CN111476475A - Short-term optimized scheduling method for stepped hydropower station under multi-constraint condition - Google Patents

Short-term optimized scheduling method for stepped hydropower station under multi-constraint condition Download PDF

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CN111476475A
CN111476475A CN202010250152.2A CN202010250152A CN111476475A CN 111476475 A CN111476475 A CN 111476475A CN 202010250152 A CN202010250152 A CN 202010250152A CN 111476475 A CN111476475 A CN 111476475A
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辛阔
李泽宏
廖胜利
武新宇
楼楠
陈恩黔
周凌安
宋万礼
肖鹏
余玲
吴永琦
王荣欣
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Dalian University of Technology
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Abstract

The invention discloses a short-term optimized scheduling method of a stepped hydropower station under multi-constraint conditions, which comprises the following processes: s1, establishing an objective function with the aim of maximizing the power generation benefit; s2, establishing constraint conditions of the objective function; and S3, solving the objective function to obtain the short-term dispatching process of each power station reservoir in the cascade hydropower station. The method determines the short-term dispatching process of the reservoirs of each hydropower station, so that the system has the maximum power generation benefit.

Description

Short-term optimized scheduling method for stepped hydropower station under multi-constraint condition
Technical Field
The invention belongs to the technical field of optimal scheduling of cascade hydropower stations, and particularly relates to a short-term optimal scheduling method of cascade hydropower stations under multi-constraint conditions.
Background
With the continuous increase of the power market reformation strength, more and more clean, cheap, efficient and sustainable hydropower participates in power market competition and realizes the optimal allocation of resources. However, due to the complex characteristics of uncertainty of water supply, large difference of adjustment performance of each reservoir, close cascade hydraulic connection and the like of hydropower, great uncertainty exists in the scheduling of whether hydropower generation enterprises can deal with the traffic electric quantity according to the electric power market, and the participation of hydropower in the electric power market faces a lot of difficulties. Meanwhile, the cascade scheduling in the market environment is no longer the maximum pursuit of the generated energy, but the maximization of the hydropower benefit is realized on the premise of meeting the national clean energy policy. Therefore, the key problem to be solved is to provide a practical hydropower station group dispatching model of the cascade hydropower station in the north Pangjiang river basin under the condition of the electric power market based on the price evolution mechanism of hydropower of the cascade hydropower station in the north Pangjiang river under the condition of the multivariate electric power market and multiple uncertain influence factors.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a short-term optimized dispatching method of cascade hydropower stations under multi-constraint conditions, and determines the short-term dispatching process of reservoirs of each hydropower station so as to maximize the power generation benefit of the system.
In order to solve the technical problems, the invention provides a short-term optimized scheduling method of a cascade hydropower station under a multi-constraint condition, which is characterized by comprising the following steps of:
s1, establishing an objective function with the aim of maximizing the power generation benefit;
s2, establishing constraint conditions of the objective function;
and S3, solving the objective function to obtain the short-term dispatching process of each power station reservoir in the cascade hydropower station.
Further, the expression of the objective function is:
Figure BDA0002435187550000021
wherein: f is a power generation benefit function; t, M is the number of dispatch period periods and the number of hydro-electric stations;
Figure BDA0002435187550000022
the output and the electricity price of the hydropower station m in the t period are shown; deltatFor a period of t hours, ElmThe delay electric quantity after the control period of the No. m power station,
Figure BDA0002435187550000023
average electricity price after the control period of the m-number power station.
Further, the constraint conditions include:
(1) balance of water
Figure BDA0002435187550000024
wherein ：
Figure BDA0002435187550000025
is the initial water storage capacity of the reservoir at the time period t +1,
Figure BDA0002435187550000026
the initial water storage capacity of the reservoir at the time period t of m;
Figure BDA0002435187550000027
Figure BDA0002435187550000028
respectively representing the total warehousing flow, the departure point flow and the abandoned water flow of the reservoir m in the t period;
Figure BDA0002435187550000029
the method comprises the steps of (1) representing warehousing flow of a t-time interval of an m hydropower station, and regarding a most upstream hydropower station, representing total warehousing flow; kmThe number of direct upstream power stations of the number m hydropower stations; u shapemThe number group of the marks is the number group of the power station directly upstream of the number m hydropower station; by a function f (m, U)m[k]T) calculating the sum of the flow of the outlet of the kth direct upstream power station of the number m hydropower station in each time period and reaching the power station m in the time period t,
Figure BDA00024351875500000210
Figure BDA00024351875500000211
Figure BDA00024351875500000212
l k,mrepresenting the maximum and minimum water flow delay time section number between the kth direct upstream power station of the number m hydropower stations and the number m hydropower stations;
Figure BDA00024351875500000213
representing the immediate upstream U of hydropower station number mm[k]Warehouse-out of number power station in n time period
Figure BDA00024351875500000214
The flow of the m power stations is achieved in the t time period;
Figure BDA00024351875500000215
is Um[k]Discharge flow of hydropower station
Figure BDA0002435187550000031
The corresponding number of lag time segments;
(2) end water level control
Figure BDA0002435187550000032
wherein ：
Figure BDA0002435187550000033
scheduling end-of-term water level, zend, for reservoir mmA control target value therefor;
(3) power generation flow restriction
Figure BDA0002435187550000034
wherein ：
Figure BDA0002435187550000035
the maximum power generation reference flow of the reservoir m in the t period is obtained;
(4) power station output constraints
Figure BDA0002435187550000036
wherein ：
Figure BDA0002435187550000037
the minimum and maximum output limits of the hydropower station m in the t period are set;
(5) step total output limit
Figure BDA0002435187550000038
wherein ：h t，
Figure BDA0002435187550000039
the lower limit and the upper limit of the total output of the hydroelectric system are represented;
(6) grid partition output limit
The system comprises a plurality of primary sub-partitions and a plurality of machine sets, each primary sub-partition can also comprise a plurality of secondary sub-partitions and a plurality of machine sets, and so on, then:
Figure BDA00024351875500000310
Figure BDA00024351875500000311
wherein ,
Figure BDA00024351875500000312
representing the lower limit of the output of the i-number subarea in the t-th time period;
Figure BDA00024351875500000313
the total effective output of the number i subareas is obtained; i represents the total number of partitions;
Figure BDA00024351875500000314
a recursion function for calculating the effective output of the partition I in the period t;
(7) reservoir level restriction
Figure BDA00024351875500000315
wherein ：
Figure BDA0002435187550000041
the initial water level and the upper and lower limits of the t time period of the hydropower station m are represented;
(8) outbound flow constraint
Figure BDA0002435187550000042
wherein ：
Figure BDA0002435187550000043
the minimum comprehensive water use restriction and the maximum delivery flow limit of the reservoir m at the time t are defined;
(9) hydropower station vibration zone constraints
Figure BDA0002435187550000044
wherein ：
Figure BDA0002435187550000045
representing the upper and lower limits of the kth output vibration region of the m hydropower station in the t period, and
Figure BDA0002435187550000046
in connection with this, the present invention is,
Figure BDA0002435187550000047
the average tail water level of the m hydropower stations in the t period;
(10) minimum boot output constraint
Figure BDA0002435187550000048
Wherein: pminmIndicating minimum starting up force of m hydropower stations, i.e.
Figure BDA0002435187550000049
Greater than pminmOr is 0;
(11) hydropower station output climbing limitation
Figure BDA00024351875500000410
wherein ：ΔpmRepresenting the maximum output lifting limit of the m hydropower stations in the adjacent time period;
(12) hydropower station output fluctuation limitation
Figure BDA00024351875500000411
wherein ：tνmThe minimum interval time period number of output lifting of the m hydropower station, namely minimum t v needs to be continued at the highest and lowest points in the process of one round of output liftingmA time period;
(13) minimum force lift period number limit
The time interval from the beginning of rising to the beginning of falling of the output of the m hydropower station or from the beginning of falling to the beginning of rising is not less than t νmA plurality of time periods.
Furthermore, an associated search method is adopted to solve the objective function.
Furthermore, the generating flow is taken as a decision variable in the process of solving the objective function.
Further, the single-step correlation search process is composed of four basic operations of initial search, expansion of the influence range, correction of the edge of the influence range and correction of the difference of water amount in and out of the reservoir.
Compared with the prior art, the invention has the following beneficial effects: the short-term optimized dispatching method of the stepped hydropower stations under the multi-constraint condition determines the short-term dispatching process of the reservoirs of each hydropower station, so that the system has the maximum power generation benefit.
Drawings
Fig. 1 is a schematic diagram of a three-variable association search method: (a) the search mode satisfying the output fluctuation constraint (b) the search mode satisfying the output fluctuation and the output climbing constraint;
FIG. 2 is a diagram of a three-variable correlation search contribution process;
FIG. 3 is a schematic diagram of an associated search pattern;
FIG. 4 is a partial adjustment to meet a unit output fluctuation limit;
FIG. 5 is a partial correction for the number of segments that meet the flow rise and fall;
FIG. 6 is a partial adjustment to meet an edge ramp constraint during a traffic adjustment period;
FIG. 7 is a partial adjustment to meet the edge power generation flow stationarity at the flow adjustment interval;
FIG. 8 is a flow time interval association constraint in local regulation to meet constant difference in water volume in and out of a reservoir;
FIG. 9 is a schematic view of water balance;
FIG. 10 is a process of construction of a feasible solution phase neighborhood for a single hydropower station;
FIG. 11 is a schematic illustration of the regulation of a downstream reservoir group;
FIG. 12 is a topological diagram of a stepped hydropower station in a north-China river basin;
FIG. 13 shows electricity prices at different stations during different time periods of a day;
FIG. 14 shows typical daily calculation results of maximum dry season of power generation benefits-power output of each power station;
FIG. 15 shows typical daily calculation results of maximum power generation efficiency in dry season — step total output;
FIG. 16 is typical daily calculation results of the maximum dry season of power generation benefits-water levels of each power station;
FIG. 17 shows typical daily calculation results of maximum power generation benefits in flood seasons-the output of each power station;
FIG. 18 shows typical daily calculation results of maximum power generation benefits in flood season, namely step total output;
fig. 19 shows typical daily calculation results of power generation efficiency in the maximum flood season — each power station water level.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
And (3) giving a warehousing flow process and initial and final water levels of the reservoirs in a dispatching period, and determining a short-term dispatching process of each hydropower station reservoir with long-term regulation capacity under the condition of considering various constraint conditions, so that the system has the maximum power generation benefit. The model is suitable for the situation that power generation enterprises carry out optimized dispatching on the hydropower stations of the subordinate steps under the market condition according to the difference of peak-valley and withered electricity prices.
The invention discloses a short-term optimized scheduling method of an elevator hydropower station under multi-constraint conditions, which comprises the following processes:
step 1, establishing an objective function with the aim of maximizing the power generation benefit.
The expression of the objective function is:
Figure BDA0002435187550000061
wherein: f is a power generation benefit function; t, M is the number of dispatch period periods and the number of hydro-electric stations;
Figure BDA0002435187550000062
the output and the electricity price of the hydropower station m in the t period are shown; deltatFor a period of t hours, ElmThe delay electric quantity after the control period of the No. m power station,
Figure BDA0002435187550000063
average electricity price after the control period of the m-number power station.
And 2, establishing constraint conditions of the objective function.
The constraint conditions include:
(1) balance of water
Figure BDA0002435187550000071
wherein ：
Figure BDA0002435187550000072
is the initial water storage capacity of the reservoir at the time period t +1,
Figure BDA0002435187550000073
the initial water storage capacity of the reservoir at the time period t of m;
Figure BDA0002435187550000074
Figure BDA0002435187550000075
respectively representing the total warehousing flow, the departure point flow and the abandoned water flow of the reservoir m in the t period;
Figure BDA0002435187550000076
the method comprises the steps of (1) representing warehousing flow of a t-time interval of an m hydropower station, and regarding a most upstream hydropower station, representing total warehousing flow; kmThe number of direct upstream power stations of the number m hydropower stations; u shapemThe number group of the marks is the number group of the power station directly upstream of the number m hydropower station; by a function f (m, U)m[k]T) calculating the sum of the flow of the outlet of the kth direct upstream power station of the number m hydropower station in each time period and reaching the power station m in the time period t,
Figure BDA0002435187550000077
Figure BDA0002435187550000078
Figure BDA0002435187550000079
l k,mrepresenting the maximum and minimum water flow delay time section number between the kth direct upstream power station of the number m hydropower stations and the number m hydropower stations;
Figure BDA00024351875500000710
representing the immediate upstream U of hydropower station number mm[k]Warehouse-out of number power station in n time period
Figure BDA00024351875500000711
The flow of the m power stations is achieved in the t time period;
Figure BDA00024351875500000712
is Um[k]Discharge flow of hydropower station
Figure BDA00024351875500000713
Corresponding number of lag time segments.
(2) End water level control
Figure BDA00024351875500000714
wherein ：
Figure BDA00024351875500000715
scheduling end-of-term water level, zend, for reservoir mmFor which the target value is controlled.
(3) Power generation flow restriction
Figure BDA00024351875500000716
wherein ：
Figure BDA00024351875500000717
and (4) quoting the maximum power generation flow of the reservoir m in the t period.
(4) Power station output constraints
Figure BDA0002435187550000081
wherein ：
Figure BDA0002435187550000082
and the minimum and maximum output limits of the hydropower station m in the time period t are obtained.
(5) Step total output limit
Figure BDA0002435187550000083
wherein ：h t，
Figure BDA0002435187550000084
and the lower limit and the upper limit of the total output of the hydroelectric system are shown.
(6) Grid partition output limit
The system comprises a plurality of primary sub-partitions and a plurality of machine sets, each primary sub-partition can also comprise a plurality of secondary sub-partitions and a plurality of machine sets, and so on, then:
Figure BDA0002435187550000085
Figure BDA0002435187550000086
wherein ,
Figure BDA0002435187550000087
representing the lower limit of the output of the i-number subarea in the t-th time period;
Figure BDA0002435187550000088
the total effective output of the number i subareas is obtained; i represents the total number of partitions;
Figure BDA0002435187550000089
and (3) representing a recursive function for calculating the effective output of the partition t time period I.
(7) Reservoir level restriction
Figure BDA00024351875500000810
wherein ：
Figure BDA00024351875500000811
and the initial water level and the upper and lower limits of the t time interval of the hydropower station m are represented.
(8) Outbound flow constraint
Figure BDA00024351875500000812
wherein ：
Figure BDA00024351875500000813
the minimum comprehensive water use constraint and the maximum delivery flow limit of the reservoir m in the time period t are achieved.
(9) Hydropower station vibration zone constraints
Figure BDA00024351875500000814
wherein ：
Figure BDA00024351875500000815
representing the upper and lower limits of the kth output vibration region of the m hydropower station in the t period, and
Figure BDA00024351875500000816
in connection with this, the present invention is,
Figure BDA00024351875500000817
the average tail water level of the m hydropower stations in the t period.
(10) Minimum boot output constraint
Figure BDA0002435187550000091
Wherein: pminmIndicating minimum starting up force of m hydropower stations, i.e.
Figure BDA0002435187550000092
Greater than pminmOr 0.
(11) Hydropower station output climbing limitation
Figure BDA0002435187550000093
wherein ：ΔpmAnd the maximum output lifting limit of the m hydropower stations in the adjacent time period is represented.
(12) Hydropower station output fluctuation limitation
Figure BDA0002435187550000094
wherein ：tνmThe minimum interval time period number of output lifting of the m hydropower station, namely minimum t v needs to be continued at the highest and lowest points in the process of one round of output liftingmA plurality of time periods.
(13) Minimum force lift period number limit
The time interval from the beginning of rising to the beginning of falling of the output of the m hydropower station or from the beginning of falling to the beginning of rising is not less than t νmA plurality of time periods.
Analyzing the above constraint characteristics, it can be classified into four categories: the method comprises the following steps that firstly, single-time-interval attribute constraints of a power station, such as water quantity balance, upper and lower limits of generated flow, reservoir water level and upper and lower limits of output, vibration area constraints and the like, are obtained; power station controllability constraints such as total electric quantity, tail water level, average flow control and the like; thirdly, time coupling type constraint, such as maximum climbing speed, output stationarity, output lifting duration and the like; and fourthly, space coupling type constraint, such as the upper limit and the lower limit of the total output of water and electricity. The constraint characteristics of the types are different and have the characteristics of mutual close association, so that the method is difficult to process, and different solving strategies need to be adopted aiming at different problems so as to meet the complex application requirements.
The power generation flow is preferably used as a decision variable in short-term optimization scheduling, and the search step length is easy to set, so that the time-associated constraint condition processing is facilitated, and the calculated amount is small. The climbing output, the minimum starting output, the minimum output and the like in the constraint conditions are related to the output, and the average water consumption rate of each hydropower station in the control period is estimated at the moment, so that the constraints are converted into the flow-related constraints. E.g. for hill climbing force limiting conditions
Figure BDA0002435187550000101
Can be converted into
Figure BDA0002435187550000102
ΔqmFor amplitude limitation of the generated current, Δ qm＝Δpmηm/3600，ηm(m3the/MWh) is the average power generation water consumption rate of the hydropower station No. m. The transformation has errors, and feasibility judgment and correction are needed to be carried out on the transformation constraints after the optimization calculation is finished.
Constraints such as the upper limit of the generating flow and the upper limit of the output of the hydropower station in the constraint conditions are forcibly met in the single-time-period adjustment calculation. And the water quantity balance, the hydropower station output lower limit, the total output limit and other constraint conditions and the requirement of not increasing the water abandon are processed by a penalty function. The reservoir level constraint and the ex-reservoir flow constraint can be changed into the satisfaction of the other condition by the destruction of one of the reservoir level constraint and the ex-reservoir flow constraint, so that the high priority is forcibly satisfied in single-time-period calculation, and the low priority is processed by a penalty function method. The limitations of output climbing speed, minimum starting output, vibration area of hydropower station, output fluctuation frequency, lifting time period number and the like can be satisfied by adopting the following correlation search mode.
And 3, solving the objective function to obtain the short-term scheduling process of each power station in the cascade hydropower station.
For large-scale problems, multidimensional search by adopting a global optimization algorithm is difficult to realize in practice. And when only a specific variable is optimized each time, the time correlation constraint has a relatively large limiting effect on the solving process: if the method is strictly limited, the number and the adjustment amplitude of adjustable variables are limited, the algorithm quickly converges to a local optimal solution, and the quality of the solution is greatly influenced by the initial solution; if these constraints are relaxed first, the optimization computational efficiency is reduced. A solution algorithm for taking the thought of a neighborhood search method as a reference is provided for the solution algorithm, starting from an initial solution, a feasible solution generator is used for continuously searching a feasible solution which is better than the current solution in the neighborhood of the current solution, and the feasible solution replaces the current solution until the better solution cannot be found in the neighborhood of the current solution. Due to the constraints of output climbing, output fluctuation frequency, output lifting period number and the like, when a variable is assigned, the feasible value range of other variables is changed, and an associated search mode capable of constructing a feasible solution in the current solution neighborhood needs to be designed, namely, the feasibility of the search mode is ensured by correcting the local power generation flow process associated with the search starting point. The correlation search comprises the step of correcting the generated flow of the search initiating power station to meet the time correlation constraint, the final water level control constraint and the like, and the step of adjusting the generated flow of each downstream power station to meet the final water level control. Each optimization search needs to adjust the values of a plurality of variables to ensure the optimization in the feasible region, and the adjustment range and the adjustment direction of the variables are determined by the specific form of the constraint condition. And taking the change of a single decision variable along the search direction as a starting point, if the change falls into an infeasible domain, adjusting variables related to violation of the constraint according to the sequence of the spatio-temporal distance from the starting variable from near to far, and returning the changed solution to the infeasible domain again.
To illustrate such problems, the association search method will be described below by taking the force fluctuation constraint as an example. the output p in t-1, t +1 time periodst-1(MW)、pt(MW)、pt+1(MW) requirement satisfies (p)t-1-pt)(pt-pt+1) The condition is more than or equal to 0, the forces in 3 adjacent time periods are correlated, fig. 1(a) is a search schematic diagram aiming at the fluctuation constraint condition, the vertebral bodies CDAB and CDEF are feasible domains, and the original feasible solution is set as a point a, then p istAfter reduction, fall into the infeasible b point, by reducing pt+1Go back to point c in the feasible region, or decrease pt-1Returning to point d in the feasible region, the search force process is shown in FIG. 2 (a). At this time, the time period t is a search initiation point, pt+1 and pt-1Is constrained by output fluctuationCondition is given with ptAn associated variable. Similarly, if ptE points, which fall into infeasible domains when added, may pass through pt-1Or pt+1Back to the h or f point within the feasible region. FIG. 1(b) shows the constraint (p)t-1-pt)(pt-pt+1) Superposition force climbing constraint | p of not less than 0t-1-ptDelta p and | p are less than or equal to |t-pt+1After | ≦ Δ p, from pt-1The initiated search shows that if delta p is output climbing constraint, polyhedrons AEHBDFCG and CGKNMJI L are feasible regions, and p is the point at-1When decreasing, the search contribution process is shown in fig. 2(b) by two steps of correction back to point d in the feasible region via point c. .
And when the number of the associated variables is more, performing feasibility correction on the constraints such as the number of output lifting time sections, output fluctuation, output climbing and the like in sequence by adopting a similar method, and correcting the output of the associated time sections according to the sequence from near to far from the starting point on the premise of ensuring that the corrected constraint conditions are met until the constraint conditions are met. Fig. 3 shows several examples of modes of single-station multivariate correlation search.
The single-step associative search process can be divided into the following operations:
initial search: the specific variable search is performed from the starting point, and the increase and decrease of the output of the point a can be solved in the corresponding graphs (a) and (b) of fig. 2.
Expansion of the influence range: the variables near the onset point were adjusted to meet the constraints of output fluctuation and number of rise and fall periods, corresponding to the search from b to c, d in fig. 2 (a).
And (3) influence range edge correction: if the edge of the variation range of the operation of expanding the influence range falls into the non-feasible point b and e like in FIG. 2(a) or the point b in FIG. 2(b), the adjacent variables outside the variation range are adjusted to satisfy the time-dependent constraint.
Adjusting the water quantity difference between the warehouse and the warehouse: there are two application modes for the associated search mode: firstly, in the optimization calculation, the function of the correlation search mode is to construct a new feasible solution near the current solution, and the feasibility of the power generation flow process can be met through initial search, influence range expansion and influence range edge correction, but the final water level control condition can be damaged. Therefore, after the operation is completed, the output or the generated flow of the power station is required to be adjusted for a plurality of continuous time intervals, so that the total water quantity difference in and out of the reservoir is unchanged compared with that before the initial search, the end water level is kept unchanged, and the output process of each power station in the downstream is sequentially judged and adjusted to ensure that the end water level is unchanged. And secondly, as the basis of heuristic load distribution, output adjustment is performed on control modes such as the final water level and the like in the initial feasible solution generation and result feasible stages to gradually approach feasible solutions, and at the moment, only the feasible output process and approach to the control mode target are met after single-step correlation search, and the adjustment of the water quantity difference of entering and leaving the reservoir is not needed.
The single-step associated search process comprises four basic operations of initial search, expansion of an influence range, correction of an influence range edge and correction of a difference between water quantity in and out of a reservoir. The search process initiated by a certain hydropower station in the time period t is as follows:
(1) expansion of the range of influence
And the influence range expansion operation is used for changing the variable quantity of two adjacent variables forwards or backwards so as to meet the output fluctuation constraint and the climbing constraint of the hydropower station unit. If it is not
Figure BDA0002435187550000121
The ascending and descending trend of the output force in the time period t is changed, so that the associated constraint conditions of the time period are damaged, and the generation flow of the adjacent time period needs to be changed, so that a new dispatching operation mode is feasible.
In order to prevent the output fluctuation restriction from being violated after the generated flow rate is changed in the period t, equation (14) needs to be satisfied.
Figure BDA0002435187550000131
If the model has no solution, then set y1 to t-tvm+1, adjusting the generated flow between y1 and t period to
Figure BDA0002435187550000132
If the model has a solution, a feasible flow lifting process between the time periods y1 and t is obtained, as shown in fig. 4. If no feasible solution is available after adjustment, the time period t +1 is the end of the lifting process and is goodThe time period for the start of the lifting process is determined using equation (15).
Figure BDA0002435187550000133
The optimization model and outbound flow correction process are shown in FIG. 5. In the formula (15), t0 is the maximum value of tt and satisfies
Figure BDA0002435187550000134
And t0 < y 2. If the rising and falling trends of y2 and tt generated flow are different, meaning tt is the last time period when the unit generated flow rises (or falls), y2 is the new starting point of the unit generated flow falling (or rising) process, and the t +1 time period is the ending point of a flow rising and falling process. If t-y2+1 < tpmThe flow from y1 to the end of the period is changed in turn:
Figure BDA0002435187550000135
t0＝y1-1,y1-2,…,t-tpm+1, up to
Figure BDA0002435187550000136
Or
Figure BDA0002435187550000137
t2 is the minimum change period.
(2) Local optimization model satisfying edge climbing constraint of flow adjustment time period
After the model and flow correction satisfied by the first two constraints, a hill climbing constraint violation may occur at the edge of the flow adjustment period. If corrected at the flow change interval, t2 must satisfy the formula:
Figure BDA0002435187550000141
the correction process of the outbound traffic is described by equation (16).
Figure BDA0002435187550000142
This model can be solved by varying the flow rate at each time interval back in sequence from t2, with the optimal model and flow rate modification as shown in fig. 6.
(3) Local optimization model meeting stability of power generation flow at edge of flow adjustment period
If the minimum output fluctuation constraint cannot be met after the step of correcting the generated flow from the time period t to the dispatching range direction, a step of connecting two lifting processes needs to be searched, and z (formula (17)) of the flow stability constraint is met, because the correction can reduce the time period number ending at the time period t5 of the flow lifting process, at the moment, z needs to be further corrected to enable the two flow lifting processes to be connected until the two lifting processes are combined into one, and all peaks and valleys between the two lifting processes which can be solved originally are cut off or filled up (as shown in fig. 7).
Figure BDA0002435187550000143
The model may use a search algorithm to find the z value. Setting the flow rate between x and y periods to z if
Figure BDA0002435187550000144
And increasing the generating flow at the left time interval of y, possibly failing to meet the output fluctuation limit of the hydroelectric generating set, and further correcting by adopting an edge correction method.
(4) Correcting the generated current backwards from the t +1 time period
And (4) carrying out influence range expansion and edge correction from the t +1 time period backwards by using the same adjusting method from the time period t to the initial control time period in the previous step.
(5) Balance of water
a. Water balance of current reservoir m
The calculation of the water quantity of the reservoir may change the warehousing flow and the delivery flow of the reservoir. In order to keep the reservoir end-of-term storage capacity (labeled t6) of the last affected term constant, a local water balance model (equation (19)) needs to be satisfied, in which the period coupling constraint is maintained, as shown in fig. 8.
Figure BDA0002435187550000151
In formula (19), y and
Figure BDA0002435187550000152
all are variables, the constraint (c) is a time period that the output fluctuation of the hydraulic turbine set is less than or equal to t6, and the constraint (d) is used for ensuring that the minimum generating flow lifting time period in the output lifting process is less than or equal to t 6.
Because the model is difficult to solve, a trial and error method is adopted for solving. In order to keep the difference between the flow rate of the reservoir leaving the reservoir and the flow rate of the reservoir entering the reservoir, i.e. the net flow rate of the reservoir entering the reservoir, constant, it is necessary to keep the time interval t', t6 constant]The flow rate in the inner chamber is uniformly changed. However, over time, [ t', t6 may not be satisfied]The output rise and fall process may be damaged by the increase of the flow rate in different periods. Therefore, the temporal coupling constraint should be prevented from being broken by extending the modification time interval. In order to solve the water quantity balance problem in the whole scheduling period, the initial value of the maximum flow variation dt0 is set to be tvmThe solving steps are as follows:
(1) using formulas
Figure BDA0002435187550000161
The difference between the flow rate of the reservoir and the flow rate of the reservoir is calculated. If W is more than 0, the steps (2) to (8) are required to be executed to increase the reservoir delivery flow and reduce the end-of-term storage capacity of the reservoir. If W is less than 0, the step (2) to the step (8) are required to be executed to reduce the delivery flow so as to increase the end-of-term storage capacity of the reservoir;
(2) setting dt0＝tvm；
(3) Setting dt to be 1;
(4) looking for [ t6-dt +1, t6]Within a time interval of (a) for a period of time during which the flow rate may be varied. N is set to 0. When t is t6, if
Figure BDA0002435187550000162
And is
Figure BDA0002435187550000163
N is set to n + 1. If, when t is t6-dt +1, t6-dt +2, …, t6-1
Figure BDA0002435187550000164
Setting n to n + 1;
(5) setting the flow variation as
Figure BDA0002435187550000165
(6) Setting up
Figure BDA0002435187550000166
And the flow rate is corrected from the dt period to the t6 direction from t6-dt +1, t6-dt +2, … and t 6. If it is
Figure BDA0002435187550000167
Then set up
Figure BDA0002435187550000168
(7) By using
Figure BDA0002435187550000169
(t-t 6-dt +1, t6-dt +2, …, t6) substitutions
Figure BDA00024351875500001610
Value to determine step (6) at time interval [ t6-dt +1, t 6%]Whether the correction in (1) is possible. If the time-related constraint is not met, the step (6) corrects
Figure BDA00024351875500001611
The value of (d) will not be saved. However, there may be a case where the feasible correction result in the dt time period cannot meet the water balance requirement in the full scheduling period, and the value of W needs to be recalculated. If W ≠ 0, setting dt to dt +1, and if t6-dt +1 is not less than 1 and dt is not more than dt0, executing the step (4) again;
(8) the value of W is recalculated. If W ≠ 0, dt0 is set to dt0+ 1. If t6-dt0+1 is not less than 1, step (3) is executed again, otherwise, the operation is stopped.
If the net warehousing flow cannot be kept unchanged, the method needs to be used for further correcting the power generation flow from the t6+1 time period to the end of the regulation period. Because if the net traffic cannot be handled during the scheduling periodRemaining unchanged means that there is no feasible solution in the neighborhood. With one hydroelectric station tvm＝4，tpmFor example, when dt < 8, the model has no feasible correction solution, as shown in fig. 9.
And if the difference between the warehousing flow and the ex-warehousing flow cannot be kept unchanged, correcting the generated flow by a similar correction method backwards to the end of the dispatching control period to meet the water quantity balance requirement.
Obviously, the construction of a feasible solution is related to the short-term scheduling process around a specific period of the reservoir. Taking hydropower station m as an example, there are
Figure BDA0002435187550000171
tp m8 and tvmStarting from the time period t, the construction process of the feasible solution neighborhood is shown in fig. 10.
b. Downstream reservoir group water balance
And after the short-term scheduling process of the upstream reservoir group changes, finding out the time interval at which the warehousing flow of the downstream reservoir group may change, and taking the end of the last time interval of the scheduling cycle as a control point of the reservoir water storage capacity. Under the condition of not changing the end-of-term storage capacity of the reservoir, in order to keep the net flow of warehousing unchanged, the flow of delivery of the reservoir must be changed along with the net flow of warehousing. The control model of the storage capacity of the upstream reservoir may also be applied to the downstream reservoir group. But if the net flow of the reservoir entering is not guaranteed to be unchanged, the feasible solution and the construction fail.
For the reservoir with the number m, the variation interval of the delivery flow of the direct upstream reservoir is [ t'm-1,0,t'm-1,1]The time interval of the warehousing traffic change is [ t ]m,0,tm,1]，
Figure BDA0002435187550000172
Figure BDA0002435187550000173
Due to influence of flow delay, tm,1-tm,0May be compared with t'm-1,1-t'm-1,0Are not equal in value. Keeping the discharge flow of the reservoir m unchanged, twoThe cause changes min (T, T)m,1) The water storage capacity of the end reservoir in the time period: the change of the reservoir warehousing flow enables the reservoir storage capacity to reach the upper limit (or the lower limit); time interval tm,0,tm,1]Partially exceeding the schedule control period (m +2 reservoir and m +3 reservoir in fig. 11). Can ensure min (T, T) by using the correction model of the discharge flow of the upstream reservoirm,1) The water storage capacity of the reservoir at the end of the period. The variation interval of m outlet flow of the reservoir is t'm,0,t'm,1]The variation interval of the warehousing flow corresponding to the reservoir m +1 is [ t ]m+1,0,tm+1,1]. The same reservoir storage capacity control model is adopted for the reservoir m +1 and the downstream reservoir thereof, and the operation is stopped until one of the following four conditions is met:
(1) for the downstream reservoir, the outlet flow of the downstream reservoir is kept unchanged, and the requirement of the water storage capacity of the scheduling end reservoir cannot be met in the storage flow change interval;
(2) the variation interval of the warehousing flow exceeds the control range (such as a m +4 reservoir in fig. 11);
(3) all downstream reservoirs have been inspected and corrected;
(4) the feasible solution structure fails, namely, a feasible solution which can meet the requirement of the water storage capacity of the reservoir at the end of the dispatching period in the warehousing flow change interval does not exist.
Examples
The stepped power station (as shown in fig. 12) in the north-disk river basin administered by the qian source company comprises a muddy slope power station (with daily regulation performance, loader 185.5MW), an illumination power station (with incomplete years regulation performance, loader 1040MW), a maja cliff power station (with daily regulation performance, loader 558MW), a dungeon power station (with daily regulation performance, loader 880MW), and the total amount of loader is 2663.5 MW. The four hydropower stations of the north Panjiang step have two adjusting performances of day and incomplete years, and two hydropower stations belong to the Guizhou Zhonghui and south net general adjusting pipes, wherein the illumination power station is a downstream tap power station of the north Panjiang and plays a role in controlling, compensating and adjusting the downstream power station. As can be seen from the grid-connected relationship of north panel river in fig. 12, the lighting, maja and dungeon are incorporated into the same rack through the xingren converter station.
And solving the scheduling scheme of typical year of abundance, average and withering by adopting the maximum power generation benefit model. Since the north-disk river basin never adopts the extremely-dry time-of-use electricity price, a virtual electricity price curve adopted for the test model is shown in fig. 13.
FIG. 14 shows typical daily calculation results of maximum dry season of power generation benefits-power output of each power station; FIG. 15 shows typical daily calculation results of maximum power generation efficiency in dry season — step total output; FIG. 16 is typical daily calculation results of the maximum dry season of power generation benefits-water levels of each power station; FIG. 17 shows typical daily calculation results of maximum power generation benefits in flood seasons-the output of each power station; FIG. 18 shows typical daily calculation results of maximum power generation benefits in flood season, namely step total output; fig. 19 shows typical daily calculation results of power generation efficiency in the maximum flood season — each power station water level.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.

Claims (6)

1. The short-term optimized scheduling method of the gradient hydropower station under the multi-constraint condition is characterized by comprising the following processes:
s1, establishing an objective function with the aim of maximizing the power generation benefit;
s2, establishing constraint conditions of the objective function;
and S3, solving the objective function to obtain the short-term dispatching process of each power station reservoir in the cascade hydropower station.
2. The short-term optimized scheduling method of the gradient hydropower station under the multi-constraint condition as claimed in claim 1, wherein the expression of the objective function is as follows:
Figure FDA0002435187540000011
wherein: f is a power generation benefit function; t, M is the number of dispatch period periods and the number of hydro-electric stations;
Figure FDA0002435187540000012
for hydropower stations m atOutput and electricity price at time t; deltatFor a period of t hours, ElmThe delay electric quantity after the control period of the No. m power station,
Figure FDA0002435187540000013
average electricity price after the control period of the m-number power station.
3. The short-term optimized scheduling method of the multi-constraint elevator hydropower station, according to claim 1, wherein the constraint conditions comprise:
(1) balance of water
Figure FDA0002435187540000014
wherein ：
Figure FDA0002435187540000015
is the initial water storage capacity of the reservoir at the time period t +1,
Figure FDA0002435187540000016
the initial water storage capacity of the reservoir at the time period t of m;
Figure FDA0002435187540000017
Figure FDA0002435187540000018
respectively representing the total warehousing flow, the departure point flow and the abandoned water flow of the reservoir m in the t period; deltatIs the time period t hours;
(2) end water level control
Figure FDA0002435187540000019
wherein ：
Figure FDA00024351875400000110
scheduling end-of-term water level, zend, for reservoir mmA control target value therefor;
(3) power generation flow restriction
Figure FDA0002435187540000021
wherein ：
Figure FDA0002435187540000022
the maximum power generation reference flow of the reservoir m in the t period is obtained;
(4) power station output constraints
Figure FDA0002435187540000023
wherein ：
Figure FDA0002435187540000024
the minimum and maximum output limits of the hydropower station m in the t period are set;
(5) step total output limit
Figure FDA0002435187540000025
wherein ：h t，
Figure FDA0002435187540000026
the lower limit and the upper limit of the total output of the hydroelectric system are represented;
(6) grid partition output limit
The system comprises a plurality of primary sub-partitions and a plurality of machine sets, each primary sub-partition can also comprise a plurality of secondary sub-partitions and a plurality of machine sets, and so on, then:
Figure FDA0002435187540000027
wherein ,
Figure FDA0002435187540000028
representing the lower limit of the output of the i-number subarea in the t-th time period;
Figure FDA0002435187540000029
the total effective output of the number i subareas is obtained; i represents the total number of partitions;
(7) reservoir level restriction
Figure FDA00024351875400000210
wherein ：
Figure FDA00024351875400000211
the initial water level and the upper and lower limits of the t time period of the hydropower station m are represented;
(8) outbound flow constraint
Figure FDA00024351875400000212
wherein ：
Figure FDA00024351875400000213
the minimum comprehensive water use restriction and the maximum delivery flow limit of the reservoir m at the time t are defined;
(9) hydropower station vibration zone constraints
Figure FDA00024351875400000214
wherein ：
Figure FDA0002435187540000031
representing the upper and lower limits of the kth output vibration region of the m hydropower station in the t period, and
Figure FDA0002435187540000032
in connection with this, the present invention is,
Figure FDA0002435187540000033
the average tail water level of the m hydropower stations in the t period;
(10) minimum boot output constraint
Figure FDA0002435187540000034
Wherein: pminmThe minimum starting-up output of the number m hydropower station is represented;
(11) hydropower station output climbing limitation
Figure FDA0002435187540000035
wherein ：ΔpmRepresenting the maximum output lifting limit of the m hydropower stations in the adjacent time period;
(12) hydropower station output fluctuation limitation
Figure FDA0002435187540000036
wherein ：tνmThe minimum interval time period number of output lifting of the m hydropower station, namely minimum t v needs to be continued at the highest and lowest points in the process of one round of output liftingmA time period;
(13) minimum force lift period number limit
The time interval from the beginning of rising to the beginning of falling of the output of the m hydropower station or from the beginning of falling to the beginning of rising is not less than t νmA plurality of time periods.
4. The short-term optimized scheduling method of gradient hydropower stations under multi-constraint conditions as claimed in claim 1, wherein the objective function is solved by adopting a correlation search method.
5. The multi-constraint-condition echelon hydropower station short-term optimization scheduling method as claimed in claim 1, wherein the generation flow is taken as a decision variable in the process of solving the objective function.
6. The multi-constraint condition elevator hydropower station short-term optimization scheduling method as claimed in claim 1, wherein the single-step associated search process comprises four basic operations of initial search, influence range expansion, influence range edge correction and reservoir water quantity difference correction.
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