CN104682447A - Power system economic dispatching method containing multiple wind power plants - Google Patents

Abstract

The invention provides a power system economic dispatching method containing multiple wind power plants. The power system economic dispatching method comprises the following steps: S1) according to wind power prediction data and prediction error distribution characteristics in a dispatching period, obtaining a possible capacity scene of each wind power plant, and calculating a scene probability; S2) reducing the scenes of all wind power plants according to the same method, and calculating the scene probability after reduction is carried out; S3) combining all the scenes of which the wind power plants are reduced, and reducing the scenes into a plurality of typical scenes; S4) establishing a target function of the power system economic dispatching method; S5) establishing constraint conditions including load balance constraints, rotary backup contain constraints, the top and bottom limitation constraints of unit capacity, unit ramp rate constraints and minimum startup-shutdown constraints; S6) carrying out solving via a mixed integer programming method. The power system economic dispatching method provided by the invention is suitable for solving the economic dispatching problem containing multiple wind power plants, and calculation difficulty generated when the scenes of the multiple wind power plants are reduced is lowered.

Description

A kind of Economic Dispatch method containing windy electric field

Technical field

The present invention relates to dispatching automation of electric power systems technical field, be specifically related to a kind of Electrical Power System Dynamic economic dispatch method be suitable for containing multiple wind energy turbine set.

Background technology

Energy environment issues impels the new forms of energy electric power comprising wind-powered electricity generation to develop rapidly, and wind-powered electricity generation has intermittent and strong stochastic volatility, its large-scale grid connection brings huge challenge to traditional dispatching of power netwoks, and the Economic Dispatch problem containing wind energy turbine set also becomes domestic and international study hotspot.To during containing the Economic Dispatch modeling of wind energy turbine set, because wind power output predicted value exists comparatively big error, need to carry out probability analysis to the uncertainty of Power Output for Wind Power Field, scene tree in Stochastic Decision-making becomes the uncertain important method of carrying out modeling of wind energy turbine set, and the generation of scene tree mainly describes the possible situation of exerting oneself of wind-powered electricity generation in dispatching cycle according to the probability distribution situation of wind power output.

Scene method is one of important method of the Electrical Power System Dynamic Economic Dispatch Problem solved containing wind energy turbine set, its basic ideas are exerted oneself by the typical case of wind energy turbine set each in the typical scene operation simulation cycle, thus the uncertain problem caused by wind-powered electricity generation is converted into certain problem solves, but, when wind energy turbine set quantity is more, the typical case of each wind energy turbine set scene scale obtained after scene composition of exerting oneself is quite large, direct solution or to carry out the difficulty of conventional reduction very large.Scene reduction, as the important step of scene method, greatly can reduce and solve difficulty.In the existing method solved based on scene method containing the Electrical Power System Dynamic Economic Dispatch Problem of wind energy turbine set, wind energy turbine set number scale is general very little, and therefore scene reduction difficulty is less; When in system, grid connected wind power number is larger, scene reduction becomes the difficult point adopting scene method to solve the Electrical Power System Dynamic Economic Dispatch Problem containing wind energy turbine set.

Summary of the invention

The object of this invention is to provide a kind of Electrical Power System Dynamic economic dispatch method be suitable for containing multiple wind energy turbine set, be suitable for the Electrical Power System Dynamic Economic Dispatch Problem solved containing two and two or more wind energy turbine set.

The present invention relates to a kind of Electrical Power System Dynamic economic dispatch method containing windy electric field, said method comprising the steps of:

Step S1, according to wind power prediction data in dispatching cycle and wind power prediction error distribution character, by discrete for the wind power output of actual capabilities in each period be some states, and using wind-powered electricity generation in the size sequence of exerting oneself of each period as a scene, the scene quantity of initially exerting oneself of each wind energy turbine set is k ₁, and calculate the probability of happening of each scene.

Step S2, by the scene quantity of each wind energy turbine set according to same scene reduction method from k ₁be reduced to k ₂, and calculate the probability of happening of the rear each scene of reduction.

Step S3, the scene of exerting oneself after the reduction of M wind energy turbine set is carried out combination can be obtained individual scene, the combination of likely exerting oneself in each moment that represents all wind energy turbine set within dispatching cycle, adopts windy electric field scene reduction method newly to obtain individual scene is reduced to S typical case and exerts oneself scene.

Step S4, set up the target function making described Electrical Power System Dynamic economic dispatch method, target function is the generating expense desired value under each typical scene:

$\min f=\underset{t=1}{\overset{T}{\mathrm{\Σ}}}\underset{s=1}{\overset{k3}{\mathrm{\Σ}}}\underset{i=1}{\overset{N}{\mathrm{\Σ}}}{\mathrm{\π}}_s\{U_i\left(t\right)\×{\mathrm{OC}}_i[P_{i,s}\left(t\right)]+S_i\left(t\right)\×U_i\left(t\right)(1-U_i(t-1))\}$

Wherein, T is the time hop count in dispatching cycle, and N is fired power generating unit quantity in system, π _srepresent the probability that scene s occurs, for unit i exerting oneself at moment t under scene s, U _it (), for unit i is in the start and stop state of moment t, " 1 " represents startup, " 0 " represents shutdown, OC _i[P _i(t)] represent the operating cost of unit i at moment t, S _i(t) represent unit i time t start-up and shut-down costs.

Step S5; set up constraints, under making arbitrary scene of described power system dispatching result in S scene, all meet account load balancing constraints, spinning reserve capacity constraint, the constraint of unit output bound, the constraint of unit climbing rate, the minimum startup-shutdown time-constrain of unit.

Step S6, is solved by mixed integer programming approach and meets constraints described in step S5 and the economic dispatch result making target function described in step S4 minimum.

Described operating cost OC _i[P _i,s(t)] be quadratic function:

${\mathrm{OC}}_i\left[P_{i,s}\left(t\right)\right]=a_i{P}_{i,s}^2\left(t\right)+b_i{P}_{i,s}\left(t\right)+c_i$

Wherein, a _i, b _iand c _ibe fuel cost coefficient.

Described start-up and shut-down costs S _it () is expressed as:

$S_i\left(t\right)=\begin{array}{c}\left\{\begin{array}{cc}{S}_i^H& T_i^{\mathrm{off}}\≤X_i^{\mathrm{off}}\left(t\right)\≤H_i^{\mathrm{off}}\\ S_i^C& X_i^{\mathrm{off}}\left(t\right)>H_i^{\mathrm{off}}\end{array}\right.\end{array}$

Wherein, represent the warm start cost of unit i, represent the cold start-up cost of unit i, represent the continuous idle time of unit i at moment t; represent the minimum continuous idle time of unit i; represent the cold start-up time of unit i; Shut down cost to process as constant 0.

System loading Constraints of Equilibrium under described each scene is: wherein, for the exerting oneself at moment t of wind energy turbine set j in scene s, D (t) is for system is at the workload demand of moment t.

System spinning reserve capacity under described each scene is constrained to: wherein, for the maximum output of unit i, Δ _rfor reserve capacity coefficient.

Unit output bound under described each scene is constrained to: wherein for the minimum load of unit i.

Unit climbing rate under described each scene is constrained to: P _i,s(t-1)-DR _i≤ P _i,s(t)≤P _i,s(t-1)+UR _i, wherein, DR _iand UR _ibe respectively the downward climbing rate restriction of conventional power unit i and ratio of slope restriction of climbing.

The minimum startup-shutdown time-constrain of unit under described each scene is:

$\left\{\begin{array}{c}{T}_i^{\mathrm{on}}\≤X_i^{\mathrm{on}}\left(t\right)\\ T_i^{\mathrm{off}}\≤X_i^{\mathrm{off}}\left(t\right)\end{array}\right.$

Wherein, for unit i is in the continuous available machine time of moment t, for the minimum continuous available machine time of unit i.

The scene reduction method of described step S2 comprises the following steps:

Step S201, calculates the distance c between all scenes _t(w _i, w _j)=|| w _i-w _j||, i, j=1,2 ..., k ₁;

Step S202, if deleted scene set J is empty set, calculates the scene l that the 1st time iteration will be deleted ₁, make to get l as deleted scene l ₁time, obtain minimum, delete scene l ₁, J ^(k)=J ^(k-1)∪ { l _k;

Step S203, makes k=2;

Step S204, calculates the scene l that kth time iteration will be deleted _k, make to get l as deleted scene l _ktime, obtain minimum, delete scene l _k, J ^(k)=J ^(k-1)∪ { l _k;

Step S205, makes k=k+1;

Step S206, if k < is k ₁-k ₂, then return step S204, otherwise carry out step S207;

Step S207, each scene i in deleted scene set J finds and makes obtain minimum j, in remaining scene set, the probability of each scene j is

The scene reduction method of described step S3 comprises the following steps:

Step S301, obtains k by the scene random combine after the 1st wind energy turbine set and the 2nd wind energy turbine set reduction ₂× k ₂individual scene, and the probability calculating that each scene occurs, according to step S2 scene reduction method used by k ₂× k ₂individual scene is reduced to S, and calculates the probability of each scene generation;

Step S302, makes j=2, and j is wind energy turbine set numbering, j=1, and 2 ..., M;

Step S303, by the k after S scene after the 1st to j wind energy turbine set reduction and jth+1 wind energy turbine set reduction ₂individual scene random combine obtains S × k ₂individual scene, and the probability calculating that each scene occurs, according to step S2 scene reduction method used by S × k ₂individual scene is reduced to S, and calculates the probability of each scene generation;

Step S304, makes j=j+1;

Step S305, if j < is M, then returns step S303; Otherwise, step S306;

Step S306, terminates to calculate.

The present invention has following remarkable advantage and beneficial effect:

(1) scene method solves the important method containing wind energy turbine set Electrical Power System Dynamic economic dispatch, the Electrical Power System Dynamic economic dispatch method based on scene method that the present invention proposes is suitable for solving the Electrical Power System Dynamic Economic Dispatch Problem containing wind energy turbine set, is particularly suitable for the Electrical Power System Dynamic Economic Dispatch Problem containing two and above wind energy turbine set.

(2) the windy electric field scene reduction method that the present invention proposes greatly can reduce amount of calculation under the prerequisite ensureing computational accuracy, solves the problem that when scene method solves windy electric field system dynamic economic dispatch problem, amount of calculation is excessive.

Accompanying drawing explanation

Fig. 1 is a kind of Electrical Power System Dynamic economic dispatch method flow diagram containing windy electric field of the present invention.

Fig. 2 is the flow chart of a kind of Electrical Power System Dynamic economic dispatch method step S2 containing windy electric field of the present invention.

Fig. 3 is the flow chart of a kind of Electrical Power System Dynamic economic dispatch method step S3 containing windy electric field of the present invention.

Embodiment

The basic ideas that scene method solves containing wind energy turbine set Electrical Power System Dynamic Economic Dispatch Problem are may exert oneself by wind energy turbine set each in the typical scene operation simulation cycle, the uncertain problem brought by wind-powered electricity generation is converted into certain problem and solves, but, when wind energy turbine set quantity is more, the typical case of each wind energy turbine set scene scale obtained after scene composition of exerting oneself is quite large, direct solution or to carry out the difficulty of conventional reduction very large, the invention provides a kind of Economic Dispatch method containing windy electric field, especially applicable use scenes method solves the Economic Dispatch problem containing multiple wind energy turbine set, the flow chart of the method as shown in Figure 1, as shown in Figure 1, the method comprises:

Step S1, according to wind power prediction data in dispatching cycle and wind power prediction error distribution character, by discrete for the wind power output of actual capabilities in each period be some states, and using wind-powered electricity generation in the size sequence of exerting oneself of each period as a scene, the wind power output scene quantity that each wind energy turbine set generates is k ₁, and calculate the probability of happening of each scene.

Step S2, by the scene quantity of each wind energy turbine set according to Same Scene reduction method from k ₁be reduced to k ₂, and calculate the probability of happening of the rear each scene of reduction.

Step S3, combines the scene of exerting oneself after the reduction of M wind energy turbine set, and is reduced to S typical case and exerts oneself scene.

Step S4, set up the target function making described Electrical Power System Dynamic economic dispatch method, target function is the generating expense desired value under each typical scene:

$\min f=\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\underset{s=1}{\overset{k3}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{\&pi;}}_s\{U_i\left(t\right)\&times;{\mathrm{OC}}_i[P_{i,s}\left(t\right)]+S_i\left(t\right)\&times;U_i\left(t\right)(1-U_i(t-1))\}$

Wherein, T is the time hop count in dispatching cycle, and N is fired power generating unit quantity in system, π _srepresent the probability that scene s occurs, P _i,st () is unit i exerting oneself at moment t under scene s, U _it (), for unit i is in the start and stop state of moment t, " 1 " represents startup, " 0 " represents shutdown, OC _i[P _i(t)] represent the operating cost of unit i at moment t, S _i(t) represent unit i time t start-up and shut-down costs.

Step S5; set up constraints, under making arbitrary scene of described power system dispatching result in S scene, all meet account load balancing constraints, spinning reserve capacity constraint, the constraint of unit output bound, the constraint of unit climbing rate, the minimum startup-shutdown time-constrain of unit.

Step S6, is solved by mixed integer programming approach and meets constraints described in step S5 and the economic dispatch result making target function described in step S4 minimum.

Concrete, operating cost OC in step S4 _i[P _i,s(t)] be quadratic function, expression formula is:

${\mathrm{OC}}_i\left[P_{i,s}\left(t\right)\right]=a_i{P}_{i,s}^2\left(t\right)+b_i{P}_{i,s}\left(t\right)+c_i,$ A _i, b _iand c _ibe fuel cost coefficient.

Concrete, S in step S4 _i(t) represent unit i time t start-up and shut-down costs, expression formula is:

$S_i\left(t\right)=\begin{array}{c}\left\{\begin{array}{cc}{S}_i^H& T_i^{\mathrm{off}}\&le;X_i^{\mathrm{off}}\left(t\right)\&le;H_i^{\mathrm{off}}\\ S_i^C& X_i^{\mathrm{off}}\left(t\right)>H_i^{\mathrm{off}}\end{array}\right.\end{array}$

Wherein, represent the warm start cost of unit i, represent the cold start-up cost of unit i, represent the continuous idle time of unit i at moment t; represent the minimum continuous idle time of unit i; represent the cold start-up time of unit i; Shut down cost to process as constant 0.

Concrete, establish account load balancing constraints, spinning reserve capacity constraint, the constraint of unit output bound, the constraint of unit climbing rate, the minimum startup-shutdown time-constrain of unit in step S5.

Account load balancing constraints is: wherein, for the exerting oneself at moment t of fired power generating unit i under scene s, for scene s wind energy turbine set j exerting oneself at moment t, D (t) is for system is at the workload demand of moment t;

Spinning reserve capacity is constrained to: wherein, for the maximum output of unit i, Δ _rfor reserve capacity coefficient;

Unit output bound is constrained to: wherein for the minimum load of unit i;

Unit climbing rate is constrained to: P _i,s(t-1)-DR _i≤ P _i,s(t)≤P _i,s(t-1)+UR _i, wherein, DR _iand UR _ibe respectively the downward climbing rate restriction of conventional power unit i and ratio of slope restriction of climbing.

The minimum startup-shutdown time-constrain of unit is: $\left\{\begin{array}{c}{T}_i^{\mathrm{on}}\&le;X_i^{\mathrm{on}}\left(t\right)\\ T_i^{\mathrm{off}}\&le;X_i^{\mathrm{off}}\left(t\right)\end{array}\right.$

Wherein, for unit i is in the continuous available machine time of moment t; for the minimum continuous available machine time of unit i.

Be illustrated in figure 2 the scene reduction method of step S2, comprise the following steps:

Step S201, calculates the distance c between all scenes _t(w _i, w _j)=|| w _i-w _j|| _,i, j=1,2, _,k ₁;

Step S202, if deleted scene set J is empty set, calculates the scene l that the 1st time iteration will be deleted ₁, make to get l as deleted scene l ₁time, obtain minimum, delete scene l ₁, J ^(k)=J ^(k-1)∪ { l _k;

Step S203, makes k=2;

Step S204, calculates the scene l that kth time iteration will be deleted _k, make to get l as deleted scene l _ktime, obtain minimum, delete scene l _k, J ^(k)=J ^(k-1)∪ { l _k;

Step S205, makes k=k+1;

Step S206, if k < is k ₁-k ₂, then return step S204, otherwise carry out step S207;

Step S207, each scene i in deleted scene set J finds and makes the probability obtaining each scene j in the set of minimum j remaining scene is

Be illustrated in figure 3 the scene reduction method of step S3, comprise the following steps:

Step S301, obtains k by the scene random combine after the 1st wind energy turbine set and the 2nd wind energy turbine set reduction ₂× k ₂individual scene, and the probability calculating that each scene occurs, according to step S2 scene reduction method used by k ₂× k ₂individual scene is reduced to S, and calculates the probability of each scene generation;

Step S302, makes j=2, and j is wind energy turbine set numbering, j=1, and 2 ..., M;

Step S303, by the k after S scene after the 1st to j wind energy turbine set reduction and jth+1 wind energy turbine set reduction ₂individual scene random combine obtains S × k ₂individual scene, and the probability calculating that each scene occurs, according to step S2 scene reduction method used by S × k ₂individual scene is reduced to S, and calculates the probability of each scene generation;

Step S304, makes j=j+1;

Step S305, if j < is M, then returns step S303; Otherwise, step S306;

Step S306, terminates to calculate, and S the typical case so just obtaining all wind energy turbine set exerts oneself scene.

Finally should be noted that: above embodiment is only in order to illustrate that technical scheme of the present invention is not intended to limit, although with reference to above-described embodiment to invention has been detailed description, those of ordinary skill in the field are to be understood that: still can modify to the specific embodiment of the present invention or equivalent replacement, and not departing from any amendment of spirit and scope of the invention or equivalent replacement, it all should be encompassed in the middle of right of the present invention.

Claims (4)

1., containing an Economic Dispatch method for windy electric field, it is characterized in that, said method comprising the steps of:

Step S1, according to wind power prediction data in dispatching cycle and wind power prediction error distribution character, by discrete for some states for exerting oneself of each wind energy turbine set actual capabilities in each period, and using each wind energy turbine set in the size sequence of exerting oneself of each period as a scene, the scene quantity of initially exerting oneself obtaining each wind energy turbine set is k ₁, and calculate the probability of happening of each scene;

Step S2, by the scene quantity of each wind energy turbine set according to unified scene reduction method from k ₁be reduced to k ₂, and calculate the probability of happening of the rear each scene of reduction;

Step S3, the scene of exerting oneself after the reduction of M wind energy turbine set is carried out combination can be obtained individual scene, represent the combination of likely exerting oneself of all wind energy turbine set day part within dispatching cycle, adopts windy electric field scene reduction method newly to obtain individual scene is reduced to S typical case and exerts oneself scene;

Step S4, sets up the target function of described Economic Dispatch method model, and target function is the generating expense desired value under each typical wind power output scene:

$\min f=\underset{t=1}{\overset{T}{\mathrm{\&Sigma;}}}\underset{s=1}{\overset{k3}{\mathrm{\&Sigma;}}}\underset{i=1}{\overset{N}{\mathrm{\&Sigma;}}}{\mathrm{\&pi;}}_s\{U_i\left(t\right)\&times;{\mathrm{OC}}_i[P_{i,s}\left(t\right)]+S_i\left(t\right)\&times;U_i\left(t\right)(1-U_i(t-1))\}$

Wherein, T is the time hop count in dispatching cycle, and N is fired power generating unit quantity in system, π _srepresent the probability that scene s occurs, P _i,st () is unit i exerting oneself at moment t under scene s, U _it (), for unit i is in the start and stop state of moment t, " 1 " represents startup, " 0 " represents shutdown, OC _i[P _i(t)] represent the operating cost of unit i at moment t, S _i(t) represent unit i time t start-up and shut-down costs.

Step S5; set up the constraints of described Economic Dispatch method model, under making arbitrary scene of described Economic Dispatch result in S scene, all meet account load balancing constraints, spinning reserve capacity constraint, the constraint of unit output bound, the constraint of unit climbing rate, the minimum startup-shutdown time-constrain of unit.

Step S6, is solved by mixed integer programming approach and meets constraints described in step S5 and the economic dispatch result making target function described in step S4 minimum.

2. the method for claim 1, is characterized in that, described operating cost OC _i[P _i,s(t)] be quadratic function: ${\mathrm{OC}}_i\left[P_{i,s}\left(t\right)\right]=a_i{P}_{i,s}^2\left(t\right)+b_i{P}_{i,s}\left(t\right)+c_i,$ Wherein, a _i, b _iand c _ibe fuel cost coefficient.

Described start-up and shut-down costs S _it () is expressed as:

$S_i\left(t\right)=\left\{\begin{array}{cc}{S}_i^H& T_i^{\mathrm{off}}\&le;X_i^{\mathrm{off}}\left(t\right)\&le;H_i^{\mathrm{off}}\\ S_i^C& X_i^{\mathrm{off}}\left(t\right)>H_i^{\mathrm{off}}\end{array}\right.$

Wherein, represent the warm start cost of unit i, represent the cold start-up cost of unit i, represent the continuous idle time of unit i at moment t; represent the minimum continuous idle time of unit i; represent the cold start-up time of unit i; Shut down cost to process as constant 0.

System loading Constraints of Equilibrium under described each scene is: wherein, for scene s wind energy turbine set j exerting oneself at moment t, D (t) is for system is at the workload demand of moment t.

System spinning reserve capacity under described each scene is constrained to: wherein, for the maximum output of unit i, Δ _rfor reserve capacity coefficient.

Unit output bound under described each scene is constrained to: wherein for the minimum load of unit i.

Unit climbing rate under described each scene is constrained to: P _i,s(t-1)-DR _i≤ P _i,s(t)≤P _i,s(t-1)+UR _i, wherein, DR _iand UR _ibe respectively the downward climbing rate restriction of conventional power unit i and ratio of slope restriction of climbing.

The minimum startup-shutdown time-constrain of unit under described each scene is:

$\left\{\begin{array}{c}{T}_i^{\mathrm{on}}\&le;X_i^{\mathrm{on}}\left(t\right)\\ T_i^{\mathrm{off}}\&le;X_i^{\mathrm{off}}\left(t\right)\end{array}\right.$

Wherein, represent the continuous available machine time of unit i at moment t; represent the minimum continuous available machine time of unit i.

3. Economic Dispatch method as claimed in claim 1, it is characterized in that, the scene reduction method of described step S2 comprises:

Step S201, calculates the probability metrics c between all scenes _t(w _i, w _j)=|| w _i-w _j||, wherein w _iand w _jbe respectively i-th scene and a jth scene, i, j=1,2 ..., k ₁;

Step S202, if deleted scene set J is empty set, calculates the scene l that the 1st time iteration will be deleted ₁, make to get l as deleted scene l ₁time, obtain minimum, delete scene l ₁, J ^(k)=J ^(k-1)∪ { l _k;

Step S203, makes k=2;

Step S204, calculates the scene l that kth time iteration will be deleted _k, make to get l as deleted scene l _ktime, obtain minimum, delete scene l _k, J ^(k)=J ^(k-1)∪ { l _k;

Step S205, makes k=k+1;

Step S206, if k<k ₁-k ₂, then return step S204, otherwise carry out step S207;

Step S207, each scene i in deleted scene set J finds and makes obtain minimum j, in remaining scene set, the probability of each scene j is

4. Economic Dispatch method as claimed in claim 1, it is characterized in that, the windy electric field scene reduction method of described step S3 comprises:

Step S301, obtains k by the scene random combine after the 1st wind energy turbine set and the 2nd wind energy turbine set reduction ₂× k ₂individual scene, and the probability calculating that each scene occurs, according to step S2 scene reduction method used by k ₂× k ₂individual scene is reduced to S, and calculates the probability of each scene generation;

Step S302, makes j=2, and j is wind energy turbine set numbering, j=1, and 2 ..., M;

Step S303, by the scene after the 1st to j wind energy turbine set S reduction and jth+1 wind energy turbine set k ₂scene random combine after individual reduction obtains S × k ₂individual scene, and the probability calculating that each scene occurs, according to step S2 scene reduction method used by S × k ₂individual scene is reduced to S, and calculates the probability of each scene generation;

Step S304, makes j=j+1;

Step S305, if j<M, then returns step S303, otherwise, step S306;

Step S306, terminates to calculate, and S the typical case finally obtaining all wind energy turbine set exerts oneself scene.