Multi-target water supply reservoir benefit-making storage capacity calculation method based on scheduling graph
Through a long series of runoff regulation calculation models driven by the particle swarm algorithm, the beneficial reservoir capacity and scheduling diagram are solved simultaneously, which solves the discrepancy between the reservoir capacity and scheduling operation in the planning and design stage, achieves the smoothness of the scheduling line and the reasonable priority of the water supply target, and meets the design requirements.
Patent Information
- Application Number
- CN202510792942.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-06-13
- Publication Date
- 2025-09-12
Smart Images
Figure CN120633441A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of reservoir benefit dispatching, in particular to a method for calculating the benefit storage capacity of a multi-objective water supply reservoir based on a dispatching diagram. Background Art
[0002] The beneficial storage capacity is a core parameter in the planning and design phase of a reservoir. This parameter directly determines the project investment and its benefits. Runoff regulation calculations analyze the relationship between water supply, beneficial storage capacity, and guarantee rate. Runoff regulation calculations primarily utilize calendar, probability, and stochastic simulation methods. Multi-objective water supply typically requires specific scheduling rules. Reservoir scheduling diagrams are the primary tool for guiding the rational operation of reservoirs and can be used as scheduling rules. Once the beneficial storage capacity is determined during the planning and design phase, reservoirs with annual regulation or above require scheduling calculations using scheduling diagrams to verify whether they meet the design water supply guarantee rate and damage depth requirements. If not, the scheduling diagrams need to be modified, and the reservoir characteristic water level adjusted if necessary.
[0003] Irregular discounting of water demand (not combined with scheduling diagrams) during the planning and design stage may lead to problems such as discrepancies between the designed beneficial storage capacity and the actual scheduling operation. In addition, the intelligent method for solving the scheduling line currently has widespread morphological distortion problems, which does not conform to industry application habits.
[0004] The present invention proposes a multi-objective water supply reservoir beneficial storage capacity calculation method based on a scheduling diagram. The scheduling diagram and the beneficial storage capacity are solved simultaneously in the planning and design stage, which can ensure the reliability of the beneficial storage capacity calculation results, make the design guarantee rate and damage depth of different water supply targets meet the specification requirements at the same time, and ensure the smoothness of the scheduling line. Summary of the Invention
[0005] The purpose of the present invention is to provide a multi-objective water supply reservoir beneficial storage capacity calculation method based on a scheduling diagram, so as to solve the problem proposed in the above background technology that the irregular discount treatment of water demand in the existing planning and design stage may lead to the design beneficial storage capacity not being consistent with the actual scheduling operation, and the current widespread problem of solving the scheduling line by intelligent method is the morphological distortion of the scheduling line, which does not conform to the industry application habits.
[0006] To achieve the above object, the present invention provides the following technical solution: a multi-objective water supply reservoir beneficial storage capacity calculation method based on a scheduling diagram, comprising the following steps:
[0007] S1: Establish an optimization model, measure the water demand process of each water supply object of the reservoir, use the urban water supply scheduling line and the agricultural water supply scheduling line as decision variables, determine the objective function of the reservoir optimization scheduling, and formulate the beneficial storage capacity given the constraint conditions;
[0008] S2: Set the parameters of the particle swarm algorithm, namely the particle swarm size g, space dimension N, maximum number of iterations, inertia weight, learning factor and function value tolerance, and determine the upper and lower bounds of particles according to the set parameters of the particle swarm algorithm [V dead , V normal ] and randomly generate initial particles, and use the particle swarm algorithm to drive the long series of runoff regulation calculation model to solve the optimization model. The constraints are included in the objective function in the form of penalty functions to calculate the fitness of the initial particles and select the optimal particles.
[0009] S3: Initial particle update iteration. The update formula is used to update the position and velocity of the initial particle. In the first iteration, the individual optimal point of the initial particle is itself. Subsequently, the best point experienced when moving in the solution space is used to calculate the fitness of the updated particle. The individual optimal position and global optimal position of the particle are recorded. It is determined whether the maximum number of iterations and the function value tolerance are met. If so, the loop is exited to obtain the optimal solution. Otherwise, the particle is updated and iterated.
[0010] S4: Determine the beneficial storage capacity and judge whether the constraint conditions corresponding to the proposed beneficial storage capacity of the reservoir meet the set threshold. If so, the proposed beneficial storage capacity is the design value. If not, the beneficial storage capacity of the reservoir is re-proposed and the particle swarm algorithm is repeated.
[0011] Preferably, the decision variables are the urban water supply scheduling line and the agricultural water supply scheduling line, the objective function is the maximum water supply guarantee rate and the smoothness of the scheduling line, and the constraints are the water supply guarantee rate and the damage depth.
[0012] Preferably, the maximum value of the objective function is:
[0013] max obj=P u +P a -S u -S a ;
[0014] Where obj is the objective function value, P u and P a are respectively the urban water supply guarantee rate and the agricultural water supply guarantee rate, S u and S a are the normalized smoothness rates of the water supply dispatch lines for urban water supply and agriculture, respectively;
[0015] The urban water supply guarantee rate P u Using the duration guarantee rate, agricultural water supply guarantee rate P a Using the annual guarantee rate, the calculation formula for the water supply guarantee rate P is:
[0016]
[0017] Where m is the number of years (or time periods) of normal water supply, and n is the number of years (or time periods) of the calculation series.
[0018] Preferably, the normalized smoothness rate S of the water supply scheduling line of the urban water supply u and the normalized smoothness rate S of the agricultural water supply scheduling line a The normalized smoothness rate S of the scheduling line is:
[0019]
[0020] Where d is the dispatch line position and is expressed in terms of reservoir storage capacity, q is the sequence number of the dispatch line position, and t is the number of dispatch line positions.
[0021] Preferably, the constraints include water balance constraints, reservoir capacity constraints, water supply guarantee rate and damage depth constraints, and water supply priority constraints.
[0022] Preferably, the runoff regulation calculation in the long series runoff regulation calculation model satisfies the water balance constraint, and the calculation formula of the water balance constraint is:
[0023] V i+1 =V i +W in，i +ΣW s，i -W l，i ;
[0024] Where V i and V i+1 are the water storage capacity of the reservoir at the beginning and end of the i-th period, W in，i is the amount of water entering the reservoir during the i-th period, ∑W s，i is the sum of water supply of various departments for comprehensive utilization of the reservoir in period i, W l，i is the sum of water loss from evaporation, leakage and freezing in the reservoir during period i;
[0025] The water storage capacity of the reservoir in the runoff regulation calculation is not allowed to fall below the dead storage capacity of the reservoir. If it exceeds the storage capacity corresponding to the normal water level, the reservoir will abandon water. The calculation formula for the storage capacity constraint is:
[0026] V dead ≤V≤V normal ;
[0027] Where V is the water storage capacity of the reservoir, V dead and V normal They are the dead storage capacity of the reservoir and the storage capacity corresponding to the normal water level.
[0028] Preferably, the urban water supply and agricultural water supply respectively adopt different water supply guarantee rates and damage depths. The calculation formulas for the water supply guarantee rate and damage depth constraints are:
[0029] P u ≥P u，d ;
[0030] P a ≥P a，d ;
[0031] D u ≤D u，max ;
[0032] D a ≤D a，max ;
[0033] Among them D u and D a are the damage depths of urban and agricultural water supply, respectively, and the damage depth is the ratio of water supply gap to water demand, P u，d and P a，d are the set values of urban and agricultural water supply guarantee rates, D u，max and D a，max Design maximum damage depths for urban and agricultural water supply respectively;
[0034] The position of the agricultural water supply scheduling line is higher than that of the urban water supply scheduling line. When the water supply capacity of the reservoir is insufficient, the agricultural water supply is reduced first and the urban water supply is prioritized. The calculation formula of the water supply priority constraint is:
[0035]
[0036] where d a，q and d u，q are the values of the agricultural water supply scheduling line and the urban water supply scheduling line at point q, respectively, and t is the number of scheduling line positions.
[0037] Preferably, the particle swarm size g is 500, the spatial dimension N is the number of data points of each type of scheduling line, the maximum number of iterations is 200, the inertia weight range is [0.5, 1.1], the learning factor is divided into individual learning factor and group learning factor and both are set to 2.0, and the function value tolerance is set to 1×10 -8 .
[0038] Compared with the prior art, the present invention has the following beneficial effects:
[0039] The present invention solves the model by using particle swarm optimization to drive a long series of runoff regulation calculations, and simultaneously solves the beneficial storage capacity and the scheduling diagram, avoiding the problem of discrepancy between the design and the actual scheduling operation caused by irregular discounting of water demand. The normalized smoothness expression of the scheduling line is introduced into the objective function for the first time, which can effectively solve the problem of morphological distortion of the scheduling line and make the scheduling diagram more in line with industry application habits. By calculating and determining the beneficial storage capacity, the water supply guarantee rate and damage depth of each water supply target meet the design requirements, the scheduling line is smooth, and the priorities of different water supply targets can be effectively distinguished. The calculation results are reasonable and feasible. BRIEF DESCRIPTION OF THE DRAWINGS
[0040] Figure 1 It is a schematic diagram of the overall process of the present invention;
[0041] Figure 2 A histogram showing the annual runoff depth of the reservoir according to an embodiment of the present invention;
[0042] Figure 3 A curve diagram showing the relationship between the water level, area, and storage capacity of a reservoir according to an embodiment of the present invention;
[0043] Figure 4 A line graph of a water supply scheduling line of a reservoir according to an embodiment of the present invention;
[0044] Figure 5 This is a step diagram of the method for calculating the beneficial storage capacity of a multi-objective water supply reservoir according to the present invention. DETAILED DESCRIPTION
[0045] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.
[0046] See also Figure 1 and Figure 5 The present invention provides an embodiment: a method for calculating the beneficial storage capacity of a multi-objective water supply reservoir based on a scheduling diagram, comprising the following steps:
[0047] S1: Establish an optimization model, measure the water demand process of each water supply object of the reservoir, use the urban water supply scheduling line and the agricultural water supply scheduling line as decision variables, determine the objective function of the reservoir optimization scheduling, and formulate the beneficial storage capacity given the constraint conditions;
[0048] S2: Set the parameters of the particle swarm algorithm, namely the particle swarm size g, space dimension N, maximum number of iterations, inertia weight, learning factor and function value tolerance, and determine the upper and lower bounds of particles according to the set parameters of the particle swarm algorithm [V dead , V normal] and randomly generate initial particles, and use the particle swarm algorithm to drive the long series of runoff regulation calculation model to solve the optimization model. The constraints are included in the objective function in the form of penalty functions to calculate the fitness of the initial particles and select the optimal particles.
[0049] S3: Initial particle update iteration. The update formula is used to update the position and velocity of the initial particle. In the first iteration, the individual optimal point of the initial particle is itself. Subsequently, the best point experienced when moving in the solution space is used to calculate the fitness of the updated particle. The individual optimal position and global optimal position of the particle are recorded. It is determined whether the maximum number of iterations and the function value tolerance are met. If so, the loop is exited to obtain the optimal solution. Otherwise, the particle is updated and iterated.
[0050] S4: Determine the beneficial storage capacity and judge whether the constraint conditions corresponding to the proposed beneficial storage capacity of the reservoir meet the set threshold. If so, the proposed beneficial storage capacity is the design value. If not, the beneficial storage capacity of the reservoir is re-proposed and the particle swarm algorithm is repeated.
[0051] The particle swarm size g is 500, the spatial dimension N is the number of data points of each scheduling line, the maximum number of iterations is 200, the inertia weight range is [0.5, 1.1], the learning factor is divided into individual learning factor and group learning factor and both are set to 2.0, and the function value tolerance is set to 1×10 -8 By setting the parameters of the particle swarm algorithm, the long series of runoff regulation calculation models can be cyclically driven by the particle swarm algorithm.
[0052] The decision variables are the urban water supply scheduling line and the agricultural water supply scheduling line. The objective function is the maximum water supply guarantee rate and the smoothness of the scheduling line. The constraints are the water supply guarantee rate and the damage depth. The maximum value of the objective function is:
[0053] max obj=P u +P a -S u -S a ;
[0054] Where obj is the objective function value, P u and P a are respectively the urban water supply guarantee rate and the agricultural water supply guarantee rate, S u and S a are the normalized smoothness rates of the water supply dispatch lines for urban water supply and agriculture, respectively;
[0055] Urban water supply guarantee rate P u Using the duration guarantee rate, agricultural water supply guarantee rate P a Using the annual guarantee rate, the calculation formula for the water supply guarantee rate P is:
[0056]
[0057] Where m is the number of years (or periods) of normal water supply, and n is the number of years (or periods) of the calculation series;
[0058] Normalized smoothness rate S of water supply dispatching line for urban water supply u and the normalized smoothness rate S of the agricultural water supply scheduling line a The normalized smoothness rate S of the scheduling line is:
[0059]
[0060] Where d is the dispatch line position and is expressed in terms of reservoir storage capacity, q is the sequence number of the dispatch line position, and t is the number of dispatch line positions.
[0061] The constraints include water balance constraint, reservoir capacity constraint, water supply guarantee rate and damage depth constraint, and water supply priority constraint. The runoff regulation calculation in the long series runoff regulation calculation model satisfies the water balance constraint. The calculation formula for the water balance constraint is:
[0062] V i+1 =V i +W in，i +∑W s，i -W l，i ;
[0063] Where V i and V i+1 are the water storage capacity of the reservoir at the beginning and end of the i-th period, W in，i is the amount of water entering the reservoir during the i-th period, ∑W s，i is the sum of water supply of various departments for comprehensive utilization of the reservoir in period i, W l，i is the sum of water loss from evaporation, leakage and freezing in the reservoir during period i;
[0064] The water storage capacity of the reservoir in the runoff regulation calculation is not allowed to fall below the dead storage capacity of the reservoir. If it exceeds the storage capacity corresponding to the normal water level, the reservoir will abandon water. The calculation formula for the storage capacity constraint is:
[0065] V dead ≤V≤V normal ;
[0066] Where V is the water storage capacity of the reservoir, V dead and V normal They are the dead storage capacity of the reservoir and the storage capacity corresponding to the normal water level.
[0067] Different water supply guarantee rates and damage depths are used for urban water supply and agricultural water supply respectively. The calculation formulas for the water supply guarantee rate and damage depth constraints are as follows:
[0068] P u≥P u，d ;
[0069] P a ≥P a，d ;
[0070] D u ≤D u，max ;
[0071] D a ≤D a，max ;
[0072] Among them D u and D a are the damage depths of urban and agricultural water supply, respectively, and the damage depth is the ratio of water supply gap to water demand, P u，d and P a，d are the set values of urban and agricultural water supply guarantee rates, D u，max and D a，max Design maximum damage depths for urban and agricultural water supply respectively;
[0073] The position of the agricultural water supply scheduling line is higher than that of the urban water supply scheduling line. When the reservoir water supply capacity is insufficient, agricultural water supply is reduced first and urban water supply is guaranteed first. The calculation formula of the water supply priority constraint is:
[0074]
[0075] where d a，q and d u，q are the values of the agricultural water supply scheduling line and the urban water supply scheduling line at point q, respectively, and t is the number of scheduling line positions.
[0076] This application selects a reservoir as Example 1:
[0077] See also Figure 2 , in the runoff from 1953 to 2020, the average annual runoff depth of the reservoir catchment area was 990.4 mm, the maximum value was 1605.8 mm and occurred in 2001, and the minimum value was 263.2 mm and occurred in 2018;
[0078] In terms of annual runoff distribution, the runoff in May accounted for the highest proportion of 22.3%, followed by April and June at 17.7% and 17.4% respectively. The runoff in December accounted for the lowest proportion of only 2.2%. The runoff from April to September accounted for 73%.
[0079] See also Figure 2The average long-term runoff depth at the reservoir dam site is 990.4 mm. During the flood season from April to September, the ecological flow is released at 30% of the long-term average flow. In other months, the ecological flow is released at 10% of the long-term average flow. When the natural inflow is less than the ecological flow, the water is discharged according to the inflow flow.
[0080] Please refer to Table 1. Reservoir water loss consists of two parts: evaporation loss and leakage loss. Based on the geological conditions of the reservoir area, leakage loss is calculated as 40L / s.
[0081] Table 1
[0082]
[0083]
[0084] The outlet section of the urban water supply reservoir is 115,000 m 3 / d uniform water supply, annual water supply scale is 41.975 million m 3 The reservoir's annual irrigation water volume is 3.521 million m 3 , the maximum annual irrigation water volume is 4.43 million m3;
[0085] The runoff regulation calculation is based on the monthly flow from 1953 to 2028 for 68 years. The reservoir has multi-year regulation performance, the urban water supply guarantee rate is not less than 97%, and the agricultural water supply guarantee rate is not less than 90%. When encountering extremely dry years, the urban water supply damage depth is 70%, and the agricultural water supply damage depth is 50%.
[0086] Please refer to Table 2 for comparison of the proposed normal water storage levels of 234m, 236m, and 238m;
[0087] Table 2
[0088]
[0089]
[0090] The normal water level of 236m corresponds to a beneficial storage capacity of 56.08 million m 3 The urban water supply guarantee rate is 97%, and the agricultural water supply guarantee rate is 90%, meeting the design guarantee rate requirements;
[0091] The normal water level of 234m corresponds to a beneficial storage capacity of 52.08 million m 3 , the urban and agricultural water supply guarantee rates do not meet the design requirements;
[0092] The normal water level of 238m corresponds to a beneficial storage capacity of 60.28 million m 3 , the urban and agricultural water supply guarantee rate is significantly higher than the design guarantee rate.
[0093] See Table 2 and Figure 3 The normal water storage level of 236m is the minimum beneficial reservoir capacity corresponding to the design requirements. The urban and agricultural water supply scheduling lines are relatively smooth, which is in line with industry application habits. This shows that introducing the normalized smoothness expression of the scheduling line in the objective function can effectively solve the problem of scheduling line morphological distortion. The agricultural water supply scheduling line is generally located above the urban water supply scheduling line, which fully reflects the priority of urban water supply over agricultural water supply in terms of water supply priority.
[0094] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.
Claims
1. A multi-objective water supply reservoir capacity calculation method based on a dispatching diagram is characterized by: The steps include: S1: Establish an optimization model, measure the water demand process of each water supply object of the reservoir, use the urban water supply scheduling line and the agricultural water supply scheduling line as decision variables, determine the objective function of the reservoir optimization scheduling, and formulate the beneficial storage capacity given the constraint conditions; S2: Set the parameters of the particle swarm algorithm, namely the particle swarm size g, space dimension N, maximum number of iterations, inertia weight, learning factor and function value tolerance, and determine the upper and lower bounds of particles according to the set parameters of the particle swarm algorithm [V dead , V normal ] and randomly generate initial particles, and use the particle swarm algorithm to drive the long series of runoff regulation calculation model to solve the optimization model. The constraints are included in the objective function in the form of penalty functions to calculate the fitness of the initial particles and select the optimal particles. S3: Initial particle update iteration. The update formula is used to update the position and velocity of the initial particle. In the first iteration, the individual optimal point of the initial particle is itself. Subsequently, the best point experienced when moving in the solution space is used to calculate the fitness of the updated particle. The individual optimal position and global optimal position of the particle are recorded. It is determined whether the maximum number of iterations and the function value tolerance are met. If so, the loop is exited to obtain the optimal solution. Otherwise, the particle is updated and iterated. S4: Determine the beneficial storage capacity and judge whether the constraint conditions corresponding to the proposed beneficial storage capacity of the reservoir meet the set threshold. If so, the proposed beneficial storage capacity is the design value. If not, the beneficial storage capacity of the reservoir is re-proposed and the particle swarm algorithm is repeated.
2. The multi-objective water supply reservoir beneficial storage capacity calculation method based on the dispatching diagram according to claim 1 is characterized in that: The decision variables are the urban water supply scheduling line and the agricultural water supply scheduling line, the objective function is the maximum water supply guarantee rate and the smoothness of the scheduling line, and the constraint conditions are the water supply guarantee rate and the damage depth.
3. The multi-objective water supply reservoir beneficial storage capacity calculation method based on the dispatching diagram according to claim 2 is characterized in that: The maximum value of the objective function is: max obj=P u +P a -S u -S a ; Where obj is the objective function value, P u and P a are respectively the urban water supply guarantee rate and the agricultural water supply guarantee rate, S u and S a are the normalized smoothness rates of the water supply dispatch lines for urban water supply and agriculture, respectively; The urban water supply guarantee rate P u Using the duration guarantee rate, agricultural water supply guarantee rate P a Using the annual guarantee rate, the calculation formula for the water supply guarantee rate P is: Where m is the number of years (or time periods) of normal water supply, and n is the number of years (or time periods) of the calculation series.
4. The multi-objective water supply reservoir beneficial storage capacity calculation method based on the dispatching diagram according to claim 3 is characterized by: The normalized smoothness rate S of the water supply scheduling line of the urban water supply u and the normalized smoothness rate S of the agricultural water supply scheduling line a The normalized smoothness rate S of the scheduling line is: Where d is the dispatch line position and is expressed in terms of reservoir storage capacity, q is the sequence number of the dispatch line position, and t is the number of dispatch line positions.
5. The multi-objective water supply reservoir beneficial storage capacity calculation method based on the dispatching diagram according to claim 4 is characterized in that: The constraints include water balance constraints, reservoir capacity constraints, water supply guarantee rate and damage depth constraints and water supply priority constraints.
6. The multi-objective water supply reservoir beneficial storage capacity calculation method based on the dispatching diagram according to claim 5 is characterized in that: The runoff regulation calculation in the long series runoff regulation calculation model satisfies the water balance constraint, and the calculation formula of the water balance constraint is: V i+1 =V i +W in，i +∑W s，i -W l，i ; Where V i and V i+1 are the water storage capacity of the reservoir at the beginning and end of the i-th period, W in，i is the amount of water entering the reservoir during the i-th period, ΣW s，i is the sum of water supply of various departments for comprehensive utilization of the reservoir in period i, W l，i is the sum of water loss from evaporation, leakage and freezing in the reservoir during period i; The water storage capacity of the reservoir in the runoff regulation calculation is not allowed to fall below the dead storage capacity of the reservoir. If it exceeds the storage capacity corresponding to the normal water level, the reservoir will abandon water. The calculation formula for the storage capacity constraint is: In dead ≤V≤V normal ; Where V is the reservoir storage capacity, V dead and V normal They are the dead storage capacity of the reservoir and the storage capacity corresponding to the normal water level.
7. The multi-objective water supply reservoir beneficial storage capacity calculation method based on the dispatching diagram according to claim 6 is characterized in that: The urban water supply and agricultural water supply use different water supply guarantee rates and damage depths, respectively. The calculation formulas for the water supply guarantee rate and damage depth constraints are: P u ≥P u，d ; P a ≥P a，d ; D u ≤D u，max ; D a ≤D a，max ; Among them D u and D a are the damage depths of urban and agricultural water supply, respectively, and the damage depth is the ratio of water supply gap to water demand, P u，d and P a，d are the set values of urban and agricultural water supply guarantee rates, D u，max and D a，max Design maximum damage depths for urban and agricultural water supply respectively; The position of the agricultural water supply scheduling line is higher than that of the urban water supply scheduling line. When the water supply capacity of the reservoir is insufficient, the agricultural water supply is reduced first and the urban water supply is prioritized. The calculation formula of the water supply priority constraint is: where d a，q and d u，q are the values of the agricultural water supply scheduling line and the urban water supply scheduling line at point q, respectively, and t is the number of scheduling line positions.
8. The multi-objective water supply reservoir beneficial storage capacity calculation method based on the dispatching diagram according to claim 1 is characterized by: The particle swarm size g is 500, the spatial dimension N is the number of data points of each type of scheduling line, the maximum number of iterations is 200, the inertia weight range is [0.5, 1.1], the learning factor is divided into individual learning factor and group learning factor and both are set to 2.0, and the function value tolerance is set to 1×10 -8 .