CN115577882A - Cascade hydropower station long-term optimization scheduling model considering incoming water nonuniformity - Google Patents
Cascade hydropower station long-term optimization scheduling model considering incoming water nonuniformity Download PDFInfo
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Abstract
The invention relates to the field of hydropower dispatching operation, in particular to a cascade hydropower station long-term optimization dispatching model for considering water inflow nonuniformity. The invention aims at the hydropower stations with weak regulating capacity and low water heads in the cascade hydropower stations and the characteristic that the generating capacity of the low water head hydropower station is limited by the warehousing flow, fits the relationship between the warehousing flow and the generating output of each hydropower station and each month, and optimizes the load distribution among the cascade hydropower stations by taking the cascade generating capacity as the maximum target. Compared with the conventional method for carrying out long-term scheduling on the cascade hydropower station only by means of output coefficient calculation, the method can quickly give out a reasonable hydropower scheduling result and meet the requirements of timeliness and practicability of actual operation of the power grid.
Description
Technical Field
The invention relates to the field of hydropower dispatching operation, in particular to a cascade hydropower station long-term optimization dispatching model for considering water inflow nonuniformity.
Background
In the hydropower construction of China, a one-reservoir multi-stage cascade hydropower station group is a common hydropower development mode. The hydropower station group has a main adjusting reservoir with larger reservoir capacity at the upstream, can execute adjusting tasks for years, years and seasons according to the reservoir capacity, and is matched with a power station with poorer multistage adjusting capacity at the downstream for cascade development. How to make a relatively accurate medium-and-long-term dispatching plan for the power station in the form is very necessary for safe and stable operation of a power grid and accurate evaluation of the self capacity of the power station. The tap power station with strong adjusting capacity generally cannot generate the water abandoning condition except the extreme end year, and the downstream power stations with poor adjusting capacity (weekly adjustment, daily adjustment and runoff type) sometimes generate the water abandoning condition due to the reservoir capacity limitation and the runoff instability. In conclusion, the influence of short-term runoff instability is considered for weekly regulation and the following power stations, and the method has important significance for long-term planning taking the year as a periodic month as a scale.
In general, in traditional hydropower optimization scheduling, output calculation is carried out by methods such as a power station output coefficient, an average water consumption rate, a water head-output-power generation flow curved surface and the like, and the accuracy of results obtained by weekly-regulated and following power stations has larger deviation from actual operation conditions. In combination with the actual production problem, the invention provides the output calculation according to the warehousing flow-output relation function fitted by actual operation data, and the result shows that the method can improve the calculation precision of the power station with poor long-term scale regulation capability and better guide the long-term hydropower operation.
Aiming at the problems, the invention provides a cascade hydropower station long-term optimization scheduling model considering the incoming water nonuniformity, and the application test is carried out on the cascade hydropower station long-term optimization scheduling model by taking 'one-base multi-stage' power station long-term plan of Guangxi red river basin as the engineering background, and the result shows that the achievement of the invention can be closer to the actual operation result than the calculation using the output coefficient.
Disclosure of Invention
The invention aims to solve the technical problems of dimension disaster optimization scheduling and result practicability of the ultra-large-scale hydropower station group, and the achievement can carry out hydropower station classification scheduling according to problem characteristics and power station characteristics, realize large-system grouping iterative solution, remarkably alleviate the problem of dimension disaster, effectively simplify a power generation scheduling mode of a power station by using power station and power grid requirements and practical experience, reduce the number of optimized hydropower stations, reduce modeling and solving difficulties, and improve the availability and practicability of results.
The technical scheme of the invention is as follows:
a long-term optimization scheduling model for a cascade hydropower station considering water supply nonuniformity mainly comprises a plurality of main parts such as a fitting output input flow relation curve and a maximum modeling and solving method for the generated energy of the cascade hydropower station in a scheduling period. The medium-long term hydropower optimization scheduling process considering the short-term runoff instability is completed according to the following steps:
(1) And carrying out hydropower station classification based on the regulation capacity characteristic. The cascade power stations are divided into two types, namely power stations with long-term regulation capacity and high water heads and power stations with poor regulation capacity and low water heads.
(2) And constructing a maximum mathematical model of the step generating capacity.
(3) The invention discloses a key problem of influence of the difference between the monthly runoff and the nonuniformity of the actual short-term runoff on the output calculation of a hydropower station with poor energy saving capacity.
(4) By using the ideas of DDDP (discrete differential dynamic programming) and DPSA (successive approximation dynamic programming) algorithms for reference, a universal variable strategy search algorithm is constructed. The method uses DDDP thought for reference, adopts a time dimension depth priority or breadth priority mode to continuously solve the two-time-period subproblem, adopts DDDP algorithm when solving the time-period subproblem, and can combine DPSA dimension reduction thought. Because the subproblem only has two time intervals, the solving algorithm actually becomes a local one-dimensional or multi-dimensional direct searching mode and is not limited by dynamic programming recursive application conditions. Specifically, a time-dimension depth-first mode or an breadth-first mode is adopted to repeatedly and sequentially solve the sub-problem search in each two time intervals, wherein the depth-first sub-problem needs to be calculated to be converged when being solved, the breadth-first mode does not need to be converged, and only one round of local optimization is performed. When the subproblem is solved, a one-dimensional or multi-bit searching mode can be adopted, the one-dimensional searching adopts an iteration mode, and the calculating speed is high; the multidimensional searching and calculating speed is low, the limitation of the system scale is large, the power stations are grouped, and the principle is that the maximum number of each group of power stations is determined at first; each cascade of continuous power stations is divided into a group, and when a plurality of upstream power stations are encountered, the cascade of continuous power stations and each upstream power station are divided into a group.
The invention has the following beneficial effects: the invention aims at the hydropower stations with weak regulating capacity and low water head in the cascade power station, and aims at the characteristic that the generating capacity of the low water head power station is limited by the warehousing flow, the invention fits the relationship between the warehousing flow and the generating output of each power station and each month, and optimizes the load distribution among the cascade power stations by taking the maximum cascade generating capacity as a target. Compared with the conventional method for carrying out long-term scheduling on the cascade hydropower station only by means of output coefficient calculation, the method can quickly give out a reasonable hydropower scheduling result and meet the requirements of timeliness and practicability of actual operation of the power grid.
Drawings
FIG. 1 (a) shows the fitting result of the large warehouse entry-expected output curve;
FIG. 1 (b) is a Bailong beach warehousing-expected output curve fitting result;
FIG. 1 (c) shows the results of Log-Sun-Bao-predicted force curve fitting;
fig. 2 (a) is a long-term optimization result of longbeach without considering short-term runoff heterogeneity;
FIG. 2 (b) is a long-term optimization result of the beach without considering the short-term runoff heterogeneity;
FIG. 2 (c) is a long-term optimization result without considering short-term runoff inhomogeneity for generalization;
FIG. 2 (d) is a long-term optimization result of Bailongtan without considering short-term runoff heterogeneity;
FIG. 2 (e) is a long-term optimization result of the Leban without considering the short-term runoff heterogeneity;
FIG. 3 (a) is a long-term optimization result of Longtan considering short-term runoff heterogeneity;
FIG. 3 (b) is a long-term optimization result of the beach considering short-term runoff heterogeneity;
FIG. 3 (c) is a long-term optimization result that generalizes short-term runoff inhomogeneities;
FIG. 3 (d) is the long-term optimization result of Bailongtan considering the non-uniformity of short-term runoff;
fig. 3 (e) is the long-term optimization result of the beach considering the short-term runoff heterogeneity.
Detailed Description
The invention is further described below with reference to the accompanying drawings and examples. The method mainly comprises five parts of classification of the hydropower station based on the characteristic of the regulating capacity, construction of a maximum mathematical model of the cascade generated energy, fitting of a relation curve of the output warehousing flow, a model solving method and practical application.
The hydropower station has huge hydropower scale in China, and besides the hydropower station with strong regulating capacity and high water head, a large number of low water head and small reservoir capacity step hydropower stations also exist. When the hydropower station with poor regulating capacity performs medium-term and long-term optimization calculation, the problem of water abandon caused by the instability of short-term power generation flow is not ignored. And performing quadratic fitting on the warehousing flow-output relation of the weekly regulation and the following power stations by using the actual operation data, and taking the quadratic fitting as the output calculation basis.
1. Power station classification and model construction method
(1) Hydropower station classification based on regulation capability characteristics
According to the hydropower station classification idea based on the regulation capacity characteristics, the cascade hydropower stations in the red river basin are divided into two types:
the first is the university, bailongsho, lesho. The low-water-head cascade hydropower stations have poor regulating capacity, and the power generation in flood seasons is seriously blocked, so that the calculation error of a dispatching model which calculates the daily average value of the incoming water is larger in the calculation of the generated water quantity and the abandoned water quantity due to the uneven incoming water in each day;
the second type is the dragon beach and the rock beach. The power stations have better regulation performance and large installation scale, and the power grid can predetermine the daily power generation amount or water level control condition of each power station according to the medium-and-long-term scheduling control requirements, and perform modeling and optimization calculation by considering the requirements of system load, peak regulation and the like.
2. Constructing step generating capacity maximum mathematical model
The target function selects the maximum generated energy:
wherein: t, M is the number of scheduling period and the number of hydropower stations;
the output of the hydropower station m in the time period t is obtained; delta t The time period t hours.
Constraint conditions
(1) Water balance constraint
Wherein:
respectively representing the time period t and the t-1 storage capacity of the reservoir m;
the storage flow is the storage flow of the reservoir at the time period m and the time period t;
the flow rate of the power station is the time t of the power station i'.
(2) End water level control
Wherein:
scheduling end-of-term water level, zend, for reservoir m m For which the target value is controlled.
(3) Power generation flow restriction
Wherein:
and (4) quoting the maximum power generation flow of the reservoir m in the t period.
(4) Reservoir level restriction
Wherein:
m is at t for reservoirAnd limiting the lowest water level and the highest water level at the beginning of the time period.
(5) Outbound flow constraint
Wherein:
the minimum comprehensive water use constraint and the maximum delivery flow limit of the reservoir m in the time period t are achieved.
(6) Power station output constraints
Wherein:
and the minimum and maximum output limits of the hydropower station m in the time period t are obtained.
(7) Tail water level down let-off flow relationship
Wherein:
the tail water level of the reservoir m in the period t;
the flow of the reservoir m out of the reservoir at the time t; fzdu m The relation of the tail water level of the flow of the reservoir m out of the reservoir.
(8) Water level reservoir capacity relationship
Wherein:
the storage capacity of the reservoir m at the time t;
the upstream reservoir level of the reservoir m in the period t; fzv m The relation of the tail water level of the flow of the reservoir m out of the reservoir.
(9) Calculation of output
Considering that the nonuniformity of short-term runoff has great influence on the power station with low energy-saving capacity, the power station uses the output coefficient to calculate the output by taking the cycle regulation capacity as a boundary, and the power station uses the warehousing output relation fitted by the invention to calculate the output.
(9.1) weekly regulation power station output calculation mode
Wherein:
outputting power for the reservoir m at the time t; a. The m The reservoir is a reservoir m output coefficient;
the generating flow of the reservoir m in the t period;
the water head of the reservoir m at the time t;
(9.2) week adjustment and following power station output calculation mode
Wherein: fq of m The output relation of the warehousing flow of the reservoir m.
3. Output and warehouse-in flow relation curve fitting method
Considering water unevenness to medium and long cascade hydropower stations with low water heads and small reservoir capacitiesThe impact of scheduling is optimized. And aiming at the characteristic that the power generation capacity of the low-water-head power station is limited by the warehousing flow, fitting the relationship between the warehousing flow and the power generation output of each power station and each month. The most common method at present is the least squares method, and the polynomial fitting is most widely used. In actual calculation, a partial derivative is calculated for each coefficient by the sum of squares of deviations, an equation set is established by making the partial derivative zero, a coefficient formula is obtained through a series of deductions, and then coefficients are calculated. The least square method curve fitting method is to minimize the sum of squares of the difference between the measured value and the fitting value, and can accurately fit the output warehousing flow relation curve. The least squares fitting procedure is as follows (where k is the polynomial degree, k =0,1,2,3 … m; y indicates the force in unit MW; x indicates the inlet flow in unit m 3 S; a is a real number; i =1,2,3 … n; ):
(1) Let the fitting polynomial be
(2) The sum of the distances of the points to the curve, i.e. the sum of the squares of the deviations, is
(3) To determine the conditional a-value, the equation is repeated to determine a k Partial derivatives, thus obtaining
…………
(4) The left side of the above equation is simplified to obtain
…………
(5) Expressing the equation in a matrix form to obtain a Van der Monde matrix
(6) The Van der Monde matrix is simplified to obtain
(7) XA = Y, the coefficient matrix a is obtained, and a fitting curve is obtained.
5. Model solving method
Specifically, a time dimension depth-first mode or a breadth-first mode is adopted to repeatedly and sequentially solve the sub-problem search of each two time periods, wherein the depth-first sub-problem needs to be calculated to be converged when being solved, the breadth-first mode does not need to be converged, and only one round of local optimization is performed. When the subproblem is solved, a one-dimensional or multi-dimensional searching mode can be adopted, the one-dimensional searching adopts an iteration mode, and the calculating speed is high; the multi-dimensional search calculation speed is low, the limitation of the system scale is large, and the power stations are required to be grouped. The basic idea of the variable search strategy method provided by the invention is to repeatedly and sequentially solve the sub-problems in two periods, and the method is divided into four modes according to different problem solving modes: a. breadth-first one-dimensional search; b. breadth-first multi-dimensional search; c. depth-first one-dimensional search; d. depth-first multidimensional search. In practical application, the calculation mode can be selected according to problems with different characteristics and scales.
4. Example analysis
The location line of the Nanchejiang red water river hydroelectric base, namely the location line of the China thirteen large hydroelectric bases, is mainly developed by the China Datang group, and the proposal of the thirteen large hydroelectric bases plays a great role in promoting the western economic development by realizing the cascade rolling development of hydropower basins in China, implementing resource optimization configuration and driving the western economic development. The invention selects 'one-bank multi-stage' power stations in a red river basin as a research object, and the specific characteristic parameters of the power stations are as follows.
TABLE 1 hydropower station principal parameters
Firstly, fitting is carried out according to historical data of the large chemical hydropower stations from 2005 to 2022, bailong beach from 1997 to 2022 and Lentan from 2000 to 2022 by using a least square method binomial mode, and fitting results of warehouse entry-expected output curves of the power stations are shown in fig. 1 (a), 1 (b) and 1 (c).
The output calculation is carried out according to a conventional comprehensive output coefficient mode, the maximum generated energy is calculated as a target, the obtained calculation result is shown in fig. 2 (a) -fig. (e), all power stations do not generate water abandon, which is inconsistent with the actual scheduling situation, and the reason is that the storage capacity of the power stations with strong adjusting capacity, namely the longshoal and the beach, can adjust the runoff with uneven short-term scale, while the large, the bailongshoal and the happy shoal are day-adjusting power stations and the following power stations, and the uneven incoming water with the short-term scale is difficult to adjust and control, and certain water abandon is inevitably generated.
The calculation example shows that when the 'one-pool multi-stage' cascade power station is optimally scheduled for a long time, the output calculation mode of the power station with poor regulation capacity needs to be changed, so that the influence of short-term runoff nonuniformity can be represented, the warehouse entry flow-expected output curve is used for carrying out output calculation of the large, bailong beach and happy beach, as can be found in fig. 3 (a) -3 (e), after the influence of runoff nonuniformity is considered, the large, bailong beach and happy beach generate water abandons to different degrees in each month, the obtained result is similar to actual operation data, and the method provided by the invention can be proved to be correct and has practical significance.
Claims (1)
1. A cascade hydropower station long-term optimization scheduling model for considering water supply nonuniformity is characterized by comprising the following steps:
(1) Hydropower station classification based on regulation capability characteristics
The cascade power stations are divided into two types, namely power stations with long-term regulation capacity and high water heads, power stations with poor regulation capacity and power stations with low water heads;
(2) Constructing step generating capacity maximum mathematical model
The target function selects the maximum generated energy:
wherein: t, M is the number of scheduling period and the number of hydropower stations;
the output of the hydropower station m in the time period t is obtained; delta t Is the time period t hours;
constraint conditions
(2.1) Water balance constraint
Wherein:
respectively representing the time period t and the t-1 storage capacity of the reservoir m;
the storage flow is the storage flow of the reservoir at the time period m and the time period t;
the flow rate of the power station in the time period t is the flow rate of the power station in the warehouse;
(2.2) end Water level control
Wherein:
scheduling end-of-term water level, zend, for reservoir m m A control target value therefor;
(2.3) Power Generation flow restriction
Wherein:
the maximum power generation reference flow of the reservoir m in the t period is obtained;
(2.4) reservoir level restriction
Wherein:
the lowest and highest water level limits of the reservoir m at the beginning of the t period;
(2.5) outbound flow constraint
Wherein:
the minimum comprehensive water use restriction and the maximum delivery flow limit of the reservoir m at the time t are defined;
(2.6) station output constraints
Wherein:
the minimum and maximum output limits of the hydropower station m in the t period are set;
(2.7) Tail Water level underflow flow Rate relationship
Wherein:
the tail water level of the reservoir m in the period t;
the flow of the reservoir m out of the reservoir at the time t; fzdu m The relation of the tail water level of the delivery flow of the reservoir m;
(2.8) Water level reservoir Capacity relationship
Wherein:
the storage capacity of the reservoir m at the time t;
the upstream reservoir level of the reservoir m in the period t; fzv m The relation of the tail water level of the flow of the reservoir m out of the reservoir;
(2.9) calculation of the output
Considering that the nonuniformity of short-term runoff has great influence on the power station with low energy-saving capacity, the power station adopts a fitted warehousing output relationship to calculate the output by taking the weekly regulating capacity as a boundary;
(2.9.1) week adjustment power station output calculation mode
Wherein:
outputting power for the reservoir m at the time t; a. The m The reservoir is a reservoir m output coefficient;
the generating flow of the reservoir m in the t period;
the water head of the reservoir m at the time t;
(2.9.2) week adjustment and following power station output calculation mode
Wherein: fq of m The output relation of the warehousing flow of the reservoir m;
(3) Fitting output warehouse entry flow relation curve
Fitting an output warehousing flow relation curve by adopting a least square method curve fitting method, wherein the fitting process is as follows, k is polynomial times, and k =0,1,2,3 … m; y indicates force, in units MW; x denotes the flow rate in the warehouse, unit m 3 S; a is a real number; i =1,2,3 … n;
(3.1) setting the fitting polynomial as
(3.2) the sum of the distances from the points to the curve, i.e. the sum of the squares of the deviations, is
(3.3) in order to obtain a value of a satisfying the condition, a is obtained from the right side of the equation k Partial derivatives, thus obtaining
…………
(3.4) the left side of the intermediate formula (3.3) is simplified to obtain
…………
(3.5) expressing the equation (3.4) in matrix form to obtain Van der Mond matrix
(3.6) simplifying the Van der Monde matrix in (3.5)
(3.7) XA = Y, solving a coefficient matrix A, and meanwhile, obtaining a fitting curve;
(4) Universal variable strategy search algorithm
The scheduling optimization problem can be decomposed into a limited number of sub-problems only containing two time periods, a DDDP algorithm is adopted when the time period sub-problems are solved, and a DPSA dimension reduction thought is combined, specifically, two modes of time dimension depth priority or breadth priority are adopted to repeatedly and sequentially solve the sub-problem search of each two time periods, wherein the depth priority sub-problem needs to be calculated to be converged when being solved, the breadth priority does not need to be converged, and only one round of local optimization is carried out; when the subproblem is solved, a one-dimensional or multi-dimensional searching mode is adopted, the one-dimensional searching mode adopts an iteration mode, and the calculating speed is high; the multi-dimensional search calculation speed is low, the limitation of the system scale is large, and the power stations are grouped, the principle is that the maximum number MD of each group of power stations is determined at first, all cascade continuous MD power stations are divided into one group, and when a plurality of upstream power stations exist, the cascade continuous MD power stations and all upstream power stations are divided into one group; the basic idea of the variable search strategy method is to repeatedly and sequentially solve sub-problems in two periods, and the method is divided into four modes according to different problem solving modes: breadth-first one-dimensional search, breadth-first multi-dimensional search, depth-first one-dimensional search and depth-first multi-dimensional search, and a calculation mode is selected according to problems of different characteristics and scales.
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| CN117674293A (en) * | 2023-12-07 | 2024-03-08 | 华能西藏雅鲁藏布江水电开发投资有限公司 | Long-term power generation optimal scheduling method and device for cascade hydropower station |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117674293A (en) * | 2023-12-07 | 2024-03-08 | 华能西藏雅鲁藏布江水电开发投资有限公司 | Long-term power generation optimal scheduling method and device for cascade hydropower station |
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