CN115185243A - Dynamic optimization scheduling modeling method for plateau mountain long-distance step pump station conveying system - Google Patents
Dynamic optimization scheduling modeling method for plateau mountain long-distance step pump station conveying system Download PDFInfo
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Abstract
The invention discloses a dynamic optimization scheduling modeling method for a long-distance stepped pump station conveying system in a plateau mountain land, which mainly comprises the following steps: s1: constructing a state matrix of the operation of the step pump station; s2: building a pump station dynamic optimization scheduling model; s3: solving based on a self-adaptive dynamic programming method; a basic data model more suitable for actual conditions is obtained by constructing a state matrix of cascade pump station scheduling, a self-adaptive dynamic programming method is introduced to realize model solution, parameters such as the number of starting stations, the overflow, the frequency conversion coefficient, the lift, the pressure and the rotating speed are reasonably distributed, an optimized operation scheme with the least power consumption is obtained, and the high-pressure diaphragm pump is ensured to be operated in an optimal working condition area all the time.
Description
Technical Field
The invention belongs to the technical field of pump station dispatching modeling, and particularly relates to a dynamic optimization dispatching modeling method for a plateau mountain long-distance step pump station conveying system.
Background
The occupation of per capita water resources in China is very small, the space-time distribution is also seriously unbalanced, the water shortage seriously restricts the healthy development of industrial and agricultural in China, in order to adapt to the rapid development of economy and the continuous progress of science and technology in China, the investment in the aspects of the construction and operation management of long-distance cascade pump station engineering in China is more and more, the pump stations play an important role in the construction of national economy and various aspects of people's life in China, and the water supply scheme with high safety, small investment and low energy consumption can bring huge economic benefits to China by carrying out the optimization design on the cascade pump stations;
the optimization scheduling of the pump station not only needs to establish different optimization models according to different tasks, but also needs to select a proper solving method according to target problems and requirements so as to shorten the optimization time. If a proper optimal scheduling method is adopted, the method is an important way for realizing cost reduction and benefit improvement.
Disclosure of Invention
In order to solve the technical problems, the invention provides a dynamic optimization scheduling modeling method for a plateau mountain long-distance step pump station conveying system, which is characterized in that a basic data model more suitable for actual conditions is obtained by constructing a state matrix for step pump station scheduling, a self-adaptive dynamic programming method is introduced to realize model solution, parameters such as the number of starting-up units, the over-flow, the frequency conversion coefficient, the lift, the pressure and the rotating speed are reasonably distributed, an optimized operation scheme with the least power consumption is obtained, and the high-pressure diaphragm pump is ensured to be operated in an optimal working condition area all the time.
In order to achieve the technical purpose, the invention is realized by the following technical scheme: a dynamic optimization scheduling modeling method for a plateau mountain long-distance stepped pump station conveying system comprises the following steps:
s1: constructing a running state matrix of the cascade pump station;
s2: building a pump station dynamic optimization scheduling model, splitting cascade pump station optimization scheduling by adopting a decomposition coordination method, and decomposing into 3 subsystems according to 2 decision variables of each stage of pump station distribution flow and each pump distribution flow respectively; the method mainly comprises a power consumption optimization model under the condition that the single pump corresponds to the distributed flow, a power consumption optimization model under the condition that the single-stage pump station corresponds to the distributed flow and a multi-objective optimization scheduling model of the cascade pump station;
s3: solving based on a self-adaptive dynamic programming method;
introducing a self-adaptive dynamic programming method into the multi-objective optimization scheduling model solution of the cascade pump station, taking the real-time capacity of a pipe network at each moment as a system state quantity, taking the parameter adjustment variable quantity of a pipe network pump set at each moment as a system control quantity, taking the running cost and the starting times of the unit in a scheduling period as a dual-constraint utility function, establishing a Bellman differential equation according to a Bellman optimality principle, and then calculating the optimal control quantity according to iterative self-adaptive dynamic programming derivation, quantitatively analyzing the convergence speed while qualitatively analyzing the convergence, and realizing the rapid solution of the optimal problem;
preferably, the specific method for constructing the running state matrix of the cascade pump station comprises the following steps: constructing a pump station optimal scheduling objective function and solving the pump station optimal scheduling objective function based on a state matrix of the cascade pump station in operation, and further analyzing the operation mechanism of the cascade pump station to obtain the optimal output of a pump set;
the cascade pumping station operating state can be represented as a state matrix of N × N (N = 5):
wherein 0 represents a clear water state and 1 represents a slurry state;
preferably, the single pump corresponds to the power consumption optimization model under the flow distribution, the high-pressure diaphragm pump system is used for solving the energy consumption models of all the pump sets, and the objective function is as follows:
the constraint conditions are as follows:
q min ＜q k ＜q max
H f (Q)≤H(q k )≤1.2H f (Q)
H(q 1 )＝H(q 2 )＝L＝H(q n )
X k =0 or 1,k =1,2 …, n
In the formula, N k Energy consumption of a single pump is shown in Kw; q. q of i Is the overflow of a single pump, unit m 3 /s；q min Is the minimum overflowed of a single pump in m 3 /s；q max Is the maximum overflow of a single pump in m 3 S; n is the number of all the pumps in the pump station; h (q) i ) Is the single pump head, unit m; h f (Q) is the pump station overflow Q a The corresponding required lift is m;
preferably, the single-stage pump station corresponds to an electricity consumption optimization model under the flow distribution, the total energy consumption of the single-stage pump station is the sum of the energy consumption of each subsystem of the high-pressure diaphragm pump, and the objective function is as follows:
the constraint conditions are as follows:
in the formula, DN is the optimal total energy consumption of the pump station in the level and is in a unit Kw; f 1 The unit is the power consumption of a single pump system, namely Kw; q j Flow rate of single pump system, unit m 3 S; q is the total over-flow of the pump station; unit m 3 /s；
Preferably, the multi-objective optimization scheduling model construction method for the cascade pump station comprises the following steps:
s2.1: on the basis of optimizing the energy consumption of a single-stage pump station, the lowest energy consumption condition of the total system is researched by distributing the flow of each stage of pump station;
the objective function is:
the constraint conditions are as follows:
H ti min ≤Z i+1 -Z i ≤H ti max
0≤Q≤Q max
in the formula, Z i 、Z i+1 The quantity of slurry at the inlet and the outlet of the pump station is the level, TN is the optimal total energy consumption of the cascade pump station, and unit Kw is the optimal total energy consumption of the cascade pump station; q max The unit is the maximum over-flow of the unit 3 /s；H ti min 、H ti max The maximum ore pulp liquid level difference and the minimum ore pulp liquid level difference of the pump station of the stage are respectively unit m;
s2.2: operating cost in a scheduling time period is used as a research objective function:
in the formula, p is the electric charge of the operation of the whole pump station system in a scheduling operation time period; γ represents the severity of the slurry; h st (i, j) is the net lift of the ith pump station in the jth period; q (i, j) represents the station flow of the ith pump station in the jth time period, and the unit operated by the station is assumed to have consistent flow in the same time period; t (i, j) represents the working time of i pump stations in the j period; d (i, j) represents the local electricity price of the ith pump station in the jth time period; eta (i, j) represents the working efficiency of the pump station in the jth time period of the ith pump station;
s2.3: the minimum number of starting times of the unit in the scheduling period is calculated as an objective function:
in the formula, e represents the sum of the starting times of all units of the whole system in a scheduling time period; l (i, j) represents the number of starting units newly added to the ith pump station in the jth time period compared with j-1 time periods;
preferably, the cost function or performance index of the multi-objective optimization scheduling model system of the step pump station in the step S3 is defined as:
in the formula, xi ∈ T is the running state of the cascade pump station (5 state matrixes of a pump starting section, a clear water section, a water slurry switching section, a slurry transportation section, a pump stopping starting section and the like); ui belongs to R (inlet and outlet flow, pressure, flow fluctuation coefficient, differential pressure fluctuation coefficient, flow and pump frequency converter frequency ratio characteristic quantity and other 5 comprehensive performance index characteristic parameter set vectors); r (xi, ui) is a utility function representing the reward or penalty the system makes after applying the current control vector ui; 0< gamma.ltoreq.1 is a discount factor.
The invention has the beneficial effects that:
by analyzing the running state matrix of the cascade pump station, a multi-objective function of the dynamic scheduling of the pump station unit is constructed, an optimal scheduling model of the cascade pump station is established, and a solving method of a self-adaptive dynamic programming theory is provided, so that the high-efficiency and low-consumption running of the high-pressure diaphragm pump unit system is realized, and the efficiency, safety and economic transportation of a slurry pipeline conveying project are improved.
A basic data model more suitable for actual conditions is obtained by constructing a state matrix of cascade pump station scheduling, a self-adaptive dynamic programming method is introduced to realize model solution, parameters such as the number of starting stations, the overflow, the frequency conversion coefficient, the lift, the pressure and the rotating speed are reasonably distributed, an optimized operation scheme with the least power consumption is obtained, and the high-pressure diaphragm pump is ensured to be operated in an optimal working condition area all the time.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a series-connection ore slurry conveying process diagram of five cascade pump stations according to the invention;
FIG. 2 is a model structure diagram of the cascade pump station of the present invention;
FIG. 3 is a schematic diagram of the adaptive dynamic programming algorithm of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1
S1: constructing a state matrix of the operation of the step pump station;
the operation conditions of the pump set in the pump station are as follows: the pump station dispatching system comprises 5 stages, namely a pump starting stage, a clear water stage, a water slurry switching stage, a slurry transportation stage and a pump stopping starting stage, and the total output efficiency of the pump station dispatching system is not always the highest even when the output efficiency of each pump is the highest due to the fact that the operating condition of the high-pressure diaphragm pump is a nonlinear optimization problem; constructing a pump station optimal scheduling objective function and solving the pump station optimal scheduling objective function based on a state matrix of the cascade pump station in operation, and further analyzing the operation mechanism of the cascade pump station to obtain the optimal output of a pump set; the high-pressure diaphragm pump combined with the operating conditions of the high-pressure diaphragm pump of Yunnan Dahongshan pipeline Limited company has the following research objects: the operation state of the ore pulp conveying process of 5 cascade pump stations such as a big red mountain station, a meter ruler moat station, a new station, a Fuliang shed station, a sunset station and the like in a cascade connection manner is shown in a figure 1:
the cascade pumping station operating state can be represented as a state matrix of N × N (N = 5):
wherein 0 represents a clear water state and 1 represents a slurry state;
s2: building a pump station dynamic optimization scheduling model, splitting cascade pump station optimization scheduling by adopting a decomposition coordination method, and decomposing into 3 subsystems according to 2 decision variables of each stage of pump station distribution flow and each pump distribution flow respectively; the method mainly comprises a power consumption optimization model under the condition that a single pump corresponds to distributed flow, a power consumption optimization model under the condition that a single-stage pump station corresponds to distributed flow and a multi-objective optimization scheduling model of a cascade pump station; taking the actual operation conditions of five pumping stations of the Yunnan Dahongshan pipeline company Limited, such as Dahongshan, mi Mo Che, a new-generation Fuliang shed and Xiyang as an example, an energy consumption minimum optimization model is sequentially established from the first layer to the third layer, as shown in FIG. 2;
s3: solving based on a self-adaptive dynamic programming method;
the self-adaptive dynamic programming algorithm mainly comprises a dynamic system, an execution function and an evaluation function. The self-adaptive dynamic programming algorithm operates according to a reward-penalty mechanism of interaction between people and the environment, and people can obtain penalty or reward according to the influence of actions on the environment after carrying out specific actions according to environmental changes, so as to adjust and execute the actions. In the specific implementation, based on the characteristic that an artificial neural network approaches a nonlinear function with any precision, a cost function in a period of time in the dynamic planning process is fitted through calculation in a single stage, and the problem of dimension disaster of dynamic planning calculation can be solved; the principle of the adaptive dynamic programming algorithm is shown in FIG. 3;
introducing a self-adaptive dynamic programming method into the multi-objective optimization scheduling model solution of the cascade pump station, taking the real-time capacity of a pipe network at each moment as a system state quantity, taking the parameter adjustment variable quantity of a pipe network pump set at each moment as a system control quantity, taking the running cost and the starting times of the unit in a scheduling period as a dual-constraint utility function, establishing a Bellman differential equation according to a Bellman optimality principle, and then calculating the optimal control quantity according to iterative self-adaptive dynamic programming derivation, quantitatively analyzing the convergence speed while qualitatively analyzing the convergence, and realizing the rapid solution of the optimal problem;
preferably, the single pump corresponds to the power consumption optimization model under the flow distribution, the high-pressure diaphragm pump system is used for solving the energy consumption models of all the pump sets, and the objective function is as follows:
the constraint conditions are as follows:
q min ＜q k ＜q max
H f (Q)≤H(q k )≤1.2H f (Q)
H(q 1 )＝H(q 2 )＝L＝H(q n )
X k =0 or 1,k =1,2 …, n
In the formula, N k Energy consumption of a single pump is shown in Kw; q. q.s i Is the overflow of a single pump, unit m 3 /s；q min Is the minimum overflowed of a single pump in m 3 /s；q max Is the maximum overflow of a single pump in m 3 S; n is the number of all the pumps of the pump station; h (q) i ) Is the single pump head, unit m; h f (Q) is the pump station overflow Q a The corresponding required lift is m;
preferably, the single-stage pump station corresponds to an electricity consumption optimization model under the flow distribution, the total energy consumption of the single-stage pump station is the sum of the energy consumption of each subsystem of the high-pressure diaphragm pump, and the objective function is as follows:
the constraint conditions are as follows:
in the formula, DN is the optimal total energy consumption of the pump station in Kw; f 1 The unit is the power consumption of a single pump system, and the unit is Kw; q j Flow rate of single pump system, unit m 3 S; q is the total over-flow of the pump station; unit m 3 /s；
Preferably, the multi-objective optimization scheduling model construction method for the cascade pump station comprises the following steps:
s2.1: on the basis of optimizing the energy consumption of single-stage pump stations, the lowest energy consumption condition of the total system is researched by distributing the flow of each stage of pump station;
the objective function is:
the constraint conditions are as follows:
H ti min ≤Z i+1 -Z i ≤H ti max
0≤Q≤Q max
in the formula, Z i 、Z i+1 The quantity of the slurry at the inlet and the outlet of the pump station is the level, and TN is the optimal total energy consumption of the stepped pump station in Kw; q max Is the maximum flow of the unit, unit m 3 /s；H ti min 、H ti max The maximum ore pulp liquid level difference and the minimum ore pulp liquid level difference of the pump station of the stage are respectively unit m;
s2.2: operating cost in a scheduling time period is used as an exploration target function:
in the formula, p is the electric charge of the operation of the whole pump station system in a scheduling operation time period; γ represents the severity of the slurry; h st (i, j) is the net lift of the ith pump station in the jth period; q (i, j) represents the station flow of the ith pump station in the jth time period, and the unit operated by the station is assumed to have consistent flow in the same time period; t (i, j) represents the working time of i pump stations in the j period; d (i, j) represents the local electricity price of the ith pump station in the jth time period; eta (i, j) represents the working efficiency of the pump station in the jth time period of the ith pump station;
s2.3: the minimum number of starting times of the unit in the scheduling period is used for calculating an objective function:
in the formula, e represents the sum of the starting times of all units of the whole system in a scheduling time period; l (i, j) represents the number of starting units newly added to the ith pump station in the jth time period compared with j-1 time periods;
preferably, the cost function or performance index of the multi-objective optimization scheduling model system of the step pump station in the step S3 is defined as:
in the formula, x i The epsilon T is the running state of the cascade pump station (5 state matrixes such as a pump starting section, a clear water section, a water slurry switching section, a slurry transportation section and a pump stopping starting section); u. u i e.R (5 comprehensive performance index characteristic parameter set vectors such as inlet and outlet flow, pressure, flow fluctuation coefficient, differential pressure fluctuation coefficient, flow and frequency ratio characteristic quantity of a pump frequency converter and the like); r (x) i ,u i ) Is a utility function, and represents the control vector u under the application i The reward or penalty the system makes; 0<γ ≦ 1 is a discount factor.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand the invention for and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (6)
1. A dynamic optimization scheduling modeling method for a plateau mountain long-distance stepped pump station conveying system is characterized by comprising the following steps:
s1: constructing a state matrix of the operation of the step pump station;
s2: building a pump station dynamic optimization scheduling model, splitting cascade pump station optimization scheduling by adopting a decomposition coordination method, and decomposing into 3 subsystems according to 2 decision variables of each stage of pump station distribution flow and each pump distribution flow respectively; the method mainly comprises a power consumption optimization model under the condition that a single pump corresponds to distributed flow, a power consumption optimization model under the condition that a single-stage pump station corresponds to distributed flow and a multi-objective optimization scheduling model of a cascade pump station;
s3: solving based on a self-adaptive dynamic programming method;
the method comprises the steps of introducing a self-adaptive dynamic programming method into a multi-objective optimization scheduling model solution of a cascade pump station, taking the real-time capacity of a pipe network at each moment as a system state quantity, taking the parameter adjustment variable quantity of a pipe network pump set at each moment as a system control quantity, taking the running cost and the starting times of a unit in a scheduling period as a dual-constraint utility function, establishing a Bellman differential equation according to a Bellman optimality principle, and then calculating the optimal control quantity according to iterative self-adaptive dynamic programming derivation, quantitatively analyzing the convergence speed while qualitatively analyzing the convergence, and realizing the rapid solution of an optimal problem.
2. The dynamic optimization scheduling modeling method for the plateau mountain long-distance stepped pump station conveying system according to claim 1, wherein the specific method for constructing the running state matrix of the stepped pump station is as follows: constructing a pump station optimal scheduling objective function and solving the pump station optimal scheduling objective function based on a state matrix of the cascade pump station in operation, and further analyzing the operation mechanism of the cascade pump station to obtain the optimal output of a pump set;
the cascade pumping station operating state can be represented as a state matrix of N × N (N = 5):
where 0 represents a clear water state and 1 represents a slurried state.
3. The dynamic optimization scheduling modeling method for the plateau mountain long-distance step pump station conveying system according to claim 1, wherein the single pump corresponds to a power consumption optimization model under flow distribution, a high-pressure diaphragm pump system is used for solving energy consumption models of all pump sets, and an objective function is as follows:
the constraint conditions are as follows:
q min ＜q k ＜q max
H f (Q)≤H(q k )≤1.2H f (Q)
H(q 1 )＝H(q 2 )＝L＝H(q n )
X k =0 or 1,k =1,2 …, n
In the formula, N k Energy consumption of a single pump is shown in Kw; q. q.s i Is the overflow of a single pump, unit m 3 /s；q min Is the minimum overflowed of a single pump in m 3 /s；q max Is the maximum overflow of a single pump in m 3 S; n is the number of all the pumps in the pump station; h (q) i ) Is the single pump head, unit m; h f (Q) is the pump station overflow Q a The corresponding required lift is m.
4. The dynamic optimization scheduling modeling method for the plateau mountain long-distance step pump station conveying system according to claim 1, wherein the single-stage pump station corresponds to an electricity consumption optimization model under the distributed flow, the total energy consumption of the single-stage pump station is the sum of the energy consumption of each subsystem of the high-pressure diaphragm pump, and the objective function is as follows:
the constraint conditions are as follows:
in the formula (I), the compound is shown in the specification,DN is the optimal total energy consumption of the pump station in the unit Kw; f 1 The unit is the power consumption of a single pump system, namely Kw; q j Flow rate of single pump system, unit m 3 S; q is the total over-flow of the pump station; unit m 3 /s。
5. The modeling method for dynamic optimized dispatching of the plateau mountain long-distance stepped pump station conveying system according to claim 1, characterized in that the method for constructing the multi-objective optimized dispatching model of the stepped pump station comprises the following steps:
s2.1: on the basis of optimizing the energy consumption of a single-stage pump station, the lowest energy consumption condition of the total system is researched by distributing the flow of each stage of pump station;
the objective function is:
the constraint conditions are as follows:
H ti min ≤Z i+1 -Z i ≤H ti max
0≤Q≤Q max
in the formula, Z i 、Z i+1 The quantity of the slurry at the inlet and the outlet of the pump station is the level, and TN is the optimal total energy consumption of the stepped pump station in Kw; q max The unit is the maximum over-flow of the unit 3 /s；H ti min 、H ti max The maximum ore pulp liquid level difference and the minimum ore pulp liquid level difference of the pump station of the stage are respectively unit m;
s2.2: operating cost in a scheduling time period is used as a research objective function:
in the formula, p is the electric charge of the operation of the whole pump station system in a scheduling operation time period; γ represents the severity of the slurry; h st (i, j) is the net lift of the ith pump station in the jth period; q (i, j) indicates that the ith pump station isThe station flow in the jth time period, and supposing that the unit operated by the station has consistent flow in the same time period; t (i, j) represents the working time of i pump stations in the j period; d (i, j) represents the local electricity price of the ith pump station in the jth time period; eta (i, j) represents the working efficiency of the pump station in the jth time period of the ith pump station;
s2.3: the minimum number of starting times of the unit in the scheduling period is used for calculating an objective function:
in the formula, e represents the sum of the starting times of all units of the whole system in a scheduling time period; and L (i, j) represents the number of the starting units newly added to the ith pump station in the jth time period compared with the j-1 time period.
6. The dynamic optimization scheduling modeling method for the plateau mountain long-distance step pump station conveying system according to claim 1, wherein a cost function or a performance index of the multi-objective optimization scheduling model system for the plateau mountain long-distance step pump station in the S3 is defined as:
in the formula, xi ∈ T is the running state of the cascade pump station (5 state matrixes of a pump starting section, a clear water section, a water slurry switching section, a slurry transportation section, a pump stopping starting section and the like); ui belongs to R (inlet and outlet flow, pressure, flow fluctuation coefficient, differential pressure fluctuation coefficient, flow and pump frequency converter frequency ratio characteristic quantity and other 5 comprehensive performance index characteristic parameter set vectors); r (xi, ui) is a utility function representing the reward or penalty the system makes after applying the current control vector ui; 0< gamma.ltoreq.1 is a discount factor.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116663733A (en) * | 2023-06-02 | 2023-08-29 | 北京金河水务建设集团有限公司 | Step pump station optimization regulation and control method and system based on scheduling model |
| CN116702979A (en) * | 2023-06-07 | 2023-09-05 | 北京金河水务建设集团有限公司 | Multi-target optimal scheduling method for step pump station |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116663733A (en) * | 2023-06-02 | 2023-08-29 | 北京金河水务建设集团有限公司 | Step pump station optimization regulation and control method and system based on scheduling model |
| CN116663733B (en) * | 2023-06-02 | 2023-11-21 | 北京金河水务建设集团有限公司 | Step pump station optimization regulation and control method and system based on scheduling model |
| CN116702979A (en) * | 2023-06-07 | 2023-09-05 | 北京金河水务建设集团有限公司 | Multi-target optimal scheduling method for step pump station |
| CN116702979B (en) * | 2023-06-07 | 2023-11-21 | 北京金河水务建设集团有限公司 | Multi-target optimal scheduling method for step pump station |
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