CN109474006A - A kind of out-of-limit factor of unit day execution electricity positions and removing method - Google Patents
A kind of out-of-limit factor of unit day execution electricity positions and removing method Download PDFInfo
- Publication number
- CN109474006A CN109474006A CN201811282391.5A CN201811282391A CN109474006A CN 109474006 A CN109474006 A CN 109474006A CN 201811282391 A CN201811282391 A CN 201811282391A CN 109474006 A CN109474006 A CN 109474006A
- Authority
- CN
- China
- Prior art keywords
- unit
- electricity
- day
- wind
- formula
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 230000005611 electricity Effects 0.000 title claims abstract description 116
- 238000000034 method Methods 0.000 title claims abstract description 36
- 230000007774 longterm Effects 0.000 claims abstract description 44
- 238000005096 rolling process Methods 0.000 claims abstract description 27
- 238000005516 engineering process Methods 0.000 claims abstract description 16
- 238000006243 chemical reaction Methods 0.000 claims abstract description 11
- 238000012545 processing Methods 0.000 claims abstract description 7
- 239000011159 matrix material Substances 0.000 claims description 24
- 230000005684 electric field Effects 0.000 claims description 15
- 238000009987 spinning Methods 0.000 claims description 14
- 238000009826 distribution Methods 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 8
- 238000005315 distribution function Methods 0.000 claims description 8
- 230000005540 biological transmission Effects 0.000 claims description 6
- 238000007726 management method Methods 0.000 claims description 6
- 238000005070 sampling Methods 0.000 claims description 6
- 238000012546 transfer Methods 0.000 claims description 6
- 238000007619 statistical method Methods 0.000 claims 1
- 238000000354 decomposition reaction Methods 0.000 description 4
- 238000009472 formulation Methods 0.000 description 4
- 239000000203 mixture Substances 0.000 description 4
- 238000010586 diagram Methods 0.000 description 3
- 230000008030 elimination Effects 0.000 description 3
- 238000003379 elimination reaction Methods 0.000 description 3
- 238000005457 optimization Methods 0.000 description 3
- 238000010248 power generation Methods 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 238000012423 maintenance Methods 0.000 description 2
- 230000007246 mechanism Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 230000007704 transition Effects 0.000 description 2
- PEDCQBHIVMGVHV-UHFFFAOYSA-N Glycerine Chemical compound OCC(O)CO PEDCQBHIVMGVHV-UHFFFAOYSA-N 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000007812 deficiency Effects 0.000 description 1
- 238000012804 iterative process Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
Classifications
-
- H02J3/386—
-
- 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
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
-
- 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
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/76—Power conversion electric or electronic aspects
Landscapes
- Engineering & Computer Science (AREA)
- Power Engineering (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses the out-of-limit factor positioning of electricity and removing method is executed a kind of unit day, include the following steps: S1, input primary data and initial parameter；S2, the processing of wind speed correlation is carried out according to wind speed historical data；S3, the Uncertainty Management for carrying out wind power output, and carry out the certainty conversion for dispatching stochastic model a few days ago；S4, contract rolling model is solved, determines that unit plan day executes electricity；S5, the out-of-limit factor of electricity is determined based on Lagrange multiplier；S6, amendment unit day execute electricity；S7, according to Lagrange multiplier, judge whether Contract generation out-of-limit；The problem of accessing unenforceability of the lower medium and long-term transaction result in practical daily dispatch scheduling present method solves renewable energy such as wind-powered electricity generations of the existing technology, and alleviate existing contradiction between medium and long-term transaction electricity schedule requirement and the arrangement of practical daily dispatch scheduling.
Description
Technical field
The invention belongs to electricity market fields, and in particular to a kind of out-of-limit factor of unit day execution electricity positions and elimination side
Method.
Background technique
In current power market reform transition period, China not yet establish perfect ahead market and spot market mechanism and
Platform, marketing is still based on medium and long-term transaction.Under existing mechanism, part of generating units is allowed to participate in marketing, power generation
Factory and user can sign year or monthly electricity contract, be responsible for the decomposition of Contract generation by control centre.Under the new situation, it adjusts
Degree center should realize optimization nested with medium and long-term transaction result in system daily dispatch scheduling, and its difficult point is:
(1) it is directed to medium and long-term transaction, how rationally to decompose to obtain day execution electricity, guarantees the effective of medium and long-term transaction result
Property；
(2) how to solve conflicting between medium and long-term transaction electricity schedule requirement and the formulation of practical daily dispatch scheduling
Problem.
The generation schedule that most of existing literature is directed under traditional centralized dispatching model conducts a research, and only a small number of documents are to working as
The Optimized Operation in preceding electricity market transition period conducts a research, in the prior art, since renewable energy is uncertain, load is pre-
Surveying the factors such as precision and power grid itself element fault maintenance influences, the medium and long-term transaction day obtained by monthly Unit Combination model
Plan electricity, often there is not executable situation in practical daily dispatch scheduling, and with time stepping method, medium and long-term transaction
The contradiction that electricity is completed between daily dispatch scheduling arrangement can constantly aggravate, and execute electricity the day after decomposition in actual schedule plan
In there are not executable situations.
Summary of the invention
For above-mentioned deficiency in the prior art, execute a kind of unit day provided by the invention the out-of-limit factor positioning of electricity and
Removing method, for solve the renewable energy such as wind-powered electricity generation access lower medium and long-term transaction result in practical daily dispatch scheduling can not
The problem of feasibility, and alleviate the lance between the requirement of medium and long-term transaction electricity schedule and the arrangement of practical daily dispatch scheduling
Shield.
In order to achieve the above object of the invention, the technical solution adopted by the present invention are as follows:
A kind of out-of-limit factor of unit day execution electricity positions and removing method, includes the following steps:
S1: input primary data and initial parameter；
Primary data is wind speed historical data, and initial parameter includes medium and long-term transaction parameter and machine set technology parameter；Will in
Long-term trade parameter and machine set technology parameter are input to contract rolling model；Mould is dispatched into the input of machine set technology parameter a few days ago
Type；
S2: according to wind speed historical data, carrying out the processing of wind speed correlation, and according to wind power output characteristic, obtains corresponding wind
Electricity power output；
S3: carrying out the Uncertainty Management of wind power output, and carries out the certainty conversion for dispatching stochastic model a few days ago；
S4: determining that plan day executes electricity, i.e., obtains executing unit plan day by solving day contract rolling model
Charge value；
According to medium and long-term transaction parameter and machine set technology parameter, day contract rolling model is solved；
S5: determine that out-of-limit factor introduces that is, according to scheduling model a few days ago and slack variable relaxation unit day executes Constraint
Formula reconstructs objective function；
Slack variable size directly reacts out-of-limit factor position and out-of-limit electricity size, i.e., out-of-limit factor be optimum results not
For 0 slack variable；
S6: amendment unit day executes electricity, i.e., according to slack variable, reconstructs the constraint of unit day Contract Energy, and repeat to walk
Rapid S4 solves contract rolling model, updates and unit day executes electricity；
S7: day executes electricity and substitutes into scheduling model a few days ago after unit is updated, and according to Lagrange multiplier, judges contract
Whether electricity is out-of-limit again, if not out-of-limit, i.e., slack variable is 0, then ending method, otherwise return step S4, and elimination is more rationed the power supply
Amount.
Further, in step S1, the formula of scheduling model a few days ago are as follows:
In formula, f is scheduling model objective function a few days ago, is minimised as objective function with system operation cost；SUi,tFor hair
Electric motor starting cost；SDi,tFor generator outage cost；Pi,tFor unit output；I is unit variable；NEFor unit quantity；T is
Time variable；Fi c(Pi,t) it is unit i cost of electricity-generating function；Ii,tFor Unit Commitment state variable.
Further, in step S2, according to wind speed historical data, the processing of wind speed correlation is carried out, and according to wind power output
Characteristic obtains corresponding wind power output, includes the following steps:
S2-1: analyzing wind power plant historical data using Pearson correlation coefficient, obtains correlation matrix；
S2-2: the wind speed sample for meeting correlation matrix requirement is obtained using Latin Hypercube Sampling；
S2-3: according to wind power output characteristic, corresponding wind power output is converted by wind speed sample.
Further, in step S3, the Uncertainty Management of wind power output is carried out, and carries out dispatching stochastic model a few days ago
Certainty conversion, includes the following steps:
S3-1: constraint of improving the occasion models the uncertainty of wind power output, is dispatched stochastic model a few days ago；
S3-2: the conversion of being determined property of stochastic model will be dispatched a few days ago, and will be converted into MIXED INTEGER by scheduling model a few days ago
Linear programming model.
Further, in step S3-1, consider wind-powered electricity generation contribute at random to power-balance constraint, system spinning reserve constraint with
And the influence of Line Flow constraint, it is modeled using chance constrained programming theory, including power-balance constraint, system are rotated
Reserve Constraint and Line Flow constraint are modeled.
Further, the formula of power-balance constraint stochastic model are as follows:
In formula, Prob{ } is probability function；Pi,tFor unit output；I is unit variable；NEFor unit quantity；Pw,tFor wind
Electric field power output；W is wind-powered electricity generation field variable；W is wind-powered electricity generation number；DLd,tFor predicted load；D is load variation；NDFor load number
Amount；T is time variable；β1Meet the confidence level parameter of workload demand for the unit output of machine set technology parameter；
The formula of system spinning reserve constraint stochastic model are as follows:
In formula, Prob{ } is probability function；Pi minFor the l articles line transmission lower limit of the power；Pi maxFor the l articles line transmission
The upper limit of the power；For wind power plant nominal output；For the positive and negative stand-by requirement of period t system；αu、αdFor wind power output pair
The service demand factor of positive and negative spinning reserve；β2、β3For the confidence level parameter for meeting positive and negative spinning reserve demand；
The formula of Line Flow constraint stochastic model are as follows:
In formula, GSFl,i、GSFl,w、GSFl,dGenerating set, wind power plant, the load pair respectively obtained based on DC power flow
The power of the l articles route shifts distribution factor；β4、β5For the confidence level parameter for meeting Line Flow security constraint requirement.
Further, in step S3-2, the conversion of being determined property of stochastic model will be dispatched a few days ago, and will scheduling model a few days ago
It is converted into mixed integer linear programming model, is included the following steps:
S3-2-1: being counted using Latin Hypercube Sampling and the prediction error sample of correlation, by the sample and prediction
Air speed value is superimposed to obtain wind speed random sample matrix；
S3-2-2: according to output of wind electric field characteristic, wind power output sample matrix is converted by wind speed random sample matrix；
S3-2-3: shifting distribution factor based on output of wind electric field sample matrix and generator power, obtains wind power plant and always goes out
Power and wind power plant shift sample vector to the general power of route；
The calculation formula of wind power plant gross capability sample vector are as follows:
In formula, WGWFor wind power plant gross capability sample vector；WwFor output of wind electric field sample matrix；W is wind-powered electricity generation field variable；W
For wind-powered electricity generation number；
Calculation formula of the wind power plant to the general power transfer sample vector of route are as follows:
In formula, WLWSample vector is shifted to route general power for wind power plant；GSFl,wIt is wind power plant w to the function of the l articles route
Rate shifts distribution factor；WwFor the sample matrix of output of wind electric field；
S3-2-4: according to wind power plant gross capability and wind power plant to the sample vector of the general power transfer of route, statistics is utilized
Method obtains its discretization sequence for corresponding to probability-distribution function；
S3-2-5: it according to probability-distribution function, is solved by probability constraints determinization, and using dichotomy.
Further, in step S5, day executes the formula of Constraint are as follows:
In formula,Electricity is executed for unit day；Pi,tFor unit output；I is unit variable；T is time variable；T is scheduling
Period；NcFor contract unit set long-term in the signing of medium and long-term transaction parameter；ΔtFor unit scheduling time segment length.
Further, in step S5, the formula of objective function are as follows:
The day of reconstruct executes the formula of Constraint are as follows:
In formula,Electricity is executed for unit day；Pi,tFor unit output；I is unit variable；T is time variable；T is scheduling
Period；NcFor contract unit set long-term in the signing of medium and long-term transaction parameter；ΔtFor unit scheduling time segment length；μiFor machine
Group day executes electricity slack variable；
The formula of objective function after reconstruct are as follows:
In formula, in formula, F is the objective function after reconstruct；F is former scheduling model objective function a few days ago；M is positive number；μiFor
Unit day executes electricity slack variable；I is unit variable；NcTo sign medium and long-term transaction unit unit quantity.
Further, in step S6, day executes the more new formula of electricity are as follows:
In formula,Electricity is executed for unit day；For the last unit day for solving contract rolling model and obtaining
Execute electricity；Electricity is executed for unit minimum day；μiElectricity slack variable is executed for unit day.
The invention has the benefit that
The present invention proposes to execute the out-of-limit factor positioning of electricity and removing method a kind of unit day based on Lagrange multiplier,
The daily dispatch scheduling optimization nested with medium and long-term transaction plan under wind-powered electricity generation uncertainty and interdependence effects is realized, is realized
The positioning of electricity out-of-limit factor is executed unit day to eliminate with out-of-limit electricity, and by execute unit day the out-of-limit factor positioning of electricity with
Out-of-limit electricity is eliminated, and is improved the enforceability of medium and long-term transaction result, is alleviated medium and long-term transaction electricity schedule requirement
With the contradiction between daily dispatch scheduling arrangement.
Detailed description of the invention
Fig. 1 is to execute the out-of-limit factor positioning of electricity and removing method flow chart unit day；
Fig. 2 is the method flow diagram that the processing of wind speed correlation is carried out to wind speed historical data；
Fig. 3 is the method flow diagram for carrying out the Uncertainty Management of wind power output；
Fig. 4 is the method flow diagram for converting being determined property of stochastic model；
Fig. 5 is that out-of-limit electricity eliminates situation map；
Fig. 6 is contract rolling result figure.
Specific embodiment
A specific embodiment of the invention is described below, in order to facilitate understanding by those skilled in the art this hair
It is bright, it should be apparent that the present invention is not limited to the ranges of specific embodiment, for those skilled in the art,
As long as various change is in the spirit and scope of the present invention that the attached claims limit and determine, these variations are aobvious and easy
See, all are using the innovation and creation of present inventive concept in the column of protection.
A kind of out-of-limit factor of unit day execution electricity positions and removing method, as shown in Figure 1, including the following steps:
S1: input primary data and initial parameter；
Primary data is wind speed historical data, and initial parameter includes medium and long-term transaction parameter and machine set technology parameter；Will in
Long-term trade parameter and machine set technology parameter are input to contract rolling model；Mould is dispatched into the input of machine set technology parameter a few days ago
Type；
The formula of scheduling model a few days ago are as follows:
In formula, f is scheduling model objective function a few days ago, is minimised as objective function with system operation cost；SUi,tFor hair
Electric motor starting cost；SDi,tFor generator outage cost；Pi,tFor unit output；I is unit variable；NEFor unit quantity；T is
Time variable；Fi c(Pi,t) it is unit i cost of electricity-generating function；Ii,tFor Unit Commitment state variable；
Contract rolling model is the common knowledge of the art, and it will not be described here, formula are as follows:
Objective function Equation is described with the variance of each unit Contract generation schedule difference:
Competing generators i ends the calculation formula of the Contract generation schedule in xth day are as follows:
All units in competing generators i cut-off xth day are averaged the calculation formula of schedule are as follows:
Competing generators day Contract generation bound constraint formulations are as follows:
The monthly Contract generation constraint formulations of unit are as follows:
Competing generators day total power generation constraint formulations are as follows:
The macrocontract electricity that plan day each contract unit should be completed:
In formula, x is plan day number；L is the total number of days of the moon；Ei,x, Emax i, x, Emin i, x be respectively according to unit skill
The i days decomposition electricity of unit and its upper lower limit value that art parameter, contract performance and maintenance plan determine；Etrade i is unit
The monthly Contract generation of i；Eo i, x-1 are that Contract generation is completed before planning day x in unit i；li,xWithRespectively competing generators i
The Contract generation schedule and all units for ending xth day are averaged schedule；Eplan x is that plan day each contract unit is answered
The macrocontract electricity of completion；DxFor the demand electricity for planning day x；
S2: according to wind speed historical data, carrying out the processing of wind speed correlation, and according to wind power output characteristic, obtains corresponding wind
Electricity power output, as shown in Fig. 2, including the following steps:
S2-1: analyzing wind power plant historical data using Pearson correlation coefficient, obtains correlation matrix；
S2-2: the wind speed sample for meeting correlation matrix requirement is obtained using Latin Hypercube Sampling；
S2-3: according to wind power output characteristic, corresponding wind power output is converted by wind speed sample；
S3: carrying out the Uncertainty Management of wind power output, and carries out the certainty conversion for dispatching stochastic model a few days ago, such as schemes
Shown in 3, include the following steps:
S3-1: constraint of improving the occasion models the uncertainty of wind power output, is dispatched stochastic model a few days ago；
Consider the shadow that wind-powered electricity generation is contributed at random to power-balance constraint, the constraint of system spinning reserve and Line Flow constraint
It rings, is modeled using chance constrained programming theory, including to power-balance constraint, the constraint of system spinning reserve and route tide
Stream constraint is modeled.
The formula of power-balance constraint stochastic model are as follows:
In formula, Prob{ } is probability function；Pi,tFor unit output；I is unit variable；NEFor unit quantity；Pw,tFor wind
Electric field power output；W is wind-powered electricity generation field variable；W is wind-powered electricity generation number；DLd,tFor predicted load；D is load variation；NDFor load number
Amount；T is time variable；β1Meet the confidence level parameter of workload demand for the unit output of machine set technology parameter；
The formula of system spinning reserve constraint stochastic model are as follows:
In formula, Prob{ } is probability function；Pi minFor the l articles line transmission lower limit of the power；Pi maxFor the l articles line transmission
The upper limit of the power；For wind power plant nominal output；For the positive and negative stand-by requirement of period t system；αu、αdFor wind power output pair
The service demand factor of positive and negative spinning reserve；β2、β3For the confidence level parameter for meeting positive and negative spinning reserve demand；
The formula of Line Flow constraint stochastic model are as follows:
In formula, GSFl,i、GSFl,w、GSFl,dGenerating set, wind power plant, the load pair respectively obtained based on DC power flow
The power of the l articles route shifts distribution factor；β4、β5For the confidence level parameter for meeting Line Flow security constraint requirement；
S3-2: the conversion of being determined property of stochastic model will be dispatched a few days ago, and will be converted into MIXED INTEGER by scheduling model a few days ago
Linear programming form, as shown in figure 4, including the following steps:
S3-2-1: being counted using Latin Hypercube Sampling and the prediction error sample of correlation, by the sample and prediction
Air speed value is superimposed to obtain wind speed random sample matrix；
S3-2-2: according to output of wind electric field characteristic, wind power output sample matrix is converted by wind speed random sample matrix；
S3-2-3: shifting distribution factor based on output of wind electric field sample matrix and generator power, obtains wind power plant and always goes out
Power and wind power plant shift sample vector to the general power of route；
The calculation formula of wind power plant gross capability sample vector are as follows:
In formula, WGWFor wind power plant gross capability sample vector；WwFor output of wind electric field sample matrix；W is wind-powered electricity generation field variable；W
For wind-powered electricity generation number；
Calculation formula of the wind power plant to the general power transfer sample vector of route are as follows:
In formula, WLWSample vector is shifted to route general power for wind power plant；GSFl,wIt is wind power plant w to the function of the l articles route
Rate shifts distribution factor；WwFor the sample matrix of output of wind electric field；
S3-2-4: according to wind power plant gross capability and wind power plant to the sample vector of the general power transfer of route, statistics is utilized
Method obtains its discretization sequence for corresponding to probability-distribution function；
S3-2-5: it according to probability-distribution function, is solved by probability constraints determinization, and using dichotomy；
By taking power-balance formula as an example, the formula of probability constraints determinization are as follows:
In formula, FwtFor the probability-distribution function of wind power plant gross capability.
It solves to obtain final determinization formula using dichotomy are as follows:
In formula,For the probability-distribution function inverse function of wind power plant gross capability.
S4: determining that plan day executes electricity, i.e., obtains executing unit plan day by solving day contract rolling model
Charge value；
According to medium and long-term transaction parameter and machine set technology parameter, day contract rolling model is solved；
S5: determine that out-of-limit factor introduces that is, according to scheduling model a few days ago and slack variable relaxation unit day executes Constraint
Formula reconstructs objective function；
Day executes the formula of Constraint are as follows:
In formula,Electricity is executed for unit day；Pi,tFor unit output；I is unit variable；T is time variable；T is scheduling
Period；NcFor contract unit set long-term in the signing of medium and long-term transaction parameter；ΔtFor unit scheduling time segment length；
Slack variable size directly reacts out-of-limit factor position and out-of-limit electricity size, i.e., out-of-limit factor be optimum results not
For 0 slack variable；
The formula of objective function are as follows:
The day of reconstruct executes the formula of Constraint are as follows:
In formula,Electricity is executed for unit day；Pi,tFor unit output；I is unit variable；T is time variable；T is scheduling
Period；NcFor contract unit set long-term in the signing of medium and long-term transaction parameter；ΔtFor unit scheduling time segment length；μiFor machine
Group day executes electricity slack variable；
The formula of objective function after reconstruct are as follows:
In formula, F is the objective function after reconstruct；F is former scheduling model objective function a few days ago；M is positive number, is generally taken larger
Value；μiElectricity slack variable is executed for unit day；I is unit variable；NcTo sign medium and long-term transaction unit unit quantity；
S6: amendment unit day executes electricity, i.e., according to slack variable, reconstructs the constraint of unit day Contract Energy, and repeat to walk
Rapid S4 solves contract rolling model, updates and unit day executes electricity；
Day executes the more new formula of electricity are as follows:
In formula,Electricity is executed for unit day；For the last unit day for solving contract rolling model and obtaining
Execute electricity；Electricity is executed for unit minimum day；μiElectricity slack variable is executed for unit day；
S7: day executes electricity and substitutes into scheduling model a few days ago after unit is updated, and according to Lagrange multiplier, judges contract
Whether electricity is out-of-limit again, if not out-of-limit, i.e., slack variable is 0, then ending method, otherwise return step S4, and elimination is more rationed the power supply
Amount.
Example: the unit that medium and long-term transaction contract is signed in IEEE118 node example employed herein shares 19,
Medium and long-term transaction relevant parameter is as shown in 1 competing generators medium and long-term transaction relevant parameter table of table；In order to sufficiently verify institute of the present invention
The feasibility of method is proposed, example compared the contract rolling situation under two kinds of scenes:
Scene one: do not consider that contract rolling result has not executable situation in actual schedule in the works；
Scene two: consider that contract rolling result has not executable situation in actual schedule in the works, and pass through
The mentioned method of the present invention determines out-of-limit factor position and eliminates out-of-limit electricity.
Contract rolling result not can be performed in actual schedule in scene one, and the present invention is on this basis, consider
There may be out-of-limit situation for contract rolling result.In first time iterative process, the mentioned method of the present invention has been determined
Cause the not executable out-of-limit factor of Contract generation and its out-of-limit electricity size in initial contract rolling result.It can by Fig. 5
To know, G40, G47, G48 and G51 days execution Constraints of unit are to lead to the not executable factor of contract rolling result,
The wherein out-of-limit situation most serious of unit G51, out-of-limit electricity are 331MWh.
The mentioned method of the present invention just eliminates out-of-limit electricity by iteration twice according to out-of-limit result, and context of methods is finally
Contract rolling result and one contract decomposition result of scene are as shown in fig. 6, it will be appreciated from fig. 6 that cause contract rolling result
Not executable unit generation amount is restricted, and generated energy is reduced, and the generated energy of reduction is complete by units such as G21, G27 and G28
At ensure that the total power generation of competing generators plan day.
Table 1
The present invention proposes to execute the out-of-limit factor positioning of electricity and removing method a kind of unit day based on Lagrange multiplier,
The daily dispatch scheduling optimization nested with medium and long-term transaction plan under wind-powered electricity generation uncertainty and interdependence effects is realized, is realized
The positioning of electricity out-of-limit factor is executed unit day to eliminate with out-of-limit electricity, and by execute unit day the out-of-limit factor positioning of electricity with
Out-of-limit electricity is eliminated, and is improved the enforceability of medium and long-term transaction result, is alleviated medium and long-term transaction electricity schedule requirement
With the contradiction between daily dispatch scheduling arrangement.
Claims (10)
1. executing the out-of-limit factor positioning of electricity and removing method a kind of unit day, which comprises the steps of:
S1: input primary data and initial parameter；
The primary data is wind speed historical data, and the initial parameter includes medium and long-term transaction parameter and machine set technology parameter；
Medium and long-term transaction parameter and machine set technology parameter are input to contract rolling model；The input of machine set technology parameter is adjusted a few days ago
Spend model；
S2: according to wind speed historical data, carrying out the processing of wind speed correlation, and according to wind power output characteristic, obtains corresponding wind-powered electricity generation and goes out
Power；
S3: carrying out the Uncertainty Management of wind power output, and carries out the certainty conversion for dispatching stochastic model a few days ago；
S4: determining that plan day executes electricity, i.e., obtains unit plan day executing electricity by solving day contract rolling model
Value；
According to medium and long-term transaction parameter and machine set technology parameter, day contract rolling model is solved；
S5: determining that out-of-limit factor introduces that is, according to scheduling model a few days ago and slack variable relaxation unit day executes Constraint formula,
Reconstruct objective function；
The slack variable size directly reacts out-of-limit factor position and out-of-limit electricity size, i.e., out-of-limit factor be optimum results not
For 0 slack variable；
S6: amendment unit day executes electricity, i.e., according to slack variable, reconstructs the constraint of unit day Contract Energy, and repeat step S4,
Contract rolling model is solved, updates and unit day executes electricity；
S7: day executes electricity and substitutes into scheduling model a few days ago after unit is updated, and according to Lagrange multiplier, judges Contract generation
Whether out-of-limit again, if not out-of-limit, i.e., slack variable is 0, then ending method, and otherwise return step S4, eliminates out-of-limit electricity.
2. executing the out-of-limit factor positioning of electricity and removing method unit day according to claim 1, which is characterized in that described
In step S1, the formula of scheduling model a few days ago are as follows:
In formula, f is scheduling model objective function a few days ago, is minimised as objective function with system operation cost；SUi,tFor generator
Start-up cost；SDi,tFor generator outage cost；Pi,tFor unit output；I is unit variable；NEFor unit quantity；T is the time
Variable；Fi c(Pi,t) it is unit i cost of electricity-generating function；Ii,tFor Unit Commitment state variable.
3. executing the out-of-limit factor positioning of electricity and removing method unit day according to claim 1, which is characterized in that described
In step S2, according to wind speed historical data, the processing of wind speed correlation is carried out, and according to wind power output characteristic, obtains corresponding wind-powered electricity generation
Power output, includes the following steps:
S2-1: analyzing wind power plant historical data using Pearson correlation coefficient, obtains correlation matrix；
S2-2: the wind speed sample for meeting correlation matrix requirement is obtained using Latin Hypercube Sampling；
S2-3: according to wind power output characteristic, corresponding wind power output is converted by wind speed sample.
4. executing the out-of-limit factor positioning of electricity and removing method unit day according to claim 1, which is characterized in that described
In step S3, the Uncertainty Management of wind power output is carried out, and carries out the certainty conversion for dispatching stochastic model a few days ago, including such as
Lower step:
S3-1: constraint of improving the occasion models the uncertainty of wind power output, is dispatched stochastic model a few days ago；
S3-2: will dispatch the conversion of being determined property of stochastic model a few days ago, and will to be converted into MIXED INTEGER linear for scheduling model a few days ago
Plan model.
5. executing the out-of-limit factor positioning of electricity and removing method unit day according to claim 4, which is characterized in that described
In step S3-1, consider that wind-powered electricity generation is contributed at random to power-balance constraint, the constraint of system spinning reserve and Line Flow constraint
It influences, is modeled using chance constrained programming theory, including to power-balance constraint, the constraint of system spinning reserve and route
Trend constraint is modeled.
6. executing the out-of-limit factor positioning of electricity and removing method unit day according to claim 5, which is characterized in that described
The formula of power-balance constraint stochastic model are as follows:
In formula, Prob{ } is probability function；Pi,tFor unit output；I is unit variable；NEFor unit quantity；Pw,tFor wind power plant
Power output；W is wind-powered electricity generation field variable；W is wind-powered electricity generation number；DLd,tFor predicted load；D is load variation；NDFor load quantity；t
For time variable；β1Meet the confidence level parameter of workload demand for the unit output of machine set technology parameter；
The formula of system spinning reserve constraint stochastic model are as follows:
In formula, Prob{ } is probability function；Pi minFor the l articles line transmission lower limit of the power；Pi maxFor the l articles line transmission power
The upper limit；For wind power plant nominal output；For the positive and negative stand-by requirement of period t system；αu、αdFor wind power output to just,
The service demand factor of negative spinning reserve；β2、β3For the confidence level parameter for meeting positive and negative spinning reserve demand；
The formula of Line Flow constraint stochastic model are as follows:
In formula, GSFl,i、GSFl,w、GSFl,dThe generating set that is respectively obtained based on DC power flow, wind power plant, load are to the l articles
The power of route shifts distribution factor；β4、β5For the confidence level parameter for meeting Line Flow security constraint requirement.
7. executing the out-of-limit factor positioning of electricity and removing method unit day according to claim 4, which is characterized in that described
In step S3-2, the conversion of being determined property of stochastic model will be dispatched a few days ago, and will be converted into MIXED INTEGER line by scheduling model a few days ago
Property plan model, includes the following steps:
S3-2-1: being counted using Latin Hypercube Sampling and the prediction error sample of correlation, by the sample and prediction of wind speed
Value superposition obtains wind speed random sample matrix；
S3-2-2: according to output of wind electric field characteristic, wind power output sample matrix is converted by wind speed random sample matrix；
S3-2-3: shifting distribution factor based on output of wind electric field sample matrix and generator power, obtain wind power plant gross capability and
Wind power plant shifts sample vector to the general power of route；
The calculation formula of wind power plant gross capability sample vector are as follows:
In formula, WGWFor wind power plant gross capability sample vector；WwFor output of wind electric field sample matrix；W is wind-powered electricity generation field variable；W is wind
Electric field quantity；
Calculation formula of the wind power plant to the general power transfer sample vector of route are as follows:
In formula, WLWSample vector is shifted to route general power for wind power plant；GSFl,wTurn for power of the wind power plant w to the l articles route
Move distribution factor；WwFor the sample matrix of output of wind electric field；
S3-2-4: according to wind power plant gross capability and wind power plant to the sample vector of the general power transfer of route, statistical method is utilized
Obtain its discretization sequence for corresponding to probability-distribution function；
S3-2-5: it according to probability-distribution function, is solved by probability constraints determinization, and using dichotomy.
8. executing the out-of-limit factor positioning of electricity and removing method unit day according to claim 1, which is characterized in that described
In step S5, day executes the formula of Constraint are as follows:
In formula,Electricity is executed for unit day；Pi,tFor unit output；I is unit variable；T is time variable；T is scheduling week
Phase；NcFor contract unit set long-term in the signing of medium and long-term transaction parameter；ΔtFor unit scheduling time segment length.
9. executing the out-of-limit factor positioning of electricity and removing method unit day according to claim 1, which is characterized in that described
In step S5, the formula of objective function are as follows:
The day of reconstruct executes the formula of Constraint are as follows:
In formula,Electricity is executed for unit day；Pi,tFor unit output；I is unit variable；T is time variable；T is scheduling week
Phase；NcFor contract unit set long-term in the signing of medium and long-term transaction parameter；ΔtFor unit scheduling time segment length；μiFor unit
Day executes electricity slack variable；
The formula of objective function after reconstruct are as follows:
In formula, F is the objective function after reconstruct；F is former scheduling model objective function a few days ago；M is positive number；μiFor the execution of unit day
Electricity slack variable；I is unit variable；NcTo sign medium and long-term transaction unit unit quantity.
10. executing the out-of-limit factor positioning of electricity and removing method unit day according to claim 1, which is characterized in that institute
It states in step S6, day executes the more new formula of electricity are as follows:
In formula,Electricity is executed for unit day；Unit day electricity is executed for what last solution contract rolling model obtained
Amount；Electricity is executed for unit minimum day；μiElectricity slack variable is executed for unit day.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811282391.5A CN109474006B (en) | 2018-10-31 | 2018-10-31 | Method for positioning and eliminating daily execution electric quantity out-of-limit factors of unit |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811282391.5A CN109474006B (en) | 2018-10-31 | 2018-10-31 | Method for positioning and eliminating daily execution electric quantity out-of-limit factors of unit |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109474006A true CN109474006A (en) | 2019-03-15 |
| CN109474006B CN109474006B (en) | 2021-10-22 |
Family
ID=65666418
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811282391.5A Expired - Fee Related CN109474006B (en) | 2018-10-31 | 2018-10-31 | Method for positioning and eliminating daily execution electric quantity out-of-limit factors of unit |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109474006B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110826763A (en) * | 2019-09-19 | 2020-02-21 | 国网浙江省电力有限公司台州供电公司 | Middle-long term contract electric quantity decomposition method based on guided learning strategy |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140277797A1 (en) * | 2013-03-15 | 2014-09-18 | Open Access Technology International, Inc. | Systems and Methods of Determining Optimal Scheduling and Dispatch of Power Resources |
| US20160092622A1 (en) * | 2013-05-20 | 2016-03-31 | State Grid Corporation Of China | Method for modeling medium and long term wind power output model of medium and long term optimal operationof power system |
| CN108075494A (en) * | 2016-11-10 | 2018-05-25 | 中国电力科学研究院 | A kind of Unit Combination method a few days ago taken into account new energy consumption and performed with transaction |
| CN108711890A (en) * | 2018-06-27 | 2018-10-26 | 广东电网有限责任公司 | Ahead market goes out clearing method, system, device and computer readable storage medium |
-
2018
- 2018-10-31 CN CN201811282391.5A patent/CN109474006B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140277797A1 (en) * | 2013-03-15 | 2014-09-18 | Open Access Technology International, Inc. | Systems and Methods of Determining Optimal Scheduling and Dispatch of Power Resources |
| US20160092622A1 (en) * | 2013-05-20 | 2016-03-31 | State Grid Corporation Of China | Method for modeling medium and long term wind power output model of medium and long term optimal operationof power system |
| CN108075494A (en) * | 2016-11-10 | 2018-05-25 | 中国电力科学研究院 | A kind of Unit Combination method a few days ago taken into account new energy consumption and performed with transaction |
| CN108711890A (en) * | 2018-06-27 | 2018-10-26 | 广东电网有限责任公司 | Ahead market goes out clearing method, system, device and computer readable storage medium |
Non-Patent Citations (9)
| Title |
|---|
| C. YINGVIVATANAPONG 等: "Multi-Area Power Generation Dispatch in competitive Markets", 《IEEE TRANSACTION ON POWER SYSTEM》 * |
| 刘思捷: "适应月度直接交易的电量安全校核模型及方法", 《广东电力》 * |
| 刘文彬: "考虑日前市场价格和风电不确定性的月度合约市场购电模型", 《陕西电力》 * |
| 白杨等: "电量协调与成本控制的日内滚动发电计划", 《电网技术》 * |
| 董京营等: "风电接入下的时序渐进滚动修正调度模型", 《可再生能源》 * |
| 蔡秋娜等: "适应电力市场多成分直接交易的日内发电计划模型和方法", 《广东电力》 * |
| 邱威 等: "含大型风电场的环境经济调度模型与解法", 《中国电机工程学报》 * |
| 黄越辉等: "基于波动划分及时移技术的多风电场出力相关性研究", 《电力自动化设备》 * |
| 黎灿兵等: "合约电量分解通用模型与算法", 《电力系统自动化》 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110826763A (en) * | 2019-09-19 | 2020-02-21 | 国网浙江省电力有限公司台州供电公司 | Middle-long term contract electric quantity decomposition method based on guided learning strategy |
| CN110826763B (en) * | 2019-09-19 | 2022-07-26 | 国网浙江省电力有限公司台州供电公司 | Middle-long term contract electric quantity decomposition method based on guided learning strategy |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109474006B (en) | 2021-10-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Dinh et al. | A home energy management system with renewable energy and energy storage utilizing main grid and electricity selling | |
| Haas et al. | A multi-service approach for planning the optimal mix of energy storage technologies in a fully-renewable power supply | |
| Frew et al. | Flexibility mechanisms and pathways to a highly renewable US electricity future | |
| Rajesh et al. | Least cost generation expansion planning with solar power plant using differential evolution algorithm | |
| CN104779611B (en) | Micro-capacitance sensor economic load dispatching method based on centralized and distributed dual-layer optimization strategy | |
| US9026259B2 (en) | Power generation optimization in microgrid including renewable power source | |
| Srikantha et al. | Distributed optimization of dispatch in sustainable generation systems via dual decomposition | |
| Ke et al. | Coordinative real‐time sub‐transmission volt–var control for reactive power regulation between transmission and distribution systems | |
| CN114498639B (en) | Day-ahead scheduling method of multi-microgrid combined mutual aid considering demand response | |
| CN105006844A (en) | Electric power system day-ahead robust scheduling system on intermittent power generation grid connected condition | |
| Jia et al. | A retroactive approach to microgrid real-time scheduling in quest of perfect dispatch solution | |
| Frolov et al. | Operations-and uncertainty-aware installation of FACTS devices in a large transmission system | |
| Naderi et al. | A step toward cleaner energy production: A water saving-based optimization approach for economic dispatch in modern power systems | |
| Gonzalez et al. | Addressing flexibility in energy system models | |
| Aaslid et al. | Pricing electricity in constrained networks dominated by stochastic renewable generation and electric energy storage | |
| CN103633641B (en) | A kind ofly consider the medium and long-term transaction operation plan acquisition methods that wind-powered electricity generation is received | |
| Kelepouris et al. | Optimal scheduling of prosumer's battery storage and flexible loads for distribution network support | |
| Zhang et al. | Optimal microgrid operation based on model predictive control framework | |
| CN109474006A (en) | A kind of out-of-limit factor of unit day execution electricity positions and removing method | |
| Ignat-Balaci et al. | Day-Ahead Scheduling, Simulation, and Real-Time Control of an Islanded Microgrid. | |
| Zois et al. | Integrated platform for automated sustainable demand response in smart grids | |
| Araneda et al. | Challenges on integrating renewables into the Chilean grid | |
| CN113394820B (en) | Optimized scheduling method for new energy grid-connected power system | |
| Barth et al. | Load-flow based market coupling with large-scale wind power in Europe | |
| Lubega et al. | An integrated energy and water market for the supply side of the energy-water nexus in the engineered infrastructure |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20211022 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |