Reservoir flood control optimal scheduling objective function construction method considering influence of dynamic reservoir capacity

Abstract

The invention discloses a reservoir flood control optimization scheduling objective function construction method considering dynamic reservoir capacity influence, and belongs to the field of reservoir flood control scheduling, and the method comprises the steps: S1, taking a maximum peak clipping criterion as an optimization criterion, taking the change of wedge-shaped reservoir capacity into consideration when calculating the reservoir capacity change caused by the reservoir inflow and outflow flow according to a water balance principle, and taking the maximum peak clipping criterion as an optimization criterion; re-deducing a target function of the flood control optimization scheduling model of the river type reservoir considering the influence of the dynamic reservoir capacity; s2, according to the obtained objective function, constructing a new river-type reservoir flood control optimization scheduling model, and replacing a common water balance equation with a Saint-Venant equation set as a constraint condition of water balance; the invention provides the reservoir flood control optimization scheduling objective function construction method considering the influence of the dynamic reservoir capacity, the flood control optimization scheduling model for the river type reservoir is established based on the objective function deduced by the method, accurate simulation of flood control scheduling of the river type reservoir is realized, and then an accurate and reliable optimal scheduling scheme is obtained.

Description

Technical Field

[0001] The invention relates to the technical field of reservoir flood control dispatching, and in particular to a method for constructing a reservoir flood control optimization dispatching objective function taking into account the influence of dynamic storage capacity. Background Art

[0002] At present, in the research on flood control optimization of river-type reservoirs based on the maximum peak-cutting criterion, although the influence of wedge-shaped storage capacity and flood propagation has been considered, the objective function still uses the form of the traditional reservoir flood control optimization scheduling model. However, in the process of deriving this objective function, when calculating the storage capacity changes caused by the inflow and outflow of the reservoir, the changes in the wedge-shaped storage capacity are not taken into account, resulting in the calculated outflow process not being the optimal solution in the true sense. Therefore, it is necessary to study a method for constructing an objective function for reservoir flood control optimization scheduling that can consider the influence of dynamic storage capacity. Summary of the invention

[0003] The purpose of the present invention is to provide a method for constructing a reservoir flood control optimization scheduling objective function taking into account the influence of dynamic storage capacity, so as to solve the problem in the above-mentioned background technology that the objective function of the existing reservoir flood control scheduling model established based on the maximum peak shaving criterion cannot reflect the influence of the dynamic storage capacity of river-type reservoirs.

[0004] To achieve the above object, the present invention provides a method for constructing a reservoir flood control optimization scheduling objective function considering the influence of dynamic storage capacity, comprising the following steps:

[0005] S1. Taking the maximum peak-cutting criterion as the optimization criterion, according to the principle of water balance, when calculating the storage capacity change caused by the inflow and outflow of the reservoir, the change of the wedge storage capacity is taken into consideration, and the objective function of the flood control optimization scheduling model of the river-type reservoir considering the influence of dynamic storage capacity is re-derived as the minimum sum of the squares of the sum of the outflow and the change rate of the wedge storage capacity over time. When there is interval inflow, the objective function can be expressed as the minimum sum of the squares of the sum of the change rate of the outflow and the wedge storage capacity over time and the interval inflow;

[0006] S2. Based on the objective function obtained in S1, a new flood control optimization scheduling model for river-type reservoirs is constructed, and the Saint-Venant equations are used to replace the water balance equation as the constraint condition of water balance.

[0007] Preferably, the derivation process of the objective function in step S1 is as follows:

[0008] According to the maximum peak shaving criterion, the objective function of reservoir flood control optimization operation is expressed as:

[0009]

[0010] in, is the maximum discharge flow of the reservoir; t0 and t N are the beginning and end of the reservoir operation period respectively; N is the time period number; Ω is the reservoir operation strategy space; q t is the discharge flow of the reservoir at time t, and meets the water balance requirements. The water balance equation is:

[0011]

[0012] Among them, Q t is the reservoir inflow at time t; ΔV is the flow from t0 to t N The change in reservoir capacity at the time, including the change in the reservoir static capacity and wedge capacity, is ΔV = ΔV s +ΔV w , where ΔV s and ΔV w From t0 to t N The change in the static storage capacity and wedge storage capacity of the reservoir at the moment, formula (2) can also be expressed as:

[0013]

[0014] in, From t0 to t N The amount of water entering the reservoir at a given moment; for a given flood process, the amount of water entering the reservoir is a known constant. It can be seen from formula (3) that the downstream flow rate q t and the inflow flow Q t , Reservoir static storage capacity change ΔV s And the wedge storage capacity change ΔV w related;

[0015] set up in Indicates the rate of change of wedge storage capacity over time, the change in static storage capacity ΔV s According to t0 to t N The relationship curve between the dam front water level and water level storage capacity at the time is calculated and is a known constant. Then formula (3) is expressed as:

[0016]

[0017] in, is a known constant;

[0018] make From formula (4), we get:

[0019]

[0020] but

[0021]

[0022] From formula (6), we know that when

[0023] When t0 to t1, X+Y can reach the minimum value, so it is a necessary condition for X and Y to reach the minimum value. That is, to make X and Y reach the minimum value, for t0 to t2 N At any moment t, To be equal to As shown below:

[0024]

[0025] Due to the constraints of reservoir flood control optimization scheduling, the scheduling results are often difficult to meet the requirements of formula (7). According to the consistent continuity of the function, when As close as possible When , X and Y can also approach their respective minimum values. Therefore, the least squares method can be used to minimize and The difference between them can solve the problem of minimizing the maximum downstream flow in equation (1), as shown below:

[0026]

[0027] According to formula (4), we can get:

[0028]

[0029] Therefore, formula (8) is expressed as:

[0030]

[0031] In the actual flood control dispatch process, the discrete form of formula (10) is often used, as shown below:

[0032]

[0033] Among them, q i and are the average discharge flow of the reservoir in period i and the change of wedge storage capacity during the reservoir flood control operation process, expressed as:

[0034]

[0035] in, is the wedge storage capacity of the reservoir at time t;

[0036] Formula (11) is the re-derived reservoir flood control optimization scheduling objective function considering the influence of dynamic storage capacity, where represents the change of wedge storage capacity during reservoir flood control operation. However, in the traditional reservoir flood control optimization operation model, ΔV in formula (2) only represents the change of static storage capacity, that is, ΔV = ΔV s , obviously, the changes of wedge-shaped reservoirs are not taken into account. Therefore, the objective function of the traditional reservoir optimization operation model is not applicable to river-type reservoirs. The traditional objective function is expressed as:

[0037]

[0038] When considering interval flooding, equation (11) is expressed as:

[0039]

[0040] in, is the interval inflow of the i-th scheduling period.

[0041] Preferably, the objective function derived in step S2 according to S1 is A one-dimensional non-steady flow model is used to simulate the changes in the dynamic storage capacity of the reservoir and the flood evolution process of the river in the reservoir area. The Saint-Venant equations are used to replace the commonly used water balance equation as the constraint condition of water balance. An optimal flood control scheduling model for river-type reservoirs considering the influence of dynamic storage capacity is established, and the water level constraints in front of the reservoir dam, the reservoir downstream flow constraints, the water level in front of the reservoir dam and the downstream flow amplitude constraints and non-negative constraints are set.

[0042] Preferably, in the model established in S2, the reservoir flood control optimization scheduling model and the one-dimensional non-steady flow model are bidirectionally coupled, wherein the dam front water level calculated by the reservoir flood control optimization scheduling model is the lower boundary condition of the one-dimensional non-steady flow model, and according to the upstream water and the dam front water level, the one-dimensional non-steady flow model is used to simulate the non-steady flow of the reservoir river channel to obtain the outflow flow and the rate of change of the wedge-shaped storage capacity over time in the objective function.

[0043] Preferably, the water balance constraint is specifically:

[0044] In the model established in step S2, the dynamic storage capacity change of the reservoir and the flood evolution process are calculated by the Saint-Venant equations. The Saint-Venant equations are used to replace the water balance equation as the constraint condition of the water balance, as follows:

[0045]

[0046] Among them, Q and Z are the cross-sectional flow and water level respectively; x is the longitudinal distance along the flow direction; t is the time; B, A, and R are the river width, cross-sectional area, and hydraulic radius respectively; n is the roughness; q l is the lateral inflow per unit length of the corresponding river section; V is the average flow velocity of the section; v lis the component of the lateral inflow velocity in the x direction; α is the momentum correction coefficient; g is the gravitational acceleration.

[0047] Preferably, the water level constraint in front of the reservoir dam is specifically:

[0048] In flood control optimization scheduling, the water levels in front of the dam at the beginning and end of the scheduling period are generally given known values, which are usually flood control limit water levels and can be expressed as follows:

[0049] Z0=Z Begin ,Z N =Z End (18)

[0050] Among them, Z0 and Z N are the water levels in front of the dam in the first and last periods of the dispatching period; Z Begin and Z End are the water levels in front of the dam at the beginning and end of a given dispatch period respectively;

[0051] During the rest of the dispatching period, the water level in front of the dam changes within the permitted range as follows:

[0052]

[0053] Among them, Z i is the water level in front of the dam at the end of period i; and They respectively represent the lower and upper limits of the water level in front of the dam that are allowed to be reached at the end of time period i, usually the flood control limit water level and the design flood level.

[0054] Preferably, the reservoir discharge flow constraint is specifically:

[0055] For each period during the dispatch period, the average discharge flow of the reservoir meets the corresponding flow limit, which is:

[0056]

[0057] in, and They represent the minimum and maximum downstream flows allowed in the i-th time period respectively.

[0058] Preferably, the water level in front of the reservoir dam and the downstream flow fluctuation constraints are specifically as follows:

[0059] The changes in the dam front water level and the discharge flow in adjacent dispatching periods do not exceed the allowable values, which are:

[0060]

[0061] in, and are the maximum water level in front of the dam and the maximum discharge flow variation allowed in the i-th period respectively.

[0062] Preferably, the non-negativity constraint is that all variables in the constructed model are non-negative.

[0063] Therefore, in order to solve the problem that the objective function of the existing reservoir flood control scheduling model established according to the maximum peak shaving criterion cannot reflect the influence of the dynamic storage capacity of the river-type reservoir, the present invention proposes the above-mentioned method for constructing the objective function of reservoir flood control optimization scheduling considering the influence of dynamic storage capacity. According to the principle of water balance, the objective function of the river-type reservoir flood control optimization scheduling model considering the influence of dynamic storage capacity is re-derived, and based on this, a new river-type reservoir flood control optimization scheduling model is established to obtain a more accurate and reliable optimal scheduling plan.

[0064] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 A schematic diagram of a model coupling framework of a method for constructing a reservoir flood control optimization scheduling objective function taking into account the influence of dynamic storage capacity according to the present invention;

[0066] Figure 2 Schematic diagram of the optimization scheduling process of the model dam front water level and downstream flow rate with different objective functions in an embodiment of the present invention. DETAILED DESCRIPTION

[0067] The following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. 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.

[0068] A method for constructing an objective function for reservoir flood control optimization scheduling considering the influence of dynamic storage capacity includes the following steps:

[0069] S1. Taking the maximum peak-cutting criterion as the optimization criterion, according to the principle of water balance, when calculating the storage capacity change caused by the inflow and outflow of the reservoir, the change of the wedge storage capacity is taken into consideration, and the objective function of the flood control optimization scheduling model of the river-type reservoir is derived as the minimum sum of the squares of the sum of the outflow and the change rate of the wedge storage capacity over time. When there is interval inflow, the objective function can be expressed as the minimum sum of the squares of the sum of the change rate of the outflow and the wedge storage capacity over time and the interval inflow; specifically:

[0070] According to the maximum peak shaving criterion, the objective function of reservoir flood control optimization operation is expressed as:

[0071]

[0072] in, is the maximum discharge flow of the reservoir; t0 and t N are the beginning and end of the reservoir operation period respectively; N is the time period number; Ω is the reservoir operation strategy space; q t is the discharge flow of the reservoir at time t, and meets the water balance requirements. The water balance equation is:

[0073]

[0074] Among them, Q t is the reservoir inflow at time t; ΔV is the flow from t0 to t N The change in reservoir capacity at the time, including the change in the reservoir static capacity and wedge capacity, is ΔV = ΔV s +ΔV w , where ΔV s and ΔV w From t0 to t N The change in the static storage capacity and wedge storage capacity of the reservoir at the moment, formula (2) can also be expressed as:

[0075]

[0076] in, From t0 to t N The amount of water entering the reservoir at a given moment; for a given flood process, the amount of water entering the reservoir is a known constant. It can be seen from formula (3) that the downstream flow rate q t and the inflow flow Q t , Reservoir static storage capacity change ΔV s And the wedge storage capacity change ΔV w related;

[0077] set up in Indicates the rate of change of wedge storage capacity over time, the change in static storage capacity ΔV s According to t0 to t N The relationship curve between the dam front water level and water level storage capacity at the time is calculated and is a known constant. Then formula (3) is expressed as:

[0078]

[0079] in, is a known constant;

[0080] make From formula (4), we get:

[0081]

[0082] but

[0083]

[0084] From formula (6), we know that when

[0085] When t0 to t1, X+Y can reach the minimum value, so it is a necessary condition for X and Y to reach the minimum value. That is, to make X and Y reach the minimum value, for t0 to t2 N At any time t, To be equal to As shown below:

[0086]

[0087] Due to the constraints of reservoir flood control optimization scheduling, the scheduling results are often difficult to meet the requirements of formula (7). According to the consistent continuity of the function, when As close as possible When , X and Y can also approach their respective minimum values. Therefore, the least squares method can be used to minimize and The difference between them can solve the problem of minimizing the maximum downstream flow in equation (1), as shown below:

[0088]

[0089] According to formula (4), we can get:

[0090]

[0091] Therefore, formula (8) is expressed as:

[0092]

[0093] In the actual flood control dispatch process, the discrete form of formula (10) is often used, as shown below:

[0094]

[0095] Among them, q i and are the average discharge flow of the reservoir in period i and the change of wedge storage capacity during the reservoir flood control operation process, expressed as:

[0096]

[0097] in, is the wedge storage capacity of the reservoir at time t;

[0098] Formula (11) is the re-derived reservoir flood control optimization scheduling objective function considering the influence of dynamic storage capacity, where represents the change of wedge storage capacity during reservoir flood control operation. However, in the traditional reservoir flood control optimization operation model, ΔV in formula (2) only represents the change of static storage capacity, that is, ΔV = ΔV s , obviously, the changes of wedge-shaped reservoirs are not taken into account. Therefore, the objective function of the traditional reservoir optimization operation model is not applicable to river-type reservoirs. The traditional objective function is expressed as:

[0099]

[0100] When considering interval flooding, formula (11) is expressed as:

[0101]

[0102] in, is the interval inflow of the i-th scheduling period.

[0103] S2. Model building

[0104] According to the objective function derived from S1, a one-dimensional non-steady flow model is used to simulate the dynamic storage capacity changes of the reservoir and the flood evolution process of the river channel in the reservoir area, and the Saint-Venant equations are used to replace the commonly used water balance equation as the constraint condition of the water balance to establish a river-type reservoir flood control optimization scheduling model considering the influence of dynamic storage capacity. In the established model, the reservoir flood control optimization scheduling model and the one-dimensional non-steady flow model are bidirectionally coupled, in which the dam front water level calculated by the reservoir flood control optimization scheduling model is the lower boundary condition of the one-dimensional non-steady flow model, and according to the upstream water and the dam front water level, the one-dimensional non-steady flow model is used to simulate the non-steady flow of the reservoir river channel, and the outflow flow and the rate of change of the wedge storage capacity over time in the objective function can be obtained. The model coupling framework is as follows: Figure 1 shown.

[0105] S21. Objective function

[0106] The objective function re-derived in S1 is used in the reservoir flood control optimization operation model considering the influence of dynamic storage capacity, that is,

[0107]

[0108] S22. Constraints

[0109] (1) Water balance constraints

[0110] In the model established in step S2, the dynamic storage capacity change of the reservoir and the flood evolution process are calculated by the Saint-Venant equations. The Saint-Venant equations are used to replace the water balance equation as the constraint condition of the water balance, as follows:

[0111]

[0112] Among them, Q and Z are the cross-sectional flow and water level respectively; x is the longitudinal distance along the flow direction; t is the time; B, A, and R are the river width, cross-sectional area, and hydraulic radius respectively; n is the roughness; q l is the lateral inflow per unit length of the corresponding river section; V is the average flow velocity of the section; v l is the component of the lateral inflow velocity in the x direction; α is the momentum correction coefficient; g is the gravitational acceleration.

[0113] (2) Water level constraints in front of the reservoir dam

[0114] In flood control optimization scheduling, the water levels in front of the dam at the beginning and end of the scheduling period are generally given known values, which are usually flood control limit water levels and can be expressed as follows:

[0115] Z0=Z Begin ,Z N =Z End (18)

[0116] Among them, Z0 and Z N are the water levels in front of the dam in the first and last periods of the dispatching period; Z Begin and Z End are the water levels in front of the dam at the beginning and end of a given dispatch period respectively;

[0117] During the rest of the dispatching period, the water level in front of the dam changes within the permitted range as follows:

[0118]

[0119] Among them, Z i is the water level in front of the dam at the end of period i; and They respectively represent the lower and upper limits of the water level in front of the dam that are allowed to be reached at the end of time period i, usually the flood control limit water level and the design flood level.

[0120] (3) Reservoir discharge flow constraints

[0121] For each period during the dispatch period, the average discharge flow of the reservoir meets the corresponding flow limit, which is:

[0122]

[0123] in, and They represent the minimum and maximum downstream flows allowed in the i-th time period respectively.

[0124] (4) Constraints on the water level in front of the reservoir dam and the downstream flow rate

[0125] The changes in the dam front water level and the discharge flow in adjacent dispatching periods do not exceed the allowable values, which are:

[0126]

[0127] in, and are the maximum water level in front of the dam and the maximum discharge flow variation allowed in the i-th period respectively.

[0128] (5) Non-negative constraints

[0129] All variables in the flood control optimization operation model are non-negative.

[0130] In this embodiment, the established model and method are applied to the flood control optimization dispatch calculation of Xiangjiaba Reservoir. The backwater length of Xiangjiaba Reservoir is about 156km, and the average water surface width is about 360m. It is a typical river-type reservoir. There are 160 sections along the way, and the section spacing is generally 700m-1700m. The downstream flood control point of Xiangjiaba Reservoir is Lizhuang Station, and its safe flow is 40000m 3 / s. The flood control limit water level and design flood level are 370m and 380m respectively. The typical inflow flood process in 1998 is magnified into a 50-year design flood process as the inflow process of the established model. The dispatching cycle is 7d, the dispatching calculation period is 1h, and there are 168 periods in total. The calculation time step of the one-dimensional unsteady flow model is set to 1h. The reservoir flood control optimization dispatching model is solved by a sequential dynamic programming algorithm based on OpenMP parallelism.

[0131] The objective function re-derived by the present invention and the traditional objective function are used to establish reservoir flood control optimization scheduling models, and flood control optimization scheduling for a design flood with a return period of 50 years is carried out. The calculation results and scheduling process are shown in Table 1 and Figure 2 shown.

[0132] Table 1 Comparison of the dispatch calculation results of the reservoir flood control optimization dispatch model established by the re-derived objective function and the traditional objective function

[0133]

[0134] From Table 1 and Figure 2 It can be seen that the maximum discharge flow and the maximum flow of Lizhuang are 30608.78m respectively, which are obtained by re-deriving the objective function of the river-type reservoir flood control dispatching model considering the influence of dynamic storage capacity. 3 / s and 37993.80m 3 / s, which is 779.55m smaller than the results calculated by the traditional objective function model. 3 / s and 1855.40m 3 / s, and the downstream flow process and Lizhuang flow process obtained by re-deriving the objective function model are both smoother. This shows that the reservoir flood control optimization scheduling model considering the influence of dynamic storage capacity established by the objective function re-derived by the present invention can obtain a better reservoir scheduling plan and provide more accurate information for the actual flood control scheduling of river-type reservoirs.

[0135] The present invention uses the maximum peak-cutting criterion as the optimization criterion, and according to the principle of water balance, re-derives the objective function of the river-type reservoir flood control optimization scheduling model considering the influence of dynamic storage capacity, and based on this, establishes a reservoir flood control optimization scheduling model considering the influence of dynamic storage capacity. The results show that the flood control optimization scheduling model considering the influence of dynamic storage capacity established according to the re-derived objective function of the present invention can obtain the optimal scheduling plan in the true sense, and can provide more accurate information for the actual flood control scheduling of river-type reservoirs.

[0136] Therefore, in view of the problem that the objective function of the existing reservoir flood control scheduling model established according to the maximum peak shaving criterion cannot reflect the influence of the dynamic storage capacity of the river-type reservoir, the present invention proposes the above-mentioned method for constructing the objective function of reservoir flood control optimization scheduling considering the influence of dynamic storage capacity, and re-derives the objective function of the river-type reservoir flood control optimization scheduling model considering the influence of dynamic storage capacity according to the water balance principle. According to the re-derived objective function, the Saint-Venant equations replace the commonly used water balance equations as the constraints of the water balance, and a new river-type reservoir flood control optimization scheduling model considering the influence of dynamic storage capacity is established, so as to achieve accurate simulation of river-type reservoir flood control scheduling, so as to obtain a more accurate and reliable optimal scheduling plan.

[0137] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that they can still modify or replace the technical solution of the present invention with equivalents, and these modifications or equivalent replacements cannot cause the modified technical solution to deviate from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for constructing an objective function for reservoir flood control optimization scheduling considering the influence of dynamic storage capacity, characterized in that: The following steps are involved: S1. Taking the maximum peak-cutting criterion as the optimization criterion, according to the water balance principle, when calculating the storage capacity change caused by the inflow and outflow of the reservoir, the change of the wedge storage capacity is taken into consideration, and the objective function of the river-type reservoir flood control optimization scheduling model considering the influence of dynamic storage capacity is re-derived; S2. Based on the objective function obtained in S1, a new flood control optimization scheduling model for river-type reservoirs is constructed, and the Saint-Venant equations are used to replace the water balance equation as the constraint condition of water balance.

2. The method for constructing the objective function of reservoir flood control optimization scheduling considering the influence of dynamic storage capacity according to claim 1 is characterized in that: The derivation process of the objective function in step S1 is as follows: According to the maximum peak shaving criterion, the objective function of reservoir flood control optimization operation is expressed as: in, is the maximum discharge flow of the reservoir; t0 and t N are the beginning and end of the reservoir operation period respectively; N is the time period number; Ω is the reservoir operation strategy space; q t is the discharge flow of the reservoir at time t, and meets the water balance requirements. The water balance equation is: Among them, Q t is the reservoir inflow at time t; ΔV is the flow from t0 to t N The change in reservoir capacity at the time, including the change in the reservoir static capacity and wedge capacity, is ΔV = ΔV s +ΔV w , where ΔV s and ΔV w From t0 to t N The change in the static storage capacity and wedge storage capacity of the reservoir at the moment, formula (2) can also be expressed as: in, From t0 to t N The amount of water entering the reservoir at the time; from formula (3), the discharge flow q t and the inflow flow Q t , Reservoir static storage capacity change ΔV s And the wedge storage capacity change ΔV w related; set up in Indicates the rate of change of wedge storage capacity over time, the change in static storage capacity ΔV s According to t0 to t N The relationship curve between the dam front water level and water level storage capacity at the time is calculated and is a known constant. Then formula (3) is expressed as: in, is a known constant; make From formula (4), we get: but From formula (6), we know that when When t0 to t1, X+Y reaches the minimum value, so the above formula is the necessary condition for X and Y to reach the minimum value; to make X+Y reach the minimum value, for t0 to t2 N At any moment t, To be equal to As shown below: Use the least squares method to minimize The difference between and C can solve the problem of minimizing the maximum downstream flow in equation (1), as shown below: According to formula (4), we can get: Therefore, formula (8) is expressed as: In the actual flood control dispatch process, the discrete form of formula (10) is often used, as shown below: Among them, q i and are the average discharge flow of the reservoir in period i and the change of wedge storage capacity during the reservoir flood control operation process, expressed as: in, is the wedge storage capacity of the reservoir at time t; Formula (11) is the re-derived reservoir flood control optimization scheduling objective function. However, in the traditional reservoir flood control optimization scheduling model, ΔV in Formula (2) represents the change in static storage capacity, ΔV = ΔV s , obviously, the changes of wedge-shaped reservoirs are not taken into account; therefore, the objective function of the traditional reservoir optimization operation model is not applicable to river-type reservoirs. The traditional objective function is expressed as: When considering interval flooding, equation (11) is expressed as: in, is the interval inflow of the i-th scheduling period.

3. The method for constructing a reservoir flood control optimization scheduling objective function considering the influence of dynamic storage capacity according to claim 2 is characterized in that: The objective function derived from step S1 in step S2 is A one-dimensional non-steady flow model is used to simulate the changes in the dynamic storage capacity of the reservoir and the flood evolution process of the river channel in the reservoir area. The Saint-Venant equations are used to replace the water balance equation as the constraint condition of the water balance. An optimal flood control scheduling model for river-type reservoirs considering the influence of dynamic storage capacity is established, and the water level constraints in front of the reservoir dam, the reservoir downstream flow constraints, the water level in front of the reservoir dam and the downstream flow amplitude constraints and non-negative constraints are set.

4. The method for constructing a reservoir flood control optimization scheduling objective function considering the influence of dynamic storage capacity according to claim 3 is characterized in that: In the model established by S2, the reservoir flood control optimization scheduling model and the one-dimensional non-steady flow model are bidirectionally coupled. The dam front water level calculated by the reservoir flood control optimization scheduling model is the lower boundary condition of the one-dimensional non-steady flow model. According to the upstream water and the dam front water level, the one-dimensional non-steady flow model is used to simulate the non-steady flow of the reservoir river channel to obtain the outflow flow and the rate of change of the wedge storage capacity over time in the objective function.

5. The method for constructing a reservoir flood control optimization scheduling objective function considering the influence of dynamic storage capacity according to claim 3 is characterized in that: The constraints of water balance are as follows: In the model established in step S2, the dynamic storage capacity change of the reservoir and the flood evolution process are calculated by the Saint-Venant equations. The Saint-Venant equations are used to replace the water balance equation as the constraint condition of the water balance, as follows: Among them, Q and Z are the cross-sectional flow and water level respectively; x is the longitudinal distance along the water flow direction; t is the time; B, A, and R are the river width, cross-sectional area, and hydraulic radius respectively; n is the roughness; q l is the lateral inflow per unit length of the corresponding river section; V is the average flow velocity of the section; v l is the component of the lateral inflow velocity in the x direction; α is the momentum correction coefficient; g is the gravitational acceleration.

6. The method for constructing a reservoir flood control optimization scheduling objective function considering the influence of dynamic storage capacity according to claim 3 is characterized in that: The specific constraints of the water level in front of the reservoir dam are as follows: the water level in front of the dam at the beginning and end of the scheduling period in flood control optimization scheduling is generally a given known value, usually the flood control limit water level, expressed as: Z0=Z Begin ,WITH N =Z End (18) Among them, Z0 and Z N are the water levels in front of the dam in the first and last periods of the dispatching period; Z Begin and Z End are the water levels in front of the dam at the beginning and end of a given dispatch period respectively; During the rest of the dispatching period, the water level in front of the dam changes within the permitted range as follows: Among them, Z i is the water level in front of the dam at the end of period i; and They represent the lower and upper limits of the water level in front of the dam that can be reached at the end of period i.

7. The method for constructing a reservoir flood control optimization scheduling objective function considering the influence of dynamic storage capacity according to claim 6 is characterized in that: The specific constraints on the reservoir discharge flow are: For each period during the dispatch period, the average discharge flow of the reservoir meets the corresponding flow limit, which is: in, and They represent the minimum and maximum downstream flows allowed in the i-th time period respectively.

8. The method for constructing the objective function of reservoir flood control optimization scheduling considering the influence of dynamic storage capacity according to claim 7 is characterized in that: The specific constraints on the water level and discharge flow in front of the reservoir dam are: The changes in the dam front water level and the discharge flow in adjacent dispatching periods do not exceed the allowable values, which are: in, and are the maximum water level in front of the dam and the maximum discharge flow variation allowed in the i-th period respectively.

9. The method for constructing a reservoir flood control optimization scheduling objective function considering the influence of dynamic storage capacity according to claim 3, characterized in that: The non-negativity constraint is that all variables in the constructed model are non-negative.

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