CN103986193A - Method for acquiring maximum capacity of wind power integration - Google Patents

Abstract

The invention discloses a method for acquiring the maximum capacity of wind power integration. The method includes the steps that first, a maximum wind power integration capacity optimization model capable of considering peak shaving abundance is established; in the model, a target function takes the maximization of capacity of grid-connected wind power of a system as a target, and constraint conditions include active power balance constraint of nodes of the system, active power constraint of direct-current power flow of branches of the system, branch capacity constraint, output constraint of a conventional generator set, constraint of total wind curtailment every day, load shedding probability and annual load shedding expectation constraint, and wind curtailment probability and annual load shedding expectation constraint; second, a nonlinear equation in a wind power receiving optimization model is converted into a linear equation, and the linear maximum wind power integration capacity optimization model is acquired; third, according to acquired parameter data of a system network, historical wind power output data and the linear maximum wind power integration capacity optimization model, the maximum capacity of wind power integration is acquired. According to the method, abundance index constraint is introduced in to optimize the wind power integration capacity, and the obtained maximum wind power integration capacity meets the requirement for peak shaving abundance and power generation abundance; the nonlinear equation in the established model is converted into the linear equation, and the calculating time is remarkably shortened.

Description

A kind of method that maximum wind grid connection capacity obtains

Technical field

The invention belongs to the new energy field of electric power system, more specifically, relate to a kind of method that maximum wind grid connection capacity obtains.

Background technology

Calculating at present maximum wind grid connection capacity is studied widely.Mainly be divided into four classes: engineering method, dynamic simulation method, restraining factors analytic approach, optimization.Engineering method adopts in mainly calculating in early days, because its calculating can only the maximum grid connection capacity of approximate estimation wind-powered electricity generation, so the method is more unilateral and rough.The various working that dynamic simulation method generally may occur for electric power system is carried out Digital Simulation, and wind-electricity integration capacity limitation is determined in the impact such as the voltage fluctuation that these class methods cause electric power system according to wind-electricity integration, the quality of power supply, and amount of calculation is larger.Restraining factors analytic approach is considered certain class factor of the maximum grid connection capacity of restriction wind-powered electricity generation, such as take voltage stabilization as restraining factors, by P-V (meritorious-voltage) curve, calculate the maximum grid connection capacity of wind-powered electricity generation, because this method is difficult to consider multiple restraining factors, so have very large limitation in application.Optimization is a kind of method extensively adopting in recent years, and the method can consider a plurality of restraining factors that affect wind-electricity integration.This method be the receptive wind-powered electricity generation calculation of capacity of electrical network as being to solve the maximized problem of installed capacity of wind-driven power of Problem with Some Constrained Conditions restriction, thereby problem is summed up as to solving of optimal problem.But, current optimization method, the not abundant property of the peak regulation of taking into account system and the abundant property of generating in optimizing process, make to calculate can be grid-connected wind-powered electricity generation capacity not accurate enough.

At present when planning wind-electricity integration capacity, if the abundant property index of taking into account system planning is to ask for the maximum access capacity of grid connected wind power in conjunction with Monte Carlo simulation approach mostly.The flow process of the method is as follows: system mode is sampled, then simulate by hour management and running, calculate respectively wind-powered electricity generation under the different installed capacity of wind-driven powers time series data of exerting oneself, and then add up every abundant property index, as year generation deficiency probability, peak regulation shortfall probability etc.The wherein one or more index of usining afterwards, as the foundation of passing judgment on grid connected wind power access capacity, is selected the maximum grid connection capacity of wind-powered electricity generation under given index.This method is first to suppose wind-electricity integration capacity, then calculates abundant property index, if can not meet the abundant property requirement of power source planning, calculates after will adjusting wind-electricity integration capacity again.If it is very little to adjust the step-length of wind-electricity integration capacity at every turn, although can access the comparatively accurate wind-powered electricity generation timing curve of exerting oneself, amount of calculation is large especially.The excessive wind-powered electricity generation obtaining of the step-length timing curve of exerting oneself is a kind of "ball-park" estimate, and the maximum grid connection capacity accuracy of final like this wind-powered electricity generation of trying to achieve is not high, and amount of calculation is very large.

Summary of the invention

Defect for prior art, the object of the present invention is to provide a kind of method of obtaining maximum wind grid connection capacity, be intended to solve when planning wind-electricity integration capacity, cannot in optimizing process, take into account the abundant property of peak shaving and the abundant property Index Constraints of generating, thereby make result of calculation inaccurate, the problem that computational efficiency is low.

The invention provides a kind of method that maximum wind grid connection capacity obtains, comprise the steps:

S1: foundation can be considered the maximum wind grid connection capacity Optimized model of abundant property constraint;

Target function in described maximum wind grid connection capacity Optimized model is that to take system grid connected wind power maximum capacity be target, and described bound for objective function comprises system operation constraint and abundant property Index Constraints;

Described system operation constraint comprises abandons the constraint of node active power balance, the branch road DC power flow active power constraint of system, branch road capacity-constrained, the actual units limits of conventional power generation usage unit, the every day of system the constraint of wind total amount;

The inequality constraints of described abundant property index comprises cutting load probability and year cutting load Expectation constraint, abandons wind probability and year and abandons wind Expectation constraint;

S2: the nonlinear equation in described maximum wind grid connection capacity Optimized model is converted to linear equation, and obtains linear maximum wind grid connection capacity Optimized model;

S3: the maximum wind grid connection capacity Optimized model that goes out force data and described linearity according to the grid supplemental characteristic obtaining, historical wind-powered electricity generation obtains maximum wind grid connection capacity.

Wherein, the node active power balance equation of system described in step S1 is constrained to s is the meritorious trend incidence matrices of node injecting power and branch road, be the active power vector that d days t flow through branch road constantly, element is designated as p _{d, t, ij}, p _{d, t, ij}be the active power that d days t flow through branch road i-j constantly; g _{d, t}for normal power supplies is at d days t reality constantly force vector of gaining merit, element is designated as g _{d, t, i}, g _{d, t, i}for the normal power supplies of access node i is gained merit and is exerted oneself in the reality in the d days t moment; u _{d, t}for grid connected wind power is at d days t reality constantly force vector of gaining merit, element is designated as u _{d, t, i}, u _{d, t, i}for the wind-powered electricity generation of access node i is gained merit and is exerted oneself in the reality in the d days t moment; the binary system that is d days t cutting load operation constantly starts variable, arbitrary node when d days there is cutting load phenomenon in t constantly, value is 1, otherwise, value is 0; r _{d, t}be the cutting load vector in the d days t moment, element is r _{d, t, i}, r _{d, t, i}for the cutting load amount of node i the d days t moment; be that d days t constantly abandon the binary system that character and conduct does and start variable, grid connected wind power exists while abandoning wind phenomenon constantly at d days t, value is 1, otherwise, value is 0; l _{d, t}be the load vector in the d days t moment, element is designated as l _{d, t, i}, l _{d, t, i}it is the load of d days t moment node i; w _{d, t}for the abandon wind direction amount of grid connected wind power the d days t moment, element is designated as w _{d, t, i}, w _{d, t, i}for the wind-powered electricity generation of access node i d days t constantly abandon air quantity.

Wherein, described cutting load probability is cutting load expectation in described year is the described wind probability of abandoning is described year is abandoned wind expectation d is the number of days in timing statistics, and the statistics that T is every day is number constantly, and d is d days, and t is the t moment, the binary system that is d days t cutting load operation constantly starts variable, arbitrary node when d days there is cutting load phenomenon in t constantly, value is 1, otherwise, value is 0; be that d days t constantly abandon the binary system that character and conduct does and start variable, grid connected wind power exists while abandoning wind phenomenon constantly at d days t, value is 1, otherwise, value is 0, with the system that is respectively is in timing statistics D inscribe Load Probability and the threshold value of abandoning wind probability, with the system that is respectively year cutting load in the timing statistics D of institute expects and year is abandoned the threshold values of wind expectation.

Wherein said maximum wind grid connection capacity Optimized model comprises target function MaxP _wn; P _wnfor grid-connected wind-powered electricity generation capacity.

Wherein at maximum wind grid connection capacity Optimized model linear described in step S2, comprise: nonlinear node active power balance is converted to linear node active power balance constraint $S\×P_{d,t}^L+g_{d,t}+u_{d,t}+r_{d,t}^y=l_{d,t}+w_{d,t}^y;$ Wherein $r_{d,t,i}^y=y_{d,t}^r{r}_{d,t},w_{d,t}^y=y_{d,t}^w{w}_{d,t}.r_{d,t,i}^y=y_{d,t}^r{r}_{d,t}$ Be equivalent to following three linear inequalities: $0\≤r_{d,t,i}^y\≤\stackrel{\‾}{r_{d,t,i}};r_{d,t,i}^y\≤\stackrel{\‾}{r_{d,t,i}}{y}_{d,t}^r;r_{d,t,i}-\stackrel{\‾}{r_{d,t,i}}(1-y_{d,t}^r)\≤r_{d,t,i}^y\≤r_{d,t,i};w_{d,t}^y=y_{d,t}^w{w}_{d,t}$ Be equivalent to following three linear inequalities: be the cutting load vector in the d days t moment, element is designated as for continuous variable, represent the cutting load amount of node i the d days t moment; it is the cutting load amount upper limit of d days t moment node i; the binary system that is d days t cutting load operation constantly starts variable, arbitrary node when d days there is cutting load phenomenon in t constantly, value is 1, otherwise, value is 0; be the wind direction amount of abandoning in the d days t moment, element is designated as for continuous variable, represent the abandon air quantity of grid connected wind power the d days t moment; be d days t constantly access node i wind-powered electricity generation abandon the air quantity upper limit; be that d days t constantly abandon the binary system that character and conduct does and start variable, grid connected wind power exists while abandoning wind phenomenon constantly at d days t, value is 1, otherwise, value is 0; S is the meritorious trend incidence matrices of node injecting power and branch road, be the active power vector that d days t flow through branch road constantly, element is designated as p _{d, t, ij}, p _{d, t, ij}be the active power that d days t flow through branch road i-j constantly; g _{d, t}for normal power supplies is at d days t reality constantly force vector of gaining merit, element is designated as g _{d, t, ij}, g _{d, t, ij}for the normal power supplies of access node i is gained merit and is exerted oneself in the reality in the d days t moment; u _{d, t}for grid connected wind power is at d days t reality constantly force vector of gaining merit, element is designated as u _{d, t, i}, u _{d, t, i}for the wind-powered electricity generation of access node i is gained merit and is exerted oneself in the reality in the d days t moment; l _{d, t}for the load vector the d days t moment, element is designated as l _{d, t, i}, l _{d, t, i}it is the load of d days t moment node i;

The branch road DC power flow power constraint of system: p _{d, t, ij}-β _ij(θ _{d, t, i}-θ _{d, t, j})=0; p _{d, t, ij}be the active power that d days t flow through branch road i-j constantly; β _ijsusceptance for branch road i-j; θ _{d, t, i}and θ _{d, t, j}be respectively node i and the node j voltage phase angle the d days t moment;

The branch road capacity-constrained of system: p _{d, t, ij}be the active power that d days t flow through branch road i-j constantly; the active power upper limit that can bear for every branch road i-j;

Conventional power generation usage unit units limits: for conventional power generation usage unit maximum output, g _i for conventional power generation usage unit minimum load;

Node i is abandoned air quantity constraint 0≤w _{d, t, i}≤ u _{d, t, i}; w _{d, t, i}for the wind-powered electricity generation of access node i d days t constantly abandon air quantity; w _{d, t, i}for the wind-powered electricity generation of access node i is gained merit and is exerted oneself in the reality in the d days t moment;

System retrains the wind total amount of abandoning of d days w _{d, t, i}be d days access node i wind-powered electricity generation t constantly abandon air quantity, the sum that I is system node, a and b are respectively the upper and lower bound of abandoning wind total amount every day;

Cutting load and the probability constraints of abandoning wind $\frac{1}{\mathrm{DT}}\underset{d=1}{\overset{D}{\mathrm{\Σ}}}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}{y}_{d,t}^r\≤K_p^r$ With $\frac{1}{\mathrm{DT}}\underset{d=1}{\overset{D}{\mathrm{\Σ}}}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}{y}_{d,t}^w\≤K_p^w;$ the binary system that is d days t cutting load operation constantly starts variable, arbitrary node when d days there is cutting load phenomenon in t constantly, value is 1, otherwise, value is 0; be that d days t constantly abandon the binary system that character and conduct does and start variable, grid connected wind power exists while abandoning wind phenomenon constantly at d days t, value is 1, otherwise, value is 0; with the system that is respectively is in timing statistics D inscribe Load Probability and the threshold value of abandoning wind probability;

Cutting load and the desired value constraint of abandoning wind: $\frac{1}{D}\underset{d=1}{\overset{D}{\mathrm{\Σ}}}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{i=1}{\overset{I}{\mathrm{\Σ}}}{r}_{d,t,i}\≤K_m^r$ With $\frac{1}{D}\underset{d=1}{\overset{D}{\mathrm{\Σ}}}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{i=1}{\overset{I}{\mathrm{\Σ}}}{w}_{d,t,i}\≤K_m^w;$ R _{d, t, i}for the cutting load amount of node i the d days t moment; w _{d, t, i}for the wind-powered electricity generation of access node i d days t constantly abandon air quantity; with the system that is respectively year cutting load in the timing statistics D of institute expects and year is abandoned the threshold values of wind expectation.

Advantage of the present invention is to adopt the method for optimizing to overcome traditional analog method and calculates the defect inaccurate, efficiency is not high, in computational process, considered cutting load probability, cutting load expectation simultaneously, abandoned wind probability and abandon the abundant property indexs such as wind expectation, the maximum wind grid connection capacity that makes to calculate can meet the requirement of the abundant property of peak regulation and the abundant property of generating of power source planning, thus the admissible maximum wind grid connection capacity of evaluating system more accurately.In addition, the present invention is linear equation by the non-linear equation in maximum wind grid connection capacity Optimized model, greatly reduces model complexity and solves difficulty, can reduce computing time significantly like this.In sum, the present invention can instruct the power system planning of wind-electricity integration, realizes the rational and efficient use of wind energy resources, has practical significance.

Accompanying drawing explanation

Fig. 1 is that the power supply that contains wind power system some day is exerted oneself and workload demand curve chart;

Fig. 2 is the active power balance schematic diagram of node;

Fig. 3 is improved Garver6 node system topology schematic diagram;

Fig. 4 is a kind of realization flow figure that obtains the maximum grid connection capacity method of wind-powered electricity generation that the invention process case provides.

Embodiment

In order to make object of the present invention, technical scheme and advantage clearer, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein, only in order to explain the present invention, is not intended to limit the present invention.

The acquisition methods that the invention provides a kind of maximum wind grid connection capacity, comprises the following steps:

(1) obtain data;

Fig. 1 has described the power supply that includes wind-electricity integration system for a day for 24 hours and has exerted oneself and workload demand curve, in figure, there are three curves: solid line is load curve, dotted line is the maximum possible power curve of system, this curve is the fired power generating unit maximum output of system and the grid connected wind power sum of exerting oneself, imaginary point line is the minimum load curve of system, and this curve is the fired power generating unit minimum load of system and the grid connected wind power sum of exerting oneself.In figure, variable capacity refers to the poor of the maximum output of fired power generating unit of system and minimum load.

Shown in figure, peak regulation deficiency is divided into two time periods: the maximum output of system is less than the time period of system load demand, and system minimum load is greater than the time period of system load demand.The former belongs to the generation deficiency time period, in this time period, can by cutting load, operate the power-balance of the system that guarantees, therefore, adopts cutting load index to assess system generation deficiency; The latter belongs to the superfluous time period of generating, in this time period, can make by abandoning character and conduct the power-balance of the system that guarantees, employing is abandoned wind index system generating surplus is assessed.

From figure, it can also be seen that, when grid connected wind power capacity increases, the grid connected wind power increase of exerting oneself, the dotted line in figure and imaginary point line will on move, obviously cutting load probability and cutting load amount can reduce along with moving on dotted line, and abandon wind probability and abandon air quantity and will increase along with moving on imaginary point line, so cutting load index and the value of abandoning wind index can limit the access capacity of grid connected wind power.In like manner, when cutting load index with abandon wind index while determining, in figure during the area definition of peak regulation deficiency, fired power generating unit maximum output and minimum load can affect the access capacity of grid connected wind power again.

From analyzing above, calculate the maximum access capacity of grid connected wind power, need to collect following data:

First gathering system network parameter data.Grid parameter comprises node i, node sum I, node injecting power and the meritorious trend incidence matrices s of branch road, line reactance the circuit trend upper limit of gaining merit each accesses the load l of each node constantly _{d, t, i}.

Then the power data of gathering system.Access the conventional generator maximum output restriction of each node limit with minimum load g _i , each cutting load amount upper limit constantly of each node grid connected wind power each constantly abandon the air quantity upper limit abandon the lower limit b that abandons upper limit a, every day wind total amount of wind total amount every day.

For guaranteeing that after wind-powered electricity generation connecting system, electrical network can continue to safely and steadily run, power system planning department can determine the span of abundant property index, calculates the threshold value of four abundant property indexs according to the span of these indexs---abandon wind expectation the expectation of threshold value, cutting load threshold value, abandon wind probability threshold value, cutting load probability threshold value.

Last because the present invention exerts oneself to simulate actual wind-powered electricity generation according to historical wind-powered electricity generation to exert oneself, therefore need to collect historical wind-powered electricity generation goes out force data with historical wind-electricity integration capacity

(2) set up the maximum wind grid connection capacity Optimized model that can consider the abundant property of peak regulation;

In order to guarantee effective utilization of wind energy, optimization aim is decided to be to the grid connected wind power maximum capacity of system.And bound for objective function comprises the inequality constraints of operation constraint and abundant property index, wherein operation constraint comprises that abandoning the constraint of node active power balance, the branch road DC power flow active power constraint of system, branch road capacity-constrained, conventional power generation usage unit units limits, the every day of system wind total amount retrains, the inequality constraints of abundant property index comprises cutting load probability and year cutting load Expectation constraint, abandons wind probability and year and abandons wind Expectation constraint.

(2.1) target function

Max P _wn(1); Target function is to make system access wind-powered electricity generation capacity maximum.In formula (1), P _wnfor the access capacity of grid connected wind power, unit: MW.

(2.2) constraints

According to the grid supplemental characteristic of collecting, set up successively the constraint of node active power balance, the constraint of branch road DC power flow active power, branch road capacity-constrained, the conventional power generation usage unit units limits of system of system and abandon wind total amount every day below and retrain and describe in detail the practical significance that each retrains.

(a) node active power balance constraint:

In system, the power-balance situation of each node is as Fig. 2, and the active power that this chart free flow is crossed node i is comprised of four parts:

First is the actual g that exerts oneself of the normal power supplies of access node i _i;

Second portion is wind-driven generator or the actual u that exerts oneself of wind energy turbine set of access node i _i-w _i, u wherein _ifor wind-driven generator or the output of wind electric field of access node i, w _ithe air quantity of abandoning for this wind-driven generator or wind energy turbine set;

Third part is the active power l that flows out node i _i-r _i, l wherein _ifor the load of access node i, r _ifor cutting load amount;

The 4th part is from line flows, to cross the active power S of node i _i* P ^l, S wherein _ifor injecting power and the branch road active power Associate array of node i, its element is s _ijif, s _iwith s _jbetween have 1 by s _ipoint to s _jdirected edge time, s _ij=1, otherwise, s _ij=-1, if s _iwith s _jbetween there is no connection, s _ij=s _ji=0.Associate array s _imiddle element representation is:

P ^lfor the meritorious trend array of branch road, element is designated as p _ij, s _ij* p _ijfor timing, represent that active power flows into node i, s from node j _ij* p _ijwhen negative, represent that active power flows out to node j from node i.By figure, know that the power of inflow node i should equal to flow out the power of node i, the power balance equation of therefore setting up node i is as follows: S _i* P ^l+ g _i+ u _i-w _i=l _i-r _i(2)

In the running of electric power system reality, the active power that flows through each node is constantly to change in time, therefore but the active power that flows into each node equals to flow out the active power of this node all the time, by equation (2), can obtain each node power equilibrium equation constantly and be:

$S\×P_{d,t}^L+g_{d,t}+u_{d,t}+y_{d,t}^r=l_{d,t}+y_{d,t}^w{w}_{d,t}---\left(3\right)$

In equation (3), d represents sky, and t represents the moment, and s is the meritorious trend incidence matrices of node injecting power and branch road, be the active power vector that d days t flow through branch road constantly, element is designated as p _{d, t, ij}, p _{d, t, ij}be the active power that d days t flow through branch road i-j constantly; g _{d, t}for normal power supplies is at d days t reality constantly force vector of gaining merit, element is designated as g _{d, t, i}, g _{d, t, i}for the normal power supplies of access node i is gained merit and is exerted oneself in the reality in the d days t moment; u _{d, t}for grid connected wind power is at d days t reality constantly force vector of gaining merit, element is designated as u _{d, t, i}, u _{d, t, i}for the wind-powered electricity generation of access node i is gained merit and is exerted oneself in the reality in the d days t moment; the binary system that is d days t cutting load operation constantly starts variable, arbitrary node when d days there is cutting load phenomenon in t constantly, value is 1, otherwise, value is 0; be that d days t constantly abandon the binary system that character and conduct does and start variable, grid connected wind power exists while abandoning wind phenomenon constantly at d days t, value is 1, and the wind-powered electricity generation of access node i is not abandoned wind constantly at d days t, value is 0; r _{d, t, i}for the cutting load amount of node i the d days t moment; l _{d, t}at the load vector in the d days t moment, element is designated as l _{d, t, i}, l _{d, t, i}it is the load of d days t moment node i; w _{d, t}for the abandon wind direction amount of grid connected wind power the d days t moment, element is designated as w _{d, t, i}, w _{d, t, i}for the wind-powered electricity generation of access node i d days t constantly abandon air quantity.S wherein, l _{d, t}for known quantity, remaining variables is amount to be asked.

(b) the branch road DC power flow active power of system constraint:

In electric power system tide calculates, the meritorious trend of branch road ij can be expressed as

in formula (4): P _ij, G _ij, B _ijthe active power, the electricity that are respectively branch road ij are led and susceptance; U _i, U _jbe respectively the voltage magnitude of node i, node j; θ _ijphase angle difference for node i and node j.Near now supposition: each node voltage of electric power system of normal operation conventionally rated voltage, can be thought U approx _i=U _j=1; Line resistance is far smaller than line reactance, therefore ignores line resistance; In system, do not have heavy load circuit, circuit both end voltage phase angle difference is very little, has sin θ _ij≈ θ _i-θ _j.The DC power flow power-balance constraint of setting up thus between node i and node j is as follows:

P _ij-β _ij(θ _i-θ _j)=0 (5); P in formula (5) _ijfor the meritorious trend of branch road i-j, β _ijfor the susceptance of branch road i-j, θ _iand θ _jvoltage phase angle for node i and node j.

(c) the branch road capacity-constrained of system: because the meritorious trend of passing through in circuit can not surpass the meritorious trend upper limit that circuit can bear, therefore set up inequality as follows:

In formula (6) the meritorious trend upper limit that can bear for every branch road i-j;

(d) conventional power generation usage unit units limits.Exerting oneself of conventional power generation usage unit in a rational interval, therefore be set up inequality as follows:

In formula (7) for conventional power generation usage unit maximum output, g _i minimum load for conventional power generation usage unit.

(e) abandon air quantity constraint.Abandon and on air quantity, be limited to the actual u of exerting oneself of wind-powered electricity generation _{d, t}, the air quantity of abandoning in every day and per moment should, in rational scope, therefore be set up node i and abandon air quantity constraint: 0≤w _{d, t, i}≤ u _{d, t, i}(8); w _{d, t, i}be d days access node i wind-powered electricity generation t constantly abandon air quantity, system d days abandon the constraint of wind total amount the sum that in formula (9), I is system node, a and b are respectively the upper and lower bound of abandoning wind total amount every day.

(2.3) inequality constraints of abundant property index

According to abandoning wind expectation, year cutting load expectation year, the threshold value of abandoning wind probability, cutting load probability sets up the abundant property constraint of probability and expects abundant property constraint.

The abundant property constraint of probability.Cutting load probability in one section of timing statistics and abandon wind probability and should be controlled at the abundant property requirement that certain limit meets system.Therefore set up inequality constraints suc as formula (10) and formula (11); $\frac{1}{\mathrm{DT}}\underset{d=1}{\overset{D}{\mathrm{\Σ}}}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}{y}_{d,t}^r\≤K_p^r,\left(10\right);$ $\frac{1}{\mathrm{DT}}\underset{d=1}{\overset{D}{\mathrm{\Σ}}}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}{y}_{d,t}^w\≤K_p^w,\left(11\right);$ Introduce the unified optimization that these two abundant property constraints can realize wind-powered electricity generation capacity and abundant property index, make the maximum access capacity of wind-electricity integration of obtaining can meet cutting load and the probability level of abandoning wind.

Expect abundant property constraint.Cutting load every day in one section of timing statistics is expected and abandons wind expectation every day to be controlled at the abundant property requirement that certain limit meets system.Therefore set up inequality constraints suc as formula (12) and formula (13); $\frac{1}{D}\underset{d=1}{\overset{D}{\mathrm{\Σ}}}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{i=1}{\overset{I}{\mathrm{\Σ}}}{r}_{d,t,i}\≤K_m^r,\left(12\right);$ $\frac{1}{D}\underset{d=1}{\overset{D}{\mathrm{\Σ}}}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{i=1}{\overset{I}{\mathrm{\Σ}}}{w}_{d,t,i}\≤K_m^w,\left(13\right);$ Introduce the unified optimization that these two abundant property constraints can realize wind-powered electricity generation capacity and abundant property index, make the maximum access capacity of wind-electricity integration of obtaining can meet cutting load and the expectation index of abandoning wind. threshold value for timing statistics D inscribe Load Probability; for abandoning the threshold value of wind probability in timing statistics D; threshold value for cutting load expectation in timing statistics D year; for abandoning the threshold value of wind expectation year in timing statistics D.

The present invention is according to the historical wind-powered electricity generation of the collecting wind-powered electricity generation of the digital simulation reality situation of exerting oneself of exerting oneself, and first calculates the historical wind-powered electricity generation perunit value of exerting oneself, then is multiplied by wind-powered electricity generation capacity to be planned, as actual wind-powered electricity generation, exerts oneself.Set up equation for historical wind-powered electricity generation goes out force data, d wherein, t represents the data that d days t get constantly, i represents the node of wind-powered electricity generation access, for historical installed capacity of wind-driven power, wherein wn represents blower fan rated capacity.

(3) nonlinear equation in wind-electricity integration model is converted to linear inequality group

In equation (3) with be the product of a binary variable and a continuous variable, thereby equation (3) is MIXED INTEGER nonlinear equation, the linearisation step of this equation is as follows:

Introduce new linear variable with make them meet equation by linear new variables with substitution equation (3) produces new equation (17).

all variablees of equation (17) are all linear, so this new equation is linear equation.

Afterwards non-linear equation (15) and (16) are converted to six linear inequality groups as follows.

$0\≤r_{d,t,i}^y\≤\stackrel{\‾}{y_{d,t,i}},\left(18\right);r_{d,t,i}^y\≤\stackrel{\‾}{r_{d,t,i}}{y}_{d,t}^r,\left(19\right);r_{d,t,i}-\stackrel{\‾}{r_{d,t,i}}(1-y_{d,t}^r)\≤r_{d,t,i}^y\≤r_{d,t,i},\left(20\right);$ $0\≤w_{d,t,i}^y\≤\stackrel{\‾}{w_{d,t,i}},\left(21\right);w_{d,t,i}^y\≤\stackrel{\‾}{w_{d,t,i}}{y}_{d,t}^w,\left(22\right);w_{d,t,i}-\stackrel{\‾}{w_{d,t,i}}(1-y_{d,t}^w)\≤w_{d,t,i}^y\≤w_{d,t,i},\left(23\right);$ Wherein with be respectively r _{d, t, i}and w _{d, t, i}the upper limit, also can be greater than r with one _{d, t, i}and w _{d, t, i}several M of the upper limit replace.

When dispatcher determines to carry out cutting load operation, time, inequality group (18)-(20) become $\left[\begin{array}{c}0{\≤r}_{d,t,i}^y\≤\stackrel{\‾}{r_{d,t,i}}\\ r_{d,t,i}^y\≤\stackrel{\‾}{r_{d,t,i}}\\ r_{d,t,i}\≤r_{d,t,i}^y\≤r_{d,t,i}\end{array}\right],\left(24\right);$ In formula, the first two equation shows each cutting load variable of t node i constantly all the time be less than its cutting load higher limit the 3rd formula shows and time equation become obvious inequality group (24) and equation (15) equivalence.

When dispatcher does not carry out cutting load operation, time, inequality group (18)-(20) become $\left[\begin{array}{c}0\≤r_{d,t,i}^y\≤\stackrel{\‾}{r_{d,t,i}}\\ r_{d,t,i}^y\≤0\\ r_{d,t,i}-\stackrel{\‾}{r_{d,t,i}}\≤r_{d,t,i}^y\≤r_{d,t,i}\end{array}\right],\left(25\right);$ In formula, show $r_{d,t,i}^y=0,$ And $y_{d,t}^r=1$ Time equation $\left(15\right),r_{d,t,i}^y=y_{d,t}^r{r}_{d,t,i}$ Become obvious inequality group (25) and equation (15) equivalence.

Binary variable no matter in sum value 0 or 1, inequality group (18)-(20) all with equation (15) equivalence.In like manner, binary variable no matter value 0 or 1, inequality group (21)-(23) all with equation (16) equivalence.And all variablees in inequality group (18)-(23) are all linear, thereby inequality group (18)-(23) are all linear, and now all equations of whole model are all linear, therefore can obtain fast the globally optimal solution of model.

It is as follows that maximum wind grid connection capacity after conversion is optimized linear model:

Target function: Max P _wn;

Linear node active power balance constraint:

Wherein be translated into three linear inequalities of equal value:

$0\≤r_{d,t,i}^y\≤\stackrel{\‾}{r_{d,t,i}};r_{d,t,i}^y\≤\stackrel{\‾}{r_{d,t,i}}{y}_{d,t}^r;r_{d,t,i}-\stackrel{\‾}{r_{d,t,i}}(1-y_{d,t}^r)\≤r_{d,t,i}^y\≤r_{d,t,i};$

be translated into three linear inequalities of equal value:

$0\≤w_{d,t,i}^y\≤\stackrel{\‾}{w_{d,t,i}};w_{d,t,i}^y\≤\stackrel{\‾}{w_{d,t,i}}{y}_{d,t}^w;w_{d,t,i}-\stackrel{\‾}{w_{d,t,i}}(1-y_{d,t}^w)\≤w_{d,t,i}^y\≤w_{d,t,i};$

The branch road DC power flow power constraint of system: p _{d, t, ij}-β _ij(θ _{d, t, i}-θ _{d, t, j})=0;

The branch road capacity-constrained of system:

Conventional power generation usage unit units limits:

Node i is abandoned air quantity constraint 0≤w _{d, t, i}≤ u _{d, t, i};

System retrains the wind total amount of abandoning of d days

Cutting load and the probability constraints of abandoning wind $\frac{1}{\mathrm{DT}}\underset{d=1}{\overset{D}{\mathrm{\Σ}}}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}{y}_{d,t}^r\≤K_p^r$ With $\frac{1}{\mathrm{DT}}\underset{d=1}{\overset{D}{\mathrm{\Σ}}}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}{y}_{d,t}^w\≤K_p^w;$

Cutting load and the desired value constraint of abandoning wind: $\frac{1}{D}\underset{d=1}{\overset{D}{\mathrm{\Σ}}}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{i=1}{\overset{I}{\mathrm{\Σ}}}{r}_{d,t,i}\≤K_m^r$ With $\frac{1}{D}\underset{d=1}{\overset{D}{\mathrm{\Σ}}}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{i=1}{\overset{I}{\mathrm{\Σ}}}{w}_{d,t,i}\≤K_m^w.$

In model, known parameters is as follows: s is the meritorious trend incidence matrices of node injecting power and branch road; l _{d, t}for the load vector the d days t moment, element is designated as l _{d, t, i}, l _{d, t, i}it is the load (unit: MW) of d days t moment node i; it is the cutting load amount upper limit of d days t moment node i; be d days t constantly access node i wind-powered electricity generation abandon the air quantity upper limit; β _ijsusceptance for branch road i-j; the meritorious trend upper limit (unit: MW) that can bear for every branch road i-j; for conventional power generation usage unit maximum output (unit: MW), g _i for conventional power generation usage unit minimum load (unit: MW); A and b are respectively the upper and lower bound (unit: MW) that abandons wind total amount every day. for the threshold value of system at timing statistics D inscribe Load Probability; for system is abandoned the threshold value of wind probability in timing statistics D; threshold value for system year cutting load expectation in timing statistics D; for abandoning the threshold value of wind expectation system year in timing statistics D.

The parameter that will solve in model is as follows: be the active power vector that d days t flow through branch road constantly, element is designated as p _{d, t, ij}, p _{d, t, ij}it is the active power (unit: MW) that d days t flow through branch road i-j constantly; be the cutting load vector in the d days t moment, element is designated as for continuous variable, represent the cutting load amount (unit: MW) of node i the d days t moment; the binary system that is d days t cutting load operation constantly starts variable, arbitrary node when d days there is cutting load phenomenon in t constantly, value is 1, otherwise, value is 0; be the wind direction amount of abandoning in the d days t moment, element is designated as for continuous variable, represent the abandon air quantity of grid connected wind power the d days t moment; be that d days t constantly abandon the binary system that character and conduct does and start variable, grid connected wind power exists while abandoning wind phenomenon constantly at d days t, value is 1, otherwise, value is 0; β _ijsusceptance for branch road i-j; θ _{d, t, i}and θ _{d, t, j}be respectively node i and node j in voltage phase angle constantly of d days t (unit: °); w _{d, t, i}for the wind-powered electricity generation of access node i d days t constantly abandon air quantity (unit: MW); for the binary variable that determines whether to carry out cutting load operation, value 0 or 1; for determining whether abandon the binary variable that character and conduct is done, value 0 or 1; g _{d, t}for normal power supplies is at d days t reality constantly force vector of gaining merit, element is designated as g _{d, t, i}, g _{d, t, i}for the normal power supplies of access node i is in d days t meritorious (unit: MW) of exerting oneself of reality constantly; u _{d, t}for grid connected wind power is at d days t reality constantly force vector of gaining merit, element is designated as u _{d, t, i}, u _{d, t, i}for the wind-powered electricity generation of access node i is in d days t meritorious (unit: MW) of exerting oneself of reality constantly; P _wnfor grid connected wind power capacity (unit: MW).

The present invention has 2 innovations, innovation one: in the maximum grid connection capacity Optimized model of traditional wind-powered electricity generation, add four abundant property Index Constraints, thereby set up the maximum wind grid connection capacity Optimized model of the abundant property of taking into account system peak regulation and the abundant property of generating, this model can be used Solution of Optimization can meet the abundant property of peak regulation of system and the maximum wind grid connection capacity that the abundant property of generating requires, and do not need to use the method for souning out to iterate, when improving result of calculation accuracy, reduced computing time greatly.Innovation two: nonlinear node power equilibrium equation is converted into linear equation and linear inequality group of equal value with it, make nonlinear maximum wind grid connection capacity optimization problem become linear planning problem, further improve the efficiency of calculating, according to the optimal solution of obtaining, can be met comparatively exactly the maximum wind grid connection capacity of the abundant sexual demand of power source planning.By above 2 known the present invention of innovation, the planning of wind-electricity integration capacity can be made accurately and being instructed, there is very strong actual use value.

(4) by data substitution model, calculate

For existing methodical deficiency, the present invention has set up a maximum access capacity model of the wind-powered electricity generation based on abundant property constraint, and this model not only comprises cutting load constraint, has also comprised can directly reflect that wind-powered electricity generation access and system are interactional and abandon wind constraint.By non-linear equation in model, be the linear equation with full scale equation equivalence afterwards, thereby can solve target function with the optimization method of linear programming, therefore computational methods of the present invention have advantages of that precision is high, computational speed is fast.

Below in conjunction with accompanying drawing, the invention process case is described in further detail.

Implementation step 1: obtain measured data and parameter.The implementation case is elaborated to the acquisition methods of the maximum access capacity of wind-electricity integration on improved Garver6 node system.As shown in Figure 3, system contains 3 generators, 8 circuits, 1 Fans to the structure of system.According to implementation step 1, collect historical wind-powered electricity generation and go out force data, system parameters, concrete measured data is as shown in table 1-table 4.

Table 1 network parameter

Each node motor of table 2 is exerted oneself and workload demand

Table 3 constrained parameters

Abandon air quantity constrained parameters: constant a gets 0, b and gets 20%*P _wn, 20% of grid connection capacity.

Table 4 node injecting power and the branch road trend incidence matrices S that gains merit

Node i	1-2	1-4	1-5	2-3	2-4	2-6	3-5	4-6
									1	-1	-1	1	0	0	0	0	0
2	1	0	0	1	-1	-1	0	0
									3	0	0	0	-1	0	0	1	0
4	0	1	0	0	1	0	0	-1
									5	0	0	-1	0	0	0	-1	0
6	0	0	0	0	0	1	0	1

Table 1 has provided analogue system network parameter, wherein for wind energy turbine set or wind-driven generator are exerted oneself, can reach each node, by the meritorious trend upper limit of every branch road be set near 800MW.Each node generator output and workload demand are as shown in table 2, node 1,3,6 access motors, and node 6 does not connect load.

This simulation example timing statistics is 1 year, i.e. D=365, and in 1 year, every 15 minutes of every day gathered data, within one day, gathered 96 data, i.e. T=96, node sum I=6, the cutting load amount upper limit get 500MW; Abandon the air quantity upper limit get 500MW; Historical wind-powered electricity generation goes out force data get the value of a year, historical wind-electricity integration capacity for 12200MW.

Implementation step 2: foundation can be considered the maximum wind receiving model of abundant property constraint.

According to the data of collecting, set up model.

Implementation step 3: receive nonlinear equation in model to be converted to linear inequality group maximum wind.

Equation in model (4) is converted to linear inequality group, sets up new maximum wind and receive model.

Implementation step 4: substitution data, computation model.

The implementation case is used GAMS (General Algebraic Modeling System) software transfer gurobi solver to solve, each stage following 1. of using gurobi to solve MILP solves the stage in advance: simplified model, the constraint of cancellation redundancy, whether decision problem unbounded, or infeasible.

2. solve the stage: utilize heuritic approach to obtain integer feasible solution, by root method of relaxation, obtain a lower bound of former problem, finally use branch-bound algorithm, find the optimal solution of former problem.

3. aggregation stages: complete when solving, output MILP is optimized the information that solves of engine.

Wholely solve flow process as shown in Figure 4, after calculating, obtaining the maximum access capacity of wind-electricity integration is 374.85MW.

Those skilled in the art will readily understand; the foregoing is only preferred embodiment of the present invention; not in order to limit the present invention, all any modifications of doing within the spirit and principles in the present invention, be equal to and replace and improvement etc., within all should being included in protection scope of the present invention.

Claims (5)

1. the method that maximum wind grid connection capacity obtains, is characterized in that, comprises the steps:

S1: foundation can be considered the maximum wind grid connection capacity Optimized model of abundant property constraint;

Target function in described maximum wind grid connection capacity Optimized model is that to take system grid connected wind power maximum capacity be target, and described bound for objective function comprises system operation constraint and abundant property Index Constraints;

Described system operation constraint comprises abandons the constraint of node active power balance, the branch road DC power flow active power constraint of system, branch road capacity-constrained, the actual units limits of conventional power generation usage unit, the every day of system the constraint of wind total amount;

The inequality constraints of described abundant property index comprises cutting load probability and year cutting load Expectation constraint, abandons wind probability and year and abandons wind Expectation constraint;

S2: the nonlinear equation in described maximum wind grid connection capacity Optimized model is converted to linear equation, and obtains linear maximum wind grid connection capacity Optimized model;

S3: the maximum wind grid connection capacity Optimized model that goes out force data and described linearity according to the grid supplemental characteristic obtaining, historical wind-powered electricity generation obtains maximum wind grid connection capacity.

2. the method for claim 1, is characterized in that, the node active power balance equation of system described in step S1 is constrained to $S\×P_{d,t}^L+g_{d,t}+u_{d,t}+y_{d,t}^r{r}_{d,t}=l_{d,t}+y_{d,t}^w{w}_{d,t};$

S is the meritorious trend incidence matrices of node injecting power and branch road, be the active power vector that d days t flow through branch road constantly, element is designated as p _{d, t, ij}, p _{d, t, ij}be the active power that d days t flow through branch road i-j constantly; g _{d, t}for normal power supplies is at d days t reality constantly force vector of gaining merit, element is designated as g _{d, t, i}, g _{d, t, i}for the normal power supplies of access node i is gained merit and is exerted oneself in the reality in the d days t moment; u _{d, t}for grid connected wind power is at d days t reality constantly force vector of gaining merit, element is designated as u _{d, t, i}, u _{d, t, i}for the wind-powered electricity generation of access node i is gained merit and is exerted oneself in the reality in the d days t moment; the binary system that is d days t cutting load operation constantly starts variable, arbitrary node when d days there is cutting load phenomenon in t constantly, value is 1, otherwise, value is 0; r _{d, t}be the cutting load vector in the d days t moment, element is r _{d, t, i}, r _{d, t, i}for the cutting load amount of node i the d days t moment; be that d days t constantly abandon the binary system that character and conduct does and start variable, grid connected wind power exists while abandoning wind phenomenon constantly at d days t, value is 1, otherwise, value is 0; l _{d, i}be the load vector in the d days t moment, element is designated as l _{d, t, i}, l _{d, t, i}it is the load of d days t moment node i; w _{d, t}for the abandon wind direction amount of grid connected wind power the d days t moment, element is designated as w _{d, t, i}, w _{d, t, i}for the wind-powered electricity generation of access node i d days t constantly abandon air quantity.

3. method as claimed in claim 1 or 2, is characterized in that, described cutting load probability is cutting load expectation in described year is the described wind probability of abandoning is $\frac{1}{\mathrm{DT}}\underset{d=1}{\overset{D}{\mathrm{\Σ}}}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}{y}_{d,t}^w\≤K_p^w;$ Described year is abandoned wind expectation $\frac{1}{D}\underset{d=1}{\overset{D}{\mathrm{\Σ}}}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{i=1}{\overset{I}{\mathrm{\Σ}}}{w}_{d,t,i}\≤K_m^w;$

D is the number of days in timing statistics, and the statistics that T is every day is number constantly, and d is d days, and t is the t moment, the binary system that is d days t cutting load operation constantly starts variable, arbitrary node when d days there is cutting load phenomenon in t constantly, value is 1, otherwise, value is 0; be that d days t constantly abandon the binary system that character and conduct does and start variable, grid connected wind power exists while abandoning wind phenomenon constantly at d days t, value is 1, otherwise, value is 0, with the system that is respectively is in timing statistics D inscribe Load Probability and the threshold value of abandoning wind probability, with the system that is respectively year cutting load in the timing statistics D of institute expects and year is abandoned the threshold values of wind expectation.

4. the method for claim 1, is characterized in that, described maximum wind grid connection capacity Optimized model comprises target function Max P _wn; P _wnfor grid-connected wind-powered electricity generation capacity.

5. method as claimed in claim 2, is characterized in that, at maximum wind grid connection capacity Optimized model linear described in step S2, comprises:

Nonlinear node active power balance is converted to linear node active power balance constraint $S\×P_{d,t}^L+g_{d,t}+u_{d,t}+r_{d,t}^y=l_{d,t}+w_{d,t}^y;$ Wherein $r_{d,t,i}^y=y_{d,t}^r{r}_{d,t},w_{d,t}^y=y_{d,t}^w{w}_{d,t}.r_{d,t,i}^y=y_{d,t}^r{r}_{d,t}$ Be equivalent to following three linear inequalities: $0\≤r_{d,t,i}^y\≤\stackrel{\‾}{r_{d,t,i}};r_{d,t,i}^y\≤\stackrel{\‾}{r_{d,t,i}}{y}_{d,t}^r;r_{d,t,i}-\stackrel{\‾}{r_{d,t,i}}(1-y_{d,t}^r)\≤r_{d,t,i}^y\≤r_{d,t,i};w_{d,t}^y=y_{d,t}^w{w}_{d,t}$ Be equivalent to following three linear inequalities: be the cutting load vector in the d days t moment, element is designated as for continuous variable, represent the cutting load amount of node i the d days t moment; it is the cutting load amount upper limit of d days t moment node i; the binary system that is d days t cutting load operation constantly starts variable, arbitrary node when d days there is cutting load phenomenon in t constantly, value is 1, otherwise, value is 0; be the wind direction amount of abandoning in the d days t moment, element is designated as for continuous variable, represent the abandon air quantity of grid connected wind power the d days t moment; be d days t constantly access node i wind-powered electricity generation abandon the air quantity upper limit; be that d days t constantly abandon the binary system that character and conduct does and start variable, grid connected wind power exists while abandoning wind phenomenon constantly at d days t, value is 1, otherwise, value is 0; S is the meritorious trend incidence matrices of node injecting power and branch road, be the active power vector that d days t flow through branch road constantly, element is designated as p _{d, t, ij}, p _{d, t, ij}be the active power that d days t flow through branch road i-j constantly; g _{d, t}for normal power supplies is at d days t reality constantly force vector of gaining merit, element is designated as g _{d, t, i}, g _{d, t, i}for the normal power supplies of access node i is gained merit and is exerted oneself in the reality in the d days t moment; u _{d, t}for grid connected wind power is at d days t reality constantly force vector of gaining merit, element is designated as u _{d, t, i}, u _{d, t, i}for the wind-powered electricity generation of access node i is gained merit and is exerted oneself in the reality in the d days t moment; l _{d, t}for the load vector the d days t moment, element is designated as l _{d, t, i}, l _{d, t, i}it is the load of d days t moment node i;

The branch road DC power flow power constraint of system: p _{d, t, ij}-β _ij(θ _{d, t, i}-θ _{d, t, j})=0; p _{d, t, ij}be the active power that d days t flow through branch road i-j constantly; β _ijsusceptance for branch road i-j; θ _{d, t, i}and θ _{d, t, j}be respectively node i and the node j voltage phase angle the d days t moment;

The branch road capacity-constrained of system: p _{d, t, ij}be the active power that d days t flow through branch road i-j constantly; the active power upper limit that can bear for every branch road i-j;

Conventional power generation usage unit units limits: for conventional power generation usage unit maximum output, g _i for conventional power generation usage unit minimum load;

Node i is abandoned air quantity constraint 0≤w _{d, t, i}≤ u _{d, t, i}; w _{d, t, i}for the wind-powered electricity generation of access node i d days t constantly abandon air quantity; u _{d, t, i}for the wind-powered electricity generation of access node i is gained merit and is exerted oneself in the reality in the d days t moment;

System retrains the wind total amount of abandoning of d days w _{d, t, i}be d days access node i wind-powered electricity generation t constantly abandon air quantity, the sum that I is system node, a and b are respectively the upper and lower bound of abandoning wind total amount every day;

Cutting load and the probability constraints of abandoning wind $\frac{1}{\mathrm{DT}}\underset{d=1}{\overset{D}{\mathrm{\Σ}}}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}{y}_{d,t}^r\≤K_p^r$ With $\frac{1}{\mathrm{DT}}\underset{d=1}{\overset{D}{\mathrm{\Σ}}}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}{y}_{d,t}^w\≤K_p^w;$ the binary system that is d days t cutting load operation constantly starts variable, arbitrary node when d days there is cutting load phenomenon in t constantly, value is 1, otherwise, value is 0; be that d days t constantly abandon the binary system that character and conduct does and start variable, grid connected wind power exists while abandoning wind phenomenon constantly at d days t, value is 1, otherwise, value is 0; with the system that is respectively is in timing statistics D inscribe Load Probability and the threshold value of abandoning wind probability;

Cutting load and the desired value constraint of abandoning wind: $\frac{1}{D}\underset{d=1}{\overset{D}{\mathrm{\Σ}}}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{i=1}{\overset{I}{\mathrm{\Σ}}}{r}_{d,t,i}\≤K_m^r$ With $\frac{1}{D}\underset{d=1}{\overset{D}{\mathrm{\Σ}}}\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{i=1}{\overset{I}{\mathrm{\Σ}}}{w}_{d,t,i}\≤K_m^w;$ R _{d, t, i}for the cutting load amount of node i the d days t moment; w _{d, t, i}for the wind-powered electricity generation of access node i d days t constantly abandon air quantity; with the system that is respectively year cutting load in the timing statistics D of institute expects and year is abandoned the threshold values of wind expectation.