Regional water supply system optimization scheduling method considering influence of salt tide

Abstract

The invention discloses a regional water supply system optimization scheduling method considering the influence of a salt tide. The method comprises the following steps: S1, constructing a multi-water-source-multi-water-plant-multi-water-demand-area layered water supply topological structure covering a target area influenced by the salt tide; s2, judging a salt tide period according to real-time water quality monitoring data of a reservoir in an area influenced by the salt tide, dynamically adjusting a target function and constraint conditions of the multi-target multi-water-source optimal scheduling model, and preferentially scheduling water sources with safe water quality; and S3, on the basis of the topological structure model constructed in the step S1, by taking the salt tide state in the step S2 as a dynamic parameter, constructing a three-dimensional objective function of'water deficit, salt tide influence and cost difference 'of the multi-objective multi-water-source optimal scheduling model, and solving a Pareto optimal solution set through an improved NSGA-III algorithm in combination with constraint conditions. The method is suitable for collaborative scheduling optimization of the multi-water-source system influenced by the salt tide, and has wide application value in the aspects of improving the water resource allocation efficiency, guaranteeing the water supply safety and the like.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of water resource management and urban water supply scheduling, and particularly relates to a regional water supply system optimal scheduling method considering the influence of salt tide. BACKGROUND

[0002] Reservoir scheduling is the core link of water resource system management. The traditional scheduling needs to coordinate multiple objectives such as flood control, power generation, ecology and water supply. In the salt tide affected areas with dense population and developed economy, water supply safety has become the primary task of reservoir scheduling. Salt tide intrusion is a unique hydrological phenomenon in coastal and estuarine areas, which refers to the intrusion of seawater along the river under the action of tides, resulting in the increase of chloride concentration in river water. When the concentration exceeds the limit value of 250 mg / L in the Standards for Drinking Water Health, it will directly threaten the safety of drinking water, and also affect industrial water, agricultural irrigation and the stability of estuarine ecosystems.

[0003] There are three major water supply challenges in the salt tide affected areas: 1) complex hydrological conditions: affected by both river runoff and ocean tides, when runoff decreases in dry season, salt tide intrusion distance can extend to tens to hundreds of kilometers inland, and water quality in water source area fluctuates dramatically; 2) prominent supply-demand contradiction: such areas are usually densely populated and economically active, with daily water demand often exceeding 5 million m³, while water supply capacity of water source area is limited during salt tide period, which is prone to double risks of "water supply gap" and "water quality exceeding standard"; 3) great scheduling difficulty: existing scheduling models mostly focus on "water balance and cost minimization", without quantifying water quality risks caused by salt tide into the objective system, and only rely on passive response of "salt avoidance and fresh water storage" engineering, which is prone to the contradiction of "protecting water quantity while water quality exceeds standard, and protecting water quality while water supply is insufficient" during extreme salt tide period; and mainstream multi-objective optimization algorithms have insufficient diversity of solution set and slow convergence speed in the three-dimensional objective space of "water quantity-water quality-cost", which is difficult to meet the flexibility demand of scheduling decision.

[0004] Taking the Yangtze River Estuary as an example, as a typical salt tide affected area, the permanent population in this area reached 24.7589 million in 2022, and the daily water demand exceeded 8 million m³. When the Yangtze River runoff is less than 12000 m³ / s in dry season, the salt tide intrusion distance can reach more than 100 km, and the chloride concentration at the water intake is often more than 300 mg / L. The existing scheduling strategy has obvious limitations: excessive reliance on experience rules, lack of mechanism to dynamically respond to salt tide changes; insufficient multi-objective coordination, unable to achieve global trade-off of water shortage, cost and water quality; poor algorithm adaptability, difficult to cope with the trend of intensifying salt tide caused by climate change.

[0005] Therefore, it is urgent to build a general optimal scheduling method that is not dependent on specific regional exclusive conditions and can adapt to different salt tide affected scenarios, to solve the technical bottlenecks of "water quality-water quantity-cost" coordinated optimization and dynamic response to salt tide changes. SUMMARY

[0006] The present application aims to provide a method for optimizing scheduling of a regional water supply system considering the influence of salt tide, to solve the above technical problems existing in the prior art.

[0007] To solve the above technical problems, the present application adopts the following scheme: The method for optimizing scheduling of a regional water supply system considering the influence of salt tide comprises the following steps: Step S1: Construct a multi-source-multi-plant-multi-demand area layered water supply topology structure covering the target area affected by salt tide, abstract the reservoirs, water plants and water demand areas in the area as directed graph nodes based on graph theory method, abstract the connection relationship between nodes as directed edges, and define the flow on the edge as a decision variable; specifically including: Step S1.1: Abstract the core reservoirs, water plant groups and water demand areas in the salt tide affected area as directed graph nodes to form a "water source-water plant group-water demand area" three-layer network structure; wherein the water source is a set of reservoirs in the area that undertake the main water supply task; the water plant group is a combination of several water plants in the area divided according to the water supply range or function; the water demand area is a number of regional units in the area divided according to the water demand characteristics.

[0008] Step S1.2: Collect daily water demand, reservoir water supply capacity, water level-storage capacity relationship and water quality monitoring data of the salt tide affected area, determine the decision variables as the allocation amount of water source to water plant group and the allocation amount of water plant group to water demand area, respectively as shown in formula (1), formula (2): (1) In the above formula, R is a set of core reservoirs in the salt tide affected area, P is a set of water plant groups in the area, T is a set of scheduling periods. X r,p,t represents the water supply amount of the reservoir r to the water plant group t in the period p .

[0009] (2) In the above formula, Z is a set of water demand areas in the salt tide affected area; Y p,z,t represents the water supply amount of the water plant group p to the water demand area t in the period z .

[0010] Step S2: Determine the salt tide period through real-time water quality monitoring data of reservoirs in the salt tide affected area, dynamically adjust the objective function and constraint conditions of the multi-objective multi-water source optimal scheduling model, and preferentially schedule water sources with safe water quality.

[0011] Step S3: Based on the topological structure model constructed in step S1, taking the salt tide state in step S2 as a dynamic parameter, a multi-objective multi-water source optimal scheduling model is constructed, and a three-dimensional objective function of "water shortage, salt tide influence, and cost difference" is constructed. Combined with the constraint conditions, the Pareto optimal solution set is solved by the improved NSGA-III algorithm; specifically including: Step S3.1: Construct the objective function of the multi-objective multi-water source optimal scheduling model, as shown in formula (3), formula (4), and formula (5): Objective 1: Minimize water shortage: (3) In the formula, is the water shortage objective function; is the water demand of the water demand area z in the time period t , D z,t is the water demand of the water demand area z in the time period t .

[0012] Objective 2: Minimize salt tide affected water supply: (4) In the formula, f 2 ( X ) is the salt tide affected water supply objective function; , is the chloride concentration monitored by the water intake of the reservoir r in the first t time period, is the salt tide influence threshold; is the reservoir affected by salt tide invasion.

[0013] Objective 3: Minimize water supply cost difference: (5) In the formula, f 3 ( X ) is the water supply cost difference objective function; is the unit cost of the reservoir r in the time period t , is the total water supply of the reservoir r in the time period t , K0 Actual water supply cost.

[0014] Step S3.2: Set constraints, such as formula (6), formula (7), formula (8), formula (9), formula (9), formula (10), formula (11), formula (12), formula (13): 1) Reservoir water supply capacity constraint: (6) In the formula, is the reservoir r The maximum water supply capacity in the time period t .

[0015] 2) Water plant water supply capacity constraint: (7) In the formula, is the water plant p The maximum water supply capacity in the time period t .

[0016] 3) Pipeline transportation capacity constraint: (8) (9) In the formula, is the reservoir r The maximum water supply capacity of the corresponding pipeline to the water plant p , is the water plant p The maximum water supply capacity of the corresponding pipeline to the water demand area z .

[0017] 4) Water plant water balance constraint: (10) 5) Water supply demand satisfaction constraint: (11) 6) Non-negative constraint: (12) (13).

[0018] Step S3.3: Solve using NSGA-III algorithm: encode decision variables into a multi-dimensional vector, generate an initial population through diversification strategy; introduce a penalty function to handle constraint violation problems in fitness evaluation; use adaptive crossover, mutation operators and feasibility repair operators based on engineering heuristic rules to optimize the search process; dynamically evaluate the convergence MIGD and distribution MHV of the solution set, and output the Pareto optimal solution set.

[0019] Further optimization, in step S1.1, the division of water plant groups is based on the layout of water supply projects and the distribution of water pipelines in the areas affected by saltwater tides, including northern water plant groups, central water plant groups, eastern water plant groups, southern water plant groups, western water plant groups, suburban water plant groups, and remote suburban water plant groups. Each water plant group contains at least one water plant with conventional water treatment capabilities.

[0020] The water-demanding areas are divided according to the administrative divisions, population density and water load characteristics of the area, including central urban areas, northern areas, eastern areas, western areas and southern areas. Each water-demanding area contains at least one town or functional area with stable water demand.

[0021] Further optimization, in step S2, the judgment condition for the salt tide period is: the chloride concentration at the water intake of any reservoir in the area affected by the salt tide exceeds 250 mg / L; during the salt tide period, the scheduling priority coefficient of water sources with safe water quality is increased by 1.2-1.5 times, and the upper limit of water supply for water sources affected by the salt tide is reduced by 20%-30%.

[0022] Further optimization, the specific process of using the NSGA-III algorithm to solve the problem in step S3.3 includes: Step S3.3.1: Decision variable encoding: Convert the core decision variables of water supply scheduling into a mathematical form that can be processed by the algorithm, that is, the water supply volume of the reservoir to the water plant group is set and the total water delivery volume from water plants to water demand areas Encoded as a one-dimensional real chromosome, the chromosome length is | R |×| P |×| T |+| P |×| Z |×| T |, the value range of each gene position is ([0, Q max ], Q max It corresponds to the maximum water supply of the reservoir or the maximum water delivery of the water plant, ensuring that the coding result meets the project capacity boundary.

[0023] Step S3.3.2: Diversified initial population generation: A weighted fusion of seven engineering-oriented strategies is used to generate an initial population of 100 to 300. This strategy group includes a demand-oriented strategy, a water supply capacity balancing strategy, a historical scheduling fitting strategy, a random focusing strategy, a regional priority strategy, a reservoir balance strategy, and a hybrid strategy. The weight of each sub-strategy in the hybrid strategy is dynamically adjusted according to the scheduling deviation rate of the region in the previous three years.

[0024] Step S3.3.3: Fitness evaluation and constraint processing, specifically including: S3.3.3.1: Calculate multi-objective fitness: for each individual in the population, i.e., a complete set of water supply scheduling scheme, the water shortage target f1, the salt tide affected water supply target f2 and the water supply cost difference target f3 are calculated simultaneously, and the calculation results of the three targets are integrated into a three-dimensional fitness vector, which directly reflects the comprehensive performance of the individual in the "water supply reliability-water quality safety-economy" dimension.

[0025] S3.3.3.2: Constraint violation penalty: introduce a penalty function to modify the fitness, the modified fitness = original fitness + λ1max(0, water shortage limit) + λ2max(0, water supply over the upper limit); where λ1 is the supply and demand balance penalty coefficient, when the water demand area water shortage exceeds 5% of its total water demand, λ1 value doubles to strengthen the punishment for serious water supply gap; λ2 is the capacity constraint penalty coefficient, when the reservoir water supply exceeds 10% of its maximum water supply capacity, λ2 value doubles to strictly limit the over-capacity water supply.

[0026] S3.3.3.3: For individuals that still have constraint violations after being punished, repair them according to the engineering priority rules.

[0027] Step S3.3.4: Balance the "global exploration" and "local optimization" capabilities by the cooperative operation of crossover and mutation, which specifically includes: Crossover operation: adopt simulated binary crossover SBX, crossover probability p c Initial value is 0.85~0.95, decrease by 0.05 every 50 generations, lower limit is 0.6, gradually turn to local optimization; crossover distribution index is fixed at 20 to ensure the diversity and stability of the offspring.

[0028] Mutation operation: adopt polynomial mutation strategy, mutation probability p m Initial value is 0.05~0.15, when the population diversity index is less than 0.3, p m Increase by 0.02 to avoid premature convergence of the algorithm; mutation distribution index is fixed at 20 to control the rationality of mutation amplitude.

[0029] Step S3.3.5: Environmental selection and population update: in order to maintain the non-dominance and uniform distribution of the solution set, update the population according to the following three steps, which specifically includes: Step S3.3.5.1: Merge the current parent population and offspring population, and perform fast non-dominated sorting on the merged population, divide the non-dominated front of different levels according to the dominance relationship of individuals, and the first front is the current optimal candidate solution set.

[0030] Step S3.3.5.2: In the "water shortage-salt tide impact-cost difference" three-dimensional target space, a uniform distribution of reference points is preset, with a quantity of 1.2-1.5 times the target population size, the correlation degree of non-dominated solutions and each reference point is calculated, and the target value is normalized to eliminate dimensional differences and ensure fair competition of different targets.

[0031] Step S3.3.5.3: Select individuals by correlation degree from high to low, and finally retain 100-150 individuals to form a new parent population, ensuring that the new population contains non-dominated solutions and can uniformly cover different trade-off areas of the target space.

[0032] Step S3.3.6: Determine the iteration termination time and output the optimal scheme by dynamically monitoring the convergence of the algorithm and the distribution of the solution set: Step S3.3.6.1: Dynamically calculate the average inverse generation distance MIGD and the average hyper volume MHV of the solution set; Step S3.3.6.2: Terminate evolution when any of the following conditions is met: 1) The number of evolution generations reaches the preset maximum value; 2) The average inverse generation distance MIGD of the last 50 generations is less than 0.001, and the hyper volume variation coefficient is less than 5%.

[0033] Step S3.3.6.3: After terminating the evolution, output the final non-dominated solution set, containing 30-50 Pareto optimal solutions, each solution containing a complete and allocation scheme, fully covering the multi-objective trade-off space of "water shortage-salt tide impact-cost difference", providing diversified alternative schemes for scheduling decisions.

[0034] Further optimization, in step S3.3.2, the strategy includes: demand-oriented strategy: predict water demand in water demand areas and allocate water supply in reverse; water supply capacity balancing strategy: allocate according to the proportion of the maximum water supply capacity of each reservoir; historical scheduling fitting strategy: fit the scheduling data of the same period in the previous three years in the salt tide affected area to generate initial solutions; random focus strategy: randomly select 1-2 water demand areas (such as core areas) to prioritize water supply; regional priority strategy: allocate according to the priority of water demand areas, with the priority of core areas being higher than that of non-core areas; reservoir balancing strategy: make the water supply difference rate of each reservoir less than 15%; Hybrid strategy: A weighted fusion of the above six strategies is adopted, with the weights dynamically adjusted based on the scheduling deviation rate over the same period of the previous three years. If the scheduling deviation rate of a strategy is higher than 10%, its weight is reduced by 5% to 10%, thereby taking into account both the diversity of the initial population and the rationality of the project.

[0035] Further optimization, in step S3.3.3.3, the engineering priority rules of the feasibility repair operator include: If the water supply of the reservoir exceeds the limit, priority will be given to reducing the saltwater source with the highest chloride concentration; for every 50 mg / L increase in concentration, the proportion of excess water supply reduction in the corresponding water source will increase by 10%.

[0036] If the water volume of a water plant is unbalanced, that is, the deviation between the water inlet and outlet exceeds the limit, the water delivery volume of the water plant that is geographically closest to the water demand area shall be adjusted first, and the single adjustment range shall not exceed 15% of the designed water delivery capacity of the water plant.

[0037] If the water shortage in the water-demand area exceeds the standard, priority will be given to protecting the core area with the highest population density, and the water shortage in the core area will be controlled to <5%. The water shortage in the non-core area will be shared according to the population density ratio of each area, balancing people's livelihood security and water supply fairness.

[0038] Further optimization, in step S3.3.6.1, the average reverse generation distance MIGD for dynamically evaluating the convergence of the solution set and the average hypervolume MHV for evaluating the distribution of the solution set specifically include: Step S3.3.6.1.1: Calculate the mean reverse generation distance (MIGD) and determine convergence: First, let the current optimization solution set be S ={ x 1, x 2,..., x n};in, x i To solve the concentration i Pareto candidate solutions, corresponding to a set of water supply from the reservoir to the water plant group , water supply from water plants to water-demanding areas A complete water supply scheduling plan, n is the total number of solutions in the current solution set.

[0039] Secondly, set the theoretical optimal solution set to S *={ x 1*, x 2*,..., x k *};in, x j * is the j A theoretically optimal solution.

[0040] Third, calculate thei Candidate solutions x i The reverse generation distance IGD( x i ) and the mean reverse generation distance MIGD, specifically: (14) (15) In the above formula, IGD( x i )for x i To the theoretical optimal solution set S * is the 3D Euclidean distance to the nearest solution, f 1( x i ), f 2( x i ), f 3( x i ) are candidate solutions x i target values ​​for water shortage, water supply impact of saltwater tide and water supply cost difference; f 1( x j *), f 2( x j *), f 3( x j *) are theoretical optimal solutions x j *The corresponding target value.

[0041] Fourth, convergence judgment rule: when the calculated MIGD < 0.001, it is determined that the current solution set has converged to the theoretical optimal region and meets the convergence requirements of the algorithm.

[0042] Step S3.3.6.1.2: Calculate the mean hypervolume MHV and determine the distribution equilibrium: First, the “water shortage target” f 1. Salt tide affects water supply targets f 2 and water supply cost difference target f 3" is the coordinate axis, forming a three-dimensional Euclidean space O - f 1 f 2 f 3. Spatial origin O (0,0,0) is the ideal optimal state of “zero water shortage, zero saltwater impact, and zero cost difference”.

[0043] Secondly, the effective super volume units in the solution set are screened to form a set of effective super volume units: V ={ V 1, V 2,..., V m};in, V j For the j The volume of a valid super volume unit, a valid super volume unit refers to a unit that is not dominated by other candidate solutions and can independently contribute to the target space coverage. m is the total number of valid hypervolume units, m≤n; MHV is the average hypervolume, that is, the sum of the volumes of all valid hypervolume units.

[0044] Third, calculate the j The volume of a valid hypervolume unit V j and the coefficient of variation of the mean hypervolume MHV CV , specifically: (16) (17) (18) In the above formula, x j is the candidate solution corresponding to the effective super volume unit, and its target value f 1( x j ), f 2( x j ), f 3( x j ) are the water shortage, water supply affected by saltwater tide and water supply cost difference of the solution respectively; μ is the mean value of MHV, is the standard deviation of the effective hypervolume unit volume.

[0045] Fourth, the distribution balance judgment rule: when the calculated CV When it is <5%, it is determined that the current solution set is evenly distributed in the three-dimensional target space, without obvious aggregation or faults, and meets the distribution requirements of the algorithm.

[0046] Compared with the prior art, the present invention has the following beneficial effects: 1. The method described in the present invention has strong versatility. The core models such as topological modeling, saltwater tide discrimination and NSGA-III calculation do not rely on the exclusive parameters of a specific area. They can be adapted by simply inputting the "number of reservoirs, water plant layout, water quality data, and water demand characteristics" of the target area. It can be widely applied to all areas affected by saltwater tides.

[0047] 2. Dynamic response efficiency, based on general water quality threshold to trigger salt tide period mode, fast response, can automatically identify salt tide period according to real-time water quality data, compared with traditional experience scheduling, can greatly respond to salt tide invasion in advance, and the weight coefficient can be adaptively adjusted according to the regional salt tide intensity, adapt to the difference of salt tide characteristics in different regions, and enhance the system resilience.

[0048] 3. Multi-objective collaborative optimization effect is remarkable, realizes the collaborative optimization of "water quantity-water quality-cost" three targets in water supply scheduling, and significantly improves the water quality safety guarantee capability.

[0049] 4. The application adopts NSGA-III multi-objective evolutionary algorithm, solves the problems of uneven distribution of Pareto solution set under multi-objective, easy to fall into local optimum and the like, improves the diversity and globality of solution set. And through multi-source initialization and intelligent repair, the convergence speed and the proportion of feasible solutions of the algorithm are improved, and the actual complex constraints are adapted. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 The calculation flowchart of the regional water supply system optimization scheduling method considering salt tide influence is described in the application. Figure 2 The topological relationship diagram of the present situation of Shanghai water supply system is shown. Figure 3 The multi-objective optimization Pareto frontier graph in 2023 is shown. Figure 4 The comparison diagram of the water supply process of the multi-objective optimization result, the traditional scheduling result and the actual scheduling scheme in 2023 is shown. DETAILED DESCRIPTION

[0051] The technical solutions of the application will be described in detail below in combination with embodiments, but the protection scope of the application is not limited to the embodiments.

[0052] In this embodiment, the water supply system in the Yangtze River estuary is taken as an example for description. The Yangtze River estuary is an important economic center and a sensitive area of water resources in China, which has been facing the serious influence of salt tide invasion on water sources for a long time. Important water sources such as Chenhang and Qingcaosha are limited by reservoir capacity, and the water supply guarantee capacity is insufficient during the continuous salt tide period. In addition, there is a lack of efficient two-way interconnection mechanism between raw water systems, which leads to weak emergency scheduling ability and seriously affects the efficiency of regional water resources supply. The current scheduling mode mostly adopts single economic guidance or segmented strategy, which is difficult to realize the collaborative optimization of water supply reliability, economic benefit and water quality safety.

[0053] In this embodiment, as shown in Figure 1 The regional water supply system optimization scheduling method considering salt tide influence takes the water supply scheduling of the Yangtze River estuary in 2023 as an application scene, and specifically includes the following steps: Step S1: Construct a multi-source-multi-plant-multi-demand area layered water supply topology structure covering the salt tide affected area of the Yangtze River estuary, as shown in FIG. 1, define decision variables, specifically including: Figure 2 Step S1.1: In this embodiment, the water source includes Chenhang Reservoir, Qingcaosha Reservoir, and Jinze Reservoir, a total of 3; the water plant group includes north, center, Pudong, south, west, Songjin, and suburban, a total of 7 groups, including 28 water plants, with a total design water supply capacity of 9 million m³ / day; the main water demand area includes the central urban area, the northern area, the western area, and the southern area, with a daily water demand of 7.5-8.5 million m³. The connection relationship and water transmission capacity of the water source-water plant group-water demand area are defined.

[0054] Step S1.2: Collect the annual operation data in 2023, including the actual daily water demand of the salt tide affected area in the Yangtze River estuary, the water supply capacity of the reservoir and the water plant, the relationship between the water level and the effective storage capacity of the reservoir, the chloride concentration data of the reservoir water intake, as well as the actual water supply scheme in 2023 and the traditional scheduling scheme. Among them, the annual daily water demand, the water supply capacity of the reservoir, and the chloride concentration data come from the routine monitoring platform of Shanghai Water Affairs Bureau, the historical scheduling data come from the water supply annual report from 2020 to 2022, and they are all types of data that can be routinely obtained in the salt tide affected area.

[0055] The decision variables are determined as the allocation amount of the water source to the water plant group and the allocation amount of the water plant group to the water demand area, specifically as follows: The allocation amount of the water source to the water plant: (19).

[0056] In the formula, R = {Chenhang Reservoir, Qingcaosha Reservoir, Jinze Reservoir}, P = {North Water Plant Group, Center Water Plant Group, Pudong Water Plant Group, South Water Plant Group, West Water Plant Group, Songjin Water Plant Group, Suburban Water Plant Group}, T is a set of scheduling periods.

[0057] The allocation amount of the water plant to the water demand area: (20) In the formula, Z = {Central Urban Area, Northern Area, Western Area, Southern Area}.

[0058] Step S2: Determine the salt tide period through real-time water quality monitoring data of the reservoir in the salt tide affected area, dynamically adjust the objective function and constraint conditions of the multi-objective multi-source optimization scheduling model, and preferentially schedule the water source with safe water quality.

[0059] In this embodiment, the chloride concentration is monitored every 2 hours, and any reservoir with chloride concentration exceeding 250 mg / L is determined as a salt tide period.

[0060] ​January-March and November-December 2023 are the peak periods of salt tides in the Yangtze River Estuary area. The chloride concentration in Qingcaosha Reservoir will reach 420 mg / L, in Chenhang Reservoir 380 mg / L, and in Jinze Reservoir 280 mg / L. On January 5, 2023, the chloride concentration in Chenhang Reservoir reached 260 mg / L, triggering the salt tide period mode: the weight coefficient of the impact of salt tide on water supply will be adjusted to 0.6. Since the concentration in Chenhang Reservoir was close to 350 mg / L on that day, the upper limit of the weight will be taken. The upper limit of water supply in Chenhang Reservoir will be reduced to 1.44 million m³ / day (80% of the original 1.8 million m³ / day), and Jinze Reservoir will be given priority (the water supply on that day was 1.1 million m³, and the proportion increased to 15%).

[0061] On April 10, 2023, the chloride concentrations in all reservoirs were lower than 200 mg / L, and the salt tide period mode was exited: the weight coefficient was restored to 0.3, the upper limit of the reservoir water supply was restored to the design value, and the scheduling priority was adjusted according to "optimal cost".

[0062] Step S3: Based on the topological structure model constructed in step S1, and taking the saltwater tide state in step S2 as the dynamic parameter, a three-dimensional objective function of the multi-objective multi-water source optimization scheduling model, namely, water shortage, saltwater tide impact, and cost difference, is constructed. Combined with the constraints, the improved NSGA-III algorithm is used to solve the Pareto optimal solution set. Specifically, it includes: Step S3.1: Construct the objective function of the multi-objective multi-water source optimization scheduling model, as shown in Equations (21), (22), and (23): Goal 1: Minimize water shortages (twenty one) In the above formula, is the water shortage objective function; For water-demanding areas z In the period t The water shortage, D z,t For water-demanding areas z In the period t water demand.

[0063] Goal 2: Minimize the impact of saltwater tides on water supply: (twenty two) In the above formula, f 2 ( X ) is the objective function of water supply affected by saltwater tide; , For reservoirs r No. t Monitor the chloride concentration at the water intake during the period. is the saltwater impact threshold; water supply reservoirs affected by saltwater intrusion.

[0064] Objective 3: Minimize the water supply cost difference: (23) In the above formula, f 3 ( X ) is the water supply cost difference objective function; is the water supply reservoir r The unit cost in the time period t , is the total water supply of the water supply reservoir r in the time period t , K 0 is the actual water supply cost.

[0065] Step S3.2: Set the constraint conditions, as shown in formula (24), formula (25), formula (26), formula (27), formula (28), formula (29), formula (30), and formula (31): 1) Water supply reservoir capacity constraint: (24) In the above formula, is the maximum water supply capacity of the water supply reservoir r in the time period t , unit: m³.

[0066] 2) Water supply plant capacity constraint: (25) In the above formula, is the maximum water supply capacity of the water supply plant p in the time period t , unit: m³.

[0067] 3) Pipeline transportation and distribution capacity constraint: (26) (27) In the above formula, is the maximum water transportation capacity of the corresponding pipeline of the water supply reservoir r to the water supply plant p , unit: m³. is the maximum water transportation capacity of the corresponding pipeline of the water supply plant p to the water demand area z , unit: m³.

[0068] 4) Water supply plant water balance constraint: (28) 5) Water supply demand satisfaction constraint: (29) 6) Non-negative constraint: (30) (31) Step S3.3: Solve using NSGA-III algorithm: encode decision variables as a multi-dimensional vector, generate initial population through diversification strategy; introduce penalty function to handle constraint violation problem in fitness evaluation; use adaptive crossover, mutation operators and feasibility repair operators based on engineering heuristic rules to optimize search process; dynamically evaluate the convergence MIGD and distribution MHV of the solution set, and output the Pareto optimal solution set.

[0069] In this embodiment, the parameters are set: according to the complexity of the water supply system in the Yangtze River estuary region, the population size is 120, the maximum evolution generation is 200, the crossover probability is 0.9, which is reduced by 0.05 every 50 generations, the mutation probability is 0.1, and the diversity index is lower than 0.3, which is increased to 0.12; parameter selection only depends on the complexity of the regional water supply system.

[0070] Initial population generation: based on the Yangtze River estuary scheduling data from January 2020 to January 2022, the demand-oriented strategy weight is 22%. Because the demand deviation rate in January 2022 is 12%, according to the general rule "high deviation rate reduces weight by 5%-10%", the weight is reduced by 3% compared with the benchmark of 25%, the water supply capacity balance strategy weight is 18%, and the rest of the strategies are allocated according to the benchmark weight.

[0071] Feasibility repair: on January 10, 2023, the Qingcaosha Reservoir water supply exceeds the upper limit by 150,000 m³, according to the general repair rule, the water supply to the southern water plant group is preferentially reduced by 100,000 m³, accounting for 67% of the excess, and the remaining 50,000 m³ is reduced to the water plant group in the suburbs to ensure water balance.

[0072] Convergence determination: when evolution reaches 200 generations, MIGD=0.0008<0.001, MHV variation coefficient=4.2%<5%, stop evolution, output 42 Pareto optimal schemes.

[0073] Table 1 is the simulation results of daily optimal scheduling water supply scheme in 2023, and Table 2 is the simulation results of daily optimal scheduling of each reservoir in 2023. Due to the limited space, only part of the data is provided here.

[0074] Table 1 Simulation results of daily optimal scheduling water supply scheme in 2023 in the Yangtze River estuary region affected by salt tide

[0075] Table 2 Daily optimized scheduling simulation results of each reservoir in the salt tide affected region of the Yangtze River Estuary in 2023

[0076] Figure 3 For the method described in the present application, the Pareto front of multi-objective optimization is simulated for the salt tide affected region of the Yangtze River Estuary in each quarter of 2023. Among them, in the first quarter, the runoff is low and the salt tide intensity is high in the dry season of the Yangtze River Estuary, and the Pareto front is significantly dispersed in the "salt tide influence-water shortage" dimension, and the solution set extends along the direction of "high water quality safety (low salt tide influence) → high water supply reliability (low water shortage)", which is consistent with the dynamic target of "water quality safety priority during salt tide period" in the scheme. In the second and third quarters, the Yangtze River Estuary enters the flood season, and the salt tide threat is weakened, and the front is contracted and concentrated in the "water shortage-cost difference" two-dimensional dominant area, and the salt tide influence target value tends to be stable, which reflects the logic of the scheme "water supply cost / stability priority during non-salt tide period". In the fourth quarter, it is the transition period of salt tide, because the dry season restarts in October-December, the salt tide appears initially and is accompanied by dry season precursors, the solution set form is between winter and summer, the multi-objective game complexity rises (salt tide initial appearance + dry season precursor), and the ability of the scheme "dynamic adaptation during seasonal transition period" is verified.

[0077] Figure 4 For the comparison of the water supply process of the multi-objective optimization results, the traditional scheduling results and the actual scheduling scheme in 2023, Figure 4 Fig. (a) is the comparison result of the water supply process of the multi-objective optimization results, the traditional scheduling results and the actual scheduling scheme of Qingcaosha Reservoir in 2023; Figure 4 Fig. (b) is the comparison result of the water supply process of the multi-objective optimization results, the traditional scheduling results and the actual scheduling scheme of Jinze Reservoir in 2023; Figure 4 Fig. (c) is the comparison result of the water supply process of the multi-objective optimization results, the traditional scheduling results and the actual scheduling scheme of Chenhang Reservoir in 2023. As can be seen from the figure, the water supply curve of the optimization scheme is highly consistent with the actual water supply curve, the average water supply of the optimization scheme described in the present application deviates from the actual average water supply by less than 4.5%, and the average water supply of the traditional scheduling water supply scheme deviates from the actual average water supply by between 24-45%.

[0078] In summary, the verification of the Yangtze River Estuary example shows that the method described in the present application can significantly improve the water supply safety and scheduling economy during the salt tide period. In addition, the method is also applicable to other regions affected by salt tide, and has wide application prospects, providing general technical support for intelligent scheduling of urban water resources in all salt tide affected regions under the background of climate change.

[0079] As above, although the present application has been shown and expressed in connection with a certain embodiments thereof, it is to be understood that the application is not limited to the details of the foregoing description, but rather can be practiced with the exclusion of the features and aspects. It is the intention, therefore, to encompass within the scope of the application, all variations and equivalents that fall within the spirit and scope of the claims following.

Claims

1. A method for optimal scheduling of a regional water supply system considering the influence of salt tide, characterized in that, The steps include: Step S1: Construct a multi-water source-multi-water plant-multi-water demand area hierarchical water supply topology covering the target area affected by saltwater tide. Based on graph theory, the reservoirs, water plants and water demand areas in the area are abstracted as directed graph nodes, the connection relationship between the nodes is abstracted as directed edges, and the flow on the edge is defined as the decision variable; Specifically include: Step S1.1: The core reservoirs, water plant clusters, and water demand areas in the area affected by saltwater tides are abstracted into directed graph nodes to form a three-layer network structure of "water source-water plant cluster-water demand area". The water source is the collection of reservoirs that undertake the main water supply task in the area; the water plant cluster is a combination of several water plants in the area divided by water supply range or function; and the water demand area is a number of regional units in the area divided by water demand characteristics. Step S1.2: Collect the daily water demand, reservoir water supply capacity, water level-storage capacity relationship and water quality monitoring data of the area affected by the salt tide, and determine the decision variables as the allocation amount from the water source to the water plant group and the allocation amount from the water plant group to the water demand area, as shown in Equation (1) and Equation (2), respectively: （1） In the above formula, R is a set of core reservoirs in a saltwater affected area, P is a set of water plants in the area, T is a set of dispatching periods; X r,p,t represents a reservoir r in a period t to a water plant p supplying the amount of water; （2） In the above formula, Z a water demand area set affected by saltwater intrusion; Y p,z,t denotes a water plant group p in a time period t to a water demand area z a water amount delivered; Step S2: Determine the saltwater tide period through real-time water quality monitoring data of reservoirs in the area affected by the saltwater tide, dynamically adjust the objective function and constraints of the multi-objective multi-water source optimization scheduling model, and give priority to scheduling water sources with safe water quality; Step S3: Based on the topological structure model constructed in step S1, the saltwater tide state in step S2 is used as a dynamic parameter to construct a three-dimensional objective function of the multi-objective multi-water source optimization scheduling model of "water shortage, saltwater tide impact, and cost difference". Combined with the constraints, the improved NSGA-III algorithm is used to solve the Pareto optimal solution set; specifically, it includes: Step S3.1: Construct the objective function of the multi-objective multi-water source optimization scheduling model, as shown in Equations (3), (4), and (5): Goal 1: Minimize water shortage: （3） In the above formula, is the water deficit target function; water deficit area z water deficit in the time period t water deficit in the time period, D z,t water deficit area z water deficit in the time period t water deficit in the time period; Goal 2: Minimize the impact of saltwater tides on water supply: （4） In the above formula, f 2 X is a salt tide influence water supply target function; , is a reservoir r The t The water intake in the time period is monitored for chloride concentration, is a salt tide influence threshold value; is a reservoir affected by salt tide intrusion;​ Goal 3: Minimize water supply cost differences: （5） In the above formula, f 3 X is a water supply cost difference target function; is the water reservoir r in the time period t unit cost, is the water reservoir r total water supply in the time period t K 0 is the actual water supply cost;​​ Step S3.2: Set the constraints, as shown in Equations (6), (7), (8), (9), (9), (10), (11), (12), and (13): 1) Constraints on reservoir water supply capacity: （6） In the above formula, is a reservoir r At the time period t of the maximum water supply capacity; 2) Constraints on water plant’s water supply capacity: （7） In the above formula, for water plants p at a time period t maximum water supply capacity; 3) Pipeline transmission and distribution capacity constraints: （8） （9） In the above formula, is a reservoir r to a water plant p the maximum water delivery capacity of the corresponding pipe supplying water to the water plant, is a water plant p to a water demand area z the maximum water delivery capacity of the corresponding pipe supplying water to the water demand area; 4) Water balance constraints of water plants: （10） 5) Water supply demand meets constraints: （11） 6) Non-negative constraints: （12） （13） Step S3.3: Solve using the NSGA-III algorithm: Encode the decision variables into multidimensional vectors and generate the initial population through a diversification strategy; Introduce a penalty function in the fitness evaluation to deal with constraint violations; Use adaptive crossover and mutation operators and a feasibility repair operator based on engineering heuristic rules to optimize the search process; Dynamically evaluate the convergence MIGD and distribution MHV of the solution set and output the Pareto optimal solution set.

2. The method for optimal scheduling of a regional water supply system considering saltwater intrusion according to claim 1, wherein, In step S1.1, the water plant groups are divided according to the layout of water supply projects and water pipelines in the areas affected by saltwater tides, including northern water plant group, central water plant group, eastern water plant group, southern water plant group, western water plant group, suburban water plant group, and remote suburban water plant group. Each water plant group contains at least one water plant with conventional water treatment capacity. The division of the water demand area is based on the administrative division, population density and water load characteristics of the area, including central urban area, northern area, eastern area, western area, southern area type, and each water demand area contains at least one town or functional area with stable water demand.

3. The method for optimal scheduling of a regional water supply system considering saltwater intrusion according to claim 2, wherein, In step S2, the salinity tide period determination condition is that the chloride concentration at any reservoir water intake in the salinity tide affected area is more than 250 mg / L; during the salinity tide period, the priority coefficient of the scheduling of the water quality safety water source is increased by 1.2-1.5 times, and the upper limit of the water supply amount of the salinity tide affected water source is reduced by 20%-30%.

4. The method according to claim 3, wherein, The specific process of solving the NSGA-III algorithm in step S3.3 includes: Step S3.3.1: Decision variable encoding: transform the core decision variables of water supply scheduling into a mathematical form that can be processed by the algorithm, i.e., transform the water supply amount set of reservoirs to water plant groups and the water delivery amount set of water plant groups to water demand areas into one-dimensional real number chromosomes, with a chromosome length of R | × | P | × | T | + | P | × | Z | × | T |, with the value range of each gene site being ([0, Q max ], Q max is the maximum water supply amount of the corresponding reservoir or the maximum water delivery amount of the water plant, ensuring that the encoding result conforms to the engineering capacity boundary; Step S3.3.2: Diversified initial population generation: 7 kinds of engineering-oriented strategies are used to generate an initial population with a size of 100-300; the strategy group includes demand-oriented strategy, water supply capacity balancing strategy, historical scheduling fitting strategy, random focusing strategy, regional priority strategy, reservoir balancing strategy and mixed strategy, and the weight of each sub-strategy in the mixed strategy is dynamically adjusted according to the regional same period scheduling deviation rate in the previous 3 years; Step S3.3.3: fitness evaluation and constraint processing, specifically including: S3.3.3.1: Calculate multi-objective fitness: for each individual in the population, i.e. a complete water supply scheduling scheme, calculate the water shortage amount target f1, the salinity tide affected water supply amount target f2 and the water supply cost difference target f3 simultaneously, and integrate the calculation results of the three targets into a three-dimensional fitness vector; S3.3.3.2: Constraint violation penalty: introduce a penalty function to modify the fitness, the modification formula is: modified fitness = original fitness + λ1max(0, water shortage limit) + λ2max(0, water supply amount exceeds the upper limit); Wherein, λ1 is the supply and demand balance penalty coefficient, when the water demand area water shortage amount exceeds 5% of the total water demand amount, λ1 value is doubled to strengthen the punishment for serious water supply gap; λ2 is the capacity constraint penalty coefficient, when the reservoir water supply amount exceeds 10% of its maximum water supply capacity, λ2 value is doubled to strictly limit the over-capacity water supply; S3.3.3.3: For individuals that still have constraint violations after being punished, repair them according to the engineering priority rules; Step S3.3.4: Promote population iteration through the cooperative operation of crossover and mutation, specifically including: Crossover operation: simulated binary crossover (SBX) with crossover probability p c Initial value: 0.85~0.95, linearly decrease 0.05 every 50 generations, gradually turn to local optimization; crossover distribution index is fixed at 20; Mutation operation: polynomial mutation strategy, mutation probability p m Initial 0.05~0.15, when the population diversity index is less than 0.3, p m 0.02; mutation distribution index is fixed at 20; Step S3.3.5: To maintain the non-dominance and uniformity of the solution set, update the population according to the following three steps, specifically including: Step S3.3.5.1: Merge the current parent population and the child population, and perform fast non-dominated sorting on the merged population, divide the non-dominated front into different levels according to the dominance relationship of individuals, and the first front is the current optimal candidate solution set; Step S3.3.5.2: In the "water shortage-salinity tide impact-cost difference" three-dimensional target space, preset uniformly distributed reference points, the number is 1.2-1.5 times the size of the target population, calculate the correlation degree of non-dominated solutions and each reference point; Step S3.3.5.3: Select individuals according to the correlation degree from high to low, and finally reserve 100-150 individuals to form a new parent population, ensuring that the new population contains both non-dominated solutions and uniformly covers different trade-off regions of the target space; Step S3.3.6: Determine the iteration termination time and output the optimal scheme by dynamically monitoring the convergence of the algorithm and the distribution of the solution set: Step S3.3.6.1: Dynamically calculate the average inverse generation distance MIGD and the average hyper volume MHV of the solution set; Step S3.3.6.2: Terminate evolution when any of the following conditions is met: 1) The number of evolution generations reaches the preset maximum value; 2) The average inverse generation distance MIGD of the last 50 generations is less than 0.001, and the coefficient of variation of hyper volume is less than 5%; Step S3.3.6.3: After termination of the evolution, output the final non-dominated solution set, containing 30~50 Pareto optimal solutions, each solution containing the complete and The allocation scheme fully covers the multi-objective trade-off space of "water shortage-saltwater intrusion-cost difference", providing diversified alternative solutions for scheduling decisions.

5. The method for optimal scheduling of a regional water supply system considering saltwater intrusion according to claim 4, wherein, In the step S3.3.2, the strategy includes: Demand-oriented strategy: predict the water demand of the water demand area and inversely allocate the water supply capacity of the water source; Water supply capacity balancing strategy: distributing according to the proportion of the maximum water supply capacity of each reservoir ; Historical scheduling fitting strategy: fit the scheduling data of the same period in the previous three years in the salt tide affected area to generate an initial solution; Random focusing strategy: randomly select 1-2 water demand areas to prioritize water supply; Zone priority strategy: on-demand water zone priority allocation ; Reservoir balance strategy: make the water supply capacity difference rate of each reservoir less than 15%; Hybrid strategy: weighted fusion of the above six strategies, the weight is dynamically adjusted according to the scheduling deviation rate in the same period in the previous three years, if the scheduling deviation rate of a certain strategy is higher than 10%, the weight is reduced by 5%-10%.

6. The method for optimizing and dispatching a regional water supply system considering the influence of saltwater tide according to claim 5, characterized in that: In the step S3.3.3.3, the engineering priority rules of the feasibility repair operator include: If the water supply capacity of the reservoir exceeds the limit, the salt tide water source with the highest chloride concentration is preferentially reduced; for every 50mg / L increase in concentration, the excess water supply capacity of the water source is reduced by 10%; If the water plant water capacity is unbalanced, the water capacity of the water plant with the shortest geographical distance to the water demand area is preferentially adjusted, and the single adjustment amplitude does not exceed 15% of the design water supply capacity of the water plant; If the water demand area is short of water, the core area with the highest population density is preferentially guaranteed, and the water shortage in the non-core area is proportionally allocated according to the population density of each region to balance the livelihood guarantee and water supply fairness.

7. The method for optimal scheduling of a regional water supply system considering saltwater intrusion according to claim 6, wherein, In the step S3.3.6.1, the average inverse generation distance MIGD for dynamically evaluating the convergence of the solution set and the average hyper volume MHV for evaluating the distribution of the solution set, specifically include: Step S3.3.6.1.1: Calculate the average inverse generation distance MIGD and the convergence criterion: First, let the current optimization solution set be S ={ x 1, x 2,..., x n };in, x i To solve the concentration i Pareto candidate solutions, corresponding to a set of water supply from the reservoir to the water plant group , water supply from water plants to water-demanding areas A complete water supply scheduling plan, n is the total number of solutions in the current solution set; Second, the set of theoretical optimal solutions is set as S x 1, x 2, x k ; wherein, x j is the first theoretical optimal solution; and j is the second theoretical optimal solution.​ Third, the Inverse Generational Distance IGD of the candidate solution and the Mean Inverse Generational Distance MIGD are calculated, specifically as follows: i x i x i ​​​ （14） （15） In the above formula, IGD( x i )for x i To the theoretical optimal solution set S * is the 3D Euclidean distance to the nearest solution, f 1( x i ), f 2( x i ), f 3( x i ) are candidate solutions x i target values ​​for water shortage, water supply impact of saltwater tide and water supply cost difference; f 1( x j *), f 2( x j *), f 3( x j *) are theoretical optimal solutions x j *The corresponding target value; Fourth, the convergence criterion: when the calculated MIGD is less than 0.001, it is determined that the current solution set has converged to the theoretical optimal region, meeting the convergence requirement of the algorithm; Step S3.3.6.1.2: Calculate the average hyper volume MHV and the distribution balance criterion: First, the "water shortage target f 1 " and the "salinity impact on water supply target f 2 " are used as coordinate axes to form a three-dimensional Euclidean space f 3 . O - f 1 f 2 f 3 , the origin of the space O (0, 0, 0) is the ideal optimal state of "zero water shortage, zero salinity impact, zero cost difference". Secondly, the effective super volume units in the solution set are screened to form a set of effective super volume units: V ={ V 1, V 2,..., V m };in, V j For the j The volume of a valid super volume unit, a valid super volume unit refers to a unit that is not dominated by other candidate solutions and can independently contribute to the target space coverage. m is the total number of valid hypervolume units, m≤n; MHV is the mean hypervolume, i.e. the sum of the volumes of all valid hypervolume units; Third, the volume of the effective hyper-volume unit is calculated j V j and the coefficient of variation of the mean hyper-volume MHV CV , in particular:​ （16） （17） （18） In the above formula, x j The target value of the candidate solution corresponding to the effective hyper-volume unit is f 1( x j )、 f 2( x j )、 f 3( x j ) respectively the water shortage, the salt tide affected water supply and the water supply cost difference of the solution; μ is the mean value of the MHV, is the standard deviation of the effective hyper-volume unit volume; Fourth, the distribution balance determination rule: when the calculated CV <5% is determined that the current solution set is distributed evenly in the three-dimensional target space, without obvious aggregation or fault, meeting the algorithm distribution requirements.

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