CN107506894A - A kind of water power group's dispatching method for considering non-constant coupling constraint - Google Patents
A kind of water power group's dispatching method for considering non-constant coupling constraint Download PDFInfo
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Abstract
The present invention relates to a kind of water power group's dispatching method for considering non-constant coupling constraint, comprise the following steps：1) random natural water and the market guidance variable for having correlation to Hydropower Stations using Latin hypercube is sampled, and obtains information of forecasting sample matrix；2) arrangement reduction is carried out to all sample scenes in information of forecasting sample matrix by scene "flop-out" method, and obtains classical scene set；3) coupled relation of flow between the superior and the subordinate power station in Hydro Power Systems with Cascaded Reservoirs is established using Muskingun method；4) according to classical scene set and coupled relation, the risk dispatching model of flood season Hydropower Stations is established；5) MILP method solving model is used to obtain the scheduling scheme with risk partiality.Compared with prior art, the present invention has the advantages that accurate reliable, simple linear, considers water and electricity price randomness, the risk partiality for considering Power Generation.
Description
Technical field
The present invention relates to a kind of water power group dispatching method, more particularly, to a kind of water power group for considering non-constant coupling constraint
Dispatching method.
Background technology
In Hydro Power Systems with Cascaded Reservoirs, the storage outflow in power station by adjacent river course evolve to subordinate power station turn into
Storehouse flow, this process are referred to as the traffic camouflaging process in the superior and the subordinate power station.For the traffic camouflaging on river course in existing literature
Process, generally use current time expander methods describe.I.e. by flow on river course between power station evolution be approximately that current pass through one
Individual delay process travels to subordinate's reservoir from higher level's reservoir.It is few to carry out water for reservoir during dry season, and storage capacity change is stable, flow rate of water flow
Gently, approximate evolution process of the current on river course can be replaced with a delay.But flood season water is swift and violent, water flow advance mistake
Journey is rapid, and the error of scheduling decision can be caused with the traffic camouflaging process in current delay roughening processing river course, causes to be proposed
Model can not apply in practice.Therefore it is particularly significant how to portray evolution process of the current on river course.
Muskingun method is a kind of streamflow routing method that equation and water balance equation are stored based on groove.In recent years one
Directly it is widely used in the traffic camouflaging in flood period river course.Water is rich during flood season, and streamflow evolution is similar to flood period,
The relation between flood season upper pond storage and storage outflow can more meticulously be described using Muskingum equation.But extremely
The present applies Muskingum model in short-term flood season cascade hydropower Optimal Operation Model there has been no document.
In addition, when to cascade hydropower traffic control founding mathematical models, in traditional method, by between step power station
The current delay deterministic models related to the given foundation of market clearing price.But in practical power systems, due to prediction
The limitation of technology, the water and market guidance of reservoir are all to have probabilistic, therefore establish cascade hydropower random schedule model
More conform to reality.Therefore the uncertainty models of cascade hydropower turn into research emphasis in the last few years.Consider probabilistic
Cascade hydropower Short-term Optimal Operation belongs to stochastic optimization problems, and uncertainty represents risk in actual motion, how to coordinate
Contradiction between risk and power benefit, just turn into urgent problem to be solved.Conditional Lyapunov ExponentP describes loss and exceedes risk
The conditional mean of value, more trailer informations are contained, decision-making is potential when more can suitably reflect consideration uncertain factor
Loss, thus being widely used in recent years.However, the dual uncertainty of natural water and market guidance optimizes to cascade hydropower
The influence of decision-making, still need to further study.
Therefore, it is badly in need of a kind of new step power station flood season Optimization Scheduling, can either abundant simple description river course stream
The consecutive variations of amount, and can enough rapidly and accurately obtain Optimized Operation result.
The content of the invention
It is an object of the present invention to overcome the above-mentioned drawbacks of the prior art and provide a kind of accurate reliable, simple
Linearly, consider water and electricity price randomness, consider that the water power group of the non-constant coupling constraint of consideration of risk partiality of Power Generation adjusts
Degree method.
The purpose of the present invention can be achieved through the following technical solutions：
A kind of water power group's dispatching method for considering non-constant coupling constraint, comprises the following steps：
1) there is the random natural water and market electricity of correlation using Latin hypercube to Hydropower Stations
Valency variable is sampled, and obtains information of forecasting sample matrix；
2) arrangement reduction is carried out to all sample scenes in information of forecasting sample matrix by scene "flop-out" method, and obtains warp
Allusion quotation scene set；
3) coupled relation of flow between the superior and the subordinate power station in Hydro Power Systems with Cascaded Reservoirs is established using Muskingun method；
4) according to classical scene set and coupled relation, risk is quantified using Conditional Lyapunov ExponentP, with all sample scenes
Interior power benefit is regulation goal function, establishes the risk dispatching model of flood season Hydropower Stations；
5) MILP method solving model is used to obtain the scheduling scheme with risk partiality.
Described step 1) specifically includes following steps：
11) one group of classical natural water and market guidance data are obtained, and it is average to set the standard deviations of this group of data
6%；
12) sample size is set as K, and natural water and the market of K group equiprobability scenes are generated using Latin Hypercube Sampling
Research on electricity price prediction message sample matrix.
Described step 2) is specially：
The natural water of the K group equiprobability scenes of generation and market guidance message sample matrix are contracted using scene "flop-out" method
Reduce to the classical scene set of L group unequal probabilities.
Described step 3) specifically includes following steps：
31) water balance equation is established to power station m：
32) be based on Muskingum equation, according to traffic camouflaging process establish in Hydro Power Systems with Cascaded Reservoirs the superior and the subordinate power station it
Between flow coupled relation：
Qk,t=C0Ik,t-Δt+C1Ik,t+C2Qk,t-Δt
Ik,t=Qi,t+Qj,t
Wherein, n is the Hydropower Unit corresponding to the m of power station, and N is that Hydropower Unit is total, vm,t、vm,t-1Respectively power station m
Storage capacity under t the and t-1 periods, Rm,tFor natural waters of the power station m under the t periods, qn,tIt is Hydropower Unit n under the t periods
Generating flow, sm,tBe power station m abandons water, Q under the t periodsk,t、Qk,t-ΔtGo out stream, C in period t and t-1 for river course k0、
C1、C2To calculate coefficient, Ik,t-Δt、Ik,tFor river course k becoming a mandarin in period t and t-1, Qi,t、Qj,tIt is power station i and j in period t
Go out stream.
The object function of the risk dispatching model of described flood season Hydropower Stations is：
Wherein, Y is power benefit, and ω, j, t are respectively scene, power station, when segment number, Ω is the set of all scenes,
J is power station sum, and T is dispatching cycle end ηωSegment number when corresponding to >=0, α are specific gravity factor, λt,ωFor the t periods under scene ω
Market guidance, Wj,t,ωIt is power station j under scene ω in the generated energy of t periods, ρωFor the probability under scene ω, ζ is VAR values,
VAR is that water power business obtains minimum yield within following certain time, and β is confidential interval in the case of certain probability level,
ηωFor auxiliary variable.
The constraints of the risk dispatching model of described flood season Hydropower Stations includes：
A, Risk Constraint：
ηω≥0
Wherein, ηωFor auxiliary variable, when power benefit Y is more than VAR, ηωValue be zero, when power benefit is less than VAR
When, ηωFor difference between the two, ζωFor the VAR values under scene ω.
B, traffic camouflaging constrains
WhereinFor river course k calculation coefficient, three's sum is 1, Ik,t,ω、Qk,t,ωRespectively field
Scape ω river course k the t periods become a mandarin and go out stream；
C, water balance constrains
Wherein, vj,t,ω、vj,t-1,ωFor storage capacity of the power station j under t and t-1 period scenes ω, Rj,t,ωFor under scene ω,
Power station j is in the natural water of t periods, qh,j,t,ωFor under scene ω, generating streams of the Hydropower Unit h in the t periods in the j of power station
Amount, sj,t,ωFor power station j water-carrying capacity, A are abandoned under t period scenes ωk,jFor 0/1 variable, when river course k and power station j are connected
When being, Ak,j=1, otherwise value is 0,Power station j upstream set is represented,Represent what is be associated with power station
Upper river goes out to flow summation, Ω j Power station j downstream set is represented,Expression is associated down with power station
Swim the summation that becomes a mandarin in river course；
D, storage capacity constrains：
Wherein,For storage capacity lower limit,For the storage capacity upper limit；
E, Hydropower Unit reservoir traffic constraints：
Wherein,For the generating flow lower limit of Hydropower Unit h in the j of power station,For Hydropower Unit h in the j of power station
The generating flow upper limit；
F, water Climing constant is abandoned：
sj,t-1-Δsj≤sj,t≤sj,t-1+Δsj
sj,t≥0
Wherein,The upper and lower limit of water-carrying capacity is abandoned for power station j, is sj,tFor power station j current are abandoned in the t periods
Amount, Δ sjTo abandon the maximum of water climbing；
G, just last storage capacity constraint
vj,0,ω=vini,j
vj,T,ω=vterm,j
Wherein, vini,jFor power station j initial storage, vterm,jFor power station j end of term storage capacity, vj,0,ωFor power station j
Initial storage under scene ω, vj,T,ωFor the end of term storage capacity under the j scenes ω of power station.
H, unit output constrains
Wherein,For the upper and lower limit of output of Hydropower Unit h in the j of power station；
I, water energy electric energy conversion constraint
Hydropower Unit output p and generating flow q functional relation, expression formula are as follows under different storage capacity：
pj,t,ω=ej,rqj,t,ω+fj,r,Vj,r-1≤vh,t,ω≤Vj,r
Wherein, r is storage capacity number-of-fragments,Vj,r、Vj,r-1Hold for r, r-1 phase library in generating curve,
And set Vj,0=0, ej,rAnd fj,rRespectively Hydropower Unit j r phase libraries hold the first order and constant of generated output linearity curve
.
In described step 5), the Power Generation that the described scheduling scheme with risk partiality refers to avoid risk can select
Larger α values are to minimize risk, and the Power Generation of risk neutral selects less α values to maximize power benefit.
Compared with prior art, the present invention has advantages below：
First, it is accurate reliable：Compared with the conventional method, method disclosed by the invention can accurately, reliable describe the discharge of river
Evolution process, the flow coupled relation established between Hydropower Stations the superior and the subordinate power station.
2nd, simple linear：Compared with the conventional method, method disclosed by the invention is established by Muskingum linear equality
Flow coupled relation between subordinate power station, makes scheduling model more succinct.
3rd, water and electricity price randomness are considered：Due to the limitation of Predicting Technique, using water and Research on electricity price prediction information as really
Fixed scheduling decision, the deviation of scheduling scheme can be caused, accordingly, it is considered to which water and the dual uncertainty of electricity price, can be obtained
More meet the scheduling scheme of running.
4th, the risk partiality of Power Generation is considered：Conditional Lyapunov ExponentP (CVAR) can quantify to balance prospective earnings and risk
Between relation, Power Generation can dislike degree according to itself happiness to risk and select corresponding scheduling scheme.
Brief description of the drawings
Fig. 1 is discharge of river schematic diagram.
Fig. 2 is two kinds of storage capacity contrasts of scheduling model power station 10.
Prospective earnings and CVAR value changes curve maps when Fig. 3 is considers water and electricity price uncertainty.
Embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.
Embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings.
The present invention proposes a kind of water power group's dispatching method for considering non-constant coupling constraint, and the present invention has initially set up ladder
The mathematical modeling of level short-term optimal operation of hydropower, the model are up to target with total gene-ration revenue within one day, simultaneously full
The a variety of generatings of foot and the constraint with water.
Secondly, the risk of scheduling model is caused to carry out specific modeling for the randomness of water and electricity price, foundation contains
CVAR risk dispatching model, it is contemplated that the risk partiality degree of Power Generation.
In conventional model, by upstream power station i to power station k go out to be put in storage flux coupling close relation with current it is stagnant when τi,k
Represent, generally current are considered as integer or foundation and the functional relation of storage outflow when stagnant.And in fact, due to Predicting Technique
Deviation, using current when stagnant method the contact that the superior and the subordinate go out between reservoir inflow accurately can not be described meticulously.Therefore propose Maas
Jing Genfa describes the superior and the subordinate's discharge of river evolution process, going out the superior and the subordinate to be put in storage discharge relation to be closely connected and stand up.
Then, the model for solving and establishing is combined using mixed integer programming approach, is comprised the following steps that：
Step 1：Based on Muskingum establishing equation discharge of river evolution model, the power station flow coupling of joint the superior and the subordinate is closed
System establishes water balance equation, as shown in Figure 1；
Step 2：K group equiprobability contextual datas are generated using Latin hypercube, recycle scene "flop-out" method to obtain
100 groups of unequal probabilities, the more rational classical scene collection of distribution；
Step 3：Risk dispatching model is established using Conditional Lyapunov ExponentP method, by specific gravity factor α by CVAR models and mesh
Scalar functions are combined together.
Step 4：Value by changing α can obtain different risk partiality degree；
Step 5：Constrained with reference to above-mentioned traffic camouflaging, water balance constraint, Risk Constraint and the scheduling of other cascade hydropowers
The flood season cascade hydropower risk dispatching model based on Muskingun method is established in constraint；
Step 6：Above-mentioned model is solved using MILP method, obtains corresponding scheduling scheme.
Embodiment 1：
Lower mask body combines a stepped system comprising 10 power stations and carries out labor.In order to verify the present invention's
Model is contrasted when reasonability and validity and stagnant current, as shown in Fig. 2 (grey in figure when being cast in model 1 using Dynamic Water
Colo(u)r streak)；Muskingum traffic camouflaging constraint (black line in figure) in model 2；Variable, constraint number, water corresponding to two kinds of models
Result of calculation and objective function optimization results contrast situation are as shown in table 1 during curtain coating.As can be seen from the table, using Maas capital
The quantity and amount of constraint for the variable being related in the water power scheduling model of root equation are all obvious less, therefore Muskingum model is more
To be succinct.As can be seen here using the current delay scheduling model of more accurate continuous variable, Hydro Power Systems with Cascaded Reservoirs can be improved
Gene-ration revenue.
The current of table 1 delay result of calculation and objective function optimization results contrast situation
| Traffic camouflaging computational methods | Continuous variable | Discrete variable | Bound variable |
| Current are delayed | 420205 | 336000 | 1126201 |
| Muskingum | 407000 | 320500 | 927505 |
Choose the scheduling scheme that two kinds of different scheduling models optimize to obtain and carry out further comparative analysis, figure is two kinds of scheduling
The storage capacity change curve in power station 10 under model.It is as can be seen that substantially excellent using the scheduling model effect of optimization of Muskingun method
Scheduling model when current are stagnant, the change of its storage capacity tend towards stability in the scheduling end of term, and change rises and falls smaller.Therefore, in flood season
In cascade hydropower Optimized Operation, rational reservoir operation meter can be obtained by describing traffic camouflaging process using Muskingum equation
Draw so that scheduling model more meets practical operation situation.
The risk that Power Generation has been considered to further analyze the present invention likes evil degree, confidence level 0.95, examines
The scheduling scheme economy and computational efficiency for considering water and the dual uncertain factor of electricity price contrast as shown in Table 2 and Figure 3.
The scheduling scheme economy of table 2 and computational efficiency contrast
From table 2 and Fig. 3 as can be seen that with the increase of risk proportion, i.e. Power Generation risk aversion degree increases, scheduling
The prospective earnings of scheme reduce therewith, and corresponding CVAR values increase therewith, and gene-ration revenue standard deviation is gradually reduced, and also just anticipate
The distribution that taste power benefit is more concentrated, and the situation of extreme income is reduced therewith, therefore Power Generation can ensure certain economy
Risk caused by uncertain factor is minimized in the case of income, but the solution time of scheduling model can be caused to increase.
Therefore Power Generation dislikes degree according to itself happiness to risk, corresponding α values are selected to obtain contemplated system economy and risk
It is required that.
Claims (7)
1. a kind of water power group's dispatching method for considering non-constant coupling constraint, it is characterised in that comprise the following steps：
1) the random natural water and market guidance for having correlation to Hydropower Stations using Latin hypercube become
Amount is sampled, and obtains information of forecasting sample matrix；
2) arrangement reduction is carried out to all sample scenes in information of forecasting sample matrix by scene "flop-out" method, and obtains Classical Fields
Scape set；
3) coupled relation of flow between the superior and the subordinate power station in Hydro Power Systems with Cascaded Reservoirs is established using Muskingun method；
4) according to classical scene set and coupled relation, risk is quantified using Conditional Lyapunov ExponentP, with all sample scenes
Power benefit is regulation goal function, establishes the risk dispatching model of flood season Hydropower Stations；
5) MILP method solving model is used to obtain the scheduling scheme with risk partiality.
A kind of 2. water power group's dispatching method for considering non-constant coupling constraint according to claim 1, it is characterised in that institute
The step 1) stated specifically includes following steps：
11) one group of classical natural water and market guidance data are obtained, and it is average to set the standard deviations of this group of data
6%；
12) sample size is set as K, and the natural water and market guidance of K group equiprobability scenes are generated using Latin Hypercube Sampling
Information of forecasting sample matrix.
A kind of 3. water power group's dispatching method for considering non-constant coupling constraint according to claim 2, it is characterised in that institute
The step 2) stated is specially：
Using scene "flop-out" method by the natural water of the K group equiprobability scenes of generation and market guidance message sample Matrix condensation extremely
The classical scene set of L group unequal probabilities.
A kind of 4. water power group's dispatching method for considering non-constant coupling constraint according to claim 2, it is characterised in that institute
The step 3) stated specifically includes following steps：
31) water balance equation is established to power station m：
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32) Muskingum equation is based on, is established in Hydro Power Systems with Cascaded Reservoirs according to traffic camouflaging process and is flowed between the superior and the subordinate power station
The coupled relation of amount：
Qk,t=C0Ik,t-Δt+C1Ik,t+C2Qk,t-Δt
Ik,t=Qi,t+Qj,t
Wherein, n is the Hydropower Unit corresponding to the m of power station, and N is that Hydropower Unit is total, vm,t、vm,t-1Respectively power station m is in t
With the storage capacity under the t-1 periods, Rm,tFor natural waters of the power station m under the t periods, qn,tFor hairs of the Hydropower Unit n under the t periods
The magnitude of current, sm,tBe power station m abandons water, Q under the t periodsk,t、Qk,t-ΔtGo out stream, C in period t and t-1 for river course k0、C1、C2
To calculate coefficient, Ik,t-Δt、Ik,tFor river course k becoming a mandarin in period t and t-1, Qi,t、Qj,tFor power station i and j going out in period t
Stream.
A kind of 5. water power group's dispatching method for considering non-constant coupling constraint according to claim 2, it is characterised in that institute
The object function of the risk dispatching model for the flood season Hydropower Stations stated is：
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</munderover>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>t</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>T</mi>
</munderover>
<msub>
<mi>&lambda;</mi>
<mrow>
<mi>t</mi>
<mo>,</mo>
<mi>&omega;</mi>
</mrow>
</msub>
<msub>
<mi>W</mi>
<mrow>
<mi>j</mi>
<mo>,</mo>
<mi>t</mi>
<mo>,</mo>
<mi>&omega;</mi>
</mrow>
</msub>
<msub>
<mi>&rho;</mi>
<mi>&omega;</mi>
</msub>
<mo>+</mo>
<mi>&alpha;</mi>
<mo>(</mo>
<mrow>
<mi>&zeta;</mi>
<mo>-</mo>
<mfrac>
<mn>1</mn>
<mrow>
<mn>1</mn>
<mo>-</mo>
<mi>&beta;</mi>
</mrow>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>&omega;</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>&Omega;</mi>
</munderover>
<msub>
<mi>&rho;</mi>
<mi>&omega;</mi>
</msub>
<msub>
<mi>&eta;</mi>
<mi>&omega;</mi>
</msub>
</mrow>
<mo>)</mo>
<mo>)</mo>
</mrow>
</mrow>
Wherein, Y is power benefit, and ω, j, t are respectively scene, power station, when segment number, Ω is the set of all scenes, and J is
Power station sum, T is dispatching cycle end ηωSegment number when corresponding to >=0, α are specific gravity factor, λt,ωFor the t periods under scene ω
Market guidance, Wj,t,ωIt is power station j under scene ω in the generated energy of t periods, ρωFor the probability under scene ω, ζ is VAR values,
VAR is that water power business obtains minimum yield within following certain time, and β is confidential interval in the case of certain probability level,
ηωFor auxiliary variable.
A kind of 6. water power group's dispatching method for considering non-constant coupling constraint according to claim 5, it is characterised in that institute
The constraints of the risk dispatching model for the flood season Hydropower Stations stated includes：
A, Risk Constraint：
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>t</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>T</mi>
</munderover>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>J</mi>
</munderover>
<msub>
<mi>&lambda;</mi>
<mrow>
<mi>t</mi>
<mo>,</mo>
<mi>&omega;</mi>
</mrow>
</msub>
<msub>
<mi>W</mi>
<mrow>
<mi>j</mi>
<mo>,</mo>
<mi>t</mi>
<mo>,</mo>
<mi>&omega;</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>&zeta;</mi>
<mi>&omega;</mi>
</msub>
<mo>-</mo>
<msub>
<mi>&eta;</mi>
<mi>&omega;</mi>
</msub>
<mo>&le;</mo>
<mn>0</mn>
</mrow>
ηω≥0
Wherein, ηωFor auxiliary variable, when power benefit Y is more than VAR, ηωValue be zero, when power benefit is less than VAR,
ηωFor difference between the two, ζωFor the VAR values under scene ω.
B, traffic camouflaging constrains
<mrow>
<msub>
<mi>Q</mi>
<mrow>
<mi>k</mi>
<mo>,</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
<mo>,</mo>
<mi>&omega;</mi>
</mrow>
</msub>
<mo>=</mo>
<msubsup>
<mi>C</mi>
<mn>0</mn>
<mi>k</mi>
</msubsup>
<msub>
<mi>I</mi>
<mrow>
<mi>k</mi>
<mo>,</mo>
<mi>t</mi>
<mo>+</mo>
<mn>1</mn>
<mo>,</mo>
<mi>&omega;</mi>
</mrow>
</msub>
<mo>+</mo>
<msubsup>
<mi>C</mi>
<mn>1</mn>
<mi>k</mi>
</msubsup>
<msub>
<mi>I</mi>
<mrow>
<mi>k</mi>
<mo>,</mo>
<mi>t</mi>
<mo>,</mo>
<mi>&omega;</mi>
</mrow>
</msub>
<mo>+</mo>
<msubsup>
<mi>C</mi>
<mn>2</mn>
<mi>k</mi>
</msubsup>
<msub>
<mi>Q</mi>
<mrow>
<mi>k</mi>
<mo>,</mo>
<mi>t</mi>
<mo>,</mo>
<mi>&omega;</mi>
</mrow>
</msub>
</mrow>
WhereinFor river course k calculation coefficient, three's sum is 1, Ik,t,ω、Qk,t,ωRespectively scene ω
River course k the t periods become a mandarin and go out stream；
C, water balance constrains
<mrow>
<msub>
<mi>v</mi>
<mrow>
<mi>j</mi>
<mo>,</mo>
<mi>t</mi>
<mo>,</mo>
<mi>&omega;</mi>
</mrow>
</msub>
<mo>=</mo>
<msub>
<mi>v</mi>
<mrow>
<mi>j</mi>
<mo>,</mo>
<mi>t</mi>
<mo>-</mo>
<mn>1</mn>
<mo>,</mo>
<mi>&omega;</mi>
</mrow>
</msub>
<mo>+</mo>
<mrow>
<mo>(</mo>
<msub>
<mi>R</mi>
<mrow>
<mi>j</mi>
<mo>,</mo>
<mi>t</mi>
<mo>,</mo>
<mi>&omega;</mi>
</mrow>
</msub>
<mo>-</mo>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>j</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>J</mi>
</munderover>
<msub>
<mi>q</mi>
<mrow>
<mi>h</mi>
<mo>,</mo>
<mi>j</mi>
<mo>,</mo>
<mi>t</mi>
<mo>,</mo>
<mi>&omega;</mi>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>s</mi>
<mrow>
<mi>j</mi>
<mo>,</mo>
<mi>t</mi>
<mo>,</mo>
<mi>&omega;</mi>
</mrow>
</msub>
<mo>+</mo>
<munder>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>&Element;</mo>
<msub>
<mi>&Omega;</mi>
<mover>
<mi>j</mi>
<mo>&OverBar;</mo>
</mover>
</msub>
</mrow>
</munder>
<msub>
<mi>A</mi>
<mrow>
<mi>k</mi>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<msub>
<mi>Q</mi>
<mrow>
<mi>k</mi>
<mo>,</mo>
<mi>t</mi>
<mo>,</mo>
<mi>&omega;</mi>
</mrow>
</msub>
<mo>)</mo>
</mrow>
</mrow>
<mrow>
<munderover>
<mo>&Sigma;</mo>
<mrow>
<mi>h</mi>
<mo>=</mo>
<mn>1</mn>
</mrow>
<mi>H</mi>
</munderover>
<msub>
<mi>q</mi>
<mrow>
<mi>h</mi>
<mo>,</mo>
<mi>j</mi>
<mo>,</mo>
<mi>t</mi>
<mo>,</mo>
<mi>&omega;</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>S</mi>
<mrow>
<mi>j</mi>
<mo>,</mo>
<mi>t</mi>
<mo>,</mo>
<mi>&omega;</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>R</mi>
<mrow>
<mi>j</mi>
<mo>,</mo>
<mi>t</mi>
<mo>,</mo>
<mi>&omega;</mi>
</mrow>
</msub>
<mo>=</mo>
<munder>
<mo>&Sigma;</mo>
<mrow>
<mi>k</mi>
<mo>&Element;</mo>
<msub>
<mi>&Omega;</mi>
<munder>
<mi>j</mi>
<mo>&OverBar;</mo>
</munder>
</msub>
</mrow>
</munder>
<msub>
<mi>A</mi>
<mrow>
<mi>k</mi>
<mo>,</mo>
<mi>j</mi>
</mrow>
</msub>
<msub>
<mi>I</mi>
<mrow>
<mi>k</mi>
<mo>,</mo>
<mi>t</mi>
<mo>,</mo>
<mi>&omega;</mi>
</mrow>
</msub>
</mrow>
Wherein, vj,t,ω、vj,t-1,ωFor storage capacity of the power station j under t and t-1 period scenes ω, Rj,t,ωFor under scene ω, water power
Stand natural waters of the j in the t periods, qh,j,t,ωFor under scene ω, generating flows of the Hydropower Unit h in the t periods in the j of power station,
sj,t,ωFor power station j water-carrying capacity, A are abandoned under t period scenes ωk,jFor 0/1 variable, when river course k is associated with power station j
When, Ak,j=1, otherwise value is 0,Power station j upstream set is represented,Expression is associated upper with power station
Trip river course goes out to flow summation, Ω j Power station j downstream set is represented,Represent the downstream being associated with power station
The summation that becomes a mandarin in river course；
D, storage capacity constrains：
<mrow>
<msubsup>
<mi>v</mi>
<mi>j</mi>
<mi>min</mi>
</msubsup>
<mo>&le;</mo>
<msub>
<mi>v</mi>
<mrow>
<mi>j</mi>
<mo>,</mo>
<mi>t</mi>
<mo>,</mo>
<mi>&omega;</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msubsup>
<mi>v</mi>
<mi>j</mi>
<mi>max</mi>
</msubsup>
</mrow>
Wherein,For storage capacity lower limit,For the storage capacity upper limit；
E, Hydropower Unit reservoir traffic constraints：
<mrow>
<msubsup>
<mi>q</mi>
<mrow>
<mi>h</mi>
<mo>,</mo>
<mi>j</mi>
</mrow>
<mi>min</mi>
</msubsup>
<mo>&le;</mo>
<msub>
<mi>q</mi>
<mrow>
<mi>h</mi>
<mo>,</mo>
<mi>j</mi>
<mo>,</mo>
<mi>t</mi>
<mo>,</mo>
<mi>&omega;</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msubsup>
<mi>q</mi>
<mrow>
<mi>h</mi>
<mo>,</mo>
<mi>j</mi>
</mrow>
<mi>max</mi>
</msubsup>
</mrow>
Wherein,For the generating flow lower limit of Hydropower Unit h in the j of power station,For the generating of Hydropower Unit h in the j of power station
Flow rate upper limit；
F, water Climing constant is abandoned：
<mrow>
<msubsup>
<mi>s</mi>
<mi>j</mi>
<mi>min</mi>
</msubsup>
<mo>&le;</mo>
<msub>
<mi>s</mi>
<mrow>
<mi>j</mi>
<mo>,</mo>
<mi>t</mi>
<mo>,</mo>
<mi>&omega;</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msubsup>
<mi>s</mi>
<mi>j</mi>
<mi>max</mi>
</msubsup>
</mrow>
sj,t-1-Δsj≤sj,t≤sj,t-1+Δsj
sj,t≥0
Wherein,The upper and lower limit of water-carrying capacity is abandoned for power station j, is sj,tWater-carrying capacity is abandoned in the t periods for power station j,
ΔsjTo abandon the maximum of water climbing；
G, just last storage capacity constraint
vj,0,ω=vini,j
vj,T,ω=vterm,j
Wherein, vini,jFor power station j initial storage, vterm,jFor power station j end of term storage capacity, vj,0,ωFor power station j scenes
Initial storage under ω, vj,T,ωFor the end of term storage capacity under the j scenes ω of power station.
H, unit output constrains
<mrow>
<msubsup>
<mi>p</mi>
<mrow>
<mi>h</mi>
<mo>,</mo>
<mi>j</mi>
</mrow>
<mi>min</mi>
</msubsup>
<mo>&le;</mo>
<msub>
<mi>p</mi>
<mrow>
<mi>h</mi>
<mo>,</mo>
<mi>j</mi>
<mo>,</mo>
<mi>t</mi>
<mo>,</mo>
<mi>&omega;</mi>
</mrow>
</msub>
<mo>&le;</mo>
<msubsup>
<mi>p</mi>
<mrow>
<mi>h</mi>
<mo>,</mo>
<mi>j</mi>
</mrow>
<mi>max</mi>
</msubsup>
</mrow>
Wherein,For the upper and lower limit of output of Hydropower Unit h in the j of power station；
I, water energy electric energy conversion constraint
Hydropower Unit output p and generating flow q functional relation, expression formula are as follows under different storage capacity：
pj,t,ω=ej,rqj,t,ω+fj,r,Vj,r-1≤vh,t,ω≤Vj,r
Wherein, r is storage capacity number-of-fragments,Vj,r、Vj,r-1Hold for r, r-1 phase library in generating curve, and set
Determine Vj,0=0, ej,rAnd fj,rRespectively Hydropower Unit j r phase libraries hold the first order and constant term of generated output linearity curve.
A kind of 7. water power group's dispatching method for considering non-constant coupling constraint according to claim 1, it is characterised in that institute
In the step 5) stated, the Power Generation that the described scheduling scheme with risk partiality refers to avoid risk can select larger α values
To minimize risk, and the Power Generation of risk neutral selects less α values to maximize power benefit.
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108520317A (en) * | 2018-03-27 | 2018-09-11 | 华中科技大学 | A kind of Latin square Dynamic Programming dimension reduction method of hydroelectric system joint optimal operation |
| CN110443470A (en) * | 2019-07-18 | 2019-11-12 | 清华大学 | Honourable water combined scheduling method and device based on production confrontation network |
| CN110442914A (en) * | 2019-07-03 | 2019-11-12 | 大连理工大学 | A kind of multi-reservoir short term scheduling MILP solving model of water levels of upstream and downstream sensitivity |
| CN110838734A (en) * | 2019-11-25 | 2020-02-25 | 中国南方电网有限责任公司 | Method and device for determining new energy power dispatching output and computer equipment |
| CN113312747A (en) * | 2021-04-19 | 2021-08-27 | 广西大学 | Hydropower dynamic storage capacity economic dispatching method considering uncertainty |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040054564A1 (en) * | 2002-09-17 | 2004-03-18 | Fonseca Adolfo M. | Systems and methods for the optimization of resources in energy markets |
| CN102296562A (en) * | 2010-06-25 | 2011-12-28 | 华东电网有限公司 | Step reservoir joint flood scheduling optimization method coupling flood protection with power generation |
-
2017
- 2017-07-18 CN CN201710585210.5A patent/CN107506894B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20040054564A1 (en) * | 2002-09-17 | 2004-03-18 | Fonseca Adolfo M. | Systems and methods for the optimization of resources in energy markets |
| CN102296562A (en) * | 2010-06-25 | 2011-12-28 | 华东电网有限公司 | Step reservoir joint flood scheduling optimization method coupling flood protection with power generation |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108520317A (en) * | 2018-03-27 | 2018-09-11 | 华中科技大学 | A kind of Latin square Dynamic Programming dimension reduction method of hydroelectric system joint optimal operation |
| CN108520317B (en) * | 2018-03-27 | 2021-07-27 | 华中科技大学 | Latin square dynamic planning and dimension reduction method for hydropower system joint optimization scheduling |
| CN110442914A (en) * | 2019-07-03 | 2019-11-12 | 大连理工大学 | A kind of multi-reservoir short term scheduling MILP solving model of water levels of upstream and downstream sensitivity |
| CN110443470A (en) * | 2019-07-18 | 2019-11-12 | 清华大学 | Honourable water combined scheduling method and device based on production confrontation network |
| CN110838734A (en) * | 2019-11-25 | 2020-02-25 | 中国南方电网有限责任公司 | Method and device for determining new energy power dispatching output and computer equipment |
| CN110838734B (en) * | 2019-11-25 | 2022-05-13 | 中国南方电网有限责任公司 | Method and device for determining new energy power dispatching output and computer equipment |
| CN113312747A (en) * | 2021-04-19 | 2021-08-27 | 广西大学 | Hydropower dynamic storage capacity economic dispatching method considering uncertainty |
| CN113312747B (en) * | 2021-04-19 | 2023-07-18 | 广西大学 | Hydropower dynamic reservoir capacity economic dispatching method considering uncertainty |
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