A rain flood resource shortage-peak shaving double target coordinated regulation method based on a distributed virtual reservoir

Abstract

The application discloses a rain flood resource water shortage-peak shaving double-target coordinated regulation method based on a distributed virtual reservoir, which comprises the following steps: constructing a virtual reservoir according to the administrative region division in a basin, wherein the reservoir capacity of the virtual reservoir is determined according to the area weight method based on the reservoir capacity in the basin; collecting monthly water demand data, rainfall data and natural runoff data in the basin, and determining the decision variable as the flow discharged under the regulation of the virtual reservoir; calculating the initial water quantity of the virtual reservoir, wherein the initial water quantity is determined based on the basin area, the initial precipitation and the natural runoff data, and the surface water coefficient in total water demand is calculated according to the water resources bulletin; constructing a multi-objective optimization model, solving the multi-objective optimization model by adopting an NSGA-II algorithm, generating a Pareto optimal solution set, and thus obtaining a monthly optimization regulation scheme of the virtual reservoir; and the application can be widely applied to rain flood resource optimization in the Zhuanlongwan basin and other arid and semi-arid regions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of water resources utilization engineering, in particular to a rain flood resource water shortage-peak shaving double target coordinated regulation method based on distributed virtual reservoirs. BACKGROUND

[0002] As a non-physical water resource regulation carrier, virtual reservoirs can simulate the regulation process of rain and flood through hydrological models, not only breaking through the geographical and engineering scale limitations of traditional concrete reservoirs, but also flexibly integrating the simulation of complex basin elements such as silt dams, effectively solving the generalization problems and adaptation problems of traditional regulation methods in complex basins (such as arid and semi-arid regions), and becoming a key technical means to realize efficient utilization of rain and flood resources. Current rain and flood regulation schemes of virtual reservoirs mostly rely on empirical formulas or single-objective optimization methods: the former is based on historical regulation data to fit the regulation rules, which is difficult to adapt to the uncertainty of inflow under climate change, and does not consider the trade-off relationship between multiple objectives; the latter, although it can simplify the calculation by taking "maximizing rain and flood utilization" or "minimizing flood control risk" as a single objective, ignores the coordinated needs of multiple objectives such as rain and flood utilization, flood control safety, and ecological protection, which may lead to excessive pursuit of rain and flood utilization, thereby increasing the risk of flood control in the basin, or strict control of flood control risk, resulting in reduced utilization of rain and flood resources. The existing multi-objective water resources optimization method based on traditional optimization algorithms (such as genetic algorithm) has two limitations: first, the convergence efficiency of the algorithm is low, and in the case of multiple decision variables (such as monthly water release, water storage threshold, and water replenishment priority) and multiple constraints (such as reservoir flood control capacity, downstream ecological base flow, and water utilization guarantee rate) of virtual reservoirs, it is easy to fall into local optimal solution and difficult to generate a globally optimal Pareto solution set; second, the solution set lacks diversity, which cannot provide decision makers with a variety of target trade-off schemes, especially when climate change leads to changes in inflow scenarios, the adaptability of a single optimization result is poor; in addition, the development of existing rain and flood utilization schemes of virtual reservoirs mostly relies on manual intervention, such as adjusting regulation parameters based on expert experience, which is not only subjective and inefficient, but also difficult to achieve batch processing and dynamic updating, and cannot meet the real-time regulation needs of rain and flood resources at the basin scale. SUMMARY

[0003] The present application relates to the technical field of water resources utilization engineering, in particular to a rain flood resource water shortage-peak shaving double target coordinated regulation method based on distributed virtual reservoirs.

[0004] The technical scheme of the present application is as follows: a rain flood resource water shortage-peak shaving double target coordinated regulation method based on distributed virtual reservoirs, comprising the following steps:

[0005] (1) Constructing virtual reservoirs according to the administrative division of the basin, wherein the capacity of the virtual reservoirs is determined based on the reservoir capacity in the basin according to the area weight method;

[0006] (2) Collecting monthly water demand data, rainfall data and natural runoff data in the basin, and determining the decision variable as the flow discharged by the virtual reservoir under regulation;

[0007] (3) Calculating the initial water quantity of the virtual reservoir, wherein the initial water quantity is determined based on the basin area, initial precipitation and natural runoff data, and the coefficient of surface water in total water demand is calculated according to the water resources bulletin;

[0008] (4) Constructing a multi-objective optimization model, the multi-objective optimization model taking the minimization of water shortage and the maximization of potential peak shaving capacity as objective functions, taking the virtual reservoir carrying capacity constraint, rainwater collection and utilization constraint, ecological flow constraint and non-negative constraint as constraint conditions;

[0009] (5) Solving the multi-objective optimization model by using NSGA-II algorithm to generate a Pareto optimal solution set, so as to obtain the monthly optimization regulation scheme of the virtual reservoir.

[0010] Further, step (1) is as follows: the basin is divided into upstream parallel virtual reservoirs and downstream virtual reservoirs according to the administrative division; the total area of each administrative region and the total reservoir capacity in the region are calculated, and the reservoir capacity of each virtual reservoir is determined according to the area ratio.

[0011] Further, step (2) is as follows: using historical water consumption data set to obtain monthly water demand data; using hydrological model to simulate natural runoff data, and checking rainfall runoff data.

[0012] Further, step (3) is as follows: using time series method, selecting the water inflow model of normal water year and medium dry year, and combining with the monthly water demand and supply scheme to calculate the water balance and determine the initial water storage of the virtual reservoir; based on the water resources bulletin, obtaining the water consumption data and surface water supply quantity of each administrative region, and calculating the surface water ratio coefficient.

[0013] Further, in step (4), the objective function is as follows: minimizing water shortage:

[0014] ;

[0015] Wherein, T is the total number of calculation periods; is the total number of virtual reservoirs in the basin; is the surface water ratio coefficient; is the th virtual reservoir basin in the th period, is the available rainfall amount in the virtual reservoir basin at the t th time period;

[0016] maximize the potential peak shaving capacity:

[0017] ;

[0018] wherein, Vt is the virtual reservoir capacity at the t th time period, P is the natural precipitation amount, Q is the outflow of the basin; A is the basin area of the basin; Q' is the outflow of the basin after reservoir regulation at the t th time period: S is a symbolic function: when > the flood warning state, the reduction benefit needs to be calculated; otherwise the sum reflects the reduction capacity of the virtual reservoir to the flood process.

[0019] Further, in step (4), the constraint formula is as follows:

[0020] Virtual reservoir carrying capacity constraint:

[0021] ;

[0022] wherein, C is the maximum carrying capacity of the virtual reservoir i;

[0023] Rainwater collection and utilization constraint:

[0024] ;

[0025] wherein, R is the actual rainwater collection amount in the k basin at the t time period, R' is the rainwater collectable amount in the k basin at the t time period, Q is the rain flood outflow of the k basin at the t time period;

[0026] Ecological flow constraint:

[0027] ;

[0028] Non-negative constraint:

[0029] ; .

[0030] Further, step (5) is specifically as follows: in the initial population generation stage, a multi-strategy coordination mechanism is adopted to ensure that the population covers the solution space and improves the quality of the initial solution; the initial population with balanced water supply load is constructed by combining the virtual reservoir water carrying capacity threshold and the rain and flood law; based on the distribution characteristics of regional water demand, the initial solution meeting the basic demand of the key water use node is generated in priority; and the initial solution with the water level of each virtual reservoir maintained in the safe operation interval is generated as the target of dynamic balance of reservoir group water storage.

[0031] Further, step (5) further comprises: constructing a multi-criteria evaluation system by using the analytic hierarchy process, and comprehensively evaluating the Pareto optimal solution set based on the index weight and the normalized index value; and calculating the comprehensive evaluation score by using the weighted summation method, and screening the unique optimal solution from the monthly Pareto front.

[0032] Beneficial effects: Compared with the prior art, the present application has the following remarkable advantages: the present application incorporates the dual targets of water shortage and potential peak shaving capacity into the water resources optimization model, significantly improving the water supply capacity and flood control capacity. The present application uses the NSGA-II multi-objective evolutionary algorithm to solve the problems of uneven distribution of Pareto solution set and easy falling into local optimum under multi-objective, and improves the diversity and globality of the solution set. The present application is adapted to the scenes of increasing extreme rainstorm frequency and uneven temporal and spatial distribution of precipitation under climate change, and has stronger robustness. The present application can adaptively adjust the control parameters under different inflow scenarios to generate an optimized scheme with strong adaptability. The present application improves the convergence speed and the proportion of feasible solutions by multi-source initialization and intelligent repair, and is adapted to actual complex constraints. The present application can be widely applied to rain and flood resource optimization in the Translongwan Basin and other arid and semi-arid regions, and is also applicable to other water resources system optimization scenes with multiple reservoirs, multiple targets and complex constraints. BRIEF DESCRIPTION OF DRAWINGS

[0033] Figure 1 is a flowchart of the present application;

[0034] Figure 2 is a 1996-2023 virtual reservoir discharge line graph under regulation of the present application; wherein, Figure 2 (a) in (a) is the virtual reservoir discharge under regulation of Dongsheng District in 1996-2023; Figure 2 (b) in (b) is the virtual reservoir discharge under regulation of Ijinkuoluo Banner; Figure 2 (c) in (c) is the virtual reservoir discharge under regulation of Kangbashi. DETAILED DESCRIPTION

[0035] The technical solutions of the present application will be further described below in combination with the drawings.

[0036] As Figure 1As shown, this invention provides a dual-objective coordinated regulation method for rainwater and flood resources based on a distributed virtual reservoir, addressing both water shortage and peak shaving. The method uses the NSGA-II algorithm to solve a multi-objective optimization regulation model for the water supply system. Taking the efficient utilization of rainwater and flood resources in the Zhuanlongwan watershed from 1996 to 2023 as an application scenario, the method includes the following steps:

[0037] Step S1: Construct a virtual reservoir based on the administrative divisions within the basin. The reservoir capacity is calculated using the area-weighted method based on the reservoir capacity within the basin.

[0038] Step S2: Collect monthly water demand (HSWUD dataset), rainfall data, and natural runoff data to determine the decision variable as the flow rate of the virtual reservoir's regulated discharge.

[0039] ;

[0040] Where R = {Dongsheng Reservoir, Yijinhuoluo Banner Reservoir, Kangbashi Reservoir}, and T is the set of control periods;

[0041] Step S3: Calculate the initial water volume of the virtual reservoir and calculate the surface water as a percentage of total water demand based on the water resources bulletin;

[0042] ;

[0043] Where T represents the total number of calculation periods; This represents the total number of virtual reservoirs within the basin. for The drainage area of ​​the watershed; For the first The outflow of natural runoff during a given period. For the first The precipitation in the month preceding the initial date within a virtual reservoir basin.

[0044] The initial water storage capacity of the virtual reservoir was calculated, with the Dongsheng Reservoir having a capacity of 2.16 million cubic meters. 3 The Yijinhuoluo Banner Reservoir has a capacity of 3.19 million cubic meters. 3 The Kangbashi Reservoir has a capacity of 51.43 million cubic meters. 3 According to the water resources bulletin, the multi-year average surface water as a percentage of total water demand is calculated as follows: Dongsheng Reservoir is 0.029, Yijinhuoluo Banner Reservoir is 0.065, and Kangbashi Reservoir is 0.123.

[0045] Step S4: Construct a multi-objective function that includes minimizing water shortage and maximizing potential peak shaving capacity, and apply constraints such as water supply capacity, water balance, demand satisfaction, and non-negativity. Solve the function using the NSGA-II algorithm. Specifically, this includes:

[0046] Step S4.1: The objective function of the multi-objective rainwater resource efficient utilization model is:

[0047] Objective 1: Minimize water shortage

[0048] ;

[0049] Wherein, T is the total number of calculation periods; is the total number of virtual reservoirs in the basin; is the surface water proportion coefficient; is the demand for water of the i-th virtual reservoir basin in the t-th period, is the available rainwater in the i-th virtual reservoir basin in the t-th period.

[0050] Objective 2: Maximize potential peak shaving capacity

[0051] ;

[0052] Wherein, is the virtual reservoir capacity in the t-th period, is the natural state precipitation, is the area of the basin; is the outflow flow after reservoir regulation in the t-th period: is the indicator function: when > , , , , (in the flood warning state, the reduction benefit needs to be calculated); otherwise The summation represents the reduction capacity of the virtual reservoir to the flood process.

[0053] Step S4.2: The constraint conditions considering the efficient utilization of water resources, supply and demand balance and other aspects are:

[0054] ① Virtual reservoir carrying capacity constraint

[0055] ;

[0056] In the formula, is the maximum carrying capacity of the virtual reservoir i;

[0057] ② Rainwater collection and utilization constraint

[0058] ;

[0059] In the formula, is the actual rainwater collection amount in the k basin in the t period,​​​​ is the collectable amount of rainwater in the k watershed at time period t, is the outflow of rainwater in the k watershed at time period t.

[0060] ③ Ecological flow constraint

[0061] ;

[0062] ④ Non-negative constraint

[0063] ; .

[0064] Step S4.3: The solving process adopts the NSGA-II algorithm, and the specific implementation includes: encoding the decision variables into a multi-dimensional vector form, generating an initial population using a diversification strategy to ensure the coverage of the solution space; introducing a penalty function mechanism in the fitness evaluation section to handle the constraint violation problem and ensure the feasibility of the solution; designing adaptive crossover and mutation operators, and combining with the feasibility repair operator to optimize the search process, improving the convergence efficiency and optimization ability of the algorithm; dynamically evaluating the convergence and distribution of the solution set through the analytic hierarchy process (AHP); and finally outputting the Pareto optimal solution set that meets the optimization objective as shown in Table 1.

[0065] Table 1 Simulation results of the monthly optimized rainwater utilization model scheme from 1996 to 2023

[0066] ;

[0067] From Figure 2 It can be seen that the natural runoff presents the typical seasonal characteristics of "weak base flow in the rainy season and abrupt flood peak in the rainy season"; and the regulated discharge is significantly suppressed through the "peak clipping and valley filling" effect of the virtual reservoir, while maintaining a relatively stable discharge in the dry season. In terms of regional differentiation of regulation amplitude, the natural runoff in Kangbashi Banner has the highest extreme value, which decreases to about 35 cubic meters per second after regulation, with a regulation amplitude of 41.7%; the natural runoff coefficient of variation is 1.52, which decreases to 0.91 after regulation. The natural runoff in Ejinhoro Banner has an extreme value of about 35 cubic meters per second, and about 30 cubic meters per second after regulation, with a regulation amplitude of 14.3%; the natural runoff coefficient of variation is 1.18, which decreases to 0.75 after regulation. The natural runoff in Dongsheng District has an extreme value of about 30 cubic meters per second, and about 25 cubic meters per second after regulation, with a regulation amplitude of 16.7%; the natural runoff coefficient of variation is 1.25, which decreases to 0.82 after regulation. It can be seen that Kangbashi Banner has the largest regulation amplitude due to the more severe fluctuations of natural runoff; the virtual reservoir effectively reduces the runoff coefficient of variation in all three regions, improving the availability of water resources.

[0068] From the long-term sequence from 1996 to 2023, the regulating effect of virtual reservoir has interannual stability: whether in wet years or dry years, it can continue to achieve "peak clipping and valley filling". For example, in 2005, 2017 and other years with concentrated flood peaks, the peak of the regulated discharge of the three places is significantly lower than the natural runoff; while in 2011 and other dry years, the regulated discharge can still maintain basic discharge, avoiding the risk of natural runoff interruption.

[0069] For the multi-objective optimization results from 1996 to 2023: From the spatial and temporal distribution characteristics of water shortage and potential peak clipping capacity, the water shortage shows significant interannual and intra-annual differences in the time dimension. From 1996 to 2023, the peak water shortage in some years (such as 2005, 2006, etc.) exceeds 35 million cubic meters, and the monthly distribution of water shortage is uneven, with lower water shortage in summer (June-August) and higher water shortage in spring and winter (January-March, November-December). The potential peak clipping capacity also shows obvious spatial and temporal differentiation, with a value range of 0-50 cubic meters / second. Interannually, the potential peak clipping capacity in some years (such as 2017, 2018, etc.) is strong, reaching more than 40 cubic meters / second, and the intra-annual distribution shows that the potential peak clipping capacity is relatively prominent in summer (June-August) and relatively weak in winter (December-February). In general, water shortage and potential peak clipping capacity have certain complementarity in spatial and temporal distribution, that is, when water shortage is high, the potential peak clipping capacity also shows corresponding trends, which provides data support for the regulation and management of regional water resources, and helps to further optimize the scheduling strategy of virtual reservoirs and achieve efficient use of water resources and coordinated promotion of flood control and disaster reduction.

Claims

1. A distributed virtual reservoir-based rain flood resource shortage-peak shaving dual target coordinated regulation method, characterized in that, The method comprises the following steps: (1) constructing virtual reservoirs according to administrative region division in the basin, wherein the reservoir capacity of the virtual reservoirs is determined based on reservoir capacities in the basin according to an area weight method; specifically as follows: the basin is divided into upper upstream parallel virtual reservoirs and downstream virtual reservoirs according to administrative region division; the total area of each administrative region and the total reservoir capacity in the region are calculated, and the reservoir capacity of each virtual reservoir is determined according to the area ratio; (2) collecting monthly water demand data, rainfall data and natural runoff data in the basin, and determining the decision variable as the flow discharged by the virtual reservoirs; specifically as follows: using historical water data sets to obtain monthly water demand data; using a hydrological model to simulate natural runoff data, and verifying the rainfall runoff data; (3) calculating the initial water quantity of the virtual reservoirs, wherein the initial water quantity is determined based on the basin area, initial precipitation and natural runoff data, and the surface water coefficient is calculated according to the water resources bulletin; specifically as follows: using a time calendar method, selecting a water model of a normal water year and a medium dry water year, and combining a monthly water demand scheme to calculate water balance, so as to determine the initial water storage capacity of the virtual reservoirs; based on the water resources bulletin, obtaining water demand data and surface water supply quantity of each administrative region, and calculating the surface water ratio coefficient; the formula is as follows: ； Wherein, T is the total number of calculation periods; is the total number of virtual reservoirs in the basin; is the basin area of the basin; is the first outflow of the natural runoff of the period; is the first the precipitation of the previous month before the initial date in the virtual reservoir basin; (4) constructing a multi-objective optimization model, the multi-objective optimization model taking the minimum water shortage and the maximum potential peak shaving capacity as objective functions, taking virtual reservoir carrying capacity constraints, rainwater collection and utilization constraints, ecological flow constraints and non-negative constraints as constraint conditions; the objective functions are specifically as follows: minimizing water shortage: ； Wherein, T is the total number of calculation periods; is the total number of virtual reservoirs in the basin; is the surface water proportion coefficient; is the total number of virtual reservoirs in the basin; is the water demand of the th virtual reservoir basin in the th period, is the available rainwater in the th period in the th virtual reservoir basin; maximizing potential peak shaving capacity: ； wherein, is the first period virtual reservoir capacity, is the natural precipitation, is the basin area of the basin; is the first period outflow flow after reservoir regulation: is the indicator function: when > in the flood warning state, the reduction benefit needs to be calculated; otherwise the sum embodies the reduction capacity of the virtual reservoir on the flood process; The constraint condition formulas are as follows: Virtual reservoir carrying capacity constraint: ； wherein, is the maximum carrying capacity of the virtual reservoir i; Rainwater collection and utilization constraint: ； wherein, is the actual rainwater collection in the k-basin at time period t, is the rainwater collectable in the k-basin at time period t, is the stormwater outflow in the k-basin at time period t; Ecological flow constraint: ； Non-negative constraint: ； ； (5) solving the multi-objective optimization model by using an NSGA-II algorithm to generate a Pareto optimal solution set, so as to obtain a monthly optimization regulation scheme of the virtual reservoirs.

2. The rainwater resource shortage-peak shaving dual-target coordinated regulation method based on a distributed virtual reservoir according to claim 1, characterized in that, Step (5) is specifically as follows: in the initial population generation stage, a multi-strategy cooperative mechanism is used to ensure that the population covers the solution space and improve the quality of the initial solution; an initial population with balanced water supply load is constructed based on the virtual reservoir water capacity threshold and rainwater rules; based on the distribution characteristics of regional water demand, the initial solution meeting the basic demand of key water nodes is generated preferentially; the initial solution is generated to maintain the water level of each virtual reservoir in the safe operation interval based on the dynamic balance of the reservoir group water storage capacity.

3. The rainwater resource shortage-peak shaving dual-target coordinated regulation method based on distributed virtual reservoir according to claim 1, characterized in that, Step (5) further comprises: constructing a multi-criteria evaluation system by using an analytic hierarchy process, and comprehensively evaluating the Pareto optimal solution set based on the index weight and the normalized index value; using a weighted summation method to calculate the comprehensive evaluation score, and selecting a unique optimal solution from the monthly Pareto frontier.

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