CN116011656A - Pump gate group flood control scheduling method and system based on model predictive control - Google Patents
Pump gate group flood control scheduling method and system based on model predictive control Download PDFInfo
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Abstract
The invention discloses a pump gate group flood control scheduling method and system based on model predictive control, relates to the cross field of urban water system pump gate flood control station group scheduling technology and computing technology, and aims at solving the problem that the existing pump gate group scheduling method is not uniform in flood control scheduling standard, and provides a pump gate group real-time scheduling strategy of pump gate group system scheduling capability, cost expense and equipment service life.
Description
Technical Field
The invention relates to the crossing field of urban water system pump gate flood drainage station scheduling technology and computing technology, in particular to a pump gate flood drainage station group flood control scheduling strategy comprehensively considering the flood drainage capacity, cost and equipment operation and maintenance cost of a pump gate station system.
Background
Urban flood control and drainage scheduling is an important work of urban flood control emergency scheduling. However, there are three problems with current pump, gate station scheduling: (1) Scheduling is carried out based on a set rule, uncertainty of rainfall is difficult to deal with, and flexibility of real-time scheduling is lacked; (2) In the research of the optimized operation of the pump brake group, only the drainage waterlogging prevention effect is usually emphasized, and indexes related to the economical efficiency and the safety of project operation, such as unit maintenance cost, water pump start-up time and frequency, and the like, are ignored. (3) Flood control dispatching is formulated by dispatching personnel according to experience, randomness is high, a real-time dispatching calculation process does not have unified standard, urban inland river water level is difficult to control accurately, a specific dispatching target cannot be met, risk control capability is weak, and meanwhile real-time short-term dispatching, stable running of equipment, economic benefit and the like are difficult to produce instant effects.
Compared with the drainage system of a common river channel, the urban drainage system is more complex, and the concept of urban construction in China is added. The emphasis degree of the urban drainage pump gate station optimizing operation and investment management in China is lower. Urban water logging disasters caused by urban storms are also common.
Therefore, how to unify flood control scheduling standards based on the existing pump gate group scheduling method is a problem that needs to be solved by those skilled in the art.
Disclosure of Invention
In view of the above, the invention provides a pump gate group flood control scheduling method based on model predictive control, and in order to achieve the above purpose, the invention adopts the following technical scheme:
step one: constructing a pump gate group optimization scheduling model according to the drainage capacity, the application cost and the later maintenance operation of the pump gate drainage station, wherein the pump gate group optimization scheduling model consists of a scheduling target and constraint conditions;
step two: solving a dispatching optimization problem, and solving by using an artificial swarm algorithm imitating bee swarm acquisition bee sources to obtain a combined scheme of flood design and pump station opening and closing and gate opening;
step three: setting a pump station and gate allocation mode, performing intelligent optimization allocation by using an artificial bee colony algorithm, optimizing a pump gate group scheduling model established in the first step and the second step, obtaining a result in a scheduling period in a constructed SWMM-based urban water system river channel hydraulics model, and finally obtaining a flood control scheduling model by configuring the opening of a city pump gate drainage station gate;
step four: the pump gate drainage station real-time scheduling strategy based on the artificial bee colony algorithm is used for solving the established flood control scheduling model, and a raindrop real-time scheduling strategy scheme can be obtained.
Optionally, the pump brake group optimization scheduling model includes the following scheduling objectives:
(1-1) target water level optimization:
wherein: ΔL 1 An indicator of target water level optimization is shown,
for the set period tOptimal water level target, L t For a period T water level, T is the scheduling period, T is the total scheduling period, N is the set nth stage pump gate drainage station, and N is the total number of the total pump gate drainage stations;
(1-2) minimum pumping costs during operation:
wherein: f is pumping and discharging cost in a dispatching period, k is an electric charge unit/kW.h, ρ is the density of water, g is the local gravity acceleration, and 9.8m/s is taken 2 Q is the pump flow passing in the period t of the pump station, H n,t The pump station is the lift in the period T, and DeltaT is the time length of the unit time period;
(1-3) the pump station is opened and closed the minimum times:
wherein: c is the sum of the times of the start and stop of the pump station unit in the dispatching period; l (i, j) is the number of pump station units in the j period and the last period of the ith pump station;
(1-4) the end of the schedule period has the lowest water level peak:
minΔL 2 ＝min{maxZ n,t }；
wherein: ΔL 2 Z is an index for optimizing the last water level peak value of a dispatching cycle pump station n,t The last water level of the nth pump station at the moment t; t is the total scheduled period.
Optionally, the pump brake group optimization scheduling model includes the following constraints:
(2-1) Water level constraint:
the constraint condition is a water level constraint condition of a target pump gate drainage station, and the water level is lower than
For dead water, the target pump gate drains the flood station to prevent flood and limit the water level, +.>
As a highest water level control assessment target;
(2-2) flow restriction:
wherein:
the upper limit and the lower limit of the pump-passing flow of the upstream pump station and the pump-passing flow of the downstream pump station of the nth pump station are respectively restrained;
(2-3) pump head constraint:
wherein:
representing upper and lower limit constraints of the pump station lift of the upstream pump station and the pump station lift of the downstream pump station of the nth pump station;
(2-4) gate opening constraint:
K i,min <K i <K i,max ；
wherein: k (K) i,min The minimum value of the gate opening; k (K) i,max Is the maximum value of the gate opening;
(2-5) non-negative conditional constraints:
all of the variables described above are non-negative variables.
Optionally, the artificial bee colony algorithm in the second step includes the following steps:
and 7, judging whether the iteration times t reach the maximum iteration times maxcycles, if so, stopping the algorithm, outputting an optimal solution, and if not, adding 1 to the iteration times and returning to the step 3 for continuous loop execution.
Optionally, the method for obtaining the flood control scheduling model in the third step includes the following steps:
and 5, judging whether the time T is smaller than the total time period T, if so, updating the initial value again, returning to the step 3, continuing to circularly execute until the optimization of the whole time axis is completed, and if not, outputting the flood control scheduling model.
In another aspect, a pump gate group flood control scheduling system based on model predictive control is provided, including the following modules:
the scheduling strategy generation module is used for constructing a pump gate group optimization scheduling model according to the drainage capacity, the application cost and the later maintenance operation of the pump gate drainage station;
the dispatching optimization problem solving module is used for solving by using an artificial bee colony algorithm imitating a bee colony to collect bee sources to obtain a combined scheme of flood design and pump station opening and closing and gate opening;
the intelligent optimization distribution module is used for carrying out intelligent optimization distribution by using an artificial bee colony algorithm, the established pump gate group optimization scheduling model is used for obtaining the result in the scheduling period in the constructed urban water system river hydraulic model based on SWMM, and finally, the flood control scheduling model is obtained by configuring the opening of the urban pump gate drainage station gate;
and the real-time scheduling strategy scheme generation module is used for solving the established flood control scheduling model by using an artificial bee colony algorithm, so that a raindown real-time scheduling strategy scheme can be obtained.
Optionally, the scheduling strategy generating module further comprises a scheduling target generating module, which is used for generating a scheduling target with optimized target water level, minimum pumping cost during operation, minimum pump station opening and closing times and minimum water level peak value at the end of the scheduling period.
Optionally, the scheduling policy generating module further includes a constraint condition generating module, configured to generate a water level constraint, a flow constraint, a pump lift constraint, a gate opening constraint, and a non-negative condition constraint.
Optionally, the scheduling optimization problem solving module further comprises a manual bee colony algorithm module, which is used for solving an optimal solution of the pump brake colony joint scheduling optimization problem.
Optionally, the intelligent optimization distribution module further comprises a flood control scheduling model acquisition module, which is used for acquiring a flood control scheduling model.
Compared with the prior art, the pump gate group flood control scheduling method and system based on model predictive control are beneficial to maintaining stable water level in a scheduling period and reducing operation cost in the scheduling operation period by selecting optimization targets and constraint conditions according to different scheduling requirements; in addition, the invention optimizes the starting and closing of the pump station as an important dispatching target, reduces the maintenance times and prolongs the service life of the equipment.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of an artificial bee colony algorithm;
FIG. 2 is a flow chart of a pump gate group real-time scheduling strategy;
FIG. 3 is a plot of a tidal level process;
FIG. 4 is a dispatch diagram of an upstream primary pump gate station for 100 years;
FIG. 5 is a 100 year first-encounter downstream secondary pump gate station schedule.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention discloses a pump gate group flood control scheduling method based on model predictive control, which comprises the following steps:
step one: constructing a pump gate group optimization scheduling model according to the drainage capacity, the application cost and the later maintenance operation of the pump gate drainage station, wherein the pump gate group optimization scheduling model consists of a scheduling target and constraint conditions;
step two: solving a dispatching optimization problem, and solving by using an artificial swarm algorithm imitating bee swarm acquisition bee sources to obtain a combined scheme of flood design and pump station opening and closing and gate opening;
step three: setting a pump station and gate allocation mode, performing intelligent optimization allocation by using an artificial bee colony algorithm, introducing the results in the scheduling period into an urban water system river channel hydraulics model constructed based on SWMM, and finally obtaining a flood control scheduling model by configuring the opening of a city pump gate drainage station gate;
step four: the pump gate group real-time scheduling strategy based on the artificial bee colony algorithm is used for solving the established flood control scheduling model, and a raindown real-time scheduling strategy scheme can be obtained.
In one embodiment, the pump gate optimization scheduling model includes the following scheduling objectives:
(1-1) target water level optimization:
wherein: ΔL 1 An indicator of target water level optimization is shown,
for the set period t of optimal water level target, L t For a period T water level, T is the scheduling period, T is the total scheduling period, N is the set nth stage pump gate drainage station, and N is the total number of the total pump gate drainage stations;
(1-2) minimum pumping costs during operation:
wherein: f is pumping and discharging cost in a dispatching period, k is an electric charge unit/kW.h, ρ is the density of water, g is the local gravity acceleration, and 9.8m/s is taken 2 Q is the pump flow passing in the period t of the pump station, H n,t The pump station is the lift in the period T, and DeltaT is the time length of the unit time period;
(1-3) the pump station is opened and closed the minimum times:
wherein: c is the sum of the times of the start and stop of the pump station unit in the dispatching period; l (i, j) is the number of pump station units in the j period and the last period of the ith pump station;
(1-4) the end of the schedule period has the lowest water level peak:
minΔL 2 ＝min{maxZ n,t }；
wherein: ΔL 2 Z is an index for optimizing the last water level peak value of a dispatching cycle pump station n,t The last water level of the nth pump station at the moment t; t is the total scheduled period.
In one embodiment, the pump gate group optimization scheduling model includes the following constraints:
(2-1) Water level constraint:
the constraint condition is a water level constraint condition of a target pump gate drainage station, and the water level is lower than
For dead water, the target pump gate drains the flood station to prevent flood and limit the water level, +.>
As a highest water level control assessment target;
(2-2) flow restriction:
wherein:
respectively the nth pumping stationUpstream pump station pump-through flow and downstream pump station pump-through flow upper and lower limit constraint;
(2-3) pump head constraint:
wherein:
representing upper and lower limit constraints of the pump station lift of the upstream pump station and the pump station lift of the downstream pump station of the nth pump station;
(2-4) gate opening constraint:
K i,min <K i <K i,max ；
wherein: k (K) i,min The minimum value of the gate opening; k (K) i,max Is the maximum value of the gate opening;
(2-5) non-negative conditional constraints:
all of the variables described above are non-negative variables.
In one embodiment, as shown in fig. 1, the artificial bee colony algorithm in the second step includes the following steps:
and 7, judging whether the iteration times t reach the maximum iteration times maxcycles, if so, stopping the algorithm, outputting an optimal solution, and if not, adding 1 to the iteration times and returning to the step 3 for continuous loop execution.
In a specific embodiment, the method for acquiring the flood control scheduling model in the third step comprises the following steps:
and 5, judging whether the time T is smaller than the total time period T, if so, updating the initial value again, returning to the step 3, continuing to circularly execute until the optimization of the whole time axis is completed, and if not, outputting the flood control scheduling model.
In one embodiment, taking a pump gate group as an example, a scheduling plan is compiled, a flow chart of a real-time scheduling strategy is shown in figure 2,
3000kW of upstream pump gate station assembly machine, the net lift range of the pump station is 0.0-7.0 m, and the design flow of the pump station is 25m 3 And/s. Pumping out the lake water to a downstream river channel through a pump station before flood comes, emptying the reservoir capacity, and starting the pump station to drain after flood comes, so that the lake water level is ensured not to exceed the signal limit water level.
The tail end of the downstream pump gate station is affected by the local tide level, when the outside river tide level is higher than the water level of the inland river, the gate is closed, the drainage station is opened, and if the outside river tide level is lower than the water level of the inland river, the drainage station is closed, and water is drained through the gate. The working flow of the waterlogging draining station is 120m 3 The number of the water pumps is 3, and the average design flow of the water pumps is 40m respectively 3 /s。
Building a river channel hydrodynamic model based on SWMM to simulate a flood evolution process, inputting the warehouse-in flow into a pump gate group combined flood control optimizing and scheduling model, setting an initial water level of a pump gate station, and solving the pump gate group combined flood control optimizing and scheduling model in a control time domain to obtain an initial warehouse flow optimal control sequence in the control time domain; but in the current t period only the instructions of the first control sequence are executed. And simultaneously obtaining the final water level at the time t, namely the initial water level at the time t+ 1, and enabling t=t+1 to update the initial value again. And repeating the previous process until the optimization of the whole time axis is completed.
And (3) rainfall setting: the typical design rainfall for 100 years in the above region was generated using the chicago rain model according to the stormwater intensity formula, wherein the total amount of rainfall was 251.18mm and the average rain intensity i= 0.1744mm/min.
Setting the tide level: because the pump gate water drainage station, the gate and pump opening and closing are affected by downstream tide level during actual operation, the tide level data selected in the process are monitored by the regional tide level monitoring station, and the tide level process graph is shown in figure 3.
Initial water level setting: assume herein that the primary pump gate station initial water level is 4.15m and the secondary pump gate initial water level is 4.02m.
The optimal scheduling is a pump station group flood control scheduling method based on model predictive control.
The regular scheduling is that when the upstream primary pump gate station in the area arrives at the river water level in the flood season, a pump station is started more than 5 meters, and the secondary pump gate station is maintained at about 4.2 meters.
The dispatch diagrams of the primary and secondary pump gate stations under the flood designed for 100 years are shown in fig. 4 and 5. Under the design flood of 100 years, the on-off statistics of one-stage pump stations of the gate group combined flood control scheduling based on model predictive control are shown in tables 1 and 2. The opening degree and the lower discharge amount of the two-stage pump gate station gate based on the pump gate group combined flood control optimization scheduling of model predictive control under the design flood in 100 years are shown in table 3.
Table 1 model-based predictive control of pump-gate group combined flood control optimization scheduling startup statistics at each moment
Table 2 pump gate group rule schedule startup statistics at various moments
Table 3100 model predictive control-based pump-gate group combined flood control optimization scheduling downstream gate opening and downstream discharge under flood design water
As can be seen from the graph, the peak flow of the primary pump gate station at the upstream of the flood water is 147.33m in the design of 100 years 3 Peak flow of/s downstream secondary pump gate station is 962.08m 3 Each of the two units arrives at the 16 th time period, and the downstream tide is also started. As can be taken from fig. 4, in the 1 st to 3 rd time periods, the water pump is not required to be turned on temporarily because the water is less. The water level rises to 4.39m. Over time, rolling optimization proceeds, upstream primary pumpBefore flood peak of the gate station comes, the upstream first-stage pump gate station starts a water pump unit, pre-discharge and pre-discharge are carried out in advance, the maximum storage capacity is set for the flood peak, and the water level of the upstream first-stage pump gate station is reduced to 0m. And (5) starting a pump station unit in the future from a flood peak, and finally controlling the highest water level in the dispatching period of the upstream primary pump gate station to be 6.15m. In contrast, in the flood of the design of 100 years, the upstream first-stage pump gate station is scheduled to be 7.11m in the scheduling period. The pump gate group flood control optimization scheduling based on model predictive control is 13.5% lower than the highest water level of the first-stage pump gate station at the upstream of the regular scheduling.
Before flood peak comes, the downstream secondary pump gate station keeps the water level at 4.1m or above by adjusting the opening of the gate, and the gate of the flood drainage station and the pump station unit can only adopt one of the gate and the pump station unit to control the water level of the downstream secondary pump gate station below the warning water level. As can be seen from fig. 5, when the downstream secondary pump gate station flood peak is reached, the flood peak just encounters the rise of the tide level, and only the pump station set can be used for flood discharge. The highest water level in the dispatching cycle of the downstream secondary pump gate station is 5.621m, and the peak water level of the downstream secondary pump gate station is 5.732m when the dispatching is carried out according to a rule. The pump gate group combined flood control optimization scheduling based on model predictive control is reduced by 1.93% compared with the highest water level of a downstream secondary pump gate station of regular scheduling.
By observing fig. 5, it can be clearly found that the fluctuation of the last water level of the downstream secondary pump gate station adopting regular scheduling is larger, and the last water level of the optical downstream secondary pump gate station adopting model predictive control Jin Anhe pump gate group combined flood control optimization scheduling is smoother.
And the pump gate group combined flood control optimization scheduling scheme pump station based on model predictive control is started and stopped 43 times and the regular scheduling scheme pump station is started and stopped 46 times through statistics in tables 1 and 2, and the pump gate group combined flood control optimization scheduling scheme pump station based on model predictive control is started and stopped 7% lower than the regular scheduling scheme pump station. The total cost of pumping by the pump gate group combined flood control optimization scheduling based on model predictive control is 2.36 ten thousand yuan, the total cost of pumping by the regular scheduling is 2.48 ten thousand yuan, and the total cost of pumping by the pump gate group combined flood control optimization scheduling based on model predictive control is reduced by 4.8 percent compared with the total cost of pumping by the regular scheduling.
And the table 3 is the gate opening and the lower discharge amount of the downstream secondary pump gate station of the pump gate group combined flood control optimization scheduling based on model prediction control under the design flood of 100 years, wherein the gate opening is the gate opening of the downstream secondary pump gate station gate of the pump gate group combined flood control optimization scheduling optimization based on model prediction control according to Jin Anhe, the gate opening of the downstream secondary pump gate station of the SWMM model river channel hydrodynamic model is repeatedly debugged, and when the debugged lower discharge amounts are consistent, the gate opening at the moment is used as the gate opening of the downstream secondary pump gate station gate of the pump gate group combined flood control optimization scheduling scheme based on model prediction control.
The result shows that the real-time scheduling process of a certain power station has obvious optimization effect, the water level process is more stable, the operation cost is obviously reduced, and most importantly, scientific guidance is provided for the real-time scheduling process of a certain power station.
In one embodiment, a pump gate group flood control scheduling system based on model predictive control is disclosed, comprising the following modules:
the scheduling strategy generation module is used for constructing a pump gate group optimization scheduling model according to the drainage capacity, the application cost and the later maintenance operation of the pump gate drainage station;
the dispatching optimization problem solving module is used for solving by using an artificial bee colony algorithm imitating a bee colony to collect bee sources to obtain a combined scheme of flood design and pump station opening and closing and gate opening;
the intelligent optimization distribution module is used for carrying out intelligent optimization distribution by using an artificial bee colony algorithm, the established pump gate group optimization scheduling model is used for obtaining the result in the scheduling period in the constructed urban water system river hydraulic model based on SWMM, and finally, the flood control scheduling model is obtained by configuring the opening of the urban pump gate drainage station gate;
and the real-time scheduling strategy scheme generation module is used for solving the established flood control scheduling model by using an artificial bee colony algorithm, so that a raindown real-time scheduling strategy scheme can be obtained.
Further, the scheduling strategy generating module further comprises a scheduling target generating module for generating a scheduling target with optimized target water level, minimum pumping cost in the operation period, minimum pump station opening and closing times and minimum water level peak value at the end of the scheduling period.
Further, the scheduling strategy generation module further comprises a constraint condition generation module for generating water level constraint, flow constraint, water pump lift constraint, gate opening constraint and non-negative condition constraint.
Further, the dispatching optimization problem solving module further comprises a manual bee colony algorithm module for solving an optimal solution of the pump brake colony joint dispatching optimization problem.
Furthermore, the intelligent optimization distribution module further comprises a flood control scheduling model acquisition module for acquiring a flood control scheduling model.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A pump gate group flood control scheduling method based on model predictive control is characterized by comprising the following steps:
step one: constructing a pump gate group optimization scheduling model according to the drainage capacity, the application cost and the later maintenance operation of the pump gate drainage station, wherein the pump gate group optimization scheduling model consists of a scheduling target and constraint conditions;
step two: solving a dispatching optimization problem, and solving by using an artificial bee colony algorithm imitating a bee colony to collect bee sources to obtain a combined scheme for designing opening and closing of a pump station under flood and opening of a gate;
step three: setting a pump station and gate allocation mode, performing intelligent optimization allocation by using an artificial bee colony algorithm, introducing the results in the scheduling period into an urban water system river channel hydraulics model constructed based on SWMM, and finally obtaining a flood control scheduling model by configuring the opening of a city pump gate drainage station gate;
step four: the pump gate drainage station real-time scheduling strategy based on the artificial bee colony algorithm is used for solving the established flood control scheduling model, and a raindrop real-time scheduling strategy scheme can be obtained.
2. The pump brake group flood control scheduling method based on model predictive control according to claim 1, wherein the pump brake group optimal scheduling model comprises the following scheduling targets:
(1-1) target water level optimization:
wherein: ΔL 1 An indicator of target water level optimization is shown,
for the set period t of optimal water level target, L t For a period T water level, T is the scheduling period, T is the total scheduling period, N is the set nth stage pump gate drainage station, and N is the total number of the total pump gate drainage stations;
(1-2) minimum pumping costs during operation:
wherein: f is pumping and discharging cost in a dispatching period, k is an electric charge unit/kW.h, ρ is the density of water, g is the local gravity acceleration,taking 9.8m/s 2 Q is the pump flow passing in the period t of the pump station, H n,t The pump station is the lift in the period T, and DeltaT is the time length of the unit time period;
(1-3) the pump station is opened and closed the minimum times:
wherein: c is the sum of the times of the start and stop of the pump station unit in the dispatching period; l (i, j) is the number of pump station units in the j period and the last period of the ith pump station;
(1-4) the end of the schedule period has the lowest water level peak:
minΔL 2 ＝min{maxZ n,t }；
wherein: ΔL 2 Z is an index for optimizing the last water level peak value of a dispatching cycle pump station n,t The last water level of the nth pump station at the moment t; t is the total scheduled period.
3. The pump brake group flood control scheduling method based on model predictive control according to claim 1, wherein the pump brake group optimal scheduling model comprises the following constraint conditions:
(2-1) Water level constraint:
the constraint condition is a water level constraint condition of a target pump gate drainage station, and the water level is lower than
For dead water, the target pump gate drains the flood station to prevent flood and limit the water level, +.>
As a highest water level control assessment target;
(2-2) flow restriction:
wherein:
the upper limit and the lower limit of the pump-passing flow of the upstream pump station and the pump-passing flow of the downstream pump station of the nth pump station are respectively restrained;
(2-3) pump head constraint:
wherein:
representing upper and lower limit constraints of the pump station lift of the upstream pump station and the pump station lift of the downstream pump station of the nth pump station;
(2-4) gate opening constraint:
K i,min <K i <K i,max ；
wherein: k (K) i,min The minimum value of the gate opening; k (K) i,max Is the maximum value of the gate opening;
(2-5) non-negative conditional constraints:
all of the variables described above are non-negative variables.
4. The method for pump gate group flood control scheduling based on model predictive control according to claim 1, wherein the artificial bee colony algorithm in the second step comprises the following steps:
step 1, initializing parameters of a manual bee colony algorithm, wherein the parameters comprise maximum iteration times maxcycles, a threshold limit, new honey sources Vi, honey sources Xi, probability Pi of each honey source selection, and parameters SN and t=1;
step 2, generating an initial population X;
step 3, hiring peak i to search honey sources to generate new honey sources Vi;
step 4, judging whether fit (Vi) is larger than fit (Xi), if yes, replacing Xi with Vi, and if not, reserving honey source Xi;
step 5, judging whether the honey source i is smaller than half of the SN, if so, calculating the probability Pi of each honey source selection, observing bees to perform greedy selection according to the probability, updating the population, and recording the optimal solution; if not, the hiring peak number is increased by 1 and returns to the step 3 to continue the loop execution;
step 6, judging whether the honey source reaches a threshold limit, if so, executing step 7, and if not, generating new bees and executing step 7;
and 7, judging whether the iteration times t reach the maximum iteration times maxcycles, if so, stopping the algorithm, outputting an optimal solution, and if not, adding 1 to the iteration times and returning to the step 3 for continuous loop execution.
5. The method for pump gate group flood control scheduling based on model predictive control according to claim 1, wherein the method for obtaining the flood control scheduling model in the third step comprises the following steps:
step 1, constructing a river channel hydraulics model based on SWMM, and providing a design flood forecast;
step 2, constructing a water balance-based prediction model according to the state variables and the control variables;
step 3, initializing an incoming water forecast result, an initial water level before a pump gate station, setting a total time period as T, and setting the T as a time T, and further setting a parameter k;
step 4, performing primary optimization based on the pump brake group optimization scheduling model, solving the pump brake group optimization scheduling model to obtain k control variable sequences, wherein only the control variable of the first sequence is adopted, and t=t+1;
and 5, judging whether the time T is smaller than the total time period T, if so, updating the initial value again, returning to the step 3, continuing to circularly execute until the optimization of the whole time axis is completed, and if not, outputting the flood control scheduling model.
6. A pump gate group flood control scheduling system based on model predictive control is characterized by comprising
The scheduling strategy generation module is used for constructing a pump gate group optimization scheduling model according to the drainage capacity, the application cost and the later maintenance operation of the pump gate drainage station;
the scheduling optimization problem solving module is used for solving by using an artificial bee colony algorithm imitating a bee colony to collect bee sources to obtain a combined scheme for designing the opening and closing of a pump station and the opening of a gate under flood water;
the intelligent optimization distribution module is used for carrying out intelligent optimization distribution by using an artificial bee colony algorithm, the established pump gate group optimization scheduling model is brought into an urban water system river hydraulic model constructed based on SWMM in a scheduling period, and finally, a flood control scheduling model is obtained by configuring the opening of a city pump gate drainage station gate;
and the real-time scheduling strategy scheme generation module is used for solving the established flood control scheduling model by using an artificial bee colony algorithm, so that a raindown real-time scheduling strategy scheme can be obtained.
7. The pump brake crowd flood control scheduling system based on model predictive control of claim 6, wherein the scheduling strategy generation module further comprises a scheduling target generation module for generating a scheduling target with optimized target water level, minimum pumping cost during operation, minimum pump station opening and closing times and minimum peak water level at the end of a scheduling period.
8. The pump-gate crowd flood control scheduling system based on model predictive control of claim 6, wherein the scheduling policy generation module further comprises a constraint condition generation module for generating a water level constraint, a flow constraint, a pump lift constraint, a gate opening constraint, and a non-negative condition constraint.
9. The pump brake swarm flood control scheduling system based on model predictive control of claim 6, wherein the scheduling optimization problem solving module further comprises an artificial bee colony algorithm module for solving an optimal solution of the pump brake swarm joint scheduling optimization problem.
10. The pump brake crowd flood control scheduling system based on model predictive control of claim 6, wherein the intelligent optimal allocation module further comprises a flood control scheduling model acquisition module for acquiring a flood control scheduling model.
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| CN116700039A (en) * | 2023-08-04 | 2023-09-05 | 山东科能电气设备有限公司 | Control method, system, equipment and medium for urban emergency drainage system |
| CN116719252A (en) * | 2023-08-08 | 2023-09-08 | 广东省广业装备科学技术研究院有限公司 | Control method of integrated pump brake equipment |
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| CN116777135B (en) * | 2023-05-12 | 2023-11-14 | 大连海事大学 | Reservoir-impounded flood area refined flood control combined dispatching method based on efficient optimization algorithm |
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