CN107506894B - Hydropower group scheduling method considering non-constant coupling constraint - Google Patents

Hydropower group scheduling method considering non-constant coupling constraint Download PDF

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CN107506894B
CN107506894B CN201710585210.5A CN201710585210A CN107506894B CN 107506894 B CN107506894 B CN 107506894B CN 201710585210 A CN201710585210 A CN 201710585210A CN 107506894 B CN107506894 B CN 107506894B
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葛晓琳
钟俊玲
金言
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Abstract

The invention relates to a hydropower group scheduling method considering non-constant coupling constraint, which comprises the following steps: 1) sampling random natural incoming water and market electricity price variables with correlation of the cascade hydropower station group by adopting a Latin hypercube sampling method to obtain a prediction information sample matrix; 2) sorting and reducing all sample scenes in the prediction information sample matrix through a scene reduction method, and acquiring a classical scene set; 3) establishing a flow coupling relation between upper and lower hydropower stations in the step hydropower system by adopting a MaskIn method; 4) establishing a risk scheduling model of the flood season step hydropower station group according to the classical scene set and the coupling relation; 5) and solving the model by adopting a mixed integer linear programming method to obtain a scheduling scheme with risk preference. Compared with the prior art, the method has the advantages of accuracy, reliability, simplicity, linearity, consideration of randomness of incoming water and electricity prices, consideration of risk preference of power generators and the like.

Description

Hydropower group scheduling method considering non-constant coupling constraint
Technical Field
The invention relates to a hydropower group scheduling method, in particular to a hydropower group scheduling method considering non-constant coupling constraint.
Background
In the cascade hydroelectric system, the outbound flow of the hydropower station evolves to the next-level hydropower station through an adjacent river channel to become the inbound flow, and the process is called the flow evolution process of the next-level hydropower station. In the existing literature, the flow evolution process on the river channel is generally described by adopting a water flow delay method. The evolution of the flow on the riverway between the hydropower stations is similar to the propagation of the water flow from a superior reservoir to a subordinate reservoir through a time delay process. In the dry season, the reservoir has less water volume, stable reservoir capacity change and smooth water flow velocity, and can approximately use a delay to replace the evolution process of water flow on the river channel. However, water is rapid in the flood season, the water flow evolution process is rapid, and the flow evolution process of the river channel is roughened during water casting, so that the scheduling decision error is caused, and the proposed model cannot be applied in practice. Therefore, how to depict the evolution process of the water flow on the river channel is very important.
The Masjing root method is a river flow algorithm based on a tank storage equation and a water balance equation. The method has been widely applied to the flow rate evolution of the river channel in the flood period in recent years. The water coming during the flood season is abundant, the river water flow evolution is similar to that during the flood season, and the relation between the warehousing and ex-warehousing flow of the upstream reservoir in the flood season can be more finely described by utilizing the Masjing's equation. However, no literature exists so far to apply the masjing root model to the short-term flood season step hydropower optimization scheduling model.
In addition, when a mathematical model is established for the cascade hydropower station operation scheduling, in the traditional method, a relevant certainty model is established by setting the water flow delay between the cascade hydropower stations and the price of the electric power market. However, in an actual power system, due to the limitation of a prediction technology, incoming water of a reservoir and market electricity prices are uncertain, and therefore, the establishment of a step hydropower random dispatching model is more practical. The uncertainty model of stepped hydropower has therefore become a focus of research in recent years. The uncertain cascade hydropower short-term optimization scheduling belongs to a random optimization problem, uncertainty represents risk in actual operation, and the problem to be solved urgently is how to coordinate contradiction between the risk and the power generation benefit. The conditional risk value describes a conditional mean value of the loss exceeding the risk value, more tail information is included, and the potential loss of decision making under the consideration of uncertainty factors can be reflected more appropriately, so that the application is wide in recent years. However, the impact of the dual uncertainty of natural water and market price on the decision to optimize the cascade hydropower remains to be further studied.
Therefore, a new optimal scheduling method for the flood season of the cascade hydropower station is urgently needed, continuous change of river channel flow can be fully and simply described, and an optimal scheduling result can be quickly and accurately obtained.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a hydropower group dispatching method which is accurate, reliable, simple and linear, considers the randomness of incoming water and electricity prices, considers the risk preference of power generators and considers the non-constant coupling constraint.
The purpose of the invention can be realized by the following technical scheme:
a hydropower group scheduling method considering non-constant coupling constraints, comprising the steps of:
1) sampling random natural incoming water and market electricity price variables with correlation of the cascade hydropower station group by adopting a Latin hypercube sampling method to obtain a prediction information sample matrix;
2) sorting and reducing all sample scenes in the prediction information sample matrix through a scene reduction method, and acquiring a classical scene set;
3) establishing a flow coupling relation between upper and lower hydropower stations in the step hydropower system by adopting a MaskIn method;
4) according to the classical scene set and the coupling relation, risk quantification is carried out by adopting condition risk values, and a risk scheduling model of the flood season step hydropower station group is established by taking the power generation benefits in all sample scenes as a scheduling objective function;
5) and solving the model by adopting a mixed integer linear programming method to obtain a scheduling scheme with risk preference.
The step 1) specifically comprises the following steps:
11) acquiring a group of classical natural incoming water and market electricity price data, and setting the standard deviation of the group of data as 6% of the mean value;
12) and (4) setting the sampling scale as K, and generating K groups of natural incoming water and market electricity price prediction information sample matrixes of equal probability scenes by utilizing Latin hypercube sampling.
The step 2) is specifically as follows:
and reducing the generated natural incoming water and market price information sample matrix of the K groups of equal probability scenes to an L group of unequal probability classical scene sets by using a scene subtraction method.
The step 3) specifically comprises the following steps:
31) establishing a water quantity balance equation for the hydropower station m:
Figure BDA0001353335650000031
32) based on the MaskAccu equation, the coupling relation of the flow between the upper and lower hydropower stations in the cascade hydropower system is established according to the flow evolution process:
Qk,t＝C0Ik,t-Δt+C1Ik,t+C2Qk,t-Δt
Ik,t＝Qi,t+Qj,t
wherein N is the hydroelectric generating set corresponding to the hydropower station m, N is the total number of the hydroelectric generating sets, vm,t、vm,t-1Respectively the storage capacity, R, of the hydropower station m in the time periods t and t-1m,tFor the natural water of the hydropower station m in the time period t, qn,tIs the generating flow of the hydroelectric generating set n in the time period t, sm,tFor the water abandonment of the hydropower station m at the time period t, Qk,t、Qk,t-ΔtFor the outflow of river k at time t and t-1, C0、C1、C2To calculate the coefficients, Ik,t-Δt、Ik,tFor the inflow of river k at time t and t-1, Qi,t、Qj,tThe outflows for the hydropower stations i and j during the time period t.
The objective function of the risk scheduling model of the flood season step hydropower station group is as follows:
Figure BDA0001353335650000032
wherein Y is the generating benefit, omega, J and T are scenes, hydropower stations, time interval numbers, omega is the set of all scenes, J is the total number of the hydropower stations, and T is the scheduling period end ηωTime period number corresponding to not less than 0, α is specific gravity coefficient, lambdat,ωIs the market price of electricity, W, at t time under the scene omegaj,t,ωGenerating capacity rho of the hydropower station j in the time period t under the scene omegaωZeta is the VAR value for the probability under the scene omega, VAR is the minimum gain obtained by the hydropower provider in a certain time in the future under a certain probability level, β is a confidence interval, ηωAre auxiliary variables.
The constraint conditions of the risk scheduling model of the flood season step hydropower station group comprise:
A. and (4) risk constraint:
Figure BDA0001353335650000033
ηω≥0
wherein, ηωη when the power generation benefit Y is greater than VAR as an auxiliary variableωIs zero, when the power generation benefit is less than VAR, ηωAs the difference between the two, ζωIs the VAR value in scene ω.
B. Traffic evolution constraints
Figure BDA0001353335650000034
Wherein
Figure BDA0001353335650000035
Is the calculation coefficient of river k, the sum of the three is 1, Ik,t,ω、Qk,t,ωRespectively inflow and outflow of a scene omega river k in a time period t;
C. water balance constraint
Figure BDA0001353335650000041
Figure BDA0001353335650000042
Wherein v isj,t,ω、vj,t-1,ωIs the reservoir capacity R of the hydropower station j under the scene omega at the time period t and the time period t-1j,t,ωIs the natural water coming from the hydropower station j in the t period under the scene omega, qh,j,t,ωUnder the scene omega, the generating flow of a water motor set h in a hydropower station j in the time period t is sj,t,ωIs the water discharge quantity of the hydropower station j under the scene omega of the time period t, Ak,j0/1 variable, when channel k is associated with hydropower station j, A k,j1, otherwise the value is 0,
Figure BDA0001353335650000043
an upstream set of hydroelectric power stations j is shown,
Figure BDA0001353335650000044
representing the sum of the outflows of the upstream channels associated with the hydroelectric power station, omega j A downstream set of hydroelectric power stations j is represented,
Figure BDA0001353335650000045
representing the sum of the inflows of the downstream channels associated with the hydroelectric power plant;
D. and (4) library capacity constraint:
Figure BDA0001353335650000046
wherein the content of the first and second substances,
Figure BDA0001353335650000047
the lower limit of the storage capacity is,
Figure BDA0001353335650000048
is the upper limit of the storage capacity;
E. reservoir flow restraint of the hydroelectric generating set:
Figure BDA0001353335650000049
wherein the content of the first and second substances,
Figure BDA00013533356500000410
is the lower limit of the generating flow of the water motor group h in the hydropower station j,
Figure BDA00013533356500000411
the upper limit of the power generation flow of the water motor set h in the hydropower station j is set;
F. and (3) abandoning water and climbing restraint:
Figure BDA00013533356500000412
sj,t-1-Δsj≤sj,t≤sj,t-1+Δsj
sj,t≥0
wherein the content of the first and second substances,
Figure BDA00013533356500000413
the lower limit and the upper limit of j water discharge of the hydropower station are sj,tIs the water flow rate, deltas, of the hydropower station j during the period tjThe maximum value of the waste water climbing is obtained;
G. initial and final storage capacity constraints
vj,0,ω＝vini,j
vj,T,ω＝vterm,j
Wherein v isini,jIs the initial reservoir capacity, v, of the hydropower station jterm,jIs the end-of-term storage capacity, v, of the hydropower station jj,0,ωIs the initial reservoir capacity v under the condition of j scene omega of the hydropower stationj,T,ωAnd the end storage capacity is the end storage capacity of the hydropower station j under the scene omega.
H. Unit output constraint
Figure BDA00013533356500000414
Wherein the content of the first and second substances,
Figure BDA0001353335650000051
the lower limit and the upper limit of the output of a water motor set h in a hydropower station j are set;
I. constraint of water energy and electric energy conversion
The functional relation between the output p of the hydroelectric generating set and the generating flow q under different reservoir capacities has the following expression:
pj,t,ω＝ej,rqj,t,ω+fj,r,Vj,r-1≤vh,t,ω≤Vj,r
wherein r is a storage capacity segment number,
Figure BDA0001353335650000052
Vj,r、Vj,r-1for the r and r-1 sections of the storage capacity in the power generation curve, and setting Vj,0＝0，ej,rAnd fj,rThe first term and the constant term of the linear curve of the generated power are stored in the nth section of the reservoir of the hydroelectric generating set j respectively.
In the step 5), the scheduling scheme with risk preference means that the risk-avoiding power generator selects a larger alpha value to minimize the risk, and the risk-neutral power generator selects a smaller alpha value to maximize the power generation benefit.
Compared with the prior art, the invention has the following advantages:
firstly, the method is accurate and reliable: compared with the prior art, the method disclosed by the invention can accurately and reliably describe the river channel flow evolution process and establish the flow coupling relation between the upper-level hydropower stations and the lower-level hydropower stations of the cascade hydropower station group.
II, simple linearity: compared with the prior art, the method disclosed by the invention establishes the flow coupling relation between the upper and lower hydropower stations through the MaskAccu linear equation, so that the dispatching model is simpler.
Thirdly, considering the randomness of incoming water and electricity prices: due to the limitation of the prediction technology, the deviation of the scheduling scheme can be caused by taking the water and electricity price prediction information as the determined scheduling decision, so that the scheduling scheme which is more consistent with the actual operation of the system can be obtained by considering the double uncertainty of the water and electricity prices.
Fourthly, considering risk preference of the power generator: the conditional risk value (CVAR) can quantify the relationship between the expected income and the risk, and the generator can select a corresponding scheduling scheme according to the degree of the risk like and dislike of the generator.
Drawings
Fig. 1 is a schematic view of river flow.
Fig. 2 is a comparison of the capacities of two dispatch model hydroelectric power stations 10.
FIG. 3 is a graph of expected revenue versus CVAR value change in view of incoming water and power rate uncertainty.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
Examples
The invention is described in detail below with reference to the figures and specific embodiments.
The invention provides a hydropower station group scheduling method considering non-constant coupling constraint.
Secondly, specifically modeling is carried out aiming at the risk of the scheduling model caused by the randomness of the incoming water and the electricity price, a risk scheduling model containing the CVAR is established, and the risk preference degree of a generator is considered.
In the past model, the model is composed ofThe coupling relation of the flow of the upstream power station i to the flow of the upstream power station k in and out of the reservoir is determined by the water flow delay taui,kMeaning that the water flow time lag is generally considered an integer or established as a function of the flow out of the reservoir. In fact, due to the deviation of the prediction technology, the connection between the flow of the upper and lower stages of the warehouse entry and exit cannot be accurately and carefully described by adopting the water flow time lag method. Therefore, the Masjing root method is provided for describing the evolution process of the river channel flow of the upper and lower levels, and the relationship between the flow of the upper and lower levels in and out of the reservoir is closely related.
Then, a mixed integer programming method is combined with the solving of the established model, and the concrete steps are as follows:
step 1: establishing a river channel flow evolution model based on the MaskAccu equation, and establishing a water balance equation by combining the flow coupling relation of upper and lower hydropower stations, as shown in FIG. 1;
step 2: generating K groups of equal probability scene data by using a Latin hypercube sampling method, and obtaining 100 groups of unequal probabilities by using a scene subtraction method, wherein the distribution of the unequal probabilities is more reasonable in a classical scene set;
and step 3: and establishing a risk scheduling model by adopting a conditional risk value method, and combining the CVAR model with the target function through a specific gravity coefficient alpha.
And 4, step 4: different risk preference degrees can be obtained by changing the value of alpha;
and 5: establishing a flood season step hydropower risk scheduling model based on the MasJinggen method by combining the flow evolution constraint, the water balance constraint, the risk constraint and other step hydropower scheduling constraints;
step 6: and solving the model by adopting a mixed integer linear programming method to obtain a corresponding scheduling scheme.
Example 1:
the detailed analysis is described below in particular in connection with a cascade system comprising 10 hydroelectric power stations. In order to verify the reasonableness and effectiveness of the invention and compare the water flow delay model, as shown in fig. 2, a dynamic water flow delay (a grey line in the figure) is adopted in the model 1; the evolution constraint of the Mas Jing root flow in the model 2 (black line in the figure); the comparison of the variables, the number of constraints, the water flow delay calculation result and the objective function optimization result corresponding to the two models is shown in table 1. As can be seen from the table, the hydropower dispatching model adopting the MaskAccu equation has obviously fewer variables and constraints, so that the MaskAccu model is simpler. Therefore, the more accurate continuous variable water flow delay scheduling model is adopted, and the power generation benefit of the step hydroelectric system can be improved.
TABLE 1 comparison of water delay calculation results and objective function optimization results
Flow evolution calculation method Continuous variable Discrete variable Constrained variables
Water flow time delay 420205 336000 1126201
Root of Masjing 407000 320500 927505
And selecting two scheduling schemes obtained by optimizing different scheduling models to carry out further comparative analysis, wherein the diagram is a storage capacity change curve of the hydropower station 10 under the two scheduling models. It can be seen that the optimization effect of the scheduling model adopting the MaskGen method is obviously better than that of the water flow delay scheduling model, the change of the storage capacity of the scheduling model tends to be smooth at the end of the scheduling period, and the change fluctuation is small. Therefore, in the cascade hydropower optimization scheduling in the flood season, a reasonable reservoir scheduling plan can be obtained by describing the flow evolution process by adopting the MaskAccun equation, so that the scheduling model is more consistent with the actual operation condition.
For further analysis, the present invention combines the risk likes and dislikes of the power generator with a confidence level of 0.95, and the economic and computational efficiency of the dispatch protocol taking into account the uncertainty of both water and electricity prices is shown in table 2 and fig. 3.
TABLE 2 comparison of scheduling scheme economics and computational efficiency
Figure BDA0001353335650000071
As can be seen from table 2 and fig. 3, as the risk proportion increases, that is, the degree of aversion risk of the generator increases, the expected profit of the scheduling scheme decreases, the corresponding CVAR value increases, and the standard deviation of the generation profit decreases gradually, which means that the distribution of the generation profit is more concentrated and the extreme profit situation decreases, so that the generator can minimize the risk caused by the uncertainty factor while ensuring a certain economic profit, but the solution time of the scheduling model increases. Therefore, the generator selects the corresponding alpha value according to the degree of the risk likes and dislikes of the generator per se so as to obtain the requirements of expected system economy and risk.

Claims (3)

1. A hydropower group scheduling method considering non-constant coupling constraints, comprising the steps of:
1) sampling random natural incoming water and market electricity price variables with correlation of the cascade hydropower station group by adopting a Latin hypercube sampling method to obtain a prediction information sample matrix, and specifically comprising the following steps of:
11) acquiring a group of classical natural incoming water and market electricity price data, and setting the standard deviation of the group of data as 6% of the mean value;
12) setting the sampling scale as K, and utilizing Latin hypercube sampling to generate K groups of natural incoming water and market price prediction information sample matrixes of equal probability scenes;
2) sorting and reducing all sample scenes in the prediction information sample matrix through a scene reduction method, and acquiring a classical scene set;
3) the method for establishing the flow coupling relationship between upper and lower hydropower stations in the cascade hydropower system by adopting the Maskyo method specifically comprises the following steps:
31) establishing a water quantity balance equation for the hydropower station m:
Figure FDA0002455905920000011
32) based on the MaskAccu equation, the coupling relation of the flow between the upper and lower hydropower stations in the cascade hydropower system is established according to the flow evolution process:
Qk,t＝C0Ik,t-Δt+C1Ik,t+C2Qk,t-Δt
Ik,t＝Qi,t+Qj,t
wherein N is the hydroelectric generating set corresponding to the hydropower station m, N is the total number of the hydroelectric generating sets, vm,t、vm,t-1Respectively the storage capacity, R, of the hydropower station m in the time periods t and t-1m,tFor the natural water of the hydropower station m in the time period t, qn,tIs the generating flow of the hydroelectric generating set n in the time period t, sm,tFor the water abandonment of the hydropower station m at the time period t, Qk,t、Qk,t-ΔtFor the outflow of river k at time t and t-1, C0、C1、C2To calculate the coefficients, Ik,t-Δt、Ik,tFor the inflow of river k at time t and t-1, Qi,t、Qj,tThe outflows for the hydropower stations i and j during the time period t;
4) according to the classical scene set and the coupling relation, the risk is quantified by adopting condition risk values, the power generation benefits in all sample scenes are taken as a scheduling objective function, a risk scheduling model of the flood season cascade hydropower station group is established, and the objective function of the risk scheduling model of the flood season cascade hydropower station group is as follows:
Figure FDA0002455905920000012
wherein Y is the generating benefit, omega, J and T are scenes, hydropower stations, time interval numbers, omega is the set of all scenes, J is the total number of the hydropower stations, and T is the scheduling period end ηωTime period number corresponding to not less than 0, α is specific gravity coefficient, lambdat,ωIs the market price of electricity, W, at t time under the scene omegaj,t,ωGenerating capacity rho of the hydropower station j in the time period t under the scene omegaωZeta is the VAR value for the probability under the scene omega, VAR is the minimum gain obtained by the hydropower provider in a certain time in the future under a certain probability level, β is a confidence interval, ηωIs an auxiliary variable;
the constraint conditions of the risk scheduling model of the flood season step hydropower station group comprise:
A. and (4) risk constraint:
Figure FDA0002455905920000021
ηω≥0
wherein, ηωη when the power generation benefit Y is greater than VAR as an auxiliary variableωIs zero, when the power generation benefit is less than VAR, ηωAs the difference between the two, ζωVAR value under the scene omega;
B. traffic evolution constraints
Figure FDA0002455905920000022
Wherein
Figure FDA0002455905920000023
Is the calculation coefficient of river k, the sum of the three is 1, Ik,t,ω、Qk,t,ωRespectively inflow and outflow of a scene omega river k in a time period t;
C. water balance constraint
Figure FDA0002455905920000024
Figure FDA0002455905920000025
Wherein v isj,t,ω、vj,t-1,ωIs the reservoir capacity R of the hydropower station j under the scene omega at the time period t and the time period t-1j,t,ωIs the natural water coming from the hydropower station j in the t period under the scene omega, qh,j,t,ωUnder the scene omega, the generating flow of a water motor set h in a hydropower station j in the time period t is sj,t,ωIs the water discharge quantity of the hydropower station j under the scene omega of the time period t, Ak,j0/1 variable, when channel k is associated with hydropower station j, Ak,j1, otherwise the value is 0,
Figure FDA0002455905920000026
an upstream set of hydroelectric power stations j is shown,
Figure FDA0002455905920000027
representing the sum of the outflows of the upstream channels associated with the hydroelectric power station, omega j A downstream set of hydroelectric power stations j is represented,
Figure FDA0002455905920000028
representing the sum of the inflows of the downstream channels associated with the hydroelectric power plant;
D. and (4) library capacity constraint:
Figure FDA0002455905920000029
wherein the content of the first and second substances,
Figure FDA00024559059200000210
the lower limit of the storage capacity is,
Figure FDA00024559059200000211
is the upper limit of the storage capacity;
E. reservoir flow restraint of the hydroelectric generating set:
Figure FDA0002455905920000031
wherein the content of the first and second substances,
Figure FDA0002455905920000032
is the lower limit of the generating flow of the water motor group h in the hydropower station j,
Figure FDA0002455905920000033
the upper limit of the power generation flow of the water motor set h in the hydropower station j is set;
F. and (3) abandoning water and climbing restraint:
Figure FDA0002455905920000034
sj,t-1-Δsj≤sj,t≤sj,t-1+Δsj
sj,t≥0
wherein the content of the first and second substances,
Figure FDA0002455905920000035
the lower limit and the upper limit of j water discharge of the hydropower station are sj,tIs the water flow rate, deltas, of the hydropower station j during the period tjThe maximum value of the waste water climbing is obtained;
G. initial and final storage capacity constraints
vj,0,ω＝vini,j
vj,T,ω＝vterm,j
Wherein v isini,jIs the initial reservoir capacity, v, of the hydropower station jterm,jIs the end-of-term storage capacity, v, of the hydropower station jj,0,ωIs the initial reservoir capacity v under the condition of j scene omega of the hydropower stationj,T,ωThe end storage capacity is the end storage capacity of the hydropower station j under the scene omega;
H. unit output constraint
Figure FDA0002455905920000036
Wherein the content of the first and second substances,
Figure FDA0002455905920000037
the lower limit and the upper limit of the output of a water motor set h in a hydropower station j are set;
I. constraint of water energy and electric energy conversion
The functional relation between the output p of the hydroelectric generating set and the generating flow q under different reservoir capacities has the following expression:
pj,t,ω＝ej,rqj,t,ω+fj,r,Vj,r-1≤vh,t,ω≤Vj,r
wherein r is a storage capacity segment number,
Figure FDA0002455905920000038
Vj,r、Vj,r-1for the r and r-1 sections of the storage capacity in the power generation curve, and setting Vj,0＝0，ej,rAnd fj,rRespectively storing a primary term and a constant term of a linear curve of the generated power in the nth section of reservoir of the hydroelectric generating set j;
5) and solving the model by adopting a mixed integer linear programming method to obtain a scheduling scheme with risk preference.
2. A hydropower group dispatching method considering non-constant coupling constraints according to claim 1, wherein the step 2) is specifically as follows:
and reducing the generated natural incoming water and market price information sample matrix of the K groups of equal probability scenes to an L group of unequal probability classical scene sets by using a scene subtraction method.
3. The method for dispatching hydropower groups according to claim 1, wherein in the step 5), the dispatching scheme with risk preference is that the risk-avoiding power generator selects a larger alpha value to minimize the risk, and the risk-neutral power generator selects a smaller alpha value to maximize the power generation benefit.
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