CN117314062B - Multi-stage gate combined multi-target optimized water distribution scheduling method - Google Patents
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Abstract
The invention discloses a multi-stage gate combined multi-target optimized water distribution scheduling method. It comprises the following steps: s1: constructing a one-dimensional hydrodynamic coupling model of a pressure-non-pressure gate, and determining the opening hydrodynamic evolution process of a water level and a flow gate; s2: constructing a penalty function by adopting a self-adaptive method based on feasible solution density according to constraint types to obtain a scheduling objective function as a scheduling optimization scientific basis; s3: completing the construction of a multi-stage gate combined multi-target optimized dispatching coupling model; s4: determining the number of open micro-services and the number of parallel threads by analog calculation, and rapidly obtaining a gate optimization scheduling scheme set; s5: optimizing the selection, execution and issuing of a dispatching scheme, and adjusting the opening of a gate by adopting a PI feedback algorithm according to the difference value between the real-time monitoring water level before the gate and the target water level of the dispatching scheme at the current moment in the dispatching process, so as to reduce the dispatching error. The method has the advantages of improving accuracy and realizing overall optimization and efficient calculation of the multi-stage gate multi-target scheduling scheme.
Description
Technical Field
The invention relates to the technical field of gate optimal scheduling, in particular to a multi-stage gate combined multi-target optimal water distribution scheduling method.
Background
The water resource in the region with abundant water is transported and distributed to the region with deficient water through the open channel water transporting and distributing project, which is an important engineering means for solving the imbalance of water resource supply and demand and realizing reasonable allocation of water resource. The open channel water delivery and distribution engineering is generally formed by connecting non-pressure water delivery buildings such as a plurality of channels, tunnels, culverts and the like, pressure water delivery buildings such as inverted siphons and the like in series with control engineering such as a multi-stage throttle valve, a water diversion valve and the like, and the system is complex in composition, various in water flow state and slow in flow rate. The method is influenced by the composition complexity of a large water delivery and distribution project and the multistage characteristic of the gate, and the gate scheduling is carried out by means of manual experience, so that the risk of damaging the operation safety and stability of the water delivery and distribution project exists due to poor accuracy, and the water delivery plan adjustment is difficult to finish on schedule. By adopting the channel automation control theory, although the channel accumulation can be regulated through the gate feedforward control, the target point water level is controlled, the time hysteresis caused by the slow water flow speed of the channel is reduced, and the limitations that the scheduling target is single and the accurate regulation and the feedback regulation are realized are still maintained. When the optimization algorithm is adopted to comprehensively consider the channel operation scheduling requirement and constraint and construct the water delivery and distribution engineering optimization scheduling model, the problems of low convergence speed, poor population diversity, long calculation time consumption and easy sinking of local optimal limitation still exist;
Therefore, on the premise of ensuring the safety of the water delivery and distribution engineering and smoothly completing the water supply plan on time, the comprehensive consideration of the targets in various aspects of scheduling is carried out, and the multi-stage gate scheduling scheme along the water delivery and distribution engineering is scientifically, efficiently and accurately formulated, so that the method is still a problem to be solved by the technicians in the field.
Disclosure of Invention
The invention aims to provide a multi-stage gate combined multi-target optimized water distribution scheduling method, which is a channel multi-stage gate combined water supply optimized scheduling method, so that the hydrodynamic process simulation accuracy of a complex water transmission and distribution project is greatly improved, and the overall optimization, efficient calculation and feedback correction of a multi-stage gate multi-target scheduling scheme are realized; the method is used for solving the limitation problems of single water supply scheduling target, poor intelligence, long time delay and poor accuracy of the existing channel.
In order to achieve the above purpose, the technical scheme of the invention is as follows: a multi-stage gate combined multi-target optimized water distribution scheduling method is characterized in that: the method comprises the following steps:
step S1: according to the engineering entity structure, carrying out mathematical model generalization on water distribution control engineering such as pressureless water delivery buildings, inverted siphon pressured water delivery buildings, diversion gates, check gates and the like along open channels, culverts, aqueducts, tunnels and the like of water delivery engineering, constructing a pressureless-gate one-dimensional hydrodynamic coupling model, and determining the opening hydrodynamic evolution process of water level and flow gate;
Step S2: selecting scheduling stability, accuracy and efficiency as scheduling targets, taking the operation safety of a water delivery building and the water distribution regulation and control capacity of a throttle gate as rigid constraints, taking water intake guarantee requirements along a water diversion port as flexible constraints, and constructing a punishment function according to constraint types by adopting a self-adaptive method based on feasible solution density to obtain a multi-stage gate combined multi-target optimized scheduling target function as a scheduling optimization scientific basis;
step S3: taking a multi-stage gate opening change process in the water distribution process as a scheduling decision variable, extracting a pre-gate water level, a gate passing flow and a gate opening obtained by hydrodynamic force simulation as scheduling objective function input, optimizing the scheduling decision variable by adopting a multi-objective particle swarm algorithm based on a competition mechanism, optimizing a scheduling scheme set by taking the minimum scheduling objective as an optimization direction, and completing the construction of a multi-stage gate combined multi-objective optimized scheduling coupling model;
step S4: adopting a micro-service architecture to perform distributed cluster deployment on the coupling model, determining the number of micro-services and parallel threads which are started by analog calculation according to the total scheduling analog time length and the gate opening adjustment time interval, and efficiently calculating to quickly obtain a gate optimization scheduling scheme set;
Step S5: optimizing the selection, execution and issuing of a dispatching scheme, and adjusting the opening of a gate by adopting a PI feedback algorithm according to the difference value between the real-time monitoring water level before the gate and the target water level of the dispatching scheme at the current moment in the dispatching process, so as to reduce the dispatching error.
In the above technical solution, in step S1, according to the engineering entity structure, mathematical model generalization is performed on non-pressure water delivery buildings such as open channels, culverts, aqueducts, tunnels and the like, inverted siphon pressure water delivery buildings, water diversion gates, and throttle gates and other water distribution control engineering, a one-dimensional hydrodynamic coupling model of pressure-non-pressure-gate is constructed, and the water level, flow and gate opening hydrodynamic evolution process is determined, which specifically includes the following steps:
s11: the method comprises the steps of researching and collecting control engineering drawing data of inverted siphon pressurized water delivery buildings, open channels, culverts, aqueducts, tunnel pressureless water delivery buildings and water diversion gates and throttle gates along water delivery and distribution projects;
s12: the construction of the topological relation of the pressureless water delivery buildings such as open channels, culverts, aqueducts, tunnels and the like, the construction of the water supply route topological relation of inverted siphon pipes and the setting of section generalized parameters are completed, and the numerical simulation of the hydrodynamic process is carried out by adopting a Saint Vietnam equation and a low-pressure pipe flow equation, wherein the specific method is as follows:
(1) Pressureless water delivery buildings such as open channels, culverts, aqueducts, tunnels and the like:
the water flow state in the pressureless water delivery buildings such as open channels, culverts, aqueducts, tunnels and the like is open flow, and the hydrodynamic process can be simulated by adopting the san velam equation, as shown in formulas (2) and (3):
wherein: x is distance, m; t is time, s; q is the flow of the flow cross section, m 3 S; f is the area of the flow cross section, m 2 The method comprises the steps of carrying out a first treatment on the surface of the u is the flow rate, s; z is the water level, m; c is a thank you coefficient; r is wet week, m; q is the flow of the division of the unit channel length, and m is positive in the division 2 /s；
(2) Inverted siphon pressurized water delivery building:
for the inverted siphon, the upper water head is lower and the flow speed is smaller, the internal water flow state is low-pressure pipe flow, and the hydrodynamic process of the inverted siphon can be simulated by adopting a low-pressure pipe flow equation, as shown in formulas (2) and (4):
wherein: h is the head of the piezometer tube;
s13: completing generalization of water distribution control projects such as a water diversion gate, a throttle gate and the like, wherein the water diversion gate generalizes to a side outflow node, and describing the water diversion process by adopting a process of changing flow along with time; summarizing the throttle gate into a gate hole, finishing forming size parameter setting, calculating the hydrodynamic process by adopting a gate overflow formula based on the relative opening of the gate, and the detail is as follows:
(1) Water distribution control engineering of the water diversion gate:
generalizing the shunt gate into a side outflow node, and generalizing the influence of the hydrodynamic process of the shunt quantity on water delivery and distribution engineering by adopting a time-varying process function of the shunt quantity:
Q f ＝Q (t) (5)
wherein: q (Q) f For dividing the flow of water diversion gate, m 3 S; t is time, s; q (Q) (t) As a function of flow rate over time, m 3 /s；
(2) Water distribution control engineering of a throttle valve:
taking the relative opening of the gate as a throttle gate overcurrent calculation basis, adopting the following gate overcurrent formula to simulate water level, flow and calculation under the alternate states of gate hole outflow, weir flow and hole weir, and completing generalized modeling of throttle gate water distribution control engineering:
wherein: m is a gate hole free outflow flow coefficient and is a dimensionless number; b is the width of the gate hole, m; e is the opening degree of the gate, m; h is a u Is the depth of water before the gate, m; sigma is the gate outflow inundation coefficient; epsilon is the coefficient of the flat gate Kong Shousu; m is m c B is the free overflow flow coefficient of the side shrinkage weir s The flow coefficient of free overflow of the width of the weir after side shrinkage is h is the water depth on the weir, sigma s Is a weir flow inundation coefficient; q (Q) p Calculate flow for gate outflow, Q w Calculating flow for the weir flow, wherein Q is gate overflow;
s14: at the section change and turning position of water delivery and distribution engineering, the water flow change is severe, and the local head loss is calculated:
(1) And (3) a non-pressure section: for pressureless sections such as open channels, culverts, aqueducts, tunnels and the like, the local head loss h can be calculated according to the formula (1) j ：
h j ＝ξ*|v 2 2 -v 1 2 |/2g (1)
Wherein: h is a j The partial head loss of the pressureless section is m; g is gravity acceleration, m 2 /s；v 2 、v 1 The flow velocity of the front section and the back section of the transition section are respectively m/s; xi is a local head loss coefficient and is selected according to the reduction of the section and the expansion condition;
(2) The method comprises the following steps: for water head loss caused by inverted siphon turning, according to turning angle number, searching local water head loss coefficient according to the manual of hydraulic engineering design
S15: dividing a water supply project into a plurality of canal sections by taking a throttle valve as a boundary, solving a Saint Vinan equation and a low-pressure pipe flow equation by adopting a four-point difference method for a canal water delivery building, determining the throttle valve flow based on a gate overflow formula by taking the coordination of the throttle valve flow and the water level of an upstream water delivery building as a principle, and further completing the solution of a one-dimensional hydrodynamic coupling model of the non-pressure-throttle valve;
s16: and carrying out calibration verification on parameters of the one-dimensional hydrodynamic coupling model of the pressureless-pressured-throttling gate based on historical dispatching water transmission and distribution project key section flow, water level and gate opening time-varying process data, and providing model support for subsequent water level and flow gate opening hydrodynamic evolution calculation.
In the above technical solution, in step S2: selecting scheduling stability, accuracy and high efficiency as scheduling targets, taking operation safety of a water delivery building and water distribution regulation and control capacity of a throttle gate as rigid constraint, taking water intake guarantee requirements along a water diversion port as flexible constraint, constructing a punishment function according to constraint types by adopting a self-adaptive method based on feasible solution density, and obtaining a multi-stage gate combined multi-target optimal scheduling target function as a scheduling optimal scientific basis, wherein the method specifically comprises the following steps of:
s21: taking scheduling stability, accuracy and high efficiency as main consideration factors of scheduling evaluation, respectively selecting a square integral weighted value f of a comprehensive deviation error of a target water level before a multi-stage gate 1 Minimum and overgate flow variation absolute value integral weighting value f 2 Minimum and gate opening variation absolute value integral weighting value f 3 Minimum is the optimal scheduling target:
(a) Scheduling accuracy: to ensure the dispatching accuracy, the deviation between the water level before the gate of the multi-stage gate and the water level of the dispatching target should be reduced as far as possible, and the square integral weighting value f of the total deviation error of the water level before the gate of the multi-stage gate 1 The minimum is used as a scheduling accuracy measurement index:
wherein: f (f) 1 The square integral weight value of the comprehensive deviation error of the target water level before the gate of the multi-stage gate is given in meters; z is Z k,t The simulated water level of the gate k at the scheduling time t is given in meters; z is Z k,target The target water level is scheduled for the operation of the gate k, the unit is meter, and m is the total number of the throttle gates of the water transmission and distribution project; t represents the total scheduling time; Δt represents a scheduling time interval;
(b) Scheduling stability: in order to ensure the dispatching stability, the danger of the reciprocating fluctuation of the gate overflow to the channel and the gate safety is avoided as much as possible in the gate dispatching process, and the weighted value f is integrated by the absolute value of the flow change of the multistage gate 2 The minimum is a scheduling stability measurement index:
wherein: f (f) 2 Integrating the weighted value, m, of the absolute value of the flow change of the multistage gate 3 /s，Q k,t For the flow of the gate k at the time t, m 3 /s，Q k,t-Δt For the flow of the gate k at the time t-deltat, m 3 /s；Q k，T For the flow of the gate k at the moment T, m 3 /s，Q k，0 For the flow of the gate k at time 0, m 3 S; m is the total number of the water transmission and distribution engineering throttle gates;
(c) Scheduling efficiency: in order to ensure the dispatching efficiency, the frequent reciprocating adjustment of the opening degree of the gate should be avoided as much as possible in the process of dispatching the gate to cause the transportationThe water distribution engineering can not reach stability for a long time, and the comprehensive deviation f is adjusted by the opening degree of the multi-stage gate 3 The minimum is a scheduling efficiency measurement index:
wherein: f (f) 3 Integrating the weighted value for the absolute value of the change of the opening of the multi-stage gate; e (E) k,t The opening degree m of the gate k at the time t of the scheduling time; e (E) k,t-Δt The opening of the gate k at the time t-delta t is m; e (E) k，T The opening degree m of the gate k at the time of the scheduling time T; e (E) k,0 The opening degree m of the gate k at the scheduling time 0 moment;
s22: in the operation scheduling process of the water delivery engineering, the operation safety of the water delivery engineering and the water distribution regulation and control capability of the throttle valve are strictly ensured, and the water taking along the water diversion openings is ensured to slightly violate the constraint as hard constraint, and the constraint condition of the operation scheduling of the water delivery engineering is obtained as soft constraint;
(a) Safety of water delivery engineering operation: in the operation scheduling process, the operation safety of the inverted siphon and lining soil canal and various water delivery buildings is strictly ensured:
1) Inverted siphon operational safety: in order to prevent the overflow of the inverted siphon from being too small to form water drop and hydraulic jump at the inlet of the pipeline, the stable flow state in the pipeline is destroyed, the inverted siphon is vibrated, the structural safety is affected, and the inverted siphon is ensured to satisfy the submerged water depth under all flow conditions, namely:
H s,t ≥CVh 0.5 (10)
wherein: h s,t The water depth of the inverted siphon is submerged at the moment t, m; h is the height of the pressure pipeline, m; v is the average flow velocity of the section of the pressure pipeline, the unit is m/s, C is the submerged depth of the inverted siphon, and m;
2) Lining soil canal operation safety: in order to prevent the lining plate from turning over and damaging caused by too fast water level lowering speed of the lining soil channels, the water level lowering speed of each lining soil channel is ensured to be always smaller than the upper limit of the allowable speed, namely:
v i,t ≤v Δhmax (11)
wherein: v i,t The water level descending speed of the section i of the lining soil canal at the moment t is m/day; v Δhmax The maximum water level descending speed of the lining soil canal is m/day;
3) Operational safety of water delivery building: for open channels, culverts, aqueducts and tunnels, the water level of each section should not exceed the design water level all the time in order to ensure the operation scheduling safety of the open channels, the culverts, the aqueducts and the tunnels:
Z i,t ≤Z i,d (12)
wherein: z is Z i,t The water level of the section i at the moment t is m; z is Z i,d The water level, m, is designed for section i;
(b) Water distribution regulation capability of the throttle valve: in the operation scheduling process, the size of the throttle valve passing flow, the variation range and the opening degree adjustment size of the gate are strictly ensured to be within the range of the water distribution regulation and control capability:
1) Throttle gate overcurrent capability: in order to ensure the safety of the operation scheduling process of the throttle gates, the overcurrent flow of each throttle gate should not exceed the design flow:
Q k,t ≤Q d (13)
wherein: q (Q) k,t To throttle the flow of the gate at time t, m 3 /s；Q d Designing flow for throttle valve, m 3 /s；
2) Throttle valve flow variation amplitude: in order to ensure the flow stability of the throttle gate and the upstream and downstream channels, the single flow change should not exceed the upper limit of the change amplitude during flow adjustment:
|Q g,t+1 -Q g,t |≤ΔQ gmax (14)
Wherein: q (Q) g,t To throttle the flow of the gate k at t, m 3 /s；Q g,t+1 To throttle the flow of the gate k at t+1, m 3 /s；ΔQ g max To throttle the upper limit of the flow variation amplitude of the gate k, m 3 /s；
3) Throttle opening adjustment capability: the single throttle gate opening adjustment value is required to be larger than the throttle gate opening dead zone due to the throttle gate opening adjustment precision:
|E k,t+1 -E k,t |≥E d (15)
wherein: e (E) k,t Opening degree of the throttle valve k at the moment t, and m; e (E) k,t+1 M is the opening of the throttle valve k at the time t+1; e (E) d M is a dead zone for controlling the opening of the brake;
(c) Water intake guarantee requirements of the water diversion port: in the water distribution process, the water level of each channel section which can normally take water through the water diversion openings along the engineering line is ensured to be not lower than the water taking lower limit water level as much as possible, if special situations such as useless water demand or water diversion gate overhaul are met, the water taking level can be temporarily lower than the water taking lower limit water level (in a short time and a small amplitude) during the special situations, and the water taking guarantee of the water diversion openings is used as soft constraint:
Z i,t ≥Z i,q (16)
wherein: z is Z i,t The water level of the section i at the moment t is m; z is Z i，p The water intake lower limit water level of the section i is m;
s23: in order to ensure that the center of gravity enters a feasible region when the guiding scheme meets all constraint conditions in the initial optimization stage of the scheduling scheme, the center of gravity is transferred to a target function value to be larger after the later optimization scheduling scheme gradually enters the feasible region, and the self-adaptive penalty function penalty coefficient is determined based on the feasible decryption degree, as shown in a formula (17):
Wherein: c (C) i (ρ) is a penalty coefficient, which may be further determined based on the soft and hard penalty type and may be classified as soft penalty coefficient C is (ρ) and hard penalty coefficient C ih (ρ); ρ is the feasible decryption degree of the scheduling scheme, which is equal to the number of the scheduling schemes meeting each constraint condition divided by the total scheduling scheme number in the solving scheme, and the value range is 0-1; alpha i To deterministically penalize function adjustment coefficients according to soft and hard constraint types, the coefficients are usually a positive integer, and the hard constraint penalty coefficients alpha h Greater than the soft constraint penalty coefficient alpha s ；
S24: determining a scheduling penalty term according to the constraint violation degree of each scheduling time step calculation result, wherein the scheduling penalty term is shown in a formula (18) -a formula (24):
1)inverted siphon submerged water depth hard punishment item
2) Punishment item for water level descending rate of lining soil canal
Wherein: n represents the total number of sections of the lining soil canal of the water distribution project;
3) Water level punishment item for water delivery building design
4) Throttle gate overcurrent capability penalty term
5) Throttle valve flow variation amplitude term
6) Throttle opening adjustment accuracy penalty term
7) Water level punishment item for water intaking of diversion port
S25: multiplying the punishment terms by the corresponding punishment coefficients to obtain soft and hard constraint punishment functions, adding the soft and hard constraint punishment functions and the scheduling target values, and combining the multi-stage gate with the multi-target optimized scheduling target function as shown in a formula (25):
In the above technical solution, in step S3: the method comprises the steps of taking a multi-stage gate opening change process in a water distribution process as a scheduling decision variable, extracting a pre-gate water level, a gate passing flow and a gate opening obtained by hydrodynamic force simulation as scheduling objective function input, optimizing the scheduling decision variable by adopting a multi-objective particle swarm algorithm based on a competition mechanism, optimizing a scheduling scheme set by taking a minimum scheduling objective as an optimization direction, and completing construction of a multi-stage gate combined multi-objective optimization scheduling coupling model, and specifically comprises the following steps:
s31: introducing an elite learning mechanism into a multi-target particle swarm optimization algorithm to form a multi-target particle swarm algorithm based on a competition mechanism;
s32: determining the population particle number and iteration times of a multi-target particle swarm algorithm based on a competition mechanism, and initializing the particle position, the particle speed and an external archive;
s33: taking a multi-stage gate opening change process as an optimized scheduling decision variable, extracting a pre-gate water level, a gate passing flow and a gate opening change process which are output by a hydrodynamic model in a simulation result time step as a scheduling evaluation objective function value calculation input, determining the front face numbers of particles pareto in the current population based on non-dominant ranking and crowding distance and ranking by taking a scheduling evaluation objective value minimum as an optimization direction, and constructing an elite particle set;
S34: randomly selecting two scheduling scheme particles from a multi-stage gate scheduling scheme elite particle set, respectively calculating included angles between the two scheduling scheme elite particles and scheduling scheme particles to be updated, determining scheduling scheme winner particles, and updating the scheduling scheme particles to be updated according to a formula (27);
wherein: x is X i And V i Is the position and velocity vector, X, of the particle to be updated ω Is the location of the contention winner particle, r 0 And r i Is [0,1 ]]Two random numbers in the interior, X i(t+1) And V i(t+1) The position and speed of the updated particles;
s35: disturbance is carried out on the updated particles by adopting a polynomial variation strategy, and the particle swarm further explores an optimal area to complete one updating iteration;
s36: repeating the above operation until the iteration times meet the set iteration times, and obtaining a final multi-stage gate scheduling scheme set as a final scheduling scheme solution set.
In the above technical solution, in step S4, a micro-service architecture is used to perform distributed cluster deployment on the coupling model, and according to the scheduling simulation total duration and the gate opening adjustment time interval, the number of micro-services and parallel threads opened by simulation calculation are determined, and the method specifically includes the following steps:
s41: based on the parallelism characteristics of a multi-target particle swarm algorithm and a one-dimensional hydrodynamic model, adopting a micro-service architecture and distributed cluster deployment to perform system integration on a multi-stage gate combined multi-target optimization scheduling coupling model;
S42: optimizing a scheduling simulation time range and a gate opening adjustment time interval according to the coupling model, combining scheduling simulation time duration limitation, comprehensively considering calculation time efficiency and calculation resource utilization conditions, determining the number of open micro-services and enabling threads so as to improve the calculation efficiency and realize quick and efficient calculation of the model;
s43: after the number of micro-services is determined and threads are started, the system starts a multi-stage gate and multi-target optimization scheduling coupling model to automatically calculate, and gate optimization scheduling scheme groups in the scheduling simulation time range of gates at all stages along the water transmission and distribution project are generated to assist scheduling decisions.
In the above technical solution, in step S5, an optimized scheduling scheme is selected to execute issuing, and according to a difference value between a water level monitored in real time before a gate and a target water level of the scheduling scheme in a scheduling process, a PI feedback algorithm is adopted to adjust a gate opening so as to reduce a scheduling error, and the method specifically includes the following steps:
s51: the user selects a scheduling scheme from the generated gate optimization scheduling scheme group to issue, and the gate automatic control system executes a multi-stage gate opening change instruction according to the scheduling scheme;
s52: according to the real-time monitoring value of the gate water level at the lower end of the canal section and the gate opening set value, adopting PI feedback control algorithm technology to determine a feedback opening change value deltae according to a formula (27);
Wherein: k (K) p Is the deviation coefficient; k (K) i Is an integral coefficient; n is the number of returned monitoring values in the scheduling decision time step, for example, when one gate opening adjustment is performed for 60min and one monitoring value is returned for 5min for the downstream gate, n=12; di is a pre-gate water level monitor value for each analog time step;
s53: and in the next scheduling decision period, superposing a feedback gate opening change value delta e on the basis of the original gate opening change instruction to serve as a gate opening execution value.
The invention has the following advantages:
(1) On the premise of ensuring the safety of water transmission and distribution engineering and smooth and on-time completion of a water supply plan, the invention comprehensively considers the targets in various aspects of scheduling (such as scheduling stability, accuracy, high efficiency and the like), and scientifically, efficiently and accurately formulates a multi-stage gate scheduling scheme along the water transmission and distribution engineering;
(2) The method comprises the steps of obtaining the water level, flow and gate opening evolution process of a multi-stage gate water transmission and distribution project through a one-dimensional hydrodynamic coupling model of a pressure-non-pressure gate; selecting scheduling stability, accuracy and efficiency as scheduling targets, taking the operation safety of a water delivery building and the water distribution regulation and control capacity of a throttle gate as rigid constraints, taking water intake guarantee requirements of a water diversion port as flexible constraints, constructing a punishment function based on a feasible solution density self-adaptive method, and obtaining a scheduling target function as a scheduling optimal basis; optimizing a multi-stage gate combined multi-target optimized dispatching water distribution process by a multi-target particle swarm algorithm based on a competition mechanism; the micro-service multi-server multithreading parallel computing technology is adopted, so that the optimal dispatching computing efficiency is remarkably improved; the gate scheduling scheme is corrected in real time by adopting a PI feedback algorithm, the difference between a plurality of monitoring values and target values is fused in a limited operation interval, the scheduling accumulated error is effectively reduced in a limited operation time, a multi-target optimization and scheduling process of the multi-stage gate for complex water transmission and distribution engineering simulation-optimization-execution-feedback adjustment is formed, the difficulty of multi-stage gate joint scheduling is high, the limitations of single water supply scheduling target, poor optimization convergence, poor accuracy and long calculation time consumption of the existing channel are overcome, the simulation accuracy of the water power process of the complex water transmission and distribution engineering is greatly improved, and the overall optimization, efficient calculation and feedback correction of the multi-stage gate multi-target scheduling scheme are realized.
Drawings
FIG. 1 is a schematic diagram of a technical process of the present invention;
FIG. 2 is a diagram showing distribution of open channels, culverts, aqueducts, tunnel pressureless water delivery buildings and inverted siphon pressureless water delivery buildings within the modeling range of a one-dimensional hydrodynamic coupling model of a pressureless-gate in an embodiment of the invention;
FIG. 3 is a graph showing a multistage sluice distribution diagram within a modeling range of a one-dimensional hydrodynamic coupling model of a pressure-non-pressure sluice in an embodiment of the present invention;
fig. 4 is a diagram of a multi-stage gate opening change process in a scheduling scheme generated by optimizing a multi-stage gate scheduling scheme by a multi-target particle swarm algorithm based on a competition mechanism in an embodiment of the present invention;
FIG. 5 shows the water depth before the throttle valve A and the target water depth and the change process of the opening degree of the throttle valve A when the feedback adjustment is adopted;
FIG. 6 is a diagram illustrating a control gate level feedback control process according to an embodiment of the present invention.
Detailed Description
The following detailed description of the invention is, therefore, not to be taken in a limiting sense, but is made merely by way of example. While making the advantages of the present invention clearer and more readily understood by way of illustration.
The invention provides a multi-stage gate joint scheduling optimization water distribution method, belonging to the field of water resource allocation scheduling; the gate overflow formula is utilized to complete the construction of a one-dimensional hydrodynamic coupling model of the pressure-non-pressure-gate, and the hydrodynamic simulation of the complex water transmission and distribution project is realized; according to the multi-stage gate scheduling operation water level, flow and gate control targets, constructing a multi-stage gate joint scheduling evaluation model by combining operation constraints of a water delivery building and a control object; the multi-objective particle swarm optimization algorithm based on the competition mechanism finishes the coupling of the hydrodynamic model and the scheduling evaluation model, and adopts a micro-service architecture to integrate a coupling model system, so that intelligent optimization, efficient alternation and scientific optimization of the cascade gate scheduling process are realized; and the PI control algorithm is adopted, the opening of the gate at the later stage is adjusted according to the difference feedback of the measured water level after the execution of the scheduling scheme and the expected water level before the gate, and a powerful support is provided for the accurate execution of the scheduling scheme.
Examples
The invention is used for the multi-stage gate combined multi-target optimized water distribution schedule of a certain water transmission and distribution project to describe the invention in detail, and has a guiding function for the invention applied to the multi-stage gate combined multi-target optimized water distribution schedule of other water transmission and distribution projects.
In the embodiment, a certain water transmission and distribution project takes an A reservoir as a water source and takes a B reservoir as a terminal point; the total engineering length is 269.67 km, which consists of 53 sections of water delivery open channels, 38 blocks of culverts, 55 blocks of tunnels, 11 blocks of inverted siphons, 22 blocks of aqueducts, 19 blocks of throttle gates and 30 blocks of water diversion gates, is a complex nonlinear engineering, is influenced by the composition complexity of a large-scale water delivery engineering and the multistage characteristics of a gate, and depends on manual experience to carry out gate scheduling, the accuracy is poor, the risk of damaging the operation safety and stability of the water delivery engineering is caused, and the water supply planning adjustment is difficult to finish on schedule; in order to realize the joint scheduling of the multi-stage gates for a certain water delivery and distribution project in the embodiment, the stability and the accuracy of scheduling are improved, and the efficient execution of a water delivery plan is ensured, the method is adopted to perform the joint multi-objective and multi-stage gate optimized water distribution scheduling.
As can be seen with reference to the accompanying drawings: as shown in fig. 1, in this embodiment, a multi-stage gate combined multi-objective optimized water distribution scheduling method for a water distribution project includes the following steps:
S1: according to the engineering entity structure, the water distribution control engineering such as the pressureless water delivery buildings, inverted siphon pressured water delivery buildings, diversion gates, throttle gates and the like along the water delivery engineering such as open channels, culverts, aqueducts, tunnels and the like are reasonably generalized (namely mathematical model generalization is performed), a pressureless-gate one-dimensional hydrodynamic coupling model is constructed, and the water level and flow gate opening hydrodynamic evolution process is determined;
s2: selecting scheduling stability, accuracy and efficiency as scheduling targets, taking the operation safety of a water delivery building and the water distribution regulation and control capacity of a throttle gate as rigid constraints, taking water intake guarantee requirements along a water diversion port as flexible constraints, and constructing a punishment function according to constraint types by adopting a self-adaptive method based on feasible solution density to obtain a multi-stage gate combined multi-target optimized scheduling target function as a scheduling optimization scientific basis;
s3: taking a multi-stage gate opening change process in the water distribution process as a scheduling decision variable, extracting a pre-gate water level, a gate passing flow and a gate opening obtained by hydrodynamic force simulation as scheduling objective function input, optimizing the scheduling decision variable by adopting a multi-objective particle swarm algorithm based on a competition mechanism, optimizing a scheduling scheme set by taking the minimum scheduling objective as an optimization direction, and completing the construction of a multi-stage gate combined multi-objective optimized scheduling coupling model;
S4: adopting a micro-service architecture to perform distributed cluster deployment on the coupling model, determining the number of micro-services and parallel threads which are started by analog calculation according to the total scheduling analog time length and the gate opening adjustment time interval, and performing efficient calculation to obtain a gate optimization scheduling scheme set;
s5: optimizing the selection, execution and issuing of a dispatching scheme, and adjusting the opening of a gate by adopting a PI feedback algorithm according to the difference value between the real-time monitoring water level before the gate and the target water level of the dispatching scheme at the current moment in the dispatching process, so as to reduce the dispatching error.
Further, the implementation method of the step S1 includes:
s11: the method comprises the steps of researching and collecting control engineering drawing data of inverted siphon pressurized water delivery buildings, open channels, culverts, aqueducts, tunnel pressureless water delivery buildings and water diversion gates and throttle gates along water delivery and distribution projects;
s12: completing construction of topological relations and section generalization parameter setting of pressureless water delivery buildings such as open channels, culverts, aqueducts, tunnels and the like and water supply routes of inverted siphon pipes, wherein the topological relations and statistical lists of the water delivery buildings are shown in table 1; the hydrodynamic process is numerically simulated by adopting a Saint Vinan equation and a low-pressure pipe flow equation, and the details are as follows:
(1) Pressureless water delivery buildings such as open channels, culverts, aqueducts, tunnels and the like:
The water flow state in the pressureless water delivery buildings such as open channels, culverts, aqueducts, tunnels and the like is open flow, and the hydrodynamic process can be simulated by adopting the san velam equation, as shown in formulas (2) and (3):
wherein: x is distance, m; t is time, s; q is the flow of the flow cross section, m 3 S; f is the area of the flow cross section, m 2 The method comprises the steps of carrying out a first treatment on the surface of the u is the flow rate, s; z is the water level, m; c is a thank you coefficient; r is wet week, m; q is the flow of the division of the unit channel length, and m is positive in the division 2 /s；
(2) Inverted siphon pressurized water delivery building:
for the inverted siphon, the upper water head is lower and the flow speed is smaller, the internal water flow state is low-pressure pipe flow, and the hydrodynamic process of the inverted siphon can be simulated by adopting a low-pressure pipe flow equation, as shown in formulas (2) and (4):
wherein: h is the head of the piezometer tube;
table 1 statistics of water delivery buildings
S13: completing generalization of water distribution control projects such as a water diversion gate, a throttle gate and the like, wherein the water diversion gate generalizes to a side outflow node, and describing the water diversion process by adopting a process of changing flow along with time; summarizing the throttle gate into a gate hole, finishing forming size parameter setting, calculating the hydrodynamic process by adopting a gate overflow formula based on the relative opening of the gate, and the detail is as follows:
(1) Water distribution control engineering of the water diversion gate:
generalizing the shunt gate into a side outflow node, and generalizing the influence of the hydrodynamic process of the shunt quantity on water delivery and distribution engineering by adopting a time-varying process function of the shunt quantity:
Q f ＝Q (t) (5)
wherein: q (Q) f For dividing the flow of water diversion gate, m 3 S; t is time, s; q (Q) (t) As a function of flow rate over time, m 3 /s；
(2) Water distribution control engineering of a throttle valve:
taking the relative opening of the gate as a throttle gate overcurrent calculation basis, adopting the following gate overcurrent formula to simulate water level, flow and calculation under the alternate states of gate hole outflow, weir flow and hole weir, and completing generalized modeling of throttle gate water distribution control engineering:
wherein: m is a gate hole free outflow flow coefficient and is a dimensionless number; b is the width of the gate hole, m; e is the opening degree of the gate, m; h is a u Is the depth of water before the gate, m; sigma is the gate outflow inundation coefficient; epsilon is the coefficient of the flat gate Kong Shousu; m is m c B is the free overflow flow coefficient of the side shrinkage weir s The flow coefficient of free overflow of the width of the weir after side shrinkage is h is the water depth on the weir, sigma s Is a weir flow inundation coefficient; q (Q) p Calculate flow for gate outflow, Q w Calculating a flow for the slice flow; q is gate overflow;
s14: at the section change and turning position of water delivery and distribution engineering, the water flow change is severe, and the local head loss is calculated according to the formula (1):
(1) And (3) a non-pressure section: for pressureless sections such as open channels, culverts, aqueducts, tunnels and the like, the local head loss h can be calculated according to the formula (1) j ：
h j ＝ξ*|v 2 2 -v 1 2 |/2g (1)
Wherein: h is a j The partial head loss of the pressureless section is m; g is gravity acceleration, m 2 /s；v 2 、v 1 The flow velocity of the front section and the back section of the transition section are respectively m/s; ζ is a local head loss coefficient, which is selected according to the reduction of area and expansion, in this embodiment, 0.2 is taken for the diverging section, and 0.1 is taken for the converging section;
(2) The method comprises the following steps: for water head loss caused by inverted siphon turning, according to turning angle number, searching local water head loss coefficient according to hydraulic engineering manual; in the present embodiment, the local head loss coefficient for each turning angle is shown in table 2:
table 2 local water head coefficient at the corner of inverted siphon pressurized water pipe
S15: dividing a water supply project into a plurality of canal sections by taking a throttle valve as a boundary, solving a Saint Vinan equation and a low-pressure pipe flow equation by adopting a four-point difference method for a canal water delivery building, determining the throttle valve flow based on a gate overflow formula by taking the coordination of the throttle valve flow and the water level of an upstream water delivery building as a principle, and further completing the solution of a one-dimensional hydrodynamic coupling model of the non-pressure-throttle valve;
S16: based on historical dispatching water transmission and distribution project key section flow, water level and gate opening time-dependent change process data, calibrating and verifying the parameters of the one-dimensional hydrodynamic coupling model of the pressureless-pressured-controlled gate, and providing model support for subsequent water level and flow gate opening hydrodynamic evolution calculation;
FIG. 2 is a diagram showing the distribution of open channels, culverts, aqueducts, tunnel pressureless water delivery buildings and inverted siphon pressureless water delivery buildings within the modeling range of the one-dimensional hydrodynamic coupling model of the pressureless-gate in the present embodiment; as can be seen from fig. 2: the embodiment comprises various projects (including a throttle valve, a water diversion valve, an inverted siphon, a tunnel, a culvert and the like) and needs to realize multi-target water distribution dispatching;
FIG. 3 is a diagram showing a gate distribution diagram in the modeling range of a one-dimensional hydrodynamic coupling model of a pressure-non-pressure-gate in the present embodiment; as can be seen from fig. 3: the embodiment comprises a multi-stage gate, and the multi-stage gate joint scheduling is required to be realized;
the implementation method of the step S2 comprises the following steps:
s21: taking scheduling stability, accuracy and efficiency as main consideration factors of scheduling evaluationSelecting the square integral weighting value f of the comprehensive deviation error of the target water level before the gate of the multi-stage gate 1 Minimum and overgate flow variation absolute value integral weighting value f 2 Minimum and gate opening variation absolute value integral weighting value f 3 Minimum is the optimal scheduling target:
(a) Scheduling accuracy: to ensure the dispatching accuracy, the deviation between the water level before the gate of the multi-stage gate and the water level of the dispatching target should be reduced as far as possible, and the square integral weighting value f of the total deviation error of the water level before the gate of the multi-stage gate 1 The minimum is used as a scheduling accuracy measurement index:
wherein: f (f) 1 The square integral weight value of the comprehensive deviation error of the target water level before the gate of the multi-stage gate is given in meters; z is Z k,t The simulated water level of the gate k at the scheduling time t is given in meters; z is Z k,target The target water level is scheduled for the operation of the gate k, the unit is meter, m is the total number of the throttle gates of the water transmission and distribution project, and m is taken as 19 in the embodiment, and a throttle gate list is obtained; t represents the total scheduling time; Δt represents a scheduling time interval;
(b) Scheduling stability: in order to ensure the dispatching stability, the danger of the reciprocating fluctuation of the gate overflow to the channel and the gate safety is avoided as much as possible in the gate dispatching process, and the weighted value f is integrated by the absolute value of the flow change of the multistage gate 2 The minimum is a scheduling stability measurement index:
wherein: f (f) 2 Integrating the weighted value, m, of the absolute value of the flow change of the multistage gate 3 /s，Q k,t For the flow of the gate k at the time t, m 3 /s，Q k,t-Δt For the flow of the gate k at the time t-deltat, m 3 /s；
(c) Scheduling efficiency: in order to ensure the high efficiency of the dispatching, the gates should be avoided as much as possible in the process of dispatching the gatesThe frequent reciprocating adjustment of the opening degree leads to the failure of stable water delivery and distribution engineering for a long time, and the comprehensive deviation f is adjusted by the opening degree of the multistage gate 3 The minimum is a scheduling efficiency measurement index:
wherein: f (f) 3 Integrating the weighted value for the absolute value of the change of the opening of the multi-stage gate; e (E) k,t The opening degree m of the gate k at the time t of the scheduling time; e (E) k,t-Δt The opening of the gate k at the time t-delta t is m;
s22: in the operation scheduling process of the water delivery engineering, the operation safety of the water delivery engineering and the water distribution regulation and control capability of the throttle valve are strictly ensured, and the water taking along the water diversion openings is ensured to slightly violate the constraint as hard constraint, and the constraint condition of the operation scheduling of the water delivery engineering is obtained as soft constraint;
(a) Safety of water delivery engineering operation: in the operation scheduling process, the operation safety of the inverted siphon and lining soil canal and various water delivery buildings is strictly ensured:
1) Inverted siphon operational safety: in order to prevent the overflow of the inverted siphon from being too small to form water drop and hydraulic jump at the inlet of the pipeline, the stable flow state in the pipeline is destroyed, the inverted siphon is vibrated, the structural safety is affected, and the inverted siphon is ensured to satisfy the submerged water depth under all flow conditions, namely:
H s,t ≥CVh 0.5 (10)
Wherein: h s,t The water depth of the inverted siphon is submerged at the moment t, m; h is the height of the pressure pipeline, m; v is the average flow velocity of the section of the pressure pipeline, the unit is m/s, C is the submerged depth of the inverted siphon, and m;
2) Lining soil canal operation safety: in order to prevent the lining plate from turning over and damaging caused by too fast water level lowering speed of the lining soil channels, the water level lowering speed of each lining soil channel is ensured to be always smaller than the upper limit of the allowable speed, namely:
v i,t ≤v Δhmax (11)
wherein: v i,t To line the section of soil canali the water level drop rate at the time t, m/day; v Δhmax Taking 0.5m/day as the maximum value of the water level descending rate of the lining soil canal in the embodiment;
3) Operational safety of water delivery building: for open channels, culverts, aqueducts and tunnels, the water level of each section should not exceed the design water level all the time in order to ensure the operation scheduling safety of the open channels, the culverts, the aqueducts and the tunnels:
Z i,t ≤Z i,d (12)
wherein: z is Z i,t The water level of the section i at the moment t is m; z is Z i,d The water level, m, is designed for section i;
in this embodiment, the designed water level constraints of each channel are shown in table 3:
table 3 Water level constraint Table for channel design
(b) Water distribution regulation capability of the throttle valve: in the operation scheduling process, the size of the throttle valve passing flow, the variation range and the opening degree adjustment size of the gate are strictly ensured to be within the range of the water distribution regulation and control capability:
1) Throttle gate overcurrent capability: in order to ensure the safety of the operation scheduling process of the throttle gates, the overcurrent flow of each throttle gate should not exceed the design flow:
Q k,t ≤Q d (13)
wherein: q (Q) k,t To throttle the flow of the gate at time t, m 3 /s；Q d Designing flow for throttle valve, m 3 /s；
In this embodiment, the throttle design flow information is shown in table 4:
TABLE 4 list of throttle and design parameter information table
2) Throttle valve flow variation amplitude: in order to ensure the flow stability of the throttle gate and the upstream and downstream channels, the single flow change should not exceed the upper limit of the change amplitude during flow adjustment:
|Q g,t+1 -Q g,t |≤ΔQ gmax (14)
wherein: q (Q) g,t To throttle the flow of the gate k at t, m 3 /s；Q g,t+1 To throttle the flow of the gate k at t+1, m 3 /s，ΔQ gmax To throttle the upper limit of the flow variation of the gate k, 2m is taken in the embodiment 3 /s；
3) Throttle opening adjustment capability: the single throttle gate opening adjustment value is required to be larger than the throttle gate opening dead zone due to the throttle gate opening adjustment precision:
|E k,t+1 -E k,t |≥E d (15)
wherein: e (E) k,t Opening degree of the throttle valve k at the moment t, and m; e (E) k,t+1 M is the opening of the throttle valve k at the time t+1; e (E) d To throttle the gate opening dead zone, 0.01m is taken in this embodiment;
(c) Water intake guarantee requirements of the water diversion port: in the water distribution process, the water level of each channel section of the water diversion opening along the engineering line can be ensured to be not lower than the water taking lower limit water level as far as possible, if the water diversion opening meets special situations such as useless water demand or water diversion gate overhaul, the water taking level of the water diversion opening can be slightly lower than the water taking lower limit water level in a short time, and the water taking guarantee of the water diversion opening is used as soft constraint:
Z i,t ≥Z i,q (16)
Wherein: z is Z i,q For section i, the water intake lower limit water level, m, in this embodiment, the water intake lower limit water level of each channel is shown in table 5:
table 5 canal section water intake lower limit water level constraint table
S23: in order to ensure that the center of gravity enters a feasible region when the guiding scheme meets all constraint conditions in the initial optimization stage of the scheduling scheme, the center of gravity is transferred to a target function value to be larger after the later optimization scheduling scheme gradually enters the feasible region, and the self-adaptive penalty function penalty coefficient is determined based on the feasible decryption degree, as shown in a formula (17):
wherein: c (C) i (ρ) is a penalty coefficient, which may be further determined based on the soft and hard penalty type and may be classified as soft penalty coefficient C is (ρ) and hard penalty coefficient C ih (ρ); ρ is the feasible decryption degree of the scheduling scheme, which is equal to the number of the scheduling schemes meeting each constraint condition divided by the total scheduling scheme number in the solving scheme, and the value range is 0-1; alpha i To deterministically penalize function adjustment coefficients according to soft and hard constraint types, the coefficients are usually a positive integer, and the hard constraint penalty coefficients alpha h Greater than the soft constraint penalty coefficient alpha s ；
S24: determining a scheduling penalty term according to the constraint violation degree of each scheduling time step calculation result, wherein the scheduling penalty term is shown in a formula (18) -a formula (24):
1) Inverted siphon submerged water depth hard punishment item
2) Punishment item for water level descending rate of lining soil canal
Wherein: n represents the total number of sections of the lining soil canal of the water distribution project;
3) Water level punishment item for water delivery building design
4) Throttle gate overcurrent capability penalty term
5) Throttle valve flow variation amplitude term
6) Throttle opening adjustment accuracy penalty term
7) Water level punishment item for water intaking of diversion port
S25: multiplying the punishment terms by the corresponding punishment coefficients to obtain soft and hard constraint punishment functions, adding the soft and hard constraint punishment functions and the scheduling target values, and combining the multi-stage gate with the multi-target optimized scheduling target function as shown in a formula (25):
the implementation method of the step S3 comprises the following steps:
s31: introducing an elite learning mechanism into a multi-target particle swarm optimization algorithm to form a multi-target particle swarm algorithm based on a competition mechanism;
s32: determining the population particle number and the iteration number of a multi-target particle swarm algorithm based on a competition mechanism, initializing the particle position, the particle speed and the external archive, wherein in the embodiment, the population particle number is taken as 100, and the iteration number is set to be 50;
s33: taking a multi-stage gate opening change process as an optimized scheduling decision variable, extracting a pre-gate water level, a gate passing flow and a gate opening change process which are output by a hydrodynamic model in a simulation result time step as a scheduling evaluation objective function value calculation input, determining the front face numbers of particles pareto in the current population based on non-dominant ranking and crowding distance and ranking by taking a scheduling evaluation objective value minimum as an optimization direction, and constructing an elite particle set;
S34: randomly selecting two scheduling scheme particles from a multi-stage gate scheduling scheme elite particle set, respectively calculating included angles between the two scheduling scheme elite particles and scheduling scheme particles to be updated, determining scheduling scheme winner particles, and updating the scheduling scheme particles to be updated according to a formula (26);
wherein: x is X i And V i Is the position and velocity vector, X, of the particle to be updated ω Is the location of the contention winner particle, r 0 And r i Is [0,1 ]]Two random numbers in the interior, X i(t+1) And V i(t+1) The position and speed of the updated particles;
s35: disturbance is carried out on the updated particles by adopting a polynomial variation strategy, and the particle swarm further explores an optimal area to complete one updating iteration;
s36: repeating the above operation until the iteration times meet the set iteration times, and obtaining a final multi-stage gate scheduling scheme set as a final scheduling scheme solution set;
fig. 4 is a diagram of a multi-stage gate opening change process in a scheduling scheme generated by optimizing a multi-stage gate scheduling scheme by a multi-target particle swarm algorithm based on a competition mechanism, wherein the multi-stage gate joint scheduling is realized by adopting the method of the invention, and when the multi-stage gate joint scheduling is performed by adopting the optimization algorithm of the invention, a plurality of gates are comprehensively considered, so that the scheduling constraint condition is not damaged due to unreasonable scheduling of a certain gate;
The implementation method of the step S4 comprises the following steps:
s41: based on the parallelism characteristics of a multi-target particle swarm algorithm and a one-dimensional hydrodynamic model, adopting a micro-service architecture and distributed cluster deployment to perform system integration on a multi-stage gate combined multi-target optimization scheduling coupling model;
s42: optimizing a scheduling simulation time range and a gate opening adjustment time interval according to the coupling model, comprehensively considering calculation time efficiency and calculation resource utilization conditions by combining scheduling simulation time duration limitation, determining the number of open micro-services and starting threads, and realizing quick and efficient calculation of the model; the time for starting the 5-instance deployment is far smaller than that of starting the single-instance deployment in the prior art, the calculation time is shortened from 90 minutes for starting the single-instance deployment in the prior art to 20 minutes for starting the 5-instance deployment, and the calculation efficiency is greatly improved;
s43: after confirming the number of micro-services to be started and threads to be started, the system starts a multi-stage gate joint multi-objective optimization scheduling coupling model to perform automatic calculation, and generates gate optimization scheduling scheme groups of gates at all stages along the water transmission and distribution project within the scheduling simulation time range to assist scheduling decisions;
the implementation method of the step S5 comprises the following steps:
S51: the user selects a scheduling scheme from the generated gate optimization scheduling scheme group to issue, and the gate automatic control system executes a multi-stage gate opening change instruction according to the scheduling scheme;
s52: according to the real-time monitoring value of the gate water level at the lower end of the canal section and the gate opening set value, adopting PI feedback control algorithm technology to determine a feedback opening change value deltae according to a formula (27);
wherein: k (K) p Is the deviation coefficient; k (K) i Is an integral coefficient; n is the number of return monitoring values in the scheduling decision time step, for example, when one gate opening adjustment is performed for 60min and one monitoring value is sent back for 5min for the downstream gate, n=12, di is the pre-gate water level monitoring value of each analog time step; in this embodiment, PI feedback control parameters of each throttle gate are shown in table 6, and the fluctuation of the water depth before the throttle gate a and the variation of the gate opening are shown in fig. 5; as can be seen from table 6 and fig. 5: in the example, the channel water level reaches an initial stable state at 2023, 1 and 4 days, the water regulation strategy is changed at 2023, 1 and 6 days, the water depth fluctuation before the brake is within 0.1m only according to the water level feedback regulation of the throttle brake, and the water level before the brake is restored to the target water level after the opening of the throttle brake is regulated for several times; as can be seen from table 6 and fig. 5: after the PI feedback control is adopted in the embodiment, the water level control and target water level coincidence degree is higher;
TABLE 6 throttle PI feedback control parameter Table
S53: in the next scheduling decision period, a feedback gate opening change value deltae is superimposed on the basis of the original gate opening change instruction and is used as a gate opening execution value, and a gate opening feedback adjustment process is shown in fig. 6 (which is optimal scheduling based on hydrodynamic force and feedback based on hydrodynamic force).
Conclusion: the embodiment adopts the method, and the water level, flow and gate opening evolution process of the multi-stage gate water transmission and distribution project is obtained through a one-dimensional hydrodynamic coupling model of the pressure-non-pressure gate; selecting scheduling stability, accuracy and efficiency as scheduling targets, taking the operation safety of a water delivery building and the water distribution regulation and control capacity of a throttle gate as rigid constraints, taking water intake guarantee requirements of a water diversion port as flexible constraints, constructing a punishment function based on a feasible solution density self-adaptive method, and obtaining a scheduling target function as a scheduling optimal basis; optimizing a multi-stage gate combined multi-target optimized dispatching water distribution process by a multi-target particle swarm algorithm based on a competition mechanism; the micro-service multi-server multithreading parallel computing technology is adopted, so that the optimal dispatching computing efficiency is remarkably improved; the gate scheduling scheme is corrected in real time by adopting the PI feedback algorithm, so that scheduling errors are effectively reduced, a multi-objective optimization and scheduling flow of the multi-stage gate for complex water transmission and distribution engineering simulation, optimization, execution and feedback adjustment is formed, the limitations of high multi-stage gate joint scheduling difficulty, single scheduling objective, poor optimization convergence, poor accuracy and long calculation time consumption are broken through, the simulation accuracy of the water power process of the complex water transmission and distribution engineering is greatly improved, and the overall optimization, efficient calculation and feedback correction of the multi-objective scheduling scheme of the multi-stage gate are realized.
Other non-illustrated parts are known in the art.
Claims (8)
1. A multi-stage gate combined multi-target optimized water distribution scheduling method is characterized in that: the method comprises the following steps:
step S1: according to engineering entity structures, carrying out mathematical model generalization on non-pressure water delivery buildings, inverted siphon pressurized water delivery buildings and water distribution control engineering comprising water diversion gates and check gates along open channels, culverts, aqueducts and tunnels of water delivery engineering, constructing a one-dimensional hydrodynamic coupling model of the pressurized-non-pressurized-gates, and determining the opening hydrodynamic evolution process of water level and flow gates;
step S2: selecting scheduling stability, accuracy and efficiency measurement indexes as scheduling targets, taking water delivery building operation safety measurement indexes and throttle gate water distribution regulation capacity measurement indexes as rigid constraint, taking water intake guarantee requirement indexes along a water diversion port as flexible constraint, and constructing a punishment function according to constraint types by adopting a self-adaptive method based on feasible solution density to obtain a multi-stage gate combined multi-target optimized scheduling target function;
step S3: taking a multi-stage gate opening change process in the water distribution process as a scheduling decision variable, extracting a pre-gate water level, a gate passing flow and a gate opening obtained by hydrodynamic force simulation as scheduling objective function input, optimizing the scheduling decision variable by adopting a multi-objective particle swarm algorithm based on a competition mechanism, optimizing a scheduling scheme set by taking the minimum scheduling objective as an optimization direction, and completing the construction of a multi-stage gate combined multi-objective optimized scheduling coupling model;
Step S4: adopting a micro-service architecture to perform distributed cluster deployment on the coupling model, and determining the number of micro-services and parallel threads which are started by analog calculation according to the total scheduling analog time length and the gate opening adjustment time interval to obtain a gate optimization scheduling scheme set;
step S5: and selecting an optimized scheduling scheme to execute issuing, and adjusting the opening of the gate by adopting a PI feedback algorithm according to the difference value between the real-time monitoring water level before the gate and the target water level of the scheduling scheme at the current moment in the scheduling process.
2. The multi-stage gate combined multi-objective optimized water distribution scheduling method according to claim 1, wherein: the step S1 specifically comprises the following steps:
s11: the method comprises the steps of researching and collecting control engineering drawing data of inverted siphon pressurized water delivery buildings, open channels, culverts, aqueducts, tunnel pressureless water delivery buildings and water diversion gates and throttle gates along water delivery and distribution projects;
s12: the construction of the topological relation of the pressureless water delivery building and the inverted siphon water supply route including open channels, culverts, aqueducts and tunnels and the setting of section generalized parameters are completed, and the hydrodynamic process is numerically simulated by adopting a Santa-Vena equation and a low-pressure pipe flow equation;
s13: completing generalization of water distribution control engineering comprising a water diversion gate and a throttle gate, wherein the water diversion gate generalizes to a side outflow node, and describing the water diversion process by adopting a flow time-dependent process; summarizing the throttle gate into a gate hole, finishing forming size parameter setting, and calculating the hydrodynamic process of the throttle gate by adopting a gate overflow formula based on the relative opening of the gate;
S14: calculating the local head loss at the section change and turning part of the water transmission and distribution engineering:
A. and (3) a non-pressure section: for the pressureless section including open channel, culvert, aqueduct and tunnel, calculating local head loss h according to the following formula j ：
h j ＝ξ*|v 2 2 -v 1 2 |/2g
Wherein: h is a j The partial head loss is the pressureless section; g is gravity acceleration; v 2 、v 1 The flow velocity of the front section and the rear section of the transition section are respectively; xi is a local head loss coefficient and is selected according to the reduction of the section and the expansion condition;
B. the method comprises the following steps: for head loss caused by inverted siphon turning, determining a local head loss coefficient according to the turning angle number;
s15: dividing a water supply project into a plurality of canal sections by taking a throttle valve as a boundary, solving a Saint Vinan equation and a low-pressure pipe flow equation by adopting a four-point difference method for a canal water delivery building, determining the throttle valve flow based on a gate overflow formula by taking the coordination of the throttle valve flow and the water level of an upstream water delivery building as a principle, and further completing the solution of a one-dimensional hydrodynamic coupling model of the non-pressure-throttle valve;
s16: and carrying out calibration verification on parameters of the one-dimensional hydrodynamic coupling model of the non-pressure-control brake based on historical dispatching water transmission and distribution project key section flow, water level and gate opening time-varying process data.
3. The multi-stage gate combined multi-objective optimized water distribution scheduling method according to claim 2, wherein: the step S12 specifically includes:
A. open channel, culvert, aqueduct, tunnel pressureless water delivery building:
in open channels, culverts, aqueducts and non-pressure tunnel water delivery buildings, the hydrodynamic process of the open channels, the culverts, the aqueducts and the non-pressure tunnel water delivery buildings is simulated by adopting the Saint Vinan equation, and the hydrodynamic process is shown in the following formula:
wherein: x is distance; t is time; q is the flow of the flow cross section; f is the area of the flow cross section; u is the flow rate; z is the water level; c is a thank you coefficient; r is wet cycle; q is the flow of the division of the length of the unit channel section, and the division is positive;
B. inverted siphon pressurized water delivery building:
for the inverted siphon, a low-pressure pipe flow equation is adopted to simulate the hydrodynamic process, and the following formula is adopted:
wherein: h is the head of the piezometer tube.
4. A multi-stage gate joint multi-objective optimized water distribution scheduling method according to claim 3, wherein: the step S13 specifically includes:
A. water distribution control engineering of the water diversion gate:
generalizing the shunt gate into a side outflow node, and generalizing the influence of the hydrodynamic process of the shunt quantity on water delivery and distribution engineering by adopting a time-varying process function of the shunt quantity:
Q f ＝Q (t)
wherein: q (Q) f The diversion flow is divided into water diversion gates; t is time; q (Q) (t) A function relation of flow change with time;
B. water distribution control engineering of a throttle valve:
taking the relative opening of the gate as a throttle gate overcurrent calculation basis, adopting the following gate overcurrent formula to simulate water level, flow and calculation under the alternate states of gate hole outflow, weir flow and hole weir, and completing generalized modeling of throttle gate water distribution control engineering:
wherein: m is the free outflow flow coefficient of the brake orifice; b is the width of the gate hole; e is the opening of the gate; h is a u Is the depth of water in front of the gate; sigma is the gate outflow inundation coefficient; epsilon is the coefficient of the flat gate Kong Shousu; m is m c B is the free overflow flow coefficient of the side shrinkage weir s The flow coefficient of free overflow of the width of the weir after side shrinkage is h is the water depth on the weir, sigma s Is a weir flow inundation coefficient; q (Q) p Calculate flow for gate outflow, Q w The flow is calculated for the weir flow and Q is the gate excess flow.
5. The multi-stage gate combined multi-objective optimized water distribution scheduling method according to claim 4, wherein: the step S2 specifically comprises the following steps:
s21: taking scheduling stability, accuracy and high efficiency as scheduling evaluation consideration factors, respectively selecting a square integral weighted value f of a comprehensive deviation error of a target water level before a multi-stage gate 1 Minimum (minimum),Integral weighting value f of absolute value of change of passing gate flow 2 Minimum and gate opening variation absolute value integral weighting value f 3 Minimum is the optimal scheduling target:
A. scheduling accuracy: the square integral weighting value f of the comprehensive deviation error of the target water level before the gate of the multi-stage gate 1 The minimum is used as a scheduling accuracy measurement index:
wherein: f (f) 1 The square integral weight value of the comprehensive deviation error of the target water level before the gate of the multi-stage gate is given in meters; z is Z k,t The simulated water level of the gate k at the scheduling time t is given in meters; z is Z k,target The target water level is scheduled for the operation of the gate k, the unit is meter, and m is the total number of the throttle gates of the water transmission and distribution project; t represents the total scheduling time; Δt represents a scheduling time interval;
B. scheduling stability: integrating the weighted value f by the absolute value of the flow change of the multi-stage gate 2 The minimum is a scheduling stability measurement index:
wherein: f (f) 2 Integrating the weighted value for the absolute value of the flow change of the multistage gate, Q k,t For the flow of the gate k at the time t, Q k,t-Δt The flow of the gate k at the time t-delta t; q (Q) k，T For the flow of the gate k at the moment T, Q k，0 The flow of the gate k at the moment 0; m is the total number of the water transmission and distribution engineering throttle gates;
C. scheduling efficiency: adjusting the integrated deviation f by the opening degree of the multi-stage gate 3 The minimum is a scheduling efficiency measurement index:
wherein: f (f) 3 Integrating the weighted value for the absolute value of the change of the opening of the multi-stage gate; e (E) k,t The opening of the gate k at the time t of the scheduling time; e (E) k,t-Δt The opening of the gate k at the time t-delta t of the scheduling time; e (E) k，T The opening of the gate k at the time of the scheduling time T; e (E) k，0 The opening of the gate k at the scheduling time 0;
s22: in the operation scheduling process of the water delivery engineering, the operation safety of the water delivery engineering and the water distribution regulation and control capability of the throttle valve are used as hard constraints, water taking along a water distribution port is ensured, and as soft constraints, the operation scheduling constraint conditions of the water delivery engineering are obtained;
A. safety of water delivery engineering operation: in the operation scheduling process, the operation safety of inverted siphons, lining soil channels and various water delivery buildings is ensured:
a. inverted siphon operational safety: the inverted siphon satisfies the submerged water depth under all flow conditions, namely:
H s,1 ≥CVh 05
wherein: h s,t Is the submerged water depth of the inverted siphon at the time t; h is the pressure pipeline height; v is the average flow velocity of the section of the pressure pipeline, C is the submerged depth of the inverted siphon;
b. lining soil canal operation safety: the water level descending rate of each lining soil canal is always smaller than the upper limit of the allowable rate, namely:
v i，t ≤v Δhmaax
wherein: v i,t The water level descending rate of the section i of the lining soil canal at the moment t is set; v Δhmax The maximum water level descending speed of the lining soil canal is set;
c. operational safety of water delivery building: for open channels, culverts, aqueducts and tunnels, the water level of each section should not exceed the design water level all the time:
Z i，t ≤Z i,d
Wherein: z is Z i,t The water level of the section i at the time t; z is Z i,d The water level is designed for the section i;
B. water distribution regulation capability of the throttle valve: in the operation scheduling process, the size of the throttle valve passing flow, the variation amplitude and the opening degree adjustment size of the gate are within the water distribution regulation and control capacity range:
a. throttle gate overcurrent capability: the throttle overflow flow should not exceed the design flow:
Q k,t ≤Q d
wherein: q (Q) k,t The flow rate of the overflow at the moment t is controlled by the throttle valve; q (Q) d Designing flow for a throttle valve;
b. throttle valve flow variation amplitude: during flow adjustment, the single flow change should not exceed the upper limit of the change amplitude:
|Q g,t+1 -Q g,t |≤ΔQ gmax
wherein: q (Q) g,t The flow rate of the overflow of the throttle valve k at the moment t is controlled; q (Q) g,t+1 The flow rate of the overflow of the throttle valve k at the time t+1 is controlled; ΔQ gmax An upper limit of the flow variation amplitude of the throttle valve k;
c. throttle opening adjustment capability: the opening adjustment value of the single throttle gate is larger than the dead zone of the throttle gate opening:
|E k，t+1 -E k，t |≥E d
wherein: e (E) k,t Opening the throttle valve k at the time t; e (E) k,t+1 Opening the throttle valve k at the time t+1; e (E) d Dead zone for throttle opening;
C. water intake guarantee requirements of the water diversion port: in the water distribution process, the water level of each canal section is higher than or equal to the water taking lower limit water level, and when useless water needs or a diversion gate overhauls, the water taking level can be lower than the water taking lower limit water level, and the water taking guarantee of the diversion gate is used as soft constraint:
Z i,r ≥Z i,q
Wherein: z is Z i,t The water level of the section i at the time t; z is Z i，q The water intake lower limit water level is the section i;
s23: the construction determines an adaptive penalty function penalty coefficient based on the feasible decryption level, as shown in the following formula:
wherein: c (C) i (ρ) is punishmentPenalty coefficients, which may be further determined based on the soft and hard penalty types, may be categorized as soft penalty coefficients C is (ρ) and hard penalty coefficient C ih (ρ); ρ is the feasible decryption degree of the scheduling scheme, which is equal to the number of the scheduling schemes meeting each constraint condition divided by the total scheduling scheme number in the solving scheme, and the value range is 0-1; alpha i To deterministically penalize function adjustment coefficients according to soft and hard constraint types, the coefficients are usually a positive integer, and the hard constraint penalty coefficients alpha h Greater than the soft constraint penalty coefficient alpha s ；
S24: and determining a scheduling penalty term according to the constraint violation degree of the calculation result of each scheduling time step, wherein the scheduling penalty term is shown in the following formula:
A. inverted siphon submerged water depth hard punishment item
B. Punishment item for water level descending rate of lining soil canal
Wherein: n represents the total number of sections of the lining soil canal of the water distribution project;
C. water level punishment item for water delivery building design
D. Throttle gate overcurrent capability penaltyItems
E. Throttle valve flow variation amplitude term
F. Throttle opening adjustment accuracy penalty term
G. Water level punishment item for water intaking of diversion port
S25: multiplying the punishment terms by the corresponding punishment coefficients to obtain soft and hard constraint punishment functions, adding the soft and hard constraint punishment functions with a scheduling target value, and combining the multi-level gate with a multi-target optimization scheduling target function as shown in the following formula group:
6. the multi-stage gate combined multi-objective optimized water distribution scheduling method according to claim 5, wherein: the step S3 specifically comprises the following steps:
s31: introducing an elite learning mechanism into a multi-target particle swarm optimization algorithm to form a multi-target particle swarm algorithm based on a competition mechanism;
s32: determining the population particle number and iteration times of a multi-target particle swarm algorithm based on a competition mechanism, and initializing the particle position, the particle speed and an external archive;
s33: taking a multi-stage gate opening change process as an optimized scheduling decision variable, extracting a pre-gate water level, a gate passing flow and a gate opening change process which are output by a hydrodynamic model in a simulation result time step as a scheduling evaluation objective function value calculation input, determining the front face numbers of particles pareto in the current population based on non-dominant ranking and crowding distance and ranking by taking a scheduling evaluation objective value minimum as an optimization direction, and constructing an elite particle set;
s34: randomly selecting two scheduling scheme particles from a multi-stage gate scheduling scheme elite particle set, respectively calculating included angles between the two scheduling scheme elite particles and scheduling scheme particles to be updated, determining scheduling scheme winner particles, and updating the scheduling scheme particles to be updated according to the following formula;
V i (t+1)＝r 0 ·V i (t)+r 1 ·(X ω (t)-X i (t))
X i (t+1)＝X i (t)+V i (t+1)
Wherein: x is X i And V i Is the position and velocity vector, X, of the particle to be updated ω Is the location of the contention winner particle, r 0 And r i Is [0,1 ]]Two random numbers in the interior, X i(t+1) And V i(t+1) The position and speed of the updated particles;
s35: disturbance is carried out on the updated particles by adopting a polynomial variation strategy, and the particle swarm further explores an optimal area to complete one updating iteration;
s36: repeating the above operation until the iteration times meet the set iteration times, and obtaining a final multi-stage gate scheduling scheme set as a final scheduling scheme solution set.
7. The multi-stage gate combined multi-objective optimized water distribution scheduling method according to claim 6, wherein: the step S4 specifically comprises the following steps:
s41: based on the parallelism characteristics of a multi-target particle swarm algorithm and a one-dimensional hydrodynamic model, adopting a micro-service architecture and distributed cluster deployment to perform system integration on a multi-stage gate combined multi-target optimization scheduling coupling model;
s42: optimizing a scheduling simulation time range and a gate opening adjustment time interval according to a coupling model, combining scheduling simulation time duration limitation, comprehensively considering calculation time efficiency and calculation resource utilization conditions, and determining the number of micro-services to be started and enabling threads;
S43: after the number of micro-services is determined and threads are started, the system starts a multi-stage gate and multi-target optimization scheduling coupling model to automatically calculate, and gate optimization scheduling scheme groups in the scheduling simulation time range of gates at all stages along the water transmission and distribution project are generated to assist scheduling decisions.
8. The multi-stage gate joint multi-objective optimized water distribution scheduling method according to claim 7, wherein: the step S5 specifically comprises the following steps:
s51: the user selects a scheduling scheme from the generated gate optimization scheduling scheme group to issue, and the gate automatic control system executes a multi-stage gate opening change instruction according to the scheduling scheme;
s52: according to the real-time monitoring value of the gate water level at the lower end of the canal section and the gate opening set value, adopting PI feedback control algorithm technology to determine a feedback opening change value deltae according to the following formula;
wherein: k (K) p Is the deviation coefficient; k (K) i Is an integral coefficient; n is the number of returned monitoring values in the scheduling decision time step; di is a pre-gate water level monitor value for each analog time step;
s53: and in the next scheduling decision period, superposing a feedback gate opening change value delta e on the basis of the original gate opening change instruction to serve as a gate opening execution value.
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