multi-timescale scheduling method is more effective than the single-timescale optimization method in limiting overvoltage. This is because the single-timescale method only optimizes the distribution network day-ahead, after which the network operation is no longer under scheduling control and is in an offline state. While automatic regulation devices can maintain system power balance and relatively stable voltage during the offline process, they cannot guarantee the achievement of the distribution network optimization goals. The multi-timescale scheduling method, on the other hand, has a more granular timescale, ensuring the provision of the latest predicted time-series values ​​at all times. In particular, the real-time stage employs rolling optimization and feedback correction, achieving online control and further reducing the impact of the uncertainty and randomness of renewable energy output on the voltage at the connection point. However, the multi-timescale scheduling method has higher requirements for communication accuracy, real-time communication, computing power, and storage space. In distribution networks with a large number of distributed resources, this scheduling method is costly.

[0169] 3) Power fluctuation analysis

[0170] As renewable energy sources are increasingly integrated into the distribution network, the uncertainty and volatility of their power output not only impact the distribution network but can also affect the operation of the transmission network. Therefore, it is necessary to limit power fluctuations between the transmission and distribution networks.

[0171] This invention selects optimization methods based on prediction accuracy at different time scales. Specifically, a stochastic optimization method based on scenario method is selected for the day-ahead and intraday scheduling stages, while a deterministic optimization method is adopted for the real-time stage, in order to cope with the uncertainty and volatility of renewable energy.

[0172] To demonstrate the superiority of this method, the selected comparison method chooses the optimization method based on the prediction accuracy at different time scales. Specifically, a deterministic optimization method based on scenario-based stochastic optimization is selected for the day-ahead and intraday scheduling stages, while a scenario-based stochastic optimization method is adopted for the real-time stage. The following compares the effectiveness of the proposed method and the comparison method in limiting power fluctuations, as shown in Table 2. Based on Table 2 and... Figure 7 and Figure 8Compared with the comparative methods, the method of the present invention can better stabilize the exchange power between the transmission network and the distribution network and reduce power fluctuations.

[0173] This is because the method of this invention can select a scenario-based stochastic optimization method in the day-ahead and intraday stages, taking into account the uncertainty of the prediction results, by optimizing and scheduling typical scenarios. Therefore, its scheduling results can adapt to various typical scenarios. In the real-time stage, a deterministic optimization method is adopted based on the higher accuracy of the prediction results, which can reduce the computation time while ensuring the scheduling effect.

[0174] The comparative method, by employing deterministic optimization techniques in both the day-ahead and intraday phases, is highly susceptible to the impact of prediction accuracy on dispatch performance. Particularly during the intraday dispatch phase, inaccurate predictions can lead to inappropriate reference values ​​for power exchange in the transmission and distribution networks, resulting in significant power fluctuations.

[0175] Therefore, compared with the comparative methods, the method of the present invention is better able to adapt to the fluctuations in renewable energy output because it selects an optimization method based on the prediction accuracy at different time scales.

[0176] Example 4: This embodiment of the invention discloses a storage medium storing a computer program that can be read by a computer. The computer program is configured to execute a multi-timescale scheduling method for distribution networks that considers wind power intermittency and IMPC when it runs.

[0177] The aforementioned storage media may include, but are not limited to, USB flash drives, read-only memory, portable hard drives, magnetic disks, optical disks, and other media capable of storing computer programs.

[0178] Example 5: This embodiment of the invention discloses an electronic device, including a processor and a memory. The memory stores a computer program, which is loaded and executed by the processor to implement a multi-timescale scheduling method for distribution networks that considers wind power intermittency and IMPC.

[0179] The aforementioned processor can be a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an ASIC, an FPGA, or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. It can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc. The memory can include, but is not limited to, various media capable of storing computer programs, such as USB flash drives, read-only memory, portable hard drives, magnetic disks, or optical disks.

[0180] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The solutions in the embodiments of this application can be implemented in various computer languages, such as the object-oriented programming language Java and the interpreted scripting language JavaScript. This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, produce implementations of the flowchart illustrations and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0181] Table 1 Operating Costs and Network Losses

[0182]

[0183] Table 2. Cumulative Active and Reactive Power Fluctuations of the Invention Method and the Comparative Method

[0184]

Claims

1. A multi-time scale dispatching method for distribution network considering wind power intermittency and IMPC, characterized in that: The following steps are involved: Construct a multi-time scale active and reactive coordinated dispatch framework for active distribution networks, which includes the day-ahead dispatch stage, the intra-day dispatch stage and the real-time dispatch stage. Construct the objective functions of the day-ahead dispatching stage, intraday dispatching stage and real-time dispatching stage in the multi-time scale active and reactive power coordinated dispatching framework of active distribution network; Design the constraints for multi-time scale active and reactive coordination optimization of active distribution networks; Based on the constraints, the objective functions of the day-ahead dispatching stage, intraday dispatching stage and real-time dispatching stage in the multi-time scale active distribution network active and reactive coordinated dispatching framework are solved, and the dispatching schemes of the day-ahead dispatching stage, intraday dispatching stage and real-time dispatching stage are obtained; Scheduling at the corresponding time scale is achieved according to the scheduling plans of the day-ahead scheduling stage, intraday scheduling stage and real-time scheduling stage.

2. The multi-time scale dispatching method for distribution network considering wind power intermittency and IMPC according to claim 1 is characterized in that: Solving the objective function of the day-ahead dispatching stage in the multi-time-scale active distribution network active and reactive coordinated dispatching framework based on the constraint conditions, including: obtaining parameter information of the day-ahead dispatching stage, taking a first time interval as a resolution, using the parameter information of the day-ahead dispatching stage, and solving the objective function of the day-ahead dispatching stage in the multi-time-scale active distribution network active and reactive coordinated dispatching framework based on the constraint conditions: The parameter information of the day-ahead scheduling stage includes typical scenarios of wind power output and load forecast data corresponding to each typical scenario. The scheduling plan of the day-ahead scheduling stage includes the optimal scheduling plan corresponding to each typical scenario. Among them, the typical scenarios of wind power output and their load forecast data are obtained by analyzing the correlation of wind power output in the distribution network through copula theory.

3. The multi-time scale dispatching method for distribution network considering wind power intermittency and IMPC according to claim 1 is characterized in that: Solving the objective function of the intraday dispatching stage in the multi-time scale active distribution network active and reactive coordinated dispatching framework based on the constraint conditions, including: obtaining parameter information of the intraday dispatching stage, using the parameter information of the intraday dispatching stage with a second time interval as a resolution, and solving the objective function of the intraday dispatching stage in the multi-time scale active distribution network active and reactive coordinated dispatching framework based on the constraint conditions: The parameter information of the intraday scheduling stage includes various typical scenarios of wind power output. The scheduling plan of the intraday scheduling stage includes optimizing the scheduling of the distribution network based on typical scenarios and predicting the typical scenarios of wind power output in the next 4 hours; wherein, the typical scenarios of wind power output are obtained by analyzing the correlation of wind power output in the distribution network through copula theory.

4. The multi-time scale dispatching method for distribution network considering wind power intermittency and IMPC according to claim 1 is characterized in that: Solving the objective function of the real-time scheduling stage in the multi-time scale active distribution network active and reactive coordinated scheduling framework based on the constraint conditions, including: obtaining parameter information of the real-time scheduling stage, using the parameter information of the real-time scheduling stage with a third time interval as a resolution, and solving the objective function of the real-time scheduling stage in the multi-time scale active distribution network active and reactive coordinated scheduling framework based on the constraint conditions: The parameter information of the real-time scheduling stage includes the wind power output data and the corresponding load forecast data for the next 1 hour; the scheduling scheme of the real-time scheduling stage includes scheduling by means of deterministic optimization, wherein the deterministic optimization adopts rolling optimization, that is, only the decision value of the first cycle is executed in each optimization process, and the optimal control sequence is retrieved at the next optimization moment; wherein, the wind power output data and its load forecast data are obtained by analyzing the correlation of wind power output in the distribution network through copula theory.

5. The multi-time scale dispatching method for distribution network considering wind power intermittency and IMPC according to claim 1 is characterized in that: The objective functions of each time scale in constructing the multi-time scale active and reactive coordinated dispatch framework of active distribution network include: 1) Construct the objective functions of the day-ahead scheduling phase and the intraday scheduling phase The optimization objectives in the objective functions of the day-ahead dispatching stage and the intraday dispatching stage are to minimize the distribution network operation cost. The objective functions of the day-ahead dispatching stage and the intraday dispatching stage are: The superscript DA indicates the day-ahead scheduling phase or the intraday scheduling phase, and the superscript ID indicates the day-ahead scheduling phase or the intraday scheduling phase. is the cost of the distribution network obtaining electric energy from the transmission network in the sth scenario in the tth period; is the power generation cost of the i-th controllable DG in the s-th scenario in the t-th period; is the operating cost of the i-th energy storage device in the t-th period under the s-th scenario, p s is the probability of the sth scenario, n is the number of scenarios, M is the number of controllable DGs, N is the number of energy storage devices, and T is the number of time periods; 2) Construct the objective function of the real-time scheduling phase The optimization goal in the objective function of the real-time dispatch stage is to minimize the power fluctuation at the interface between the transmission network and the distribution network. The objective function of the real-time dispatch stage is The above RT represents the real-time scheduling stage. and is the power exchange value between the transmission network and the distribution network promised during the intraday dispatch phase, and is the optimization variable in the real-time scheduling stage, which represents the power value on the transmission network and distribution network tie line in the tth period, T is the number of periods, and α t Represents the weight of the power fluctuation in the tth period.

6. The multi-time scale dispatching method for distribution network considering wind power intermittency and IMPC according to claim 1 is characterized in that: The constraints include power flow constraints, branch capacity constraints, node voltage constraints, renewable energy generation operation constraints, controllable distributed generation operation constraints, on-load tap-changing transformer operation constraints, static VAR compensation device operation constraints, compensation capacitor bank operation constraints, and energy storage device operation constraints: 1) The equation for power flow constraint is: Among them, α(j) is the set of first nodes of the branch with node j as the last node; β(j) is the set of last nodes of the branch with node j as the first node; P ij,t and Q ij,t are respectively the active power and reactive power at the head end of branch ij; P j,t and Q j,t are the active power and reactive power injection values ​​of node j respectively; r ij +jx ij is the impedance of branch ij; I ij,t is the square of the current amplitude flowing through branch ij; k ij,t is the transformation ratio of the on-load tap-changing transformer at branch ij; U i,t is the square of the voltage amplitude at node i; 2) Branch capacity constraint is Where: I ij,t is the current of branch ij at time t, is the upper limit of the current amplitude of branch ij; 3) The node voltage constraint is Where U i,t is the current value of the node at time t, and are the upper and lower limits of node voltage amplitude respectively; 4) The operating constraints of renewable energy generation are In the formula, is the predicted value of active power output of renewable energy, P RES,i,t is the active output value of renewable energy, Q RES,i,t Reactive power output of renewable energy, S i The capacity of inverters for renewable energy generation; 5) The controllable distributed generation operation constraints are Where P CDG,i,t and Q CDG,i,t Represents the active and reactive output of controllable distributed generation; P CDG,min , P CDG,max , Q CDG,min and Q CDG,max Indicates the upper and lower limits of the active and reactive output of controllable distributed generation; S CDG,i,t The installed capacity of controllable distributed generation; Indicates the upper and lower limits of the ramping of controllable distributed generation; 6) The operating constraints of the on-load tap-changing transformer are Where k0 is the standard ratio of the on-load tap-changing transformer; Δk ij is the adjustment step length; K ij,t , They are the adjustable gears of the on-load tap-changing transformer and their upper and lower limits; 7) The operating constraints of the static VAR compensation device are In the formula, and They are respectively the upper and lower limits of the adjustable reactive output of the static reactive compensation device; 8) The operating constraints of the compensation capacitor bank are Where: H i,t is the adjustable gear; ΔQ c,i,t is the adjustment step length; H max The maximum adjustable gear position for the compensation capacitor bank; 9) The operation constraints of the energy storage device are Where: P ch,i,t and P dis,i,t are the charging and discharging power of the energy storage device respectively; η ch and η dis are the charging and discharging efficiency of the energy storage device, respectively; and are the maximum charging and discharging power of the energy storage device respectively; D ch,i,t and D dis,i,t is a 0-1 variable, indicating the charging and discharging status of the energy storage device; is the capacity of the energy storage device.

7. A multi-time scale dispatching device for distribution network considering wind power intermittency and IMPC, characterized in that: include, A framework construction unit is used to construct a multi-time scale active distribution network active and reactive coordinated dispatch framework, wherein the multi-time scale active distribution network active and reactive coordinated dispatch framework includes a day-ahead dispatch stage, an intra-day dispatch stage and a real-time dispatch stage; An objective function construction unit is used to construct objective functions of the day-ahead dispatching stage, the intraday dispatching stage and the real-time dispatching stage in the multi-time-scale active and reactive power coordinated dispatching framework of active distribution networks; Constraint design unit, which designs the constraints for multi-time scale active and reactive coordination optimization of active distribution network; The dispatching scheme acquisition unit solves the objective functions of the day-ahead dispatching stage, the intraday dispatching stage and the real-time dispatching stage in the multi-time-scale active distribution network active and reactive coordinated dispatching framework based on the constraint conditions, and obtains the dispatching schemes of the day-ahead dispatching stage, the intraday dispatching stage and the real-time dispatching stage; The scheduling implementation unit implements scheduling at corresponding time scales according to the scheduling schemes of the day-ahead scheduling stage, the intraday scheduling stage and the real-time scheduling stage.

8. The distribution network multi-time scale dispatching device considering wind power intermittency and IMPC according to claim 7 is characterized in that: It includes day-ahead scheduling module, intraday scheduling module and real-time scheduling module, among which: The day-ahead dispatching module solves the objective function of the day-ahead dispatching stage in the multi-time scale active distribution network active and reactive coordinated dispatching framework based on the constraint conditions, including: obtaining parameter information of the day-ahead dispatching stage, taking the first time interval as the resolution, using the parameter information of the day-ahead dispatching stage, and solving the objective function of the day-ahead dispatching stage in the multi-time scale active distribution network active and reactive coordinated dispatching framework based on the constraint conditions: The parameter information of the day-ahead dispatching stage includes typical scenarios of wind power output and load forecast data corresponding to each typical scenario, and the dispatching scheme of the day-ahead dispatching stage includes the optimal dispatching scheme corresponding to each typical scenario; wherein the typical scenarios of wind power output and load forecast data are obtained by analyzing the correlation of wind power output in the distribution network through copula theory; The intraday dispatching module solves the objective function of the intraday dispatching stage in the multi-time scale active distribution network active and reactive coordinated dispatching framework based on the constraint conditions, including: obtaining parameter information of the intraday dispatching stage, using the parameter information of the intraday dispatching stage with a second time interval as a resolution, and solving the objective function of the intraday dispatching stage in the multi-time scale active distribution network active and reactive coordinated dispatching framework based on the constraint conditions: The parameter information of the intraday dispatching stage includes typical scenarios of wind power output. The dispatching scheme of the intraday dispatching stage includes optimizing the dispatching of the distribution network based on the typical scenarios and predicting the typical scenarios of wind power output in the next 4 hours. The typical scenarios of wind power output are obtained by analyzing the correlation of wind power output in the distribution network through copula theory. The real-time dispatching module solves the objective function of the real-time dispatching stage in the multi-time scale active distribution network active and reactive coordinated dispatching framework based on the constraint conditions, including: obtaining parameter information of the real-time dispatching stage, using the parameter information of the real-time dispatching stage with a third time interval as a resolution, and solving the objective function of the real-time dispatching stage in the multi-time scale active distribution network active and reactive coordinated dispatching framework based on the constraint conditions: The parameter information of the real-time scheduling stage includes the wind power output data and the corresponding load forecast data for the next 1 hour; the scheduling scheme of the real-time scheduling stage includes scheduling by means of deterministic optimization, wherein the deterministic optimization adopts rolling optimization, that is, only the decision value of the first cycle is executed in each optimization process, and the optimal control sequence is retrieved at the next optimization moment; wherein, the wind power output data and its load forecast data are obtained by analyzing the correlation of wind power output in the distribution network through copula theory.

9. A storage medium, characterized in that: The storage medium stores a computer program that can be read by a computer, and the computer program is configured to execute the distribution network multi-time scale scheduling method considering wind power intermittency and IMPC as described in any one of claims 1 to 6 when running.

10. An electronic device, characterized in that: It includes a processor and a memory, wherein a computer program is stored in the memory, and the computer program is loaded and executed by the processor to implement the distribution network multi-time scale scheduling method considering wind power intermittency and IMPC as described in any one of claims 1 to 6.

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