Cascade hydropower station real-time optimization scheduling method, device and system, and storage medium

Abstract

The invention discloses a cascade hydropower station real-time optimization scheduling method, device and system and a storage medium, and belongs to the technical field of hydropower station scheduling, and the method comprises the steps: S1, constructing a target function and a constraint condition of a cascade hydropower station real-time optimization scheduling model; s2, acquiring cascade hydropower station operation data; and S3, solving the cascade hydropower station real-time optimization scheduling model by adopting a discrete differential enumeration algorithm according to the cascade hydropower station operation data and the target function and the constraint condition of the cascade hydropower station real-time optimization scheduling model. By the adoption of the technical scheme, the problem that the same water volume has different energies in different hydropower stations due to the water head effect is solved, and the phenomenon that the results of the cascade energy storage maximum criterion and the cascade energy consumption minimum criterion are inconsistent and unreasonable is avoided.

Description

Technical Field

[0001] The present invention belongs to the technical field of hydropower station dispatching, and in particular relates to a real-time optimization dispatching method and device, system and storage medium for cascade hydropower stations. Background Art

[0002] Hydropower energy is a renewable energy source that converts water energy into electrical energy. In recent years, with the development of technology and the improvement of environmental awareness, hydropower technology has also been continuously improving, including improving hydropower efficiency and reducing the impact on the environment. The joint optimization operation of cascade hydropower stations is an important non-engineering measure to improve the utilization efficiency of water resources and the potential of hydropower equipment. The "power-to-water" mode is often used in the real-time economic operation of cascade hydropower stations, that is, the operation process of the hydropower station that makes the adopted optimization criteria reach the extreme value under the conditions of the known cascade hydropower station status at the beginning of the dispatch period, the total load process of the cascade during the dispatch period, and the natural inflow process. The dispatching plan formulated by the "power-to-water" mode will be directly applied to actual production and is closely related to the power generation efficiency of cascade hydropower stations. Therefore, it is very important to conduct in-depth research on it. However, compared with the "water-to-electricity" mode, the current research on the "power-to-water" mode is not mature enough, and there is still controversy at home and abroad on the determination of its optimization criteria. Scholars have proposed many optimization criteria from different perspectives. From the perspective of water volume, there are the minimum criterion for cascade water consumption, the minimum criterion for cascade water abandonment, and the minimum criterion for leading hydropower station water consumption, but these criteria do not take into account the impact of cascade head; from the perspective of hydropower, there are the minimum cascade energy consumption and the maximum cascade residual energy, but how to calculate the energy contained in a cascade hydropower station at a certain moment still needs further research. Summary of the invention

[0003] The technical problem to be solved by the present invention is to provide a real-time optimization scheduling method and device, system and storage medium for cascade hydropower stations.

[0004] To achieve the above object, the present invention adopts the following technical solution:

[0005] A real-time optimization dispatching method for cascade hydropower stations, comprising:

[0006] Step S1, constructing the objective function and constraint conditions of the real-time optimization scheduling model of cascade hydropower stations; wherein, the objective function of the real-time optimization scheduling model of cascade hydropower stations is the total water level and maximum of the cascade hydropower stations; the constraint conditions of the real-time optimization scheduling model of cascade hydropower stations include: water balance constraint, water level constraint, output constraint, flow constraint, upstream and downstream hydraulic connection and non-negative constraint;

[0007] Step S2, obtaining the operation data of the cascade hydropower station; wherein the operation data of the cascade hydropower station includes: the storage capacity relationship curve of each cascade hydropower station, the optimal characteristic curve of each hydropower station, the inflow of the leading hydropower station and the flow data of each interval, and the total load of the cascade hydropower station;

[0008] Step S3, according to the operation data of the cascade hydropower stations, the objective function and constraint conditions of the real-time optimization scheduling model of the cascade hydropower stations, a discrete differential enumeration algorithm is used to solve the real-time optimization scheduling model of the cascade hydropower stations; specifically comprising:

[0009] Step S31, according to the operation data of the cascade hydropower stations, based on the objective function and constraint conditions of the real-time optimization scheduling model of the cascade hydropower stations, the outbound flow trial algorithm is used to perform a trial calculation on the outbound flow to solve the model and obtain an initial solution;

[0010] Step S32, using a discrete differential enumeration algorithm to perform further optimization calculations in the corridor formed near the initial solution, and obtaining the optimal solution as a new initial solution;

[0011] Step S33, reduce the discrete step length of the corridor, repeat step S32 until the total water level of the steps remains unchanged and the accuracy of the discrete step length meets the set requirements, and output the solution result.

[0012] The present invention also provides a real-time optimization dispatching device for cascade hydropower stations, comprising:

[0013] A construction module is used to construct the objective function and constraint conditions of the real-time optimization dispatching model of cascade hydropower stations; the objective function of the real-time optimization dispatching model of cascade hydropower stations is the total water level and maximum of the cascade hydropower stations; the constraint conditions of the real-time optimization dispatching model of cascade hydropower stations include: water balance constraint, water level constraint, output constraint, flow constraint, upstream and downstream hydraulic connection and non-negative constraint;

[0014] An acquisition module is used to acquire the operation data of cascade hydropower stations; wherein the operation data of cascade hydropower stations include: the reservoir capacity relationship curve of each cascade hydropower station, the optimal characteristic curve of each hydropower station, the inflow of the leading hydropower station and the flow data of each interval, and the total load of the cascade hydropower station;

[0015] The calculation module is used to solve the real-time optimization dispatching model of the cascade hydropower station by using a discrete differential enumeration algorithm according to the operation data of the cascade hydropower station, the objective function and constraint conditions of the real-time optimization dispatching model of the cascade hydropower station; it includes:

[0016] The first calculation module is used to calculate the outflow flow rate by trial calculation according to the operation data of the cascade hydropower station, based on the objective function and constraint conditions of the real-time optimization dispatching model of the cascade hydropower station, and obtain an initial solution by using the outflow flow trial algorithm;

[0017] The second calculation module is used to use a discrete differential enumeration algorithm to perform further optimization calculations in the corridor formed near the initial solution to obtain the optimal solution as a new initial solution;

[0018] The third calculation module is used to reduce the discrete step length of the corridor, repeat the calculation process of the second calculation module until the total water level of the steps remains unchanged and the accuracy of the discrete step length meets the set requirements, and output the solution result.

[0019] The present invention also provides a real-time optimization and scheduling system for cascade hydropower stations, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes a real-time optimization and scheduling method for cascade hydropower stations when executed by the processor.

[0020] The present invention also provides a storage medium, on which a computer program is stored, and when the computer program is running, the real-time optimization scheduling method for cascade hydropower stations is executed.

[0021] The present invention proposes a total water level and maximum criterion for cascade hydropower stations, i.e., trying to meet the maximum water level requirement of each hydropower station. This not only solves the problem that the same amount of water has different energies in different hydropower stations due to the head effect, but also avoids the inconsistency and unreasonableness of the maximum criterion for cascade energy storage and the minimum criterion for cascade energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0023] Figure 1 It is a flow chart of a real-time optimization scheduling method for cascade hydropower stations according to an embodiment of the present invention;

[0024] Figure 2 Schematic diagram of discrete differential enumeration algorithm. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0026] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0027] Embodiment 1:

[0028] like Figure 1 As shown, an embodiment of the present invention provides a real-time optimization scheduling method for cascade hydropower stations, comprising:

[0029] Step S1, constructing the objective function and constraint conditions of the real-time optimization scheduling model of cascade hydropower stations;

[0030] Step S2, obtaining the operation data of the cascade hydropower station;

[0031] Step S3: according to the operation data of the cascade hydropower stations, the objective function and constraint conditions of the real-time optimization scheduling model of the cascade hydropower stations, a discrete differential enumeration algorithm is used to solve the real-time optimization scheduling model of the cascade hydropower stations.

[0032] As an implementation method of the present invention, in step S1, the objective function of the real-time optimization scheduling model of the cascade hydropower station is the total water level and the maximum of the cascade hydropower station, which is expressed as:

[0033]

[0034] Among them, Zm i is the water level at the end of the period of hydropower station i; n is the total number of hydropower stations.

[0035] The constraints of the real-time optimization scheduling model of cascade hydropower stations include: water balance constraints, water level constraints, output constraints, flow constraints, upstream and downstream hydraulic connections and non-negative constraints.

[0036] (1) Water balance constraints:

[0037] V i '=V i +(Q rk,i -q i )Δt(i=1,2,3...n)

[0038] Among them, V i and V i ' is the initial and final water storage of the hydropower station i; Q rk,i is the inflow flow of hydropower station i; q i is the discharge flow of hydropower station i; n is the total number of hydropower stations.

[0039] (2) Water level constraint:

[0040] Z i min ≤Z i ≤Zi max (i=1,2,3...n)

[0041] in, are the water level Z of hydropower station i in this period i The minimum and maximum values ​​allowed; the minimum value is the dead water level Z d,i , the maximum value is the normal water level Z n,i Or flood limit water level Z x,i .

[0042] (3) Output constraints:

[0043] N i min ≤N i ≤N i max (i=1,2,3...n)

[0044] in, are the minimum and maximum output power of hydropower station i under a certain water head; the minimum is the technical minimum output of the unit or 0, and the maximum is the expected output N yxcl,i Or it can be determined according to the actual requirements of the power grid.

[0045] (4) Traffic Constraints:

[0046] q i min ≤q i ≤q i max (i=1,2,3...n)

[0047] Among them, q i ,q i min ,q i max They are the outflow flow, the minimum and maximum allowable downstream flow of hydropower station i in this period respectively; the minimum value is set according to the comprehensive utilization requirements, and the maximum value is limited by the discharge capacity of the hydropower station and the flood control discharge constraints.

[0048] (5) Upstream and downstream hydraulic connections:

[0049] When cascade hydropower stations are located upstream and downstream of a main stream, the discharge flow from the upstream hydropower station will become part of the inflow flow of the downstream hydropower station, and together with the interval flow, it will become the inflow flow of the downstream hydropower station.

[0050] Q rk,i =q i-1,t +j i-1,t (i=2,3...n)

[0051] Among them, j i-1,tis the water inflow between the i-1th hydropower station and the i-th hydropower station during period t.

[0052] (6) Non-negative constraints:

[0053] All variables are non-negative.

[0054] As an implementation method of an embodiment of the present invention, in step S2, the operating data of the cascade hydropower station includes: the reservoir capacity relationship curve of each cascade hydropower station, the optimal characteristic curve of each hydropower station, the inflow flow of the leading hydropower station and the flow data of each interval, and the total load of the cascade hydropower station.

[0055] (1) Inflow data of Longtou Hydropower Station

[0056] The inflow rate of the upstreammost power station in a cascade hydropower station group.

[0057] (2) Traffic flow data for each section

[0058] The amount of water that flows into the basin or river section between two adjacent power stations through natural runoff, rainfall or other water sources and ultimately affects the inflow to the downstream power station.

[0059] (3) Reservoir capacity relationship curve of cascade hydropower stations

[0060] Z sy,i (t+1)=f zv [V i (t+1)](i=1,2,3...n)

[0061] Among them, Z sy,i (t+1) is the upstream water level of hydropower station i at the end of period t, V i (t+1) is the water storage capacity of hydropower station i at the end of period t, f zv (·) represents the storage capacity curve function.

[0062] (4) Optimal characteristic curves of cascade hydropower stations

[0063]

[0064] Among them, N i * is the optimal output of hydropower station i, is the upstream water level of hydropower station i, q i is the outflow of hydropower station i, f NZQ (·) represents the optimal output-upstream water level-outflow flow curve function.

[0065] (5) Total load of cascade hydropower stations

[0066] N=∑N i (i=1,2,3...n)

[0067] Where N is the total output of cascade hydropower stations during the dispatch period, N i The output of hydropower station i.

[0068] As an implementation method of an embodiment of the present invention, in step S3, the discrete differential enumeration algorithm starts from an initial solution, changes a small range (i.e., increment) near the solution sequence to form a corridor, and then performs an enumeration algorithm to optimize within the corridor, iterates repeatedly until convergence, and then reduces the increment, and repeats the operation until convergence and the increment meets the accuracy requirement.

[0069] Step S3 includes:

[0070] Step S31: According to the operation data of the cascade hydropower stations, based on the objective function and constraints of the real-time optimization scheduling model of the cascade hydropower stations, a group of equal outflows of each hydropower station is given, the output is calculated and the value of the outflow is adjusted so that the output of each power station meets the total load constraint of the cascade hydropower station, and the objective function value (sum of the water level at the end of the period of the cascade hydropower station) Z is output. 0 and the initial solution (outflow of each power station) q 0 ;

[0071] Step S32: The most downstream reservoir is selected as the coordination reservoir, and the other reservoirs are formed into corridors near the initial solution by taking smaller discrete steps above and below the initial solution using the discrete differential enumeration algorithm, and the enumeration algorithm is applied in the corridor for further optimization calculation to obtain the optimal solution as the new initial solution;

[0072] Step S33, repeat step S32 until the total water level of the steps does not increase. If the discrete step length meets the requirements at this time, stop the calculation and output the solution result; if the discrete step length does not meet the requirements, reduce the step length and repeat step S32 until the total water level of the steps does not change and the discrete step length accuracy meets the set requirements, and output the solution result.

[0073] Further, in step S31, the initial solution of the model is obtained by using the outbound flow trial algorithm, which specifically includes:

[0074] Step S311: Given a set of outbound flow q 0 = {q 0 1=q 0 2 = q 0 3=...=q 0 n}, according to the water balance equation V i '=V i +(Q rk,i -q i )Δt calculates the water storage at the end of the period and according to the storage capacity relationship curve Z of each power station sy,i (t+1)=f zv [V i(t+1)] to obtain the water level of each hydropower station at the end of the time period.

[0075] Step S312: Obtain the output N of each hydropower station according to the optimal characteristic curve (optimal output-upstream water level-outflow curve) of each hydropower station. 0 = {N 0 1, N 0 2, N 0 3…, N 0 n}, adjust the outbound flow so that N 0 1+N 0 2+...+N 0 n =N.

[0076] Step S313, calculate the water level change of each hydropower station, appropriately increase the outflow of the hydropower station with the smallest water level change while satisfying the output constraint, and calculate the difference ΔN between the sum of the outputs of each hydropower station and the given total output at this time; appropriately reduce the outflow of the hydropower station with the largest water level change, and calculate the difference ΔN' between the sum of the outputs of each hydropower station and the given total output at this time, calculate the outflow value so that ΔN'=0, and then calculate the water level at the end of the time period for each hydropower station.

[0077] Step S314, repeat step S313, calculate the sum of the water levels at the end of each period of each hydropower station under each combined outflow flow, until the sum of the water levels at the end of each period of time remains unchanged, and output the objective function value (sum of the water levels at the end of each period of the cascade power station) Z 0 and the initial solution (outflow of each power station) q 0 .

[0078] Further, in step S32, if Figure 2 As shown, the discrete differential enumeration algorithm is used to further optimize the calculation including:

[0079] Step S321, define the stages and stage variables, each power station is a stage, the power station number i is the stage variable (i=1,2,3,...n), select the nth power station as the coordination power station, and select a relatively small positive number ε as the minimum accuracy of flow change according to the demand.

[0080] Step S322: Take an appropriate discrete step length h (h>ε), select an appropriate number of discrete points m (m is 3, 5, 7, ...) according to the flow constraint range, and 0 A parallel corridor with a width of 2D is generated near the sequence, that is, the initial flow sequence, where 2D = h(m-1).

[0081] Step S323, fix the flow rate of the 1st to n-2nd stage, change the discrete flow rate of the n-1th stage, calculate the output of the 1st to n-1th stage, then calculate the difference between the system output and the output of the first n-1 power stations, that is, the remaining output, which is borne by the nth power station, calculate the water level value at the end of the time period of each reservoir, and obtain the total water level of the cascade hydropower station under the flow combination of the 1st to n-1th stage.

[0082] Step S324, traverse all discrete flow combinations from stage 1 to stage n-1, and select the flow combination that maximizes the total water level of the cascade hydropower station as the optimal strategy;

[0083] Step S325: Use the optimal strategy obtained in step S324 as the initial strategy, and repeat the calculations of step S322-step S324 until the water level and the water level no longer increase; if the increment h>ε at this time, reduce the increment h=h / 2, and then go to step S322-step S324; otherwise, go to step S326;

[0084] Step S326: output the optimal objective function value Z and the flow combination q of each power station.

[0085] The embodiment of the present invention takes the total water level and maximum of the cascade hydropower station as the optimization criteria, not only considering the water volume, but also fully considering the head effect between different hydropower stations. By making the water level of each hydropower station reach the maximum value as much as possible, the criterion can more accurately reflect the actual scheduling target of the cascade hydropower station system, avoiding the deficiency of traditional criteria that only focuses on water volume and ignores energy conversion, as well as the unreasonable results of the maximum criterion of cascade energy storage and the minimum criterion of cascade energy consumption. The discrete differential enumeration algorithm is used to solve the real-time optimization scheduling model of cascade hydropower stations, which effectively solves the problems of long calculation time and unstable results during the short-term economic operation of the hydropower station group. The method for determining the real-time optimization scheduling model of cascade hydropower stations optimizes the joint operation of cascade hydropower stations, obtains greater water resources, can provide technical support for the short-term scheduling and real-time economic operation of cascade hydropower stations, and can also provide reference for the medium- and long-term scheduling of cascade hydropower stations.

[0086] Embodiment 2:

[0087] The embodiment of the present invention further provides a real-time optimization dispatching device for cascade hydropower stations, comprising:

[0088] A construction module is used to construct the objective function and constraint conditions of the real-time optimization dispatching model of cascade hydropower stations; the objective function of the real-time optimization dispatching model of cascade hydropower stations is the total water level and maximum of the cascade hydropower stations; the constraint conditions of the real-time optimization dispatching model of cascade hydropower stations include: water balance constraint, water level constraint, output constraint, flow constraint, upstream and downstream hydraulic connection and non-negative constraint;

[0089] An acquisition module is used to acquire the operation data of cascade hydropower stations; wherein the operation data of cascade hydropower stations include: the reservoir capacity relationship curve of each cascade hydropower station, the optimal characteristic curve of each hydropower station, the inflow of the leading hydropower station and the flow data of each interval, and the total load of the cascade hydropower station;

[0090] The calculation module is used to solve the real-time optimization dispatching model of the cascade hydropower station by using a discrete differential enumeration algorithm according to the operation data of the cascade hydropower station, the objective function and constraint conditions of the real-time optimization dispatching model of the cascade hydropower station; it includes:

[0091] The first calculation module is used to calculate the outflow flow rate by trial calculation according to the operation data of the cascade hydropower station, based on the objective function and constraint conditions of the real-time optimization dispatching model of the cascade hydropower station, and obtain an initial solution by using the outflow flow trial algorithm;

[0092] The second calculation module is used to use a discrete differential enumeration algorithm to perform further optimization calculations in the corridor formed near the initial solution to obtain the optimal solution as a new initial solution;

[0093] The third calculation module is used to reduce the discrete step length of the corridor, repeat the calculation process of the second calculation module until the total water level of the steps remains unchanged and the accuracy of the discrete step length meets the set requirements, and output the solution result.

[0094] Embodiment 3:

[0095] An embodiment of the present invention also provides a real-time optimization and scheduling system for cascade hydropower stations, comprising: a memory and a processor, wherein the memory stores a computer program executed by the processor, and the computer program executes a real-time optimization and scheduling method for cascade hydropower stations when executed by the processor.

[0096] Embodiment 4:

[0097] An embodiment of the present invention further provides a storage medium, on which a computer program is stored, and when the computer program is run, a real-time optimization scheduling method for cascade hydropower stations is executed.

[0098] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. A real-time optimization scheduling method for cascade hydropower stations, characterized in that: include: Step S1, constructing the objective function and constraint conditions of the real-time optimization scheduling model of cascade hydropower stations; wherein, the objective function of the real-time optimization scheduling model of cascade hydropower stations is the total water level and maximum of the cascade hydropower stations; the constraint conditions of the real-time optimization scheduling model of cascade hydropower stations include: water balance constraint, water level constraint, output constraint, flow constraint, upstream and downstream hydraulic connection and non-negative constraint; Step S2, obtaining the operation data of the cascade hydropower station; wherein the operation data of the cascade hydropower station includes: the storage capacity relationship curve of each cascade hydropower station, the optimal characteristic curve of each hydropower station, the inflow of the leading hydropower station and the flow data of each interval, and the total load of the cascade hydropower station; Step S3, according to the operation data of the cascade hydropower stations, the objective function and constraint conditions of the real-time optimization scheduling model of the cascade hydropower stations, a discrete differential enumeration algorithm is used to solve the real-time optimization scheduling model of the cascade hydropower stations; specifically comprising: Step S31, according to the operation data of the cascade hydropower stations, based on the objective function and constraint conditions of the real-time optimization scheduling model of the cascade hydropower stations, the outbound flow trial algorithm is used to perform a trial calculation on the outbound flow to solve the model and obtain an initial solution; Step S32, using a discrete differential enumeration algorithm to perform further optimization calculations in the corridor formed near the initial solution, and obtaining the optimal solution as a new initial solution; Step S33, reduce the discrete step length of the corridor, repeat step S32 until the total water level of the steps remains unchanged and the accuracy of the discrete step length meets the set requirements, and output the solution result.

2. A real-time optimization dispatching device for cascade hydropower stations, characterized in that: include: A construction module is used to construct the objective function and constraint conditions of the real-time optimization dispatching model of cascade hydropower stations; the objective function of the real-time optimization dispatching model of cascade hydropower stations is the total water level and maximum of the cascade hydropower stations; the constraint conditions of the real-time optimization dispatching model of cascade hydropower stations include: water balance constraint, water level constraint, output constraint, flow constraint, upstream and downstream hydraulic connection and non-negative constraint; An acquisition module is used to acquire the operation data of cascade hydropower stations; wherein the operation data of cascade hydropower stations include: the reservoir capacity relationship curve of each cascade hydropower station, the optimal characteristic curve of each hydropower station, the inflow of the leading hydropower station and the flow data of each interval, and the total load of the cascade hydropower station; The calculation module is used to solve the real-time optimization dispatching model of the cascade hydropower station by using a discrete differential enumeration algorithm according to the operation data of the cascade hydropower station, the objective function and constraint conditions of the real-time optimization dispatching model of the cascade hydropower station; it includes: The first calculation module is used to calculate the outflow flow rate by trial calculation according to the operation data of the cascade hydropower station, based on the objective function and constraint conditions of the real-time optimization dispatching model of the cascade hydropower station, and obtain an initial solution by using the outflow flow trial algorithm; The second calculation module is used to use a discrete differential enumeration algorithm to perform further optimization calculations in the corridor formed near the initial solution to obtain the optimal solution as a new initial solution; The third calculation module is used to reduce the discrete step length of the corridor, repeat the calculation process of the second calculation module until the total water level of the steps remains unchanged and the accuracy of the discrete step length meets the set requirements, and output the solution result.

3. A real-time optimization dispatching system for cascade hydropower stations, comprising: A memory and a processor, wherein the memory stores a computer program executed by the processor, and when the computer program is executed by the processor, the real-time optimization scheduling method for cascade hydropower stations as claimed in claim 1 is executed.

4. A storage medium having a computer program stored thereon, wherein the computer program executes the real-time optimization scheduling method for cascade hydropower stations as claimed in claim 1 when running.

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