Cascade reservoir multi-target scheduling evaluation method based on weight coefficient

Abstract

The invention discloses a cascade reservoir multi-target scheduling evaluation method based on a weight coefficient. The method comprises the following steps: collecting basic data of cascade reservoirs and multi-target scheduling rules and constraints of the reservoirs; determining evaluation indexes of the multi-target scheduling scheme and the number n of the evaluation indexes; drawing up m multi-target scheduling schemes, and constructing a reservoir scheduling model based on a reservoir multi-target scheduling rule; calculating each evaluation index value in each multi-target scheduling scheme, and constructing a multi-target decision evaluation index matrix; obtaining a multi-target decision evaluation transformation matrix; constructing a weight matrix; obtaining a weighted attribute matrix, then calculating the relative proximity, and if the relative proximity value is the maximum, indicating that the scheduling scheme is the optimal scheduling scheme. According to the method, the problem that the scheduling target cannot be common and competitive coordination during decision making of the reservoir scheduling scheme is solved, and technical support can be provided for formulating the reservoir scheduling scheme with the maximum comprehensive benefit.

Description

Technical Field

[0001] The present invention relates to the technical field of reservoir operation, and particularly to a multi-objective operation evaluation method for cascade reservoirs based on weight coefficients. Background Art

[0002] Reservoirs play multiple roles such as flood control and drought relief, and water resource regulation. With the increasing demand for water resources and the improvement of water ecological environment protection requirements, reservoir operation faces more complex multi-objective problems. Common reservoir operation objectives include ensuring the flood control safety of flood control protection objects downstream, meeting the water supply and irrigation demands of the downstream river channel, making full use of water resources to generate power generation benefits as much as possible, and improving the navigation conditions of the downstream river channel.

[0003] The formulation and evaluation of cascade reservoir joint operation plans have the characteristics of multi-objective decision-making. For example: First, there are more than one objective in the decision-making problem, such as the excess flood volume of flood control protection objects, the end-of-flood-season water storage volume of reservoirs, the power generation of reservoirs within the operation period, etc.; Second, there is incommensurability between objectives, that is, there is no unified measurement standard or measurement unit for each objective, so it is difficult to compare; Third, there is a contradiction between each objective. Improving a certain objective value in a certain plan may make another objective value worse.

[0004] Due to the existence of a certain competition and coordination relationship between different reservoir operation objectives, the traditional reservoir operation decision-making theory and methods have certain limitations in dealing with the evaluation of cascade reservoir multi-objective operation plans and cannot meet the new requirements of maximizing the comprehensive benefits of cascade reservoir multi-objective operation. Summary of the Invention

[0005] In view of the deficiencies of the prior art, the present invention proposes a multi-objective operation evaluation method for cascade reservoirs based on weight coefficients, establishes an evaluation system for different operation plans containing different operation objectives, solves the problems of incommensurability and competition coordination of operation objectives in reservoir operation plan decision-making, and can provide technical support for formulating a reservoir operation plan with maximum comprehensive benefits.

[0006] To achieve the above object, a multi-objective operation evaluation method for cascade reservoirs based on weight coefficients designed by the present invention is characterized in that it includes the following steps:

[0007] S1) Collect the basic information of cascade reservoirs, as well as the multi-objective operation rules and constraints of the reservoirs;

[0008] S2) Determine the evaluation indexes of the multi-objective operation plan and the number n of evaluation indexes;

[0009] S3) According to the requirements of reservoir operation decision-making, m multi-objective operation schemes are formulated; a reservoir operation model is constructed based on the multi-objective operation rules of the reservoir, and the reservoir operation model is the functional relationship between the evaluation indexes of the multi-objective operation scheme and the reservoir operation decision variables;

[0010] S4) Calculate the value of each evaluation index in each multi-objective operation scheme, and construct a multi-objective decision-making evaluation index matrix;

[0011] S5) Normalize each evaluation index value in the multi-objective decision-making evaluation index matrix to obtain a multi-objective decision-making evaluation transformation matrix;

[0012] S6) Construct a weight matrix, which is used to measure the importance of each evaluation index;

[0013] S7) Multiply the multi-objective decision-making evaluation transformation matrix by the weight matrix to obtain a weighted attribute matrix; Imagine a positive ideal scheme and a negative ideal scheme, respectively determine the Euclidean distances between each operation scheme in the weighted attribute matrix and the positive ideal scheme and the negative ideal scheme, and then calculate the relative closeness. If the relative closeness value is the largest, it indicates that this operation scheme is the optimal operation scheme;

[0014] The relative closeness is expressed by the following formula

[0015]

[0016]

[0017] In the formula,

[0018] C i represents the relative closeness value,

[0019] represents the Euclidean distance between the i-th operation scheme and the negative ideal scheme,

[0020] represents the Euclidean distance between the i-th operation scheme and the positive ideal scheme,

[0021] v i,j represents the value of the j-th evaluation index of the i-th operation scheme in the weighted attribute matrix,

[0022] is the value of the positive ideal scheme of the j-th evaluation index,

[0023] is the value of the negative ideal scheme of the j-th evaluation index.

[0024] Further, in S1), the rules and constraints for the multi-objective reservoir operation include flood control operation rules, flood control operation constraints, water supply operation rules, water supply operation constraints, power generation operation rules, and power generation operation constraints.

[0025] Furthermore, in S3), the reservoir operation model is expressed by the following formula

[0026] x m,n = f(Q in,t , z t , …)

[0027] In the formula,

[0028] x m,n represents the value of the nth evaluation index in the mth operation plan,

[0029] f represents the reservoir operation function,

[0030] Q in,t is the average inflow of the reservoir at time t,

[0031] z t is the water level of the reservoir at the beginning of time t.

[0032] Further, in S4), the multi-objective decision-making evaluation index matrix is expressed by the following formula

[0033]

[0034] In the formula,

[0035] X represents the multi-objective decision-making evaluation index matrix,

[0036] x m,n represents the value of the nth evaluation index in the mth operation plan.

[0037] Furthermore, in S5), the multi-objective decision-making evaluation transformation matrix is expressed by the following formula

[0038]

[0039] In the formula,

[0040] R represents the multi-objective decision-making evaluation transformation matrix,

[0041] r m,n represents the value after normalizing the value of the nth evaluation index in the mth operation plan.

[0042] Furthermore, in S5), if the evaluation index is a benefit-type index, then

[0043]

[0044] If the evaluation index is a cost-type index, then

[0045]

[0046] In the formula,

[0047] x m,n represents the value of the nth evaluation index in the mth scheduling plan,

[0048] max x m,n represents the maximum value among the n evaluation index values in the mth scheduling plan,

[0049] r m,n represents the value after normalizing the value of the nth evaluation index in the mth scheduling plan.

[0050] Furthermore, in S6), the weight matrix is represented by the following formula

[0051]

[0052] In the formula,

[0053] W represents the weight matrix,

[0054] w n represents the weight of the nth evaluation index.

[0055] Furthermore, in S7), the weighted attribute matrix is represented by the following formula

[0056]

[0057] In the formula,

[0058] V represents the weighted attribute matrix,

[0059] R represents the multi-objective decision-making evaluation transformation matrix,

[0060] W represents the weight matrix,

[0061] r m,n represents the value after normalizing the value of the nth evaluation index in the mth scheduling plan,

[0062] w n represents the weight of the nth evaluation index.

[0063] Further, in S1), the basic data of the cascade reservoir includes the reservoir storage curve, the discharge capacity curve, the flood limit water level, the flood control high water level, the flood control operation mode of the reservoir, and the power generation operation mode of the reservoir.

[0064] The advantages of the present invention are as follows:

[0065] 1. For a comprehensive utilization reservoir undertaking multiple scheduling tasks, the present invention proposes a multi-objective scheduling evaluation method for cascade reservoirs based on weight coefficients, constructs a decision-making system for reservoir scheduling schemes including multiple objectives such as flood control, power generation, and water storage, solves the problems of incommensurability of scheduling objectives and competition coordination in the decision-making of reservoir scheduling schemes, and can provide technical support for formulating a reservoir scheduling scheme with maximum comprehensive benefits.

[0066] 2. First, the present invention calculates each evaluation index value in each multi-objective scheduling scheme, constructs a multi-objective decision-making evaluation index matrix, then constructs a weight matrix for measuring the importance of each evaluation index, multiplies the multi-objective decision-making evaluation transformation matrix by the weight matrix to obtain a weighted attribute matrix, determines the relative proximity of each scheduling scheme in the weighted attribute matrix, and obtains the optimal scheduling scheme according to the magnitude of the relative proximity value.

[0067] The multi-objective scheduling evaluation method for cascade reservoirs based on weight coefficients of the present invention establishes an evaluation system for different scheduling schemes with different scheduling objectives, solves the problems of incommensurability of scheduling objectives and competition coordination in the decision-making of reservoir scheduling schemes, and can provide technical support for formulating a reservoir scheduling scheme with maximum comprehensive benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 is a flowchart of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0069] The following further describes the present invention in detail with reference to the drawings and specific embodiments.

[0070] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation of the invention.

[0071] Taking the flood season scheduling of a certain reservoir A in the Yangtze River Basin of China as an example, its main scheduling objectives include minimizing the excess flood volume of the downstream flood protection objects P1 and P2, and maintaining a high full storage rate of the reservoir at the end of the scheduling period. The multi-objective scheduling evaluation method for cascade reservoirs based on weight coefficients of the present invention is applied.

[0072] The multi-objective scheduling evaluation method for cascade reservoirs based on weight coefficients of the present invention includes the following steps:

[0073] S1) Collect the basic information of the cascade reservoir, as well as the multi-objective scheduling rules and constraints of the reservoir.

[0074] Specifically, the rules and constraints for the multi-objective operation of the reservoir include flood control operation rules, flood control operation constraints, water supply operation rules, water supply operation constraints, power generation operation rules, and power generation operation constraints.

[0075] Specifically, the basic data of the cascade reservoir include the reservoir storage curve, discharge capacity curve, flood limit water level, flood control high water level, flood control operation mode of the reservoir, and power generation operation mode of the reservoir. The basic data of the cascade reservoir should include all the data that can be used to calculate the evaluation index.

[0076] In this embodiment, the flood control operation rules include two flood protection objects, P1 and P2, and their river channel safe discharge capacities are 56700 m 3 / s and 60000 m 3 / s respectively. The operation mode of Reservoir A is to ensure that the downstream discharge of the reservoir plus the inflow in the interval is less than the safe discharge capacity when it evolves to the representative river channel section of the flood protection object. Since Reservoir A has two flood protection objects at the same time, in order to coordinate the relationship between the two flood protection objects, the water level control parameter Z is determined (this parameter is a variable, and different control parameters can obtain different operation plans).

[0077] In this embodiment, during the flood control operation in the flood season, both the reservoir inflow and the downstream discharge are large, and the water supply discharge and power generation discharge can be fully guaranteed, which do not constitute actual restrictive conditions.

[0078] S2) Determine the evaluation index of the multi-objective operation plan and the number n of evaluation indexes.

[0079] The evaluation indexes of the multi-objective operation plan include the excess flood volume at the flood control control point, the reservoir storage volume at the end of the operation period, the reservoir water abandonment volume during the operation period, etc. The total number of evaluation indexes is denoted as n.

[0080] In this embodiment, the evaluation indexes of the operation plan include the excess flood volumes of flood protection objects P1 and P2, and the reservoir full storage rate at the end of the operation period. That is, n1 is the excess flood volume at P1, n2 is the excess flood volume at P2, and n3 is the reservoir full storage rate. The total number of evaluation indexes n = 3.

[0081] S3) According to the requirements of reservoir operation decision-making, draw up m multi-objective operation plans; construct a reservoir operation model based on the multi-objective operation rules of the reservoir. The reservoir operation model is the functional relationship between each evaluation index of the multi-objective operation plan and the reservoir operation decision variables. The reservoir operation decision variables include inflow, reservoir water level, minimum downstream discharge of the reservoir, guaranteed output, etc.

[0082] Specifically, the reservoir operation model is expressed by the following formula

[0083] x m,n = f(Qin,t , z t , …)

[0084] In the formula,

[0085] x m,n represents the value of the nth evaluation index in the mth scheduling plan,

[0086] f represents the reservoir scheduling function,

[0087] Q in,t is the average inflow of the reservoir at time t,

[0088] z t is the reservoir water level at the beginning of time t.

[0089] In this embodiment, by using the variable parameter Z, four water level parameters of 155 m, 158 m, 160 m, and 162 m are determined to obtain four scheduling comparison plans, that is, m = 4.

[0090] This embodiment mainly relates to flood control scheduling. Therefore, a flood control scheduling model is mainly established. A

[0091] The flood control scheduling rule of the reservoir is:

[0092]

[0093] In the formula,

[0094] q p1 / qu,t+Δt is the sectional flow between Reservoir A and flood control object P1,

[0095] q p2 / qu,t+Δt is the sectional flow between Reservoir A and flood control object P2,

[0096] Q min is the minimum discharge of Reservoir A,

[0097] Z max is the highest water level for flood control scheduling of flood control objects P1 and P2, which is a given fixed value. To simplify the model, the scheduling method with the reservoir water level higher than Z max is not considered temporarily.

[0098] According to the discharge of Reservoir A, the time-delay method is used to evolve to flood control objects P1 and P2, and the sectional flow is added to obtain the flow processes q 1,t , (t = 1, 2, ……, T) and q 2,t , (t = 1, 2, ……, T), where T is the total number of calculation time periods. Then, the decision variable of excess flood volume is calculated.

[0099]

[0100] Similarly, n2 and n3 can be calculated by dividing the water storage at the end of reservoir operation by the total regulating storage capacity.

[0101] S4) Calculate the value of each evaluation index in each multi-objective scheduling scheme, and construct a multi-objective decision-making evaluation index matrix.

[0102] For example, let m = 1, select a typical inflow process for simulation scheduling, and calculate the value of the evaluation index of the m-th scheduling scheme. For example, according to the above method, the following is calculated and obtained

[0103] x1 = {136, 48, 0.65}

[0104] Specifically, the multi-objective decision-making evaluation index matrix is represented by the following formula

[0105]

[0106] In the formula,

[0107] X represents the multi-objective decision-making evaluation index matrix,

[0108] x m,n represents the value of the n-th evaluation index in the m-th scheduling scheme.

[0109] In this embodiment, the evaluation objective values in 4 scheduling schemes are calculated to obtain the multi-objective decision-making evaluation index matrix as follows:

[0110]

[0111] S5) Normalize each evaluation index value in the multi-objective decision-making evaluation index matrix to obtain a multi-objective decision-making evaluation transformation matrix.

[0112] Specifically, the multi-objective decision-making evaluation transformation matrix is represented by the following formula

[0113]

[0114] In the formula,

[0115] R represents the multi-objective decision-making evaluation transformation matrix,

[0116] r m,n represents the value after normalizing the value of the n-th evaluation index in the m-th scheduling scheme.

[0117] The normalization methods include: vector normalization method, linear transformation, (0-1) interval domain transformation, etc.

[0118] Taking linear transformation as an example, for profit-type indicators, the larger the better; for cost-type indicators, the smaller the better.

[0119] If the evaluation index is a benefit type index, then

[0120]

[0121] If the evaluation index is a cost type index, then

[0122]

[0123] In the formula,

[0124] x m,n represents the value of the nth evaluation index in the mth scheduling plan,

[0125] max x m,n represents the maximum value among the n evaluation index values in the mth scheduling plan,

[0126] r m,n represents the value after normalizing the value of the nth evaluation index in the mth scheduling plan.

[0127] In this embodiment, n1 and n2 are cost type indices, and the ideal values are both 0; n3 is a benefit type index, and the ideal value is 1. According to the benefit type index and cost type index formulas, for example Similarly, other r m,n values can be calculated to obtain the multi-objective decision-making evaluation transformation matrix R.

[0128]

[0129] S6) Construct a weight matrix, which is used to measure the importance of each evaluation index.

[0130] In multi-objective decision-making analysis, evaluation indices are not equally important in decision-making problems. Therefore, it is necessary to determine the relative importance of index attributes, and weights are usually used to represent the relative importance degree of attributes. Weights are a measure of index importance and a means to measure the importance of objectives. The determination of weights and the distribution of weights play a crucial role in multi-objective decision-making results.

[0131] Specifically, the weight matrix is represented by the following formula

[0132]

[0133] In the formula,

[0134] W represents the weight matrix,

[0135] w n represents the weight of the nth evaluation index.

[0136] For a multi-objective decision-making problem with n objectives, the weight of the i-th objective is w i , where

[0137] There are many methods to determine weights, but basically they can be divided into two categories: one is the subjective weighting method, and the other is the objective weighting method.

[0138] In this embodiment, the subjective weighting method is adopted to determine the weight matrix W as:

[0139]

[0140] S7) Multiply the multi-objective decision-making evaluation transformation matrix by the weight matrix to obtain the weighted attribute matrix; Imagine a positive ideal solution and a negative ideal solution, respectively determine the Euclidean distances between each scheduling solution in the weighted attribute matrix and the positive ideal solution and the negative ideal solution, and then calculate the relative closeness. If the relative closeness value is the smallest, it indicates that this scheduling solution is the optimal scheduling solution.

[0141] Specifically, the weighted attribute matrix is represented by the following formula

[0142]

[0143] In the formula,

[0144] V represents the weighted attribute matrix,

[0145] R represents the multi-objective decision-making evaluation transformation matrix,

[0146] W represents the weight matrix,

[0147] r m,n represents the value after normalizing the nth evaluation index value in the mth scheduling solution,

[0148] w n represents the weight of the nth evaluation index.

[0149] Specifically, the relative closeness is represented by the following formula

[0150]

[0151] In the formula,

[0152] C i represents the relative closeness value,

[0153] represents the Euclidean distance between the ith scheduling solution and the negative ideal solution,

[0154] represents the Euclidean distance between the ith scheduling solution and the positive ideal solution,

[0155] v i,jRepresents the value of the j-th evaluation index of the i-th scheduling plan in the weighted attribute matrix.

[0156] Is the value of the positive ideal plan for the j-th evaluation index.

[0157] Is the value of the negative ideal plan for the j-th evaluation index.

[0158] The plan that is closest to the positive ideal plan and farthest from the negative ideal plan is the optimal plan. Therefore, the largest relative closeness value indicates that this scheduling plan is the optimal scheduling plan.

[0159] In this embodiment, the weighted attribute matrix is

[0160]

[0161] The positive ideal plan is

[0162]

[0163] The negative ideal plan is

[0164]

[0165] The Euclidean distances between each calculated scheduling plan and the positive ideal plan and the negative ideal plan are

[0166] S * =[0.55 0.365 0.248 0.09];

[0167] S - =[0.09 0.195 0.311 0.55];

[0168] The calculated relative closeness

[0169]

[0170] C i The larger the value, the better the scheduling plan. It can be seen that in this embodiment, scheduling plan 4 is the optimal plan under the current evaluation weights. It should be noted that: different weights will result in different optimal plans obtained by evaluation.

[0171] The present invention proposes a multi-objective scheduling evaluation method for cascade reservoirs based on weight coefficients for a comprehensive utilization reservoir undertaking multiple scheduling tasks, constructs a decision-making system for reservoir scheduling plans including multiple objectives such as flood control, power generation, and water storage, solves the problems of incommensurability and competition coordination of scheduling objectives in the decision-making of reservoir scheduling plans, and can provide technical support for formulating a reservoir scheduling plan with maximized comprehensive benefits.

[0172] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the above embodiments. Any other changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be equivalent replacement methods and are all included in the protection scope of the present invention.

Claims

1. A multi-objective dispatching evaluation method for cascade reservoirs based on weight coefficients, characterized in that: The steps include: S1) Collect basic information of cascade reservoirs, as well as multi-objective operation rules and constraints of reservoirs; S2) determining the evaluation index of the multi-objective scheduling scheme and the number of evaluation indexes n; S3) According to the reservoir operation decision-making requirements, m multi-objective operation schemes are formulated; a reservoir operation model is constructed based on the reservoir multi-objective operation rule, wherein the reservoir operation model is a functional relationship between each evaluation index of the multi-objective operation scheme and the reservoir operation decision variables; S4) calculating each evaluation index value in each multi-objective scheduling scheme and constructing a multi-objective decision evaluation index matrix; S5) normalizing each evaluation index value in the multi-objective decision-making evaluation index matrix to obtain a multi-objective decision-making evaluation transformation matrix; S6) constructing a weight matrix, wherein the weight matrix is ​​used to measure the importance of each evaluation index; S7) multiplying the multi-objective decision evaluation transformation matrix by the weight matrix to obtain a weighted attribute matrix; assuming a positive ideal solution and a negative ideal solution, respectively determining the Euclidean distance between each scheduling solution in the weighted attribute matrix and the positive ideal solution and the negative ideal solution, and then calculating the relative proximity. If the relative proximity value is the largest, it indicates that the scheduling solution is the optimal scheduling solution; The relative proximity is expressed by the following formula In the formula, C i Represents the relative proximity value, represents the Euclidean distance between the ith scheduling solution and the negative ideal solution, represents the Euclidean distance between the ith scheduling solution and the positive ideal solution, v i,j represents the value of the jth evaluation index of the ith scheduling scheme in the weighted attribute matrix, is the positive ideal solution value of the jth evaluation index, is the negative ideal solution value of the jth evaluation index.

2. The multi-objective scheduling evaluation method for cascade reservoirs based on weight coefficients according to claim 1 is characterized in that: In S1), the rules and constraints of the multi-objective scheduling of the reservoir include flood control scheduling rules, flood control scheduling constraints, water supply scheduling rules, water supply scheduling constraints, power generation scheduling rules, and power generation scheduling constraints.

3. The multi-objective scheduling evaluation method for cascade reservoirs based on weight coefficients according to claim 2 is characterized in that: S3), the reservoir operation model is expressed by the following formula: x m,n =f(Q in,t ,z t ,…) In the formula, x m,n represents the nth evaluation index value in the mth scheduling scheme, f represents the reservoir dispatching function, Q in,t is the average inflow into the reservoir during period t, z t is the reservoir water level at the beginning of period t.

4. The multi-objective scheduling evaluation method for cascade reservoirs based on weight coefficients according to claim 1 is characterized in that: In S4), the multi-objective decision evaluation index matrix is ​​expressed by the following formula: In the formula, X represents the multi-objective decision-making evaluation index matrix, x m,n Represents the nth evaluation index value in the mth scheduling scheme.

5. The multi-objective dispatching evaluation method of cascade reservoirs based on weight coefficients according to claim 4 is characterized in that: In S5), the multi-objective decision evaluation transformation matrix is ​​expressed by the following formula: In the formula, R represents the multi-objective decision-making evaluation transformation matrix, r m,n It represents the normalized value of the nth evaluation index value in the mth scheduling scheme.

6. The multi-objective dispatching evaluation method of cascade reservoirs based on weight coefficients according to claim 5 is characterized in that: In S5), if the evaluation indicator is a benefit indicator, then If the evaluation index is a cost-based index, then In the formula, x m,n represents the nth evaluation index value in the mth scheduling scheme, max x m,n represents the maximum value of the n evaluation index values ​​in the mth scheduling scheme, r m,n It represents the normalized value of the nth evaluation index value in the mth scheduling scheme.

7. The multi-objective dispatching evaluation method of cascade reservoirs based on weight coefficients according to claim 6 is characterized in that: In S6), the weight matrix is ​​expressed by the following formula: In the formula, W represents the weight matrix, w n Represents the weight of the nth evaluation indicator.

8. The multi-objective dispatching evaluation method of cascade reservoirs based on weight coefficients according to claim 7 is characterized in that: In S7), the weighted attribute matrix is ​​expressed by the following formula: In the formula, V represents the weighted attribute matrix, R represents the multi-objective decision-making evaluation transformation matrix, W represents the weight matrix, r m,n It represents the value after normalization of the nth evaluation index value in the mth scheduling scheme. w n Represents the weight of the nth evaluation indicator.

9. The multi-objective dispatching evaluation method of cascade reservoirs based on weight coefficients according to claim 1 is characterized in that: S1), the basic information of the cascade reservoirs includes the reservoir capacity curve, discharge capacity curve, flood limit water level, flood control high water level, reservoir flood control dispatching method, and reservoir power generation dispatching method.

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