CN106786806A - A kind of power distribution network active reactive based on Model Predictive Control coordinates regulation and control method - Google Patents

Abstract

Coordinate regulation and control method the invention discloses a kind of power distribution network active reactive based on Model Predictive Control, step includes：1) Optimum Regulation model a few days ago is set up according to optimization aim and constraints；2) Optimum Regulation model a few days ago is solved, formulates the plan of power distribution network active reactive regulation and control a few days ago；3) it is worth on the basis of the regulation and control plan of power distribution network active reactive a few days ago, based on current time distribution Running State measuring value, consider the operation constraints of current and future, the in a few days forecast model in roll correction stage and active reactive coordination optimization regulation-control model are set up, the in a few days active reactive control instruction sequence of following limited period of time is solved；4) first in a few days active reactive control instruction at moment is performed, a time interval is moved after time window, repeat in a few days roll correction optimization process.The method reduces system operation cost on the premise of system operation security is ensured, reduces grid loss, realizes the maximization of active distribution network on-road efficiency.

Description

Power distribution network active and reactive power coordinated regulation and control method based on model predictive control

Technical Field

The invention belongs to the field of operation optimization of a power distribution network, and relates to a power distribution network active and reactive power coordinated regulation and control method based on model predictive control.

Background

With the access of a large number of devices such as distributed power sources, flexible loads, energy storage devices and reactive power compensation devices to the power distribution network, the traditional power distribution network is gradually evolving into an active power distribution network with numerous adjustable and controllable resources. The power generation output of the distributed power supply, especially the renewable energy, is random, intermittent and fluctuating, the prediction accuracy is low, and the prediction error is increased along with the increase of time, so that great challenges are brought to the regulation and control of a power grid. Active power distribution networks urgently need to find a regulation and control method capable of better coping with uncertainty of output of renewable energy sources, and Model Predictive Control (MPC) is an effective way for solving the problem.

Although the capability of a power distribution network in dealing with uncertainty of renewable energy resources is effectively improved through model prediction control in the existing research, active scheduling of an active power distribution network is only considered. The coupling of active power and reactive power in the power distribution network is strong, active optimization and reactive optimization analysis are respectively carried out on the power distribution network based on the traditional active and reactive decoupling theory, and the power distribution network is incomplete and comprehensive. The reactive power is adjusted to reduce the network loss and indirectly improve the economic benefit from the consideration of the operating economy of the power distribution network; considering from the operation safety of the power distribution network, the voltage of the system can be adjusted by adjusting active resources in the power distribution network, and the safe operation of the system is ensured. Therefore, from the perspective of safety and economy of operation of the active power distribution network, active and reactive power coordinated scheduling of the active power distribution network needs to be researched, and the maximization of the operation benefit of the active power distribution network is realized under the condition of ensuring safety and stability.

Disclosure of Invention

The technical problem to be solved by the invention is as follows: the existing active power distribution network optimal scheduling method based on model predictive control only aims at active scheduling, does not consider the influence of reactive power change in a power distribution network, and is incomplete and comprehensive for the power distribution network with strong active and reactive coupling.

In order to solve the technical problem, the invention provides a power distribution network active and reactive power coordinated regulation and control method based on model predictive control, which comprises the following steps:

step 10), in a day-ahead stage, establishing a power distribution network day-ahead optimization regulation and control model, wherein the model takes the minimum running cost of the power distribution network as an optimization target and takes power flow constraint, power distribution network running safety constraint, distributed power supply active and reactive power coordinated output constraint, flexible load constraint and reactive power compensation equipment constraint as constraint conditions;

step 20), solving the power distribution network day-ahead optimized scheduling model to obtain a day-ahead power distribution network active and reactive power regulation plan, and issuing the plan in advance;

step 30), in the intra-day phase, at regular time intervals, utilizing the active and reactive power regulation and control plan of the power distribution network before the day obtained by the step 20) to establish a prediction model and an active and reactive power coordinated optimization regulation and control model of the limited time period in the intra-day rolling correction phase based on the measurement value of the running state of the power distribution network at the current moment and by considering the current and future running constraint conditions, correcting the deviation phenomenon of the actual plan in the day caused by the prediction error before the day, and solving the control instruction sequence of the active and reactive power in the day of the limited time period in the future;

and step 40), executing the active and reactive control instruction in the day at the first moment, moving the time window backwards by a time interval, and repeating the rolling correction optimization process in the day.

As a further limiting scheme of the invention, in step 10), a model of a power distribution network day-ahead optimization regulation and control stage is as follows:

minF^DA(U^DA)

s.t.G^DA(U^DA)≤0

in the formula,F^DAMinimum objective function of distribution network operation cost, G, for a model of the day ahead phase^DAThe constraint conditions in the model comprise a power flow constraint, a power distribution network operation safety constraint, a distributed power supply active and reactive power coordinated output constraint, a flexible load constraint and a reactive power compensation equipment constraint, U^DAAs a control variable in the day-ahead phase model,power is exchanged for the main network links at time t ahead of day,andfor the renewable energy source active power and the controllable distributed power source active power at the node i at the time t in the day,the controlled active power of the load at node i for time t;andrespectively the charging power and the discharging power of the energy storage device at the node i at the moment t in the day,andrespectively the reactive power of a Static Var Compensator (SVC), the energy storage reactive power output, the compensation capacitor reactive power, the renewable energy reactive power output and the controllable distributed power supply reactive power at a node i at the moment t in the day,the current is the adjustable transformation ratio of the on-load voltage regulator OLTC at the branch ij at the time t.

As a further limiting scheme of the present invention, in step 10), the objective function of minimizing the operation cost of the power distribution network is:

in the formula, delta T is unit time interval rho of the day-ahead optimization regulation and control stage_G,tUnit cost rho of power exchange between regional distribution network and main network tie line at time t_RE,i、ρ_CDG,i and ρ_ESS,iRespectively are renewable energy sources at the node i, a controllable distributed power supply and unit active power on-line electricity price of stored energy,the controlled state of the load at the node i at the time t before the day, 1 represents controlled, 0 represents uncontrolled,cost of compensation for load at node i, ρ_TCost of adjustment for unit gear of OLTC, Delta U_TTotal daily adjustment for OLTC range, ρ_CCost per gear adjustment, Δ U, for a compensation capacitor_CIs the total adjustment amount of the compensation capacitor gear all day.

As a further limiting solution of the present invention, in step 10), the power flow constraint is:

in the formula,andrespectively the active power and the reactive power of the head end of the branch ij at the moment t in the day,andrespectively the net injection values of active power and reactive power of a node j at a moment t in the day,andrespectively the load active power and the load reactive power of a node j at the moment t in the day,is the voltage amplitude at node j at time t, r_ij and x_ijRespectively the resistance and reactance of the line ij,the current on branch ij at time t before day;

the power distribution network operation safety constraint is as follows:

in the formula,andrespectively an upper limit and a lower limit of the voltage amplitude of the node i,the upper current amplitude limit for branch ij.

As a further limiting scheme of the invention, in step 10), the active and reactive coordinated output constraints of the distributed power supply comprise energy storage active and reactive output constraints, renewable energy active and reactive output constraints and controllable distributed power supply active and reactive output constraints; wherein,

the energy storage active and reactive power output constraint is as follows:

in the formula,the maximum apparent power which can be provided by the energy storage inverter at the node i;

the energy storage active charging and discharging and the energy storage quantity need to comply with the following constraints:

in the formula,andrespectively is the upper limit of the charge and discharge power stored on the node i at the time t,andthe charging and discharging states of the stored energy on the node i at the time t are respectively variable from 0 to 1,for the total energy stored at node i during time t, η_ch、η_disRespectively the charge and discharge efficiency of the stored energy,is the energy storage capacity limit;

the renewable energy active and reactive power output constraints comprise photovoltaic active and reactive power output constraints and wind power active and reactive power output constraints; the photovoltaic active and reactive power output constraint is as follows:

in the formula,andrespectively the active output and the reactive output of the photovoltaic at the node i at the moment t in the day,the maximum apparent power of the photovoltaic inverter at the node i is obtained;

the wind power active and reactive power output constraint is as follows:

in the formula,andrespectively the active power output and the reactive power output of the wind power at a node i at the moment t before the day,is the maximum current of the stator winding of the doubly-fed induction wind turbine generator, s is the slip ratio of the doubly-fed induction wind turbine generator, X_s,i and X_m,iRespectively is the stator reactance and the excitation reactance of the doubly-fed induction wind turbine generator at the node i,the maximum current of a rotor side converter of the doubly-fed induction wind turbine generator set is obtained;

the active and reactive power output constraint of the controllable distributed power supply is the active and reactive power output constraint of the micro gas turbine; the active and reactive power output constraints of the micro gas turbine are as follows:

in the formula,andactive power output and reactive power output S of the micro gas turbine at a node i at the moment t before the day are respectively_MT,iThe installed capacity of the micro gas turbine at the node i is represented;

for active power output, the ramp rate constraint of the micro gas turbine is as follows:

in the formula：andthe upward ramp rate limit and the downward ramp rate limit of the micro gas turbine at node i, respectively.

As a further limitation of the present invention, in step 10), the flexible load constraint is an operation constraint capable of interrupting the load, and then the flexible load operation constraint is:

in the formula,is the upper limit value, S, of the interruptible load at node j_CL,iAn interrupt period is allowed for the interruptible load at node i.

As a further limiting scheme of the present invention, in step 10), the reactive compensation equipment constraints include OLTC operation constraints, SVC operation constraints, and compensation capacitor operation constraints; wherein the OLTC operating constraints are:

in the formula, and Δk_ijRespectively the standard transformation ratio and the adjustment step size of the OLTC in branch ij,andrespectively representing a t-time gear of the OLTC in the branch ij and an adjustable lower limit and an upper limit of the T-time gear;

the SVC operating constraints are:

in the formula,andthe upper limit value and the lower limit value of the adjustable power of the SVC are respectively;

the compensation capacitor operating constraints are:

in the formula, and ΔQ_C,iRespectively the minimum output and the adjustable capacity step length of the compensation capacitor connected on the node i, andrespectively, the t-time gear of the compensation capacitor connected at the node i and the adjustable upper and lower limits thereof.

As a further limiting aspect of the present invention, in step 30), the prediction model of the limited period of the rolling correction period in the day is:

u＝[P_G,P_RE,i,Q_RE,i,P_CDG,i,Q_CDG,i,P_ch,i,P_dis,i,Q_ESS,i,Q_SVC,i]

in the formula,t₀The moment is the current moment, delta t is the unit time interval of the rolling correction stage in the day, M is the total time interval of model solution of the rolling correction stage in the day, u represents a controllable means in the active power distribution network of the feedback correction stage, and the controllable means comprises main network interconnection line exchange power P_GRenewable energy source active power output P_RE,iRenewable energy reactive power output Q_RE,iActive power output P of controllable distributed power supply_CDG,iControllable distributed power supply reactive power output Q_CDG,iEnergy storage active power output P_ch,i and P_dsi,iEnergy storage idle workOutput Q_ESS,iAnd SVC reactive power Q_SVC,I，Is t₀T predicted by time₀The force output value of each controllable means at the moment of + k deltat,is t₀The actual measured feedback values of the controllable means at the moment,is t₀And (5) predicting the moment to obtain the increment of each controllable means, and solving variables for the optimization model.

As a further limiting scheme of the present invention, in step 30), the active and reactive power coordinated optimization regulation and control model of the limited period of the rolling correction stage in the day is as follows:

minF^DR(U^DR)

in the formula,U^DRIs t₀～t₀Set of force values from each controllable means within + M Δ t time period, F^DRCorrecting the objective function of the phase model for rolling in the day, G^DRCorrecting the constraint conditions of the phase model for rolling in the day;

the objective function of the finite-period active and reactive power coordination optimization model in the rolling correction stage in the day is as follows:

minF^DR＝||(U^DR-U^DA)·U^max||¹

in the formula,U^DAIs t day ahead₀～t₀Set of force values, U, from each controllable means within the + M Δ t time period^maxAnd for the reciprocal of the maximum value of the output of each controllable means, the constraint conditions of the limited-period active and reactive power coordinated optimization model in the rolling correction stage in the day are the same as those in the previous stage except that the constraint conditions do not comprise flexible load operation constraint, OLTC operation constraint and compensation capacitor operation constraint.

The invention has the beneficial effects that: the invention provides a multi-time scale active and reactive power coordinated scheduling method of a power distribution network based on model predictive control, aiming at the adjusting characteristics of various adjustable resources such as distributed power sources, flexible loads and reactive power adjusting equipment in an active power distribution network in the aspects of time scale, control function and the like, analyzing the coordination mechanism of active and reactive power output of the power distribution network, combining the requirements of the power distribution network in the aspects of operation economy, safety and the like, and considering the prediction error caused by the output fluctuation of large-scale renewable energy sources, and the invention further reduces the system operation cost, reduces the system network loss and more comprehensively realizes the maximization of the operation benefit of the active power distribution network on the premise of ensuring the system operation safety.

Drawings

FIG. 1 is a schematic flow chart of the method of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in detail below with reference to the accompanying drawings.

As shown in fig. 1, the method for coordinated regulation and control of active power and reactive power of a power distribution network based on model predictive control disclosed by the invention comprises the following steps:

1) firstly, establishing a power distribution network day-ahead optimization regulation and control model, wherein the model takes the minimum running cost of a power distribution network as an optimization target and takes power flow constraint, power distribution network running safety constraint, distributed power supply active and reactive power coordinated output constraint, flexible load constraint and reactive power compensation equipment constraint as constraint conditions;

the day-ahead regulation and control plan carries out active and reactive power coordinated optimization and control according to day-ahead load and renewable energy source prediction information and by considering the operation economy and safety of the active power distribution network, the unit time interval of the day-ahead optimization and control stage is delta T, and the optimization and control variables comprise: the method comprises the steps that active and reactive power output of a distributed power supply, active power exchange between a power distribution network and a main network connecting line, flexible load regulation, reactive power output of a compensation capacitor, SVC output and OLTC transformation ratio are achieved, a basic regulation and control plan of the next day is made in a day-ahead optimization stage by taking the minimum running cost of the power distribution network as an optimization target, and the plan is issued in advance;

the day-ahead optimization regulation phase model can be summarized as follows:

minF^DA(U^DA)

s.t.G^DA(U^DA)≤0

in the formula,F^DAMinimum objective function of distribution network operation cost, G, for a model of the day ahead phase^DAAre constraints in the model that are to be used,the method comprises the steps of power flow restraint, power distribution network operation safety restraint, distributed power supply active and reactive power coordinated output restraint, flexible load restraint and reactive power compensation equipment restraint, U^DAAs a control variable in the day-ahead phase model,power is exchanged for the main network links at time t ahead of day,andfor the renewable energy source active power and the controllable distributed power source active power at the node i at the time t in the day,the controlled active power of the load at node i for time t;andrespectively the charging power and the discharging power of the energy storage device at the node i at the moment t in the day,andrespectively the reactive power of a Static Var Compensator (SVC), an energy storage reactive power output (the output reactive power is positive), a compensation capacitor reactive power, a renewable energy reactive power output (the output is positive) and the reactive power of a controllable distributed power supply at a node i at the moment t in the day,the adjustable transformation ratio of the on-load voltage regulator OLTC at the branch ij at the moment t is the day before;

power distribution networkObjective function F with minimum running cost^DAAs follows:

in the formula, delta T is unit time interval rho of the day-ahead optimization regulation and control stage^G,tUnit cost rho of power exchange between regional distribution network and main network tie line at time t^RE,i、ρ^CDG,i and ρ^ESS,iRespectively are renewable energy sources at the node i, a controllable distributed power supply and unit active power on-line electricity price of stored energy,the controlled state of the load at the node i at the time t before the day, 1 represents controlled, 0 represents uncontrolled,cost of compensation for load at node i, ρ_TCost of adjustment for unit gear of OLTC, Delta U_TTotal daily adjustment for OLTC range, ρ_CCost per gear adjustment, Δ U, for a compensation capacitor_CThe total adjustment quantity of the compensation capacitor gear in the whole day;

constraint condition G of power distribution network day-ahead optimization regulation and control model^DAThe method comprises the steps of power flow restraint, power distribution network operation safety restraint, distributed power supply active and reactive power coordinated output restraint, flexible load restraint and reactive power compensation equipment restraint.

The power flow constraint is as follows:

in the formula,andrespectively the active power and the reactive power of the head end of the branch ij at the moment t in the day,andrespectively the net injection values of active power and reactive power of a node j at a moment t in the day,andrespectively the load active power and the load reactive power of a node j at the moment t in the day,is the voltage amplitude at node j at time t, r_ij and x_ijRespectively the resistance and reactance of the line ij,the current on branch ij at time t before day;

the power distribution network operation safety constraint is as follows:

in the formula,andrespectively an upper limit and a lower limit of the voltage amplitude of the node i,is the current amplitude upper limit of the branch ij;

the distributed power supply active and reactive coordinated output constraints comprise energy storage active and reactive output constraints, renewable energy active and reactive output constraints and controllable distributed power supply active and reactive output constraints; wherein,

the energy storage active and reactive power output constraint is as follows:

in the formula,the maximum apparent power which can be provided by the energy storage inverter at the node i;

the energy storage active charging and discharging and the energy storage quantity need to comply with the following constraints:

in the formula,andrespectively is the upper limit of the charge and discharge power stored on the node i at the time t,andthe charging and discharging states of the stored energy on the node i at the time t are respectively variable from 0 to 1,for the total energy stored at node i during time t, η_ch、η_disRespectively the charge and discharge efficiency of the stored energy,is the energy storage capacity limit;

the renewable energy active and reactive power output constraints comprise photovoltaic active and reactive power output constraints and wind power active and reactive power output constraints; the photovoltaic active and reactive power output constraint is as follows:

in the formula,andrespectively the active output and the reactive output of the photovoltaic at the node i at the moment t in the day,the maximum apparent power of the photovoltaic inverter at the node i is obtained;

the wind power active and reactive power output constraint is as follows:

in the formula,andrespectively the active power output and the reactive power output of the wind power at a node i at the moment t before the day,is the maximum current of the stator winding of the doubly-fed induction wind turbine generator, s is the slip ratio of the doubly-fed induction wind turbine generator, X_s,i and X_m,iRespectively is the stator reactance and the excitation reactance of the doubly-fed induction wind turbine generator at the node i,the maximum current of a rotor side converter of the doubly-fed induction wind turbine generator set is obtained;

the active and reactive power output constraint of the controllable distributed power supply is the active and reactive power output constraint of the micro gas turbine; the active and reactive power output constraints of the micro gas turbine are as follows:

in the formula,andactive power output and reactive power output S of the micro gas turbine at a node i at the moment t before the day are respectively_MT,iThe installed capacity of the micro gas turbine at the node i is represented;

for active power output, the ramp rate constraint of the micro gas turbine is as follows:

in the formula：andrespectively limiting the upward climbing speed and the downward climbing speed of the micro gas turbine at the node i;

the flexible load constraint mainly considers the operation constraint of interruptible load, and the flexible load operation constraint is as follows:

in the formula,is the upper limit value, S, of the interruptible load at node j_CL,iAllowing an interruption period for interruptible load at node i;

the reactive compensation equipment constraints mainly comprise OLTC operation constraints, SVC operation constraints and compensation capacitor operation constraints; wherein the OLTC operating constraints are:

in the formula, and Δk_ijRespectively the standard transformation ratio and the adjustment step size of the OLTC in branch ij,andrespectively representing a t-time gear of the OLTC in the branch ij and an adjustable lower limit and an upper limit of the T-time gear;

the SVC operating constraints are:

in the formula,andthe upper limit value and the lower limit value of the adjustable power of the SVC are respectively;

the compensation capacitor operating constraints are:

in the formula, and ΔQ_C,iRespectively the minimum output and the adjustable capacity step length of the compensation capacitor connected on the node i, andthe t moment gear and the adjustable upper and lower limits of the compensation capacitor connected at the node i are respectively;

2) solving a day-ahead optimization regulation and control model by using mature commercial software, making an active and reactive regulation and control plan of a day-ahead power distribution network, and issuing the plan in advance, wherein slow dynamic equipment such as OLTC (on-line stability test), a compensation capacitor, interruptible load and the like are not changed in a day-ahead stage after an output plan is determined in the day-ahead regulation and control stage due to the limitation of regulation speed;

3) establishing a prediction model and an optimized regulation and control model in an intra-day rolling correction stage by using the regulation and control plan obtained in the step 2) and considering current and future operation constraint conditions, and correcting an intra-day actual plan deviation phenomenon caused by a pre-day prediction error;

in order to eliminate the phenomenon of deviation of an actual plan in the day caused by prediction errors in the day, rolling correction is carried out in the day by taking delta T (delta T < delta T) as a period, in each period, load in the current time and a future period of time of the current time and prediction information of renewable energy are taken as input variables, actual measurement values of output of each controllable means in a power distribution network at the current time are taken as initial values, increment of output of each controllable means in a future finite time domain M delta T is taken as control variables, the state of the future M delta T of the power distribution network is predicted on the basis of a certain prediction model, meanwhile, a control instruction sequence of the future M delta T period is optimized and solved by taking current and future operation constraint conditions into consideration and establishing an active and reactive power coordination optimization model of the finite time period in the rolling correction stage in the day,

the prediction model of the limited period of the rolling correction phase in the day is as follows:

u＝[P_G,P_RE,i,Q_RE,i,P_CDG,i,Q_CDG,i,P_ch,i,P_dis,i,Q_ESS,i,Q_SVC,i]

in the formula,t₀The moment is the current moment, delta t is the unit time interval of the rolling correction stage in the day, M is the total time interval of model solution of the rolling correction stage in the day, u represents a controllable means in the active power distribution network of the feedback correction stage, and the controllable means comprises main network interconnection line exchange power P_GRenewable energy source active power output P_RE,iRenewable energy reactive power output Q_RE,iActive power output P of controllable distributed power supply_CDG,iControllable distributed power supply reactive power output Q_CDG,iEnergy storage active power output P_ch,i and P_dsi,iEnergy-storage reactive output Q_ESS,iAnd SVC reactiveOutput Q_SVC,I，Is t₀T predicted by time₀The force output value of each controllable means at the moment of + k deltat,is t₀The actual measured feedback values of the controllable means at the moment,is t₀The increment of each controllable means is obtained by predicting the moment, and the variables are solved for the optimization model;

the active and reactive power coordinated optimization regulation and control model in the limited period of the rolling correction stage in the day is as follows:

minF^DR(U^DR)

in the formula：U^DRIs t₀～t₀Set of force values from each controllable means within + M Δ t time period, F^DRCorrecting the objective function of the phase model for rolling in the day, G^DRThe constraints of the phase model are corrected for intra-day roll.

The objective function of the optimization model for a limited period of the intra-day roll correction phase is as follows:

minF^DR＝||(U^DR-U^DA)·U^max||¹

in the formula：U^DAIs t day ahead₀～t₀Set of force values, U, from each controllable means within the + M Δ t time period^maxConstraint conditions G of finite period active and reactive power coordination optimization model in rolling correction stage in day for reciprocal of maximum value of output of each controllable means^DRConstraint condition G of day-ahead optimization regulation and control stage^DASimilarly, the difference lies in: since the control plans for the full dynamic devices, such as interruptible loads, OLTC, and compensation capacitors, have been determined at the day-ahead and not changed during the in-day phase, the constraints of the in-day roll correction phase do not include compliant load operating constraints, OLTC operating constraints, and compensation capacitor operating constraints.

4) And solving to obtain an in-day active and reactive control instruction sequence of a limited period in the future according to the in-day rolling correction stage active and reactive optimization control model, executing the in-day active and reactive control instruction of the first moment, moving a time window backwards by a time interval, and repeating the in-day rolling correction optimization process.

Claims (9)

1. A power distribution network active and reactive power coordinated regulation and control method based on model predictive control is characterized by comprising the following steps:

step 10), in a day-ahead stage, establishing a power distribution network day-ahead optimization regulation and control model, wherein the model takes the minimum running cost of the power distribution network as an optimization target and takes power flow constraint, power distribution network running safety constraint, distributed power supply active and reactive power coordinated output constraint, flexible load constraint and reactive power compensation equipment constraint as constraint conditions;

step 20), solving the power distribution network day-ahead optimized scheduling model to obtain a day-ahead power distribution network active and reactive power regulation plan, and issuing the plan in advance;

step 30), in the intra-day phase, at regular time intervals, utilizing the active and reactive power regulation and control plan of the power distribution network before the day obtained by the step 20) to establish a prediction model and an active and reactive power coordinated optimization regulation and control model of the limited time period in the intra-day rolling correction phase based on the measurement value of the running state of the power distribution network at the current moment and by considering the current and future running constraint conditions, correcting the deviation phenomenon of the actual plan in the day caused by the prediction error before the day, and solving the control instruction sequence of the active and reactive power in the day of the limited time period in the future;

and step 40), executing the active and reactive control instruction in the day at the first moment, moving the time window backwards by a time interval, and repeating the rolling correction optimization process in the day.

2. The method for coordinated active and reactive power regulation and control of the power distribution network based on model predictive control according to claim 1, wherein in the step 10), the model of the day-ahead optimized regulation and control stage of the power distribution network is as follows:

min F^DA(U^DA)

s.t.G^DA(U^DA)≤0

$U^{DA}=\&lsqb;P_{G,t}^{DA},P_{RE,i,t}^{DA},Q_{RE,i,t}^{DA},P_{CDG,i,t}^{DA},Q_{CDG,i,t}^{DA},$

$P_{ch,i,t}^{DA},P_{dis,i,t}^{DA},Q_{ESS,i,t}^{DA},P_{CL,i,t}^{DA},k_{ij,t}^{DA},Q_{SVC,i,t}^{DA},Q_{C,i,t}^{DA}\&rsqb;$

in the formula,F^DAMinimum objective function of distribution network operation cost, G, for a model of the day ahead phase^DAThe constraint conditions in the model comprise a power flow constraint, a power distribution network operation safety constraint, a distributed power supply active and reactive power coordinated output constraint, a flexible load constraint and a reactive power compensation equipment constraint, U^DAAs a control variable in the day-ahead phase model,power is exchanged for the main network links at time t ahead of day,andfor the renewable energy source active power and the controllable distributed power source active power at the node i at the time t in the day,the controlled active power of the load at node i for time t;andrespectively the charging power and the discharging power of the energy storage device at the node i at the moment t in the day,andrespectively the reactive power of a Static Var Compensator (SVC), the energy storage reactive power output, the compensation capacitor reactive power, the renewable energy reactive power output and the controllable distributed power supply reactive power at a node i at the moment t in the day,the current is the adjustable transformation ratio of the on-load voltage regulator OLTC at the branch ij at the time t.

3. The method for coordinating and controlling the active power and the reactive power of the power distribution network based on the model predictive control according to claim 1, wherein in the step 10), the objective function with the minimum operation cost of the power distribution network is as follows:

$\begin{array}{c}\min F^{DA}=\underset{t\&Element;T}{\&Sigma;}{\mathrm{\&rho;}}_{G,t}{P}_{G,t}^{DA}\&CenterDot;\mathrm{\&Delta;}T+\underset{t\&Element;T}{\&Sigma;}\underset{i\&Element;{\mathrm{\&psi;}}_{RE}}{\&Sigma;}{\mathrm{\&rho;}}_{RE,i}{P}_{RE,i,t}^{DA}\&CenterDot;\mathrm{\&Delta;}T+\underset{t\&Element;T}{\&Sigma;}\underset{i\&Element;{\mathrm{\&psi;}}_{DG}}{\&Sigma;}{P}_{CDG,i}{P}_{CDG,i,t}^{DA}\&CenterDot;\mathrm{\&Delta;}T\\ +\underset{t\&Element;T}{\&Sigma;}\underset{i\&Element;{\mathrm{\&psi;}}_{IL}}{\&Sigma;}{u}_{CL,i,t}^{DA}\&CenterDot;{\mathrm{\&rho;}}_{CL,i}\&CenterDot;P_{CL,i,t}^{DA}\&CenterDot;\mathrm{\&Delta;}T+\underset{t\&Element;T}{\&Sigma;}\underset{i\&Element;{\mathrm{\&psi;}}_{ESS}}{\&Sigma;}{\mathrm{\&rho;}}_{ESS,i}(P_{ch,i,t}^{DA}+P_{dis,i,t}^{DA})\&CenterDot;\mathrm{\&Delta;}T+{\mathrm{\&rho;}}_T\&CenterDot;{\mathrm{\&Delta;U}}_T+{\mathrm{\&rho;}}_C\&CenterDot;{\mathrm{\&Delta;U}}_C\end{array}$

in the formula, delta T is unit time interval rho of the day-ahead optimization regulation and control stage_G,tUnit cost rho of power exchange between regional distribution network and main network tie line at time t_RE,i、ρ_CDG,i and ρ_ESS,iRespectively are renewable energy sources at the node i, a controllable distributed power supply and unit active power on-line electricity price of stored energy,the controlled state of the load at the node i at the time t before the day, 1 represents controlled, 0 represents uncontrolled,cost of compensation for load at node i, ρ_TCost of adjustment for unit gear of OLTC, Delta U_TTo OLTTotal daily adjustment of C gear, ρ_CCost per gear adjustment, Δ U, for a compensation capacitor_CIs the total adjustment amount of the compensation capacitor gear all day.

4. The method for coordinated active and reactive power regulation and control of the power distribution network based on the model predictive control is characterized in that in the step 10), the power flow constraint is as follows:

$\left\{\begin{array}{c}{P}_{ij,t}^{DA}=\underset{k:(j,k)}{\mathrm{\&Sigma;}}{P}_{jk,t}^{DA}+r_{ij}|I_{ij,t}^{DA}{|}^2+P_{j,t}^{DA}\\ Q_{ij,t}^{DA}=\underset{k:(j,k)}{\mathrm{\&Sigma;}}{Q}_{jk,t}^{DA}+x_{ij}|I_{ij,t}^{DA}{|}^2+Q_{j,t}^{DA}\\ P_{j,t}^{DA}=P_{d,j,t}^{DA}+P_{ch,j,t}^{DA}-P_{dis,j,t}^{DA}-P_{RE,j,t}^{DA}-P_{CDG,j,t}^{DA}-P_{CL,j,t}^{DA}\\ Q_{j,t}^{DA}=Q_{d,j,t}^{DA}-Q_{ESS,j,t}^{DA}-Q_{SVC,j,t}^{DA}-Q_{RE,j,t}^{DA}-Q_{CDG,j,t}^{DA}-Q_{C,i,t}^{DA}\\ |V_{j,t}^{DA}{|}^2*{\left(k_{ji,t}^{DA}\right)}^2=|V_{i,t}^{DA}{|}^2-2(r_{ij}{P}_{ij,t}^{DA}+x_{ij}{Q}_{ij,t}^{DA})+(r_{ij}^2+x_{ij}^2)|I_{ij,t}^{DA}{|}^2\\ |I_{ij,t}^{DA}{|}^2=\frac{{\left(P_{ij,t}^{DA}\right)}^2+{\left(Q_{ij,t}^{DA}\right)}^2}{|V_{i,t}^{DA}{|}^2}\end{array}\right.$

in the formula,andrespectively the active power and the reactive power of the head end of the branch ij at the moment t in the day,andrespectively the net injection values of active power and reactive power of a node j at a moment t in the day,andrespectively the load active power and the load reactive power of a node j at the moment t in the day,is the voltage amplitude at node j at time t, r_ij and x_ijRespectively the resistance and reactance of the line ij,the current on branch ij at time t before day;

the power distribution network operation safety constraint is as follows:

$\left\{\begin{array}{c}{V}_i^{\min }\&le;V_{i,t}^{DA}\&le;V_i^{max}\\ I_{ij,t}^{DA}\&le;I_{ij}^{\max }\end{array}\right.$

in the formula,andrespectively an upper limit and a lower limit of the voltage amplitude of the node i,the upper current amplitude limit for branch ij.

5. The method for coordinating and controlling the active power and the reactive power of the power distribution network based on the model predictive control of claim 1, wherein in the step 10), the active-reactive coordination output constraints of the distributed power supply comprise an energy storage active-reactive output constraint, a renewable energy active-reactive output constraint and a controllable distributed power active-reactive output constraint; wherein,

the energy storage active and reactive power output constraint is as follows:

${\left(P_{ch.i,t}^{DA}\right)}^2+{\left(Q_{ESS,i,t}^{DA}\right)}^2\&le;{\left(S_{ESS,i}^{\max }\right)}^2$

${\left(P_{dis,i,t}^{DA}\right)}^2+{\left(Q_{ESS,i,t}^{DA}\right)}^2\&le;{\left(S_{ESS,i}^{\max }\right)}^2$

in the formula,the maximum apparent power which can be provided by the energy storage inverter at the node i;

the energy storage active charging and discharging and the energy storage quantity need to comply with the following constraints:

$0\&le;P_{ch.i,t}^{DA}\&le;P_{ch,i}^{\max }{D}_{ch,i,t}^{DA}$

$0\&le;P_{dis,i,t}^{DA}\&le;P_{dis,i}^{\max }{D}_{dis,i,t}^{DA}$

$D_{ch,i,t}^{DA}+D_{dis,i,t}^{DA}\&le;1$

$P_{ESS,i,t}^{DA}=P_{ch,i,t}^{DA}+P_{dis,i,t}^{DA}$

$E_{SOC,i,t}^{DA}+P_{ch,i,t}^{DA}{\mathrm{\&eta;}}_{ch}\mathrm{\&Delta;}T-\frac{P_{dis,i,t}^{DA}}{{\mathrm{\&eta;}}_{dis}}\mathrm{\&Delta;}T=E_{SOC,i,t+\mathrm{\&Delta;}T}^{DA}$

$E_{SOC,i}^{\max }\&times;20\%\&le;E_{SOC,i,t}^{DA}\&le;E_{SOC,i}^{\max }\&times;90\%$

in the formula,andrespectively is the upper limit of the charge and discharge power stored on the node i at the time t,andthe charging and discharging states of the stored energy on the node i at the time t are respectively variable from 0 to 1,for the total energy stored at node i during time t, η_ch、η_disRespectively the charge and discharge efficiency of the stored energy,is the energy storage capacity limit;

the renewable energy active and reactive power output constraints comprise photovoltaic active and reactive power output constraints and wind power active and reactive power output constraints; the photovoltaic active and reactive power output constraint is as follows:

${\left(P_{PV,i,t}^{DA}\right)}^2+{\left(Q_{PV,i,t}^{DA}\right)}^2\&le;{\left(S_{PV,i}^{\max }\right)}^2$

$0.9\&le;\frac{|P_{d,i,t}^{DA}-P_{PV,i,t}^{DA}|}{\sqrt{{(P_{d,i,t}^{DA}-P_{PV,i,t}^{DA})}^2+{(Q_{d,i,t}^{DA}-Q_{PV,i,t}^{DA})}^2}}\&le;1$

in the formula,andrespectively the active output and the reactive output of the photovoltaic at the node i at the moment t in the day,the maximum apparent power of the photovoltaic inverter at the node i is obtained;

the wind power active and reactive power output constraint is as follows:

$\frac{{\left(P_{WT,i,t}^{DA}\right)}^2}{{(1-s)}^2}+{\left(Q_{WT,i,t}^{DA}\right)}^2\&le;{(V_{i,t}^{DA}\&CenterDot;I_{WT,s,i}^{\max })}^2$

$\frac{{\left(P_{WT,i,t}^{DA}\right)}^2}{{(1-s)}^2}+{(Q_{WT,i,t}^{DA}+\frac{{\left(V_{i,t}^{DA}\right)}^2}{X_{s,i}+X_{m,i}})}^2\&le;{\left(\frac{X_{m,i}}{X_{s,i}+X_{m,i}}{V}_{i,t}^{DA}{I}_{WT,r,i}^{\max }\right)}^2$

in the formula,andrespectively the active power output and the reactive power output of the wind power at a node i at the moment t before the day,is the maximum current of the stator winding of the doubly-fed induction wind turbine generator, s is the slip ratio of the doubly-fed induction wind turbine generator, X_s,i and X_m,iRespectively is the stator reactance and the excitation reactance of the doubly-fed induction wind turbine generator at the node i,the maximum current of a rotor side converter of the doubly-fed induction wind turbine generator set is obtained;

the active and reactive power output constraint of the controllable distributed power supply is the active and reactive power output constraint of the micro gas turbine; the active and reactive power output constraints of the micro gas turbine are as follows:

${\left(P_{MT,i,t}^{DA}\right)}^2+{\left(Q_{MT,i,t}^{DA}\right)}^2\&le;{\left(S_{MT,i}\right)}^2$

in the formula,andactive power output and reactive power output S of the micro gas turbine at a node i at the moment t before the day are respectively_MT,iThe installed capacity of the micro gas turbine at the node i is represented;

for active power output, the ramp rate constraint of the micro gas turbine is as follows:

$\left\{\begin{array}{c}{P}_{MT,i,t+\mathrm{\&Delta;}T}^{DA}-P_{WT,i,t}^{DA}\&le;R_{WT,i}^{up}\\ P_{MT,i,t}^{DA}-P_{WT,i,t+\mathrm{\&Delta;}T}^{DA}\&le;R_{WT,i}^{down}\end{array}\right.$

in the formula：andthe upward ramp rate limit and the downward ramp rate limit of the micro gas turbine at node i, respectively.

6. The method for coordinating and controlling the active power and the reactive power of the power distribution network based on the model predictive control according to claim 1, wherein in the step 10), the flexible load constraint is an operation constraint of interruptible loads, and then the flexible load operation constraint is as follows:

$P_{CL,j,t}^{DA}\&le;P_{CL,j}^{\max }$

$P_{CL,i,t}^{DA}\&GreaterEqual;0,t\&Element;S_{CL,i}$

$P_{CL,i,t}^{DA}=0,t\&NotElement;S_{CL,i}$

in the formula,is the upper limit value, S, of the interruptible load at node j_CL,iAn interrupt period is allowed for the interruptible load at node i.

7. The method for the active and reactive power coordinated regulation and control of the power distribution network based on the model predictive control is characterized in that in the step 10), reactive power compensation equipment constraints comprise an OLTC operation constraint, an SVC operation constraint and a compensation capacitor operation constraint; wherein the OLTC operating constraints are:

$k_{ij,t}^{DA}=k_{ij}^0+K_{ij,t}^{DA}{\mathrm{\&Delta;k}}_{ij}$

$K_{ij}^{\min }\&le;K_{ij,t}^{DA}\&le;K_{ij}^{\max }$

in the formula, and Δk_ijRespectively the standard transformation ratio and the adjustment step size of the OLTC in branch ij,andrespectively representing a t-time gear of the OLTC in the branch ij and an adjustable lower limit and an upper limit of the T-time gear;

the SVC operating constraints are:

$Q_{SVC,i}^{\min }\&le;Q_{SVC,i,t}^{DA}\&le;Q_{SVC,i}^{\max }$

in the formula,andthe upper limit value and the lower limit value of the adjustable power of the SVC are respectively;

the compensation capacitor operating constraints are:

$Q_{C,i,t}^{DA}=Q_{C,i}^{\min }+H_{C,i,t}^{DA}{\mathrm{\&Delta;Q}}_{C,i}$

$H_{C,i}^{\min }\&le;H_{C,i,t}^{DA}\&le;H_{C,i}^{\max }$

in the formula, and ΔQ_C,iRespectively the minimum output and the adjustable capacity step length of the compensation capacitor connected on the node i, andrespectively, the t-time gear of the compensation capacitor connected at the node i and the adjustable upper and lower limits thereof.

8. The method for the active and reactive power coordinated regulation and control of the power distribution network based on the model predictive control is characterized in that in the step 30), the prediction model of the limited period of the rolling correction stage in the day is as follows:

$u_{t_0+k\mathrm{\&Delta;}t}^{DR}=u_{t_0}^{real}+{\mathrm{\&Delta;u}}_{t_0+k\mathrm{\&Delta;}t}^{DR},(k=1,2,...M)$

u＝[P_G,P_RE,i,Q_RE,i,P_CDG,i,Q_CDG,i,

P_ch,i,P_dis,i,Q_ESS,i,Q_SVC,i]

in the formula,t₀The moment is the current moment, delta t is the unit time interval of the rolling correction stage in the day, M is the total time interval of model solution of the rolling correction stage in the day, u represents a controllable means in the active power distribution network of the feedback correction stage, and the controllable means comprises main network interconnection line exchange power P_GRenewable energy source active power output P_RE,iRenewable energy reactive power output Q_RE,iActive power output P of controllable distributed power supply_CDG,iControllable distributed power supply reactive power output Q_CDG,iEnergy storage active power output P_ch,i and P_dsi,iEnergy-storage reactive output Q_ESS,iAnd SVC reactive power Q_SVC,I，Is t₀T predicted by time₀The force output value of each controllable means at the moment of + k deltat,is t₀The actual measured feedback values of the controllable means at the moment,is t₀And (5) predicting the moment to obtain the increment of each controllable means, and solving variables for the optimization model.

9. The method for the active and reactive power coordinated regulation and control of the power distribution network based on the model predictive control is characterized in that in the step 30), the active and reactive power coordinated optimization regulation and control model in the limited period of the rolling correction stage in the day is as follows:

min F^DR(U^DR)

$s.t.G^{DR}(u_{t_0+k\mathrm{\&Delta;}t}^{DR},P_{CL,j,t_0+k\mathrm{\&Delta;}t}^{DA},k_{ij,t_0+k\mathrm{\&Delta;}t}^{DA},Q_{C,i,t_0+k\mathrm{\&Delta;}t}^{DA})\&le;0,(k=1,2,...M)$

$U^{DR}=\&lsqb;u_{t_0+\mathrm{\&Delta;}t}^{DR};u_{t_0+2\mathrm{\&Delta;}t}^{DR};...;u_{t_0+M\mathrm{\&Delta;}t}^{DR}\&rsqb;$

in the formula,U^DRIs t₀～t₀Set of force values from each controllable means within + M Δ t time period, F^DRCorrecting the objective function of the phase model for rolling in the day, G^DRCorrecting the constraint conditions of the phase model for rolling in the day;

the objective function of the finite-period active and reactive power coordination optimization model in the rolling correction stage in the day is as follows:

min F^DR＝||(U^DR-U^DA)·U^max||¹

$U^{DA}=\&lsqb;u_{t_0+\mathrm{\&Delta;}t}^{DA};u_{t_0+2\mathrm{\&Delta;}t}^{DA};...;u_{t_0+M\mathrm{\&Delta;}t}^{DA}\&rsqb;$

$\begin{array}{c}{U}^{\max }=\&lsqb;1/P_G^{\max };1/P_{RE,i}^{\max };1/Q_{RE,i}^{\max };1/P_{CDG,i}^{\max };1/Q_{CDG,i}^{\max };\\ 1/P_{ch,i}^{\max };1/P_{dis,i}^{\max };1/Q_{ESS,i}^{\max };1/Q_{SVC,i}^{\max }\&rsqb;\end{array}$

in the formula,U^DAIs t day ahead₀～t₀Set of force values, U, from each controllable means within the + M Δ t time period^maxAnd for the reciprocal of the maximum value of the output of each controllable means, the constraint conditions of the limited-period active and reactive power coordinated optimization model in the rolling correction stage in the day are the same as those in the previous stage except that the constraint conditions do not comprise flexible load operation constraint, OLTC operation constraint and compensation capacitor operation constraint.