CN106712035B - A kind of Economic Dispatch method - Google Patents
A kind of Economic Dispatch method Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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Abstract
The present invention discloses a kind of Economic Dispatch method, is related to electric power system optimization running technology field, improves the reliability to the economic load dispatching of electric system.The described method includes: decoupling electric system for Consultation Center and multiple regions；According to Consultation Center and multiple regions, Economic Dispatch model is established, wherein the target of Economic Dispatch model is total power generation expense and abandoning generation of electricity by new energy cutting load rejection penalty the sum of of the conventional power unit in scheduling duration in electric system；It is interregional Coordination Model and multiple regions scheduling model by Economic Dispatch model decomposition, wherein, multiple regions scheduling model and multiple regions correspond, and interregional Coordination Model is corresponding with Consultation Center, and interregional Coordination Model carries out distributed optimization to the boundary node in each region；According to interregional Coordination Model and multiple regions scheduling model, the economic load dispatching result of electric system is calculated.
Description
Technical field
The present invention relates to electric power system optimization running technology field more particularly to a kind of Economic Dispatch methods.
Background technique
Currently, the proposition of energy environment issues, has promoted the rapid development of new energy electric field, for example, wind energy electric field, the sun
Energy electric field, tide electric field etc..However, since new energy electric field has biggish randomness and fluctuation, thus cause to be incorporated with
The electric system of new energy electric field faces more severe form, and the economic load dispatching of especially electric system faces biggish choose
War.
The economic load dispatching of electric system, which refers to, to be guaranteed power system security, reliability service and is meeting power quality, electricity consumption
Under the premise of needing, the unit output of unit each in electric system is scheduled, makes energy consumption, the running cost of entire electric system
With minimum, to obtain maximum economic benefit.The economic load dispatching of existing electric system is usually uniformly made by control centre, i.e.,
The economic load dispatching of electric system is obtained by the way of centralized optimization as a result, for example, can be by conventional power unit in electric system
The total power generation expense of (such as fired power generating unit, Hydropower Unit etc.) within dispatching cycle and abandon new energy (such as abandonment, abandon the sun
Can, abandon tide) generate electricity target of the sum of the cutting load rejection penalty as Economic Dispatch, i.e., in solution electric system often
It advises unit (such as fired power generating unit, Hydropower Unit etc.) total power generation expense within dispatching cycle and abandons new energy and (such as abandonment, abandon
Solar energy abandons tide) the smallest economic load dispatching of power generation the sum of cutting load rejection penalty as a result, with realize to electric system carry out through
Ji scheduling, at this point, control centre usually requires to obtain the whole network data of electric system.However, gradually with new energy electric field
It being incorporated to, the scale of electric system constantly expands, when carrying out economic load dispatching to electric system by control centre is unified, institute, control centre
The whole network data of acquisition is huger and many and diverse, be easy to cause communication blockage and shortage of data, in turn results in the economy of electric system
The reliability of scheduling is poor.
Summary of the invention
The purpose of the present invention is to provide a kind of Economic Dispatch methods, for solving existing electric system
The poor problem of the reliability of economic load dispatching.
To achieve the goals above, the invention provides the following technical scheme:
A kind of Economic Dispatch method characterized by comprising
Step S100, electric system is decoupled as Consultation Center and multiple regions；
Step S200, according to the Consultation Center and multiple regions, Economic Dispatch model is established,
In, the target of the Economic Dispatch model is total power generation of the conventional power unit in scheduling duration in the electric system
The sum of expense and abandoning generation of electricity by new energy cutting load rejection penalty；
It step S300, is that interregional Coordination Model and multiple regions are dispatched by the Economic Dispatch model decomposition
Model, wherein multiple subdispatch models and multiple regions correspond, the interregional Coordination Model with it is described
Consultation Center is corresponding, and the interregional Coordination Model carries out distributed optimization to the boundary node in each region；
Step S400, according to the interregional Coordination Model and multiple subdispatch models, electric system is calculated
Economic load dispatching result.
In Economic Dispatch method provided by the invention, electric system is decoupled as Consultation Center and multiple areas
Then Economic Dispatch model is established according to Consultation Center and multiple regions in domain, then by Economic Dispatch
Model decomposition is interregional Coordination Model and multiple regions scheduling model, then according to interregional Coordination Model and multiple regions tune
Model is spent, the economic load dispatching result of electric system is calculated.Therefore, in the present invention, the economic load dispatching result of electric system is calculated
When, multiple regions scheduling model is calculated respectively, each subdispatch model is distributed using interregional Coordination Model
The economic load dispatching result of formula optimization, i.e., each region is calculated by the subdispatch model for corresponding to the region, utilizes region
Between Coordination Model distributed optimization is carried out to the boundary node in the region, thus, Consultation Center is utilizing interregional Coordination Model
When carrying out distributed optimization to the boundary node in each region, Consultation Center need to only obtain the variable of the boundary node in each region, and
Without obtaining other variables in each region, i.e. Consultation Center is not necessarily to obtain the whole network data of electric system, therefore will not be because needing
The whole network data to be obtained and cause communication blockage and shortage of data, so as to improve electric system economic load dispatching it is reliable
Property.
Detailed description of the invention
The drawings described herein are used to provide a further understanding of the present invention, constitutes a part of the invention, this hair
Bright illustrative embodiments and their description are used to explain the present invention, and are not constituted improper limitations of the present invention.In the accompanying drawings:
Fig. 1 is the flow chart one of Economic Dispatch method in the embodiment of the present invention；
Fig. 2 is the flowchart 2 of Economic Dispatch method in the embodiment of the present invention；
Fig. 3 is that electric system decouples schematic diagram in the embodiment of the present invention.
Specific embodiment
The Economic Dispatch method that embodiment provides in order to further illustrate the present invention, it is attached below with reference to specification
Figure is described in detail.
Referring to Fig. 1, Economic Dispatch method provided in an embodiment of the present invention includes:
Step S100, electric system is decoupled as Consultation Center and multiple regions.
Step S200, according to Consultation Center and multiple regions, Economic Dispatch model is established, wherein power train
The target of system economic load dispatching model is that total power generation expense and abandoning new energy of the conventional power unit in scheduling duration are sent out in electric system
The sum of cutting load rejection penalty.
It step S300, is that interregional Coordination Model and multiple regions dispatch mould by Economic Dispatch model decomposition
Type, wherein multiple regions scheduling model and multiple regions correspond, and interregional Coordination Model is corresponding with Consultation Center, region
Between Coordination Model distributed optimization is carried out to the boundary node in each region.
Step S400, according to interregional Coordination Model and multiple regions scheduling model, the economic load dispatching of electric system is calculated
As a result.
It specifically, can be using construction virtual region and multiple when electric system being decoupled as Consultation Center and multiple regions
The method of boundary node variable processed, for example, referring to Fig. 3, decoupling electric system for Consultation Center and two regions
For be illustrated, first with construction virtual region method electric system is configured to two regions, respectively region a and area
Domain b, two regions are connected by interconnector between a strip area, one end of the interregional interconnector and the boundary node m of region a
Connection, the other end of the interregional interconnector are connect with the boundary node n of region b, wherein boundary node can be understood as certain
The node that one region is connect with other regions is become the phase angle of boundary node m then using the method for duplication boundary node variable
Amount and the duplication of the phase angle variable of boundary node n are primary, respectivelyWithWherein,WithBelong to region a,WithBelong to region b.When forming Consultation Center, using the method for duplication boundary node variable, the phase angle of boundary node m is become
Amount and the phase angle variable of boundary node n replicate once, respectively againWithIn this way, being saved to the same boundary
The phase angle that there is one group of corresponding variable to indicate the boundary node for point, each relevant range and Consultation Center.
Electric system is decoupled as after Consultation Center and multiple regions, then it can be according to Consultation Center that decoupling is formed and more
Economic Dispatch model is established in a region, wherein the target of Economic Dispatch model can be set as electric power
Total power generation expense and abandoning generation of electricity by new energy cutting load rejection penalty the sum of of the conventional power unit in scheduling duration, that is, solve in system
The economic load dispatching of obtained electric system result it is required that in electric system total power generation expense of the conventional power unit in scheduling duration and
The sum of generation of electricity by new energy cutting load rejection penalty minimum is abandoned, which is the economy of entire electric system
Scheduling model includes the whole network data of electric system including parameter all in each region and interregional parameter.
After completing Economic Dispatch model, analytic approach is cascaded using target and is decomposed containing partially polymerized more cuttings
Economic Dispatch model decomposition is the interregional Coordination Model corresponding to Consultation Center by algorithm and is corresponded
In the multiple regions scheduling model to a region, Consultation Center receives variable (such as the phase of the boundary node uploaded by each region
Angle variable), and according to the variable for corresponding to each boundary node in interregional Coordination Model coordinating calculating center, it then will be in coordination
It is issued to corresponding region corresponding to the variable of each boundary node in the heart, is carried out with the boundary node to each region distributed excellent
Change.
After being interregional Coordination Model and multiple regions scheduling model for Economic Dispatch model decomposition, according to area
Coordination Model and multiple regions scheduling model between domain pass through repeatedly dividing between interregional Coordination Model and each subdispatch model
Cloth optimization, the economic load dispatching of electric system is calculated as a result, electric system economic load dispatching result by each region region
Scheduling result composition.
It can be seen from the above, being by electric system decoupling in Economic Dispatch method provided in an embodiment of the present invention
Then Consultation Center and multiple regions establish Economic Dispatch model according to Consultation Center and multiple regions, then will
Economic Dispatch model decomposition is interregional Coordination Model and multiple regions scheduling model, then according to interregional coordination
Model and multiple regions scheduling model calculate the economic load dispatching result of electric system.Therefore, in embodiments of the present invention, calculate
When the economic load dispatching result of electric system, multiple regions scheduling model is calculated respectively, utilizes interregional Coordination Model pair
Each subdispatch model carries out distributed optimization, i.e., the economic load dispatching result in each region is by the subdispatch corresponding to the region
Model is calculated, and carries out distributed optimization using boundary node of the interregional Coordination Model to the region, thus, Consultation Center
When carrying out distributed optimization using boundary node of the interregional Coordination Model to each region, Consultation Center need to only obtain each region
Boundary node variable, without obtaining other variables in each region, i.e., Consultation Center is without obtaining the complete of electric system
Network data, therefore communication blockage and shortage of data will not be caused because of the whole network data that needs obtain, so as to improve electric power
The reliability of the economic load dispatching of system.
In addition, in embodiments of the present invention, the economic load dispatching result in each region is by the subdispatch corresponding to the region
Model is calculated, and carries out distributed optimization using boundary node of the interregional Coordination Model to the region, thus, Consultation Center
When carrying out distributed optimization using boundary node of the interregional Coordination Model to each region, Consultation Center need to only obtain each region
Boundary node variable, without obtaining other variables in each region, i.e., Consultation Center is without obtaining the complete of electric system
Network data.It is thereby achieved that the independent scheduling in each region, and realize the protection of the data-privacy of some regions.
It furthermore is in embodiments of the present invention, interregional Coordination Model and more by Economic Dispatch model decomposition
A subdispatch model, i.e., by a biggish PROBLEM DECOMPOSITION be multiple small problems, then to multiple small problems respectively into
Row calculates, thus can simplify the process for calculating the economic load dispatching result of electric system, and can be improved and calculate electric system
The efficiency of economic load dispatching result, simultaneously as the negligible amounts of parameter involved in each small problem, so as to further
Improve the reliability of the economic load dispatching of electric system.
Fig. 1 and Fig. 2 are please referred to, before step S100, Economic Dispatch method provided in an embodiment of the present invention
Further include:
Step S10, it determines the scheduling duration for carrying out economic load dispatching to electric system, and scheduling duration is averagely divided into nT
A period, wherein nT≥2。
For example, the scheduling duration for carrying out economic load dispatching to electric system can be set as one day, i.e., 24 hours, will adjust
Degree duration is averagely divided into nTA period, wherein nTIn a period, the duration of each period is identical, for example, can be by 24 hours
24 periods are divided into, are per hour a period, alternatively, can be divided into 96 periods for 24 hours, every 15 minutes are one
A period.
Please continue to refer to Fig. 2, after step sloo, before step S200, electric system provided in an embodiment of the present invention
Economic load dispatching method further include:
Step S100 ', prediction scene is set to each region, and to the multiple error fields of region extraction with new energy electric field
Scape.
Specifically, prediction scene can be set to each region using scene method, and the region with new energy electric field is taken out
Error scene is taken, for example, 100 error scenes can be extracted to the region with new energy electric field, completes setting for prediction scene
After the fixed and extraction of error scene, when establishing Economic Dispatch model, Economic Dispatch model includes prediction
The relevant parameter of scene and the relevant parameter of error scene, Economic Dispatch model consider the random of new energy electric field
Property and waveform, and the Economic Dispatch model established with this can cope with the random of new energy electric field in electric system
Property and fluctuation.
Please continue to refer to Fig. 2, after step S300, before step S400, electric system provided in an embodiment of the present invention
Economic load dispatching method further include:
Step S300 ', by subdispatch model decomposition be regional prediction model of place and domain error model of place.
It is area by the subdispatch model decomposition in the region for the region with new energy electric field in step S300 '
Model of place and domain error model of place are predicted in domain, and in the economic load dispatching result of zoning, field is predicted in first zoning
Then it is repeatedly random to predict that the result obtained after model of place carries out to zoning using domain error model of place for scape model
Optimization.Therefore, in embodiments of the present invention, in the economic load dispatching result of zoning, also by a big problem in the region
Two minor issues for corresponding respectively to prediction scene and error scene are decomposed into, thus can simplify the economic load dispatching of zoning
As a result process, and the efficiency of the economic load dispatching result of zoning can be improved, simultaneously as involved by each small problem
Parameter negligible amounts, so as to improve region economic load dispatching reliability, and then further improve electric system
The reliability of economic load dispatching.
In above-described embodiment, Economic Dispatch model can be with are as follows:
Objective function:
Constraint condition:
The prediction context restrictions condition in region:
BaPa+Daθa≤Ea；1≤a≤N (2)
The error scene constraint condition in region:
Ba,sPa,s+Da,sθa,s≤Ea,s+Ga,sPa+Ha,sθa；1≤a≤N,1≤s≤Sa (3)
The constraint condition of Consultation Center:
Coupling constraint condition between Consultation Center and region:
In above-mentioned formula, faFor the prediction scene total cost of region a；fa,sGeneration of electricity by new energy is abandoned for the error scene of region a
Expense；N is the number in region；For the number of conventional power unit in a of region；For the number of region a new energy unit；For
In the number of the load bus of period t region a；SaFor the number of the error scene of region a；For in period t region a, pre-
Survey the active power output of conventional power unit i under scene；WithThe power generation cost coefficient of conventional power unit i in respectively region a；For in period t, the abandoning generation of electricity by new energy power of region a new energy unit w in the case where predicting scene；qWAbandoning for region a is new
Energy power generation rejection penalty coefficient；For in period t, the cutting load power of region a load bus d in the case where predicting scene；qD
For the cutting load rejection penalty coefficient of region a；psFor the probability of the error scene s of region a, ps=1/Sa；For in the period
The abandoning generation of electricity by new energy power of t, region a the new energy unit w at error scene s；For in period t, region a is in error
The cutting load power of load bus d under scene s.
PaFor region a, in the case where predicting scene, each conventional power unit is in the power output matrix of day part, and contribute matrix PaMember be region
Conventional power unit i is in the power output of period t in the case where predicting scene by a, and contribute matrix PaForMatrix orMatrix；
θaFor region a, in the case where predicting scene, for each node in the phase angle matrix of day part, node includes: node (the load section in a of region
Point, non-load bus etc.), the boundary node that is connect with region a in the boundary node of region a and other regions, phase angle matrix
θaMember be the region a phase angle of a certain node in period t in the case where predicting scene；Ba、DaAnd EaIt is region a in the case where predicting scene
Parameter matrix；Pa,sFor region a, each conventional power unit is in the power output matrix of day part at error scene s, and contribute matrix Pa,s's
Member is that conventional power unit i is in the power output of period t at error scene s by region a, and contribute matrix Pa,sForMatrix orMatrix；θa,sFor region a at error scene s phase angle matrix of each node in day part, phase angle matrix θa,sMember
For region a at error scene s phase angle of a certain node in period t；Ba,s、Da,s、Ea,s、Ga,sAnd Ha,sIt is region a in error
Parameter matrix under scene s；TLab,aFor the boundary node intersection being connected in a of region with region b；TLab,bFor the region area b Zhong Yu
The boundary node intersection that domain a is connected, and m and n is corresponding two boundary nodes of connecting line of join domain a and region b；
Correspond to phase angle matrix of the boundary node m in day part in a of region, phase angle matrix for Consultation CenterMember be Consultation Center pair
Should in a of region phase angle of the boundary node m in period t；For Consultation Center corresponding to boundary node n in a of region in day part
Phase angle matrix, phase angle matrixMember be Consultation Center correspond to region a in boundary node n period t phase angle；For
Consultation Center corresponds to phase angle matrix of the boundary node m in day part in the b of region, phase angle matrixMember be that Consultation Center is corresponding
Phase angle of the boundary node m in period t in the b of region；For Consultation Center corresponding to boundary node n in the b of region in day part
Phase angle matrix, phase angle matrixMember be Consultation Center correspond to region b in boundary node n period t phase angle；For region
Phase angle matrix of the boundary node m in day part, phase angle matrix in aMember be region a in boundary node m period t phase angle；Phase angle matrix for boundary node n in a of region in day part, phase angle matrixMember be region a in boundary node n in the period
The phase angle of t.
Above-mentioned Economic Dispatch model is compact, practically, in above-mentioned Economic Dispatch model,
The prediction context restrictions condition in region includes:
For in period t, the power output matrix of region a each conventional power unit in the case where predicting scene, matrix of contributingFor row square
Battle array or column matrix, matrix of contributingMember be in period t, the power output of region a conventional power unit i in the case where predicting scene；For
The power output matrix of period t, region a each new energy unit in the case where predicting scene, matrix of contributingFor row matrix or column matrix, out
Torque battle arrayMember be in period t, the power output of region a new energy unit w in the case where predicting scene；For in period t, region a
The matrix of loadings of each load bus, matrix of loadings in the case where predicting sceneFor row matrix or column matrix, matrix of loadingsMember
For in period t, the load of region a load bus d in the case where predicting scene；For in period t, region a is each in the case where predicting scene
The abandoning generation of electricity by new energy power matrix of new energy unit abandons generation of electricity by new energy power matrixFor row matrix or column matrix, abandon
Generation of electricity by new energy power matrixMember in period t, the abandoning generation of electricity by new energy of region a new energy unit w in the case where predicting scene
Power；For in period t, the cutting load power matrix of region a each load bus in the case where predicting scene, cutting load power matrixFor row matrix or column matrix, cutting load power matrixMember be in period t, region a load section in the case where predicting scene
The load of point d；BaFor the node admittance matrix for ignoring branch resistance and set up to ground leg of region a；For in the period
The phase angle matrix of t, region a each node in the case where predicting scene, phase angle matrixFor row matrix or column matrix, phase angle matrixMember
For in period t, the phase angle of region a a certain node in the case where predicting scene；For the active power output lower limit of conventional power unit i in a of region；For the active power output upper limit of conventional power unit i in a of region；For in period t, region a new energy unit w in the case where predicting scene
Active power output；For the maximum active power output of the new energy unit w in period t region a；For conventional power unit i in a of region
Active power output climb limitation；For the active power output landslide limitation of conventional power unit i in a of region；For in period t-1, region
The active power output of a conventional power unit i in the case where predicting scene；NJFor the number of route related with region a in electric system, route packet
Include the internal wiring of region a and the interregional interconnector of join domain a and other regions；For line related with region a
The maximum transmission power value of road j；For the reactance value of route j related with region a；It is offline in period t, prediction scene
The phase angle of the node j1 of road j；For the phase angle of the node j2 of route j under period t, prediction scene；SBOn the basis of be worth, SB=
100MW；For conventional power unit i in a of region in 10 minutes adjustable power output increment；For in period t, region a is accidentally
The active power output of conventional power unit i under poor scene s.
The error scene constraint condition in region includes:
For in period t, the power output matrix of region a each conventional power unit at error scene s, matrix of contributingFor row
Matrix or column matrix, matrix of contributingMember be in period t, the power output of region a conventional power unit i at error scene s；
In period t, the power output matrix of region a each new energy unit at error scene s, matrix of contributingFor row matrix or column square
Battle array, matrix of contributingMember be in period t, the power output of region a new energy unit w at error scene s；For in the period
The matrix of loadings of t, region a each load bus at error scene s, matrix of loadingsFor row matrix or column matrix, matrix of loadingsMember be in period t, the load of region a load bus d at error scene s；For in period t, region a is accidentally
The abandoning generation of electricity by new energy power matrix of each new energy unit under poor scene s abandons generation of electricity by new energy power matrixFor row matrix
Or column matrix, abandon generation of electricity by new energy power matrixMember in period t, region a new energy unit w at error scene s
Abandon generation of electricity by new energy power；For in period t, the cutting load power matrix of region a each load bus at error scene s,
Cutting load power matrixFor row matrix or column matrix, cutting load power matrixMember be exist in period t, region a
The load of load bus d under error scene s；For in period t, the phase angle matrix of region a each node at error scene s, phase
Angular moment battle arrayFor row matrix or column matrix, phase angle matrixMember be in period t, region a a certain node at error scene s
Phase angle；For in period t, the active power output of region a new energy unit w at error scene s；For in period t, area
The maximum active power output of domain a new energy unit w at error scene s；For in period t-1, region a is at error scene s
The active power output of conventional power unit i；For the phase angle of the node j1 of route j at period t, error scene s；For in the period
T, under error scene s the node j2 of route j phase angle；For in period t, phase of the region a in prediction scene lower boundary node m
Angle；For in period t, phase angle of the region a in error scene s lower boundary node m；For in period t, region a is in prediction field
The phase angle of scape lower boundary node n；For in period t, phase angle of the region a in error scene s lower boundary node n.
The constraint condition of Consultation Center specifically:
Coupling constraint condition between Consultation Center and region specifically:
For the phase angle for corresponding to boundary node m in a of region in period t Consultation Center；For in period t Consultation Center
Phase angle corresponding to boundary node m in the b of region；For the phase for corresponding to boundary node n in a of region in period t Consultation Center
Angle；For the phase angle for corresponding to boundary node n in the b of region in period t Consultation Center.
Regional prediction model of place are as follows:
Objective function:
Constraint condition:
BaPa+Daθa≤Ea；1≤a≤N (21)
Phase angle of the boundary node m in day part of region a is issued to for kth time distributed optimization iterative coordination center
Matrix；It is phase angular moment of the boundary node n in day part that kth time distributed optimization iterative coordination center is issued to region a
Battle array；It is the coupling constraint condition that corresponds between Consultation Center and region of kth time distributed optimization iteration each
The Lagrange multiplier of period,It is that kth time distributed optimization iteration corresponds between Consultation Center and region
Quadratic penalty function multiplier of the coupling constraint condition in day part；For the corresponding intermediate change of region a and error scene aggregation group x
Amount, total XaIt is a；E is column matrix, and the member of column matrix is 1；FaFor optimal cutling coefficient matrix；MaAnd NaIt is optimal cutling
Coefficient matrix；Pa TFor region a in the case where predicting scene each conventional power unit day part power output matrix transposed matrix；For area
The domain a transposed matrix of each node in the phase angle matrix of day part in the case where predicting scene.
Domain error model of place are as follows:
Objective function:
Constraint condition:
Ba,sPa,s+Da,sθa,s≤Ea,s+Ga,sPa,l+Ha,sθa,l；1≤a≤N,1≤s≤Sa (24)
Pa,lFor the l times random optimization iteration, the region a being calculated according to regional prediction model of place is in prediction scene
Under each conventional power unit day part power output matrix；θa,lFor the l times random optimization iteration, according to regional prediction model of place meter
The obtained region a phase angle matrix of each node in day part in the case where predicting scene.
Interregional Coordination Model are as follows:
Objective function are as follows:
Constraint condition are as follows:
For kth time distributed optimization iteration, it is calculated and is uploaded in coordination according to regional prediction model of place
Phase angle matrix of the boundary node m of the region a of the heart in day part；For kth time distributed optimization iteration, according to regional prediction
Model of place is calculated and uploads to phase angle matrix of the boundary node n in day part of the region a of Consultation Center.
Please continue to refer to Fig. 2, in embodiments of the present invention, step S400 may include:
Step S410, set electric system in parameter initial value, initial value include in Consultation Center respectively with each region
Corresponding initial distribution formula optimum results.Specifically, distributed optimization the number of iterations k=1 can be set, parameter is setThat is, the 1st distributed optimization iteration correspond to Consultation Center with
Coupling constraint condition between region is 100 in the Lagrange multiplier of day part, and the 1st time distributed optimization iteration corresponds to
Coupling constraint condition between Consultation Center and region is also 100 in the quadratic penalty function multiplier of day part, in period t, the 1st
Secondary distributed optimization iteration is in period t region a in the phase angle of prediction scene lower boundary node m, and the 1st time distributed optimization iteration exists
Period t region a is 0 in the phase angle of prediction scene lower boundary node n.
Step S420, according to the regional prediction model of place in each region, the initial economic load dispatching in each region is calculated as a result, simultaneously
Distributed optimization is carried out using boundary node of the interregional Coordination Model to each region, makes the initial economic load dispatching result in each region
It is all satisfied the first convergence criterion, wherein the first convergence criterion are as follows:
ε is convergence precision, ε=10-3；For kth time distributed optimization iteration, correspond to area in period t Consultation Center
The phase angle of boundary node m in a of domain；For kth time distributed optimization iteration, scene lower boundary section is being predicted in period t region a
The phase angle of point m；For kth time distributed optimization iteration, correspond to the phase of boundary node m in the b of region in period t Consultation Center
Angle；For kth time distributed optimization iteration, the phase of boundary node m in the b of region under prediction scene is in period t region a
Angle.
According to the regional prediction model of place in each region, the initial economic load dispatching in each region is calculated as a result, and utilizing region
Between Coordination Model distributed optimization is carried out to the boundary node in each region, a preferable glug can be provided for subsequent calculate
Bright day multiplier initial value is convenient for subsequent calculating, and reduces and calculate the time.
Step S430, according to the regional prediction model of place in each region, the prediction economic load dispatching result in each region is calculated.I.e.
According to Lagrange multiplier initial value, the regional prediction model of place obtained in step S420, the prediction economy tune in each region is calculated
Spend result.
Step S440, according to the domain error model of place in each region, the random optimization result in each region is calculated.Utilize
The prediction economic load dispatching result that is calculated in step S430, domain error model of place, calculate the random optimization knot in each region
Fruit.
Step S450, whether the prediction economic load dispatching result for judging each region and the random optimization result in each region are all satisfied
Second convergence criterion；When meeting, the parameter of boundary node in the prediction economic load dispatching result in each region is uploaded in coordination
The heart executes step S460；When being unsatisfactory for, optimal cutling model is established,
And the optimal cutling value in each region, Jiang Gequ are calculated using the random optimization result of optimal cutling model and each region
The corresponding constraint condition for being incorporated to regional prediction model of place of the optimal cutling value in domain, executes step S430.
Wherein, the second convergence criterion are as follows:
Wherein,
fa,lFor the l times random optimization iteration, the prediction scene total cost of region a；For the l times random optimization iteration,
Phase angle matrix of the boundary node m in day part in a of region；For the l times random optimization iteration, boundary node n exists in a of region
The phase angle matrix of day part.
Optimal cutling model are as follows:
πa,s,lFor the l times random optimization iteration, the dual variable of the constraint condition of domain error model of place in day part
Matrix；XaFor by the number S of the error scene of region aaThe number of the error scene aggregation group formed after average polymerization, each mistake
Poor scene aggregation group includes Sa/XaA error scene.
It is the domain error model of place using region to pre- by the region in the region that step S430 is practical to step S450
The prediction economic load dispatching result for surveying the region that model of place is calculated carries out random optimization, when the prediction economy tune in each region
When degree result and the random optimization result in each region are all satisfied the second convergence criterion, show that the random optimization in each region is restrained,
Then complete random optimization；In the prediction economic load dispatching result in each region and the random optimization result in each region, wherein at least have
When the prediction economic load dispatching result in one region and the random optimization result in the region are unsatisfactory for the second convergence criterion, then show this
The random optimization in region is restrained, and then needs to continue with the domain error model of place in the region at this time to the region by the region
The prediction economic load dispatching result in the region that prediction model of place is calculated carries out random optimization, that is, carries out next time random excellent
Change.In this way, obtaining the prediction economic load dispatching result in optimal each region by multiple random optimization.
In the above-described embodiments, optimal cutling model is used SaThe X formed after a error scene average polymerizationaA error
Scene aggregation group construction, in practical applications, S also can be directly used in optimal cutling modelaA error scene directly carries out structure
It makes, specifically, optimal cutling model can be with are as follows:
Wherein,For region a intermediate variable corresponding with error scene s, total SaIt is a.
At this point, regional prediction model of place can be with are as follows:
Objective function:
Constraint condition:
BaPa+Daθa≤Ea；1≤a≤N (21)
Step S460, according to interregional Coordination Model, the distributed optimization result for corresponding respectively to each region is calculated.It completes
Prediction using the domain error model of place in region to the region being calculated by the regional prediction model of place in the region
After economic load dispatching result carries out random optimization, then boundary is saved in the prediction economic load dispatching result that Consultation Center uploads according to each region
The parameter of point, and according to interregional Coordination Model, distributed optimization result is calculated.
Step S470, judge the prediction economic load dispatching result in each region and correspond respectively to the distributed optimization knot in each region
Whether fruit is all satisfied the first convergence criterion；When meeting, using the prediction economic load dispatching result in each region as the warp of electric system
Ji scheduling result, executes step S480；When being unsatisfactory for, parameter more new model is established, using parameter more new model, calculates and updates
Parameter afterwards executes step S430.Wherein, parameter more new model are as follows:
It is the coupling constraint that -1 distributed optimization iteration of kth corresponds between Consultation Center and region
Lagrange multiplier of the condition in day part；It is that -1 distributed optimization iteration of kth corresponds to Consultation Center
The quadratic penalty function multiplier of coupling constraint condition between region in day part；α is to adjust step parameter, 1≤α≤3, example
Such as, α=1.05.
Step S460 and step S470 is actually that Consultation Center is saved using boundary of the interregional Coordination Model to each region
Point carries out distributed optimization, optimal regional prediction economic load dispatching result is calculated；When the prediction economic load dispatching in each region
When being as a result all satisfied the first convergence criterion with the distributed optimization result for corresponding respectively to each region, at this point, Consultation Center is to each
The distributed optimization of the boundary node in region is restrained, then the prediction economic load dispatching result in each region collectively forms the warp of electric system
Ji scheduling result；When each region prediction economic load dispatching result with correspond respectively to each region distributed optimization result in,
In at least one region prediction economic load dispatching result with correspond to the region distributed optimization result be unsatisfactory for the first convergence
When criterion, then shows that Consultation Center does not restrain the distributed optimization of the boundary node in each region, then need to be divided next time
Cloth optimization since parameter is updated according to parameter more new model, then needs again when carrying out distributed optimization next time
Prediction using the domain error model of place in region to the region being calculated by the regional prediction model of place in the region
Economic load dispatching result carries out random optimization.
Step S480, the economic load dispatching result of output power system.
The above description is merely a specific embodiment, but scope of protection of the present invention is not limited thereto, any
Those familiar with the art in the technical scope disclosed by the present invention, can easily think of the change or the replacement, and should all contain
Lid is within protection scope of the present invention.Therefore, protection scope of the present invention should be based on the protection scope of the described claims.
Claims (10)
1. a kind of Economic Dispatch method characterized by comprising
Step S100, electric system is decoupled as Consultation Center and multiple regions；
Step S200, according to the Consultation Center and multiple regions, Economic Dispatch model is established, wherein institute
The target for stating Economic Dispatch model is total power generation expense of the conventional power unit in scheduling duration in the electric system
With abandon the sum of generation of electricity by new energy cutting load rejection penalty；
It step S300, is that interregional Coordination Model and multiple regions dispatch mould by the Economic Dispatch model decomposition
Type, wherein multiple subdispatch models and multiple regions correspond, for the region with new energy electric field,
Multiple subdispatch models can be decomposed into regional prediction model of place and domain error model of place, the regional prediction
Model of place is used to calculate the prediction economic load dispatching in each region as a result, the domain error model of place is for calculating each region
As a result, the interregional Coordination Model is corresponding with the Consultation Center, the interregional Coordination Model is used for each random optimization
The boundary node in the region carries out distributed optimization；
Step S400, according to the interregional Coordination Model and multiple subdispatch models, the economy of electric system is calculated
Scheduling result.
2. Economic Dispatch method according to claim 1, which is characterized in that before the step S100,
The Economic Dispatch method further include:
Step S10, it determines the scheduling duration for carrying out economic load dispatching to the electric system, and the scheduling duration is averagely divided
For nTA period, wherein nT≥2。
3. Economic Dispatch method according to claim 1, which is characterized in that after the step S100,
Before the step S200, the Economic Dispatch method further include:
Step S100 ', scene is predicted to each region setting, and to the multiple mistakes of the region extraction with new energy electric field
Poor scene.
4. Economic Dispatch method according to claim 3, which is characterized in that after the step S300,
Before the step S400, the Economic Dispatch method further include:
Step S300 ', by the subdispatch model decomposition be regional prediction model of place and domain error model of place.
5. Economic Dispatch method according to claim 4, which is characterized in that the step S400 includes:
Step S410, the initial value of parameter in the electric system is set, the initial value includes distinguishing in the Consultation Center
Initial distribution formula optimum results corresponding with each region；
Step S420, according to the regional prediction model of place in each region, the initial economic load dispatching knot in each region is calculated
Fruit, and distributed optimization is carried out using boundary node of the interregional Coordination Model to each region, make each region
Initial economic load dispatching result be all satisfied the first convergence criterion；
Step S430, according to the regional prediction model of place in each region, the prediction economic load dispatching knot in each region is calculated
Fruit；
Step S440, according to the domain error model of place in each region, the random optimization result in each region is calculated；
Step S450, whether the prediction economic load dispatching result for judging each region and the random optimization result in each region are all satisfied
Second convergence criterion；
When meeting, the parameter of boundary node in the prediction economic load dispatching result in each region is uploaded in the coordination
The heart executes step S460；
When being unsatisfactory for, optimal cutling model is established, and utilize the random optimization of the optimal cutling model and each region
The optimal cutling value correspondence in each region is incorporated to the regional prediction field by the optimal cutling value for as a result calculating each region
The constraint condition of scape model executes the step S430；
Step S460, according to the interregional Coordination Model, the iteration distributed optimization for corresponding respectively to each region is calculated
As a result；
Step S470, the prediction economic load dispatching result for judging each region is distributed with the iteration for corresponding respectively to each region
Whether formula optimum results are all satisfied first convergence criterion；
When meeting, using the prediction economic load dispatching result in each region as the economic load dispatching of the electric system as a result, holding
Row step S480；
When being unsatisfactory for, parameter more new model is established, using the parameter more new model, updated parameter is calculated, executes institute
State step S430；
Step S480, the economic load dispatching result of the electric system is exported.
6. Economic Dispatch method according to claim 5, which is characterized in that the Economic Dispatch
Model are as follows:
Objective function:
Constraint condition:
The constraint condition of the regional prediction model of place in the region:
BaPa+Daθa≤Ea；1≤a≤N；
The constraint condition of the domain error model of place in the region:
Ba,sPa,s+Da,sθa,s≤Ea,s+Ga,sPa+Ha,sθa；1≤a≤N,1≤s≤Sa；
The constraint condition of the Consultation Center:
Coupling constraint condition between the Consultation Center and the region:
Wherein, the constraint condition of the regional prediction model of place in the region includes:
The constraint condition of the domain error model of place in the region includes:
The constraint condition of the Consultation Center specifically:
Coupling constraint condition between the Consultation Center and the region specifically:
faFor the prediction scene total cost of region a；fa,sGeneration of electricity by new energy expense is abandoned for the error scene of region a；N is the region
Number；For the number of conventional power unit in a of region；For the number of region a new energy unit；For in period t region a
Load bus number；SaFor the number of the error scene of region a；For in period t region a, the routine in the case where predicting scene
The active power output of unit i；βi aWithThe power generation cost coefficient of conventional power unit i in respectively region a；For in the period
The abandoning generation of electricity by new energy power of t, region a the new energy unit w in the case where predicting scene；qWIt is punished for the abandoning generation of electricity by new energy of region a
Cost coefficient；For in period t, the cutting load power of region a load bus d in the case where predicting scene；qDIt is negative for cutting for region a
Lotus rejection penalty coefficient；psFor the probability of the error scene s of region a, ps=1/Sa；For in period t, region a is in error
The abandoning generation of electricity by new energy power of new energy unit w under scene s；For in period t, region a load section at error scene s
The cutting load power of point d；
PaFor region a in the case where predicting scene power output matrix of each conventional power unit in day part；θaIt is each in the case where predicting scene for region a
Phase angle matrix of the node in day part；Ba、DaAnd EaIt is parameter matrix of the region a in the case where predicting scene；Pa,sIt is region a accidentally
Power output matrix of each conventional power unit in day part under poor scene s；θa,sFor region a at error scene s each node in day part
Phase angle matrix；Ba,s、Da,s、Ea,s、Ga,sAnd Ha,sIt is parameter matrix of the region a at error scene s；TLab,aFor in a of region
The boundary node intersection being connected with region b；TLab,bFor the boundary node intersection being connected in the b of region with region a, and m and n
For corresponding two boundary nodes of connecting line of join domain a and region b；Correspond to boundary section in a of region for Consultation Center
Phase angle matrix of the point m in day part；For Consultation Center correspond to region a in boundary node n day part phase angle matrix；For Consultation Center correspond to region b in boundary node m day part phase angle matrix；Correspond to region b for Consultation Center
Phase angle matrix of the middle boundary node n in day part；For boundary node m in a of region day part phase angle matrix；For region
Phase angle matrix of the boundary node n in day part in a；
For in period t, the power output matrix of region a each conventional power unit in the case where predicting scene；For in period t, region a is pre-
Survey the power output matrix of each new energy unit under scene；For in period t, the load of region a each load bus in the case where predicting scene
Matrix；For in period t, the abandoning generation of electricity by new energy power matrix of region a each new energy unit in the case where predicting scene；
For in period t, the cutting load power matrix of region a each load bus in the case where predicting scene；BaIgnore branch resistance for region a
With the node admittance matrix set up to ground leg；For in period t, the phase angular moment of region a each node in the case where predicting scene
Battle array；For the active power output lower limit of conventional power unit i in a of region；For the active power output upper limit of conventional power unit i in a of region；
For in period t, the active power output of region a new energy unit w in the case where predicting scene；For the new energy source machine in period t region a
The maximum active power output of group w；For the active power output climbing limitation of conventional power unit i in a of region；For conventional power unit i in a of region
Active power output come down limitation；For in period t-1, the active power output of region a conventional power unit i in the case where predicting scene；NJFor institute
The number of route related with region a in electric system is stated, the route includes the internal wiring and join domain a of region a
With the interregional interconnector in other regions；For the maximum transmission power value of route j related with region a；For with region a
The reactance value of related route j；For the phase angle of the node j1 of route j under period t, prediction scene；For period t,
Predict the phase angle of the node j2 of route j under scene；SBOn the basis of be worth, SB=100MW；It is conventional power unit i in a of region at 10 points
Adjustable power output increment in clock；For in period t, the active power output of region a conventional power unit i at error scene s；
For in period t, the power output matrix of region a each conventional power unit at error scene s；In period t, region a is accidentally
The power output matrix of each new energy unit under poor scene s；For in period t, region a each load bus at error scene s
Matrix of loadings；For in period t, the abandoning generation of electricity by new energy power square of region a each new energy unit at error scene s
Battle array；For in period t, the cutting load power matrix of region a each load bus at error scene s；For in period t,
The phase angle matrix of region a each node at error scene s；For in period t, region a new energy unit w at error scene s
Active power output；For in period t, the maximum active power output of region a new energy unit w at error scene s；For
The active power output of period t-1, region a the conventional power unit i at error scene s；For the route j at period t, error scene s
Node j1 phase angle；For the phase angle of the node j2 of route j at period t, error scene s；For in period t, region
Phase angle of a in prediction scene lower boundary node m；For in period t, phase angle of the region a in error scene s lower boundary node m；For in period t, phase angle of the region a in prediction scene lower boundary node n；For in period t, region a is at error scene s
The phase angle of boundary node n；
For the phase angle for corresponding to boundary node m in a of region in period t Consultation Center；It is corresponding in period t Consultation Center
The phase angle of boundary node m in the b of region；For the phase angle for corresponding to boundary node n in a of region in period t Consultation Center；
For the phase angle for corresponding to boundary node n in the b of region in period t Consultation Center.
7. Economic Dispatch method according to claim 6, which is characterized in that
The regional prediction model of place are as follows:
Objective function:
Constraint condition:
BaPa+Daθa≤Ea；1≤a≤N；
Phase angle matrix of the boundary node m in day part of region a is issued to for kth time distributed optimization iterative coordination center；It is phase angle matrix of the boundary node n in day part that kth time distributed optimization iterative coordination center is issued to region a；It is the coupling constraint item that kth time distributed optimization iteration corresponds between the Consultation Center and the region
Part day part Lagrange multiplier,Be kth time distributed optimization iteration correspond to the Consultation Center with
The quadratic penalty function multiplier of coupling constraint condition between the region in day part；For region a and error scene aggregation group x
Corresponding intermediate variable, total XaIt is a；E is column matrix, and the member of column matrix is 1；FaFor optimal cutling coefficient matrix；MaAnd Na
It is optimal cutling coefficient matrix；Pa TFor region a in the case where predicting scene each conventional power unit day part power output matrix transposition
Matrix；For region a in the case where predicting scene transposed matrix of each node in the phase angle matrix of day part；
The domain error model of place are as follows:
Objective function:
Constraint condition:
Ba,sPa,s+Da,sθa,s≤Ea,s+Ga,sPa,l+Ha,sθa,l；1≤a≤N,1≤s≤Sa；
Pa,lFor the l times random optimization iteration, the region a being calculated according to the regional prediction model of place is in prediction scene
Under each conventional power unit day part power output matrix；θa,lFor the l times random optimization iteration, according to the regional prediction scene mould
The region a that type the is calculated phase angle matrix of each node in day part in the case where predicting scene；
The interregional Coordination Model are as follows:
Objective function are as follows:
Constraint condition are as follows:
For kth time distributed optimization iteration, it is calculated according to the regional prediction model of place and uploads to the coordination
Phase angle matrix of the boundary node m of the region a at center in day part；For kth time distributed optimization iteration, according to the area
Domain prediction model of place is calculated and uploads to phase angular moment of the boundary node n in day part of the region a of the Consultation Center
Battle array.
8. Economic Dispatch method according to claim 7, which is characterized in that
First convergence criterion are as follows:
ε is convergence precision, ε=10-3；For kth time distributed optimization iteration, correspond in a of region in period t Consultation Center
The phase angle of boundary node m；For kth time distributed optimization iteration, predicting scene lower boundary node m's in period t region a
Phase angle；For kth time distributed optimization iteration, correspond to the phase angle of boundary node m in the b of region in period t Consultation Center；For kth time distributed optimization iteration, the phase angle of boundary node m in the b of region under prediction scene is in period t region a；
Second convergence criterion are as follows:
Wherein,
fa,lFor the l times random optimization iteration, the prediction scene total cost of region a；For the l times random optimization iteration, region a
Phase angle matrix of the middle boundary node m in day part；For the l times random optimization iteration, boundary node n is in day part in a of region
Phase angle matrix.
9. Economic Dispatch method according to claim 7, which is characterized in that
The optimal cutling model are as follows:
πa,s,lFor the l times random optimization iteration, the dual variable of the constraint condition of the domain error model of place in day part
Matrix；XaFor by the number S of the error scene of region aaThe number of the error scene aggregation group formed after average polymerization, Mei Gesuo
Stating error scene aggregation group includes Sa/XaA error scene.
10. Economic Dispatch method according to claim 7, which is characterized in that
The parameter more new model are as follows:
It is the coupling that -1 distributed optimization iteration of kth corresponds between the Consultation Center and the region
Lagrange multiplier of the constraint condition in day part；It is described in -1 distributed optimization iteration of kth corresponds to
The quadratic penalty function multiplier of coupling constraint condition between Consultation Center and the region in day part；α is to adjust step parameter,
1≤α≤3。
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