CN111639793A - Boiler group scheduling optimization method and device - Google Patents
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- CN111639793A CN111639793A CN202010401664.4A CN202010401664A CN111639793A CN 111639793 A CN111639793 A CN 111639793A CN 202010401664 A CN202010401664 A CN 202010401664A CN 111639793 A CN111639793 A CN 111639793A
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Abstract
The invention is suitable for the technical field of steam boiler scheduling analysis, and provides a method and a device for optimizing boiler group scheduling, wherein the method comprises the following steps: setting an optimization target of a boiler group according to the gas consumption of the boilers in the boiler group in a specified time period; setting a constraint condition for optimizing an optimization target according to the steam quantity generated by the boilers in the boiler group in a specified time period; establishing a differential evolution algorithm model for optimizing an optimization target; and at least calculating the optimization target association parameters when the optimization target meets the preset conditions according to the constraint conditions and the differential evolution algorithm model so as to obtain the load distribution plan of the boiler group and complete the scheduling optimization of the boiler group. The invention is based on a differential evolution algorithm, searches and optimizes from a global level, realizes intelligent load scheduling, avoids unreasonable load distribution caused by subjective experience of operating personnel or mechanical operation, and furthest excavates the energy-saving potential of group operation caused by individual difference of boilers.
Description
Technical Field
The invention belongs to the technical field of steam boiler scheduling analysis, and particularly relates to a boiler group scheduling optimization method and device.
Background
Along with the development of industry centralized areas and industrial parks, the demands of industrial steam and centralized heat supply are increasing, and centralized heat supply stations with a plurality of gas steam boilers are increasing. For how to reasonably schedule a boiler group with a plurality of steam boilers, the problem that the running energy consumption is the lowest, the efficiency is the highest and the cost is the lowest under the condition of meeting the steam load of a user is a key problem, the problem of optimized scheduling is a key problem facing distributed energy, the user load fluctuation is frequent, the scheduling mode rationality by the subjective experience of personnel is insufficient due to a large number of energy supply devices, and the consumption reduction potential of the energy supply group cannot be exerted.
At present, few researches are conducted on the aspect of boiler group scheduling optimization, on-site operating personnel schedule and distribute boiler loads according to personal subjective experience, or a mechanical fixed value scheduling mode is used, for example, total loads are evenly distributed to each boiler, or a certain number of boilers are manually specified to be peak shaving boilers, load changes are all borne by the peak shaving boilers, the optimal scheduling state is often not achieved, and therefore the energy-saving potential of group operation cannot be given play.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for optimizing boiler group scheduling, in which an optimal optimization target association parameter is searched by using iteration and evolution of a population in a differential evolution algorithm, so as to obtain a load distribution plan within a specified time period of each boiler, and complete boiler group scheduling optimization without manually or mechanically formulating a scheduling policy.
The first aspect of the embodiments of the present invention provides a boiler group scheduling optimization method, including:
setting an optimization target of a boiler group according to the gas consumption of the boilers in the boiler group in a specified time period;
setting a constraint condition for optimizing the optimization target according to the steam quantity generated by the boilers in the boiler group in a specified time period;
establishing a differential evolution algorithm model for optimizing the optimization target;
and at least calculating an optimization target association parameter when the optimization target meets a preset condition according to the constraint condition and the differential evolution algorithm model so as to obtain a load distribution plan of the boiler group and complete the scheduling optimization of the boiler group.
A second aspect of an embodiment of the present invention provides a boiler group scheduling optimization apparatus, including:
the target setting module is used for setting an optimization target of the boiler group according to the gas consumption of the boilers in the boiler group in a specified time period;
the condition setting module is used for setting a constraint condition for optimizing the optimization target according to the steam quantity generated by the boilers in the boiler group in a specified time period;
the model establishing module is used for establishing a differential evolution algorithm model for optimizing the optimization target;
and the optimizing solving module is used for at least calculating an optimization target association parameter when the optimization target meets a preset condition according to the constraint condition and the differential evolution algorithm model so as to obtain a load distribution plan of the boiler group and finish the scheduling optimization of the boiler group.
A third aspect of the embodiments of the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method when executing the computer program.
A fourth aspect of embodiments of the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the steps of the above-described method.
Compared with the prior art, the embodiment of the invention has the following beneficial effects:
the embodiment of the invention can improve the intelligent scheduling capability of the gas-steam boiler group, avoid unreasonable load distribution caused by subjective experience of operators or mechanical operation, furthest excavate the energy-saving potential of group operation caused by individual difference of boilers, search and optimize from the global level based on a differential evolution algorithm, and realize intelligent load scheduling.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed for the embodiments or the prior art descriptions will be briefly described 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 to obtain other drawings without creative efforts.
FIG. 1 is a schematic flow chart diagram illustrating a method for optimizing boiler group scheduling according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of a boiler group scheduling optimization apparatus according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention, are within the scope of the invention. Unless otherwise specified, the technical means used in the examples are conventional means well known to those skilled in the art.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It is also to be understood that the terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in the specification of the present application and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to a determination" or "in response to a detection". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
The first embodiment is as follows:
referring to fig. 1, a method for optimizing boiler group scheduling according to an embodiment of the present invention includes:
step S11: setting an optimization target of a boiler group according to the gas consumption of the boilers in the boiler group in a specified time period;
step S12: setting a constraint condition for optimizing the optimization target according to the steam quantity generated by the boilers in the boiler group in a specified time period;
step S13: establishing a differential evolution algorithm model for optimizing the optimization target;
step S14: and at least calculating an optimization target association parameter when the optimization target meets a preset condition according to the constraint condition and the differential evolution algorithm model so as to obtain a load distribution plan of the boiler group and complete the scheduling optimization of the boiler group.
The optimization method is based on a Differential Evolution algorithm (Differential Evolution) for optimization, the Differential Evolution algorithm is a heuristic global random search algorithm based on a population, compared with other Evolution algorithms, the Differential Evolution algorithm reserves a global search strategy based on the population, and the complexity of genetic operation is reduced by adopting real number coding, simple variation operation based on the Differential and a one-to-one competition survival strategy. Meanwhile, the specific memory capacity of the differential evolution algorithm enables the differential evolution algorithm to dynamically track the current search condition so as to adjust the search strategy, and the differential evolution algorithm has stronger global convergence capacity and robustness.
The core steps of the differential evolution algorithm are as follows: setting a target, setting constraints, establishing a model, and carrying out optimization solution. Therefore, the embodiment of the invention optimizes the boiler group scheduling according to the four steps.
Firstly, setting an optimization target, namely the optimization degree which is required to be achieved by the boiler group, then setting constraint conditions of an algorithm, then establishing an algorithm model, calculating according to the algorithm model after the optimization is completed to obtain related parameters, screening the contents which accord with the constraint conditions in the related parameters to obtain an optimal result, and adding the optimal solution into the optimization target to quickly obtain a load distribution plan of the boiler group so as to complete the optimization.
Through the design, the intelligent scheduling capability of the gas-steam boiler group can be improved, unreasonable load distribution caused by subjective experience of operators or mechanical operation is avoided, the group operation energy-saving potential caused by individual differences of boilers is furthest excavated, and based on a differential evolution algorithm, the group operation energy-saving potential is searched and optimized from a global level, so that intelligent load scheduling is realized.
The optimization target of the boiler group can be selected and set according to specific items, and in this embodiment, the optimization target is preferably: the total gas consumption of the boiler group in a specified time period is minimum, namely:
wherein, i is 1, 2, …, n, n is the total number of boilers participating in optimization, t is unit time, X isitAnd T is the gas consumption of the boiler corresponding to the number i in the unit time T, and is the designated optimizing time period.
The optimization goal means that when the value of the total gas consumption of the boiler group in the specified time period is the minimum value, the optimization goal can be regarded as being achieved.
In addition, in this embodiment, the optimization constraint condition may preferably include:
the total steam production load of the boiler group in unit time is equal to the total steam load actually required by a user;
the actual load of each boiler in the boiler group cannot exceed the rated load of the boiler;
and the load lifting rate of each boiler in the boiler group is provided with a limit value.
The first condition is to ensure the balance of supply and demand of steam, and the second and third conditions are to prevent the equipment from overload operation, thereby causing damage to the equipment.
Wherein, the total load of steam production per unit time of the boiler group is as follows: the total steam production of the boiler group in unit time t is as follows:
wherein, YtI.e. the total load of steam production per unit time of the boiler group, YitAnd n is the total number of the boilers participating in optimization, wherein n is the steam production amount of the boiler corresponding to the number i in the unit time t.
Setting the total steam load required by the user in unit hour in unit time t as HtLet Y bet＝HtThe first constraint is satisfied.
The corresponding constraints further include:
0≤Yitn is less than or equal to N, wherein N is the rated load of the boiler, namely the rated steam production per hour;
-F≤Yit-Yi(t-1)≤F，Yi(t-1)and F is the limit value of the load lifting rate of the boiler per hour, wherein the steam quantity produced by the boiler corresponding to the number i at a moment in unit time t is represented by F.
When a differential evolution algorithm model for optimizing the optimization target is established, the differential evolution algorithm is selected as a model frame in the embodiment, and the setting content of the model includes: the method comprises the following steps of setting a population and algorithm parameters, wherein the population setting comprises the following steps: coding mode, population scale, creation of area descriptors and instantiation of population objects; the algorithm parameter setting comprises the following steps: instantiating an algorithm template object, maximum genetic algebra, a differential evolutionary variant scaling factor, and a cross probability.
The preferred settings for this embodiment are as follows:
setting the population scale to be 500-1000 in the population setting of the differential evolution algorithm model;
the maximum genetic algebra is set to 10000 in the algorithm setting of the differential evolution algorithm model, the variation scaling factor of the differential evolution is 0.5, and the cross probability is 0.7.
When the specific model is selected and set, the variation scaling factor and the cross probability of the differential evolution can be adjusted and set according to the distribution of the actual solution set in the calculation result and the algorithm exploration efficiency.
The algorithm is written by a programming language, such as Python, and the optimal X can be searched by utilizing the iteration and the evolution of the populationitAnd due to the common condition, Yit＝kiXit，kiThe fitting coefficient is the boiler steam consumption rate, the corresponding physical meaning is the boiler steam consumption rate, and the fitting coefficient of each boiler can be different, so that the optimal X can be passeditTo obtain the optimal Yit。
The optimization objective association parameters may include: corresponding to the gas consumption X of the boiler in the corresponding time perioditAnd the amount of steam Y produced by the corresponding boiler in the corresponding time periodit。
Example two:
referring to fig. 2, a boiler group scheduling optimization apparatus according to an embodiment of the present invention includes: a goal setting module 21, a condition setting module 22, a model building module 23, and an optimization solution module 24, wherein,
the target setting module 21 is configured to set an optimization target of the boiler group according to the gas consumption of the boilers in the boiler group in a specified time period;
the condition setting module 22 is configured to set a constraint condition for optimizing the optimization target according to the steam amount generated by the boilers in the boiler group within a specified time period;
the model establishing module 23 is configured to establish a differential evolution algorithm model for optimizing the optimization target;
the optimizing solving module 24 is configured to calculate at least an optimization target association parameter when the optimization target meets a preset condition according to the constraint condition and the differential evolution algorithm model, so as to obtain a load distribution plan of the boiler group, and complete scheduling optimization of the boiler group.
Fig. 3 is a schematic diagram of a terminal device 3 according to an embodiment of the present invention. As shown in fig. 3, the terminal device 3 of this embodiment comprises a processor 31, a memory 31 and a computer program 32, such as a boiler group scheduling optimizer, stored in said memory 31 and operable on said processor 31. The processor 30, when executing the computer program 32, implements the steps in the various method embodiments described above, such as the steps S11-S14 shown in fig. 1. Alternatively, the processor 30, when executing the computer program 32, implements the functions of the modules/units in the above-mentioned device embodiments, such as the functions of the modules 21 to 24 shown in fig. 2.
Illustratively, the computer program 32 may be partitioned into one or more modules/units that are stored in the memory 31 and executed by the processor 30 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 32 in the terminal device 3.
The terminal device 3 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device 3 may include, but is not limited to, a processor 30 and a memory 31. It will be understood by those skilled in the art that fig. 3 is only an example of the terminal device 3, and does not constitute a limitation to the terminal device 3, and may include more or less components than those shown, or combine some components, or different components, for example, the terminal device 3 may further include an input-output device, a network access device, a bus, etc.
The Processor 30 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 31 may be an internal storage unit of the terminal device 3, such as a hard disk or a memory of the terminal device 3. The memory 31 may also be an external storage device of the terminal device 3, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 3. Further, the memory 31 may also include both an internal storage unit and an external storage device of the terminal device 3. The memory 31 is used for storing the computer programs and other programs and data required by the terminal device 3. The memory 31 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Of course, the units and modules may be replaced by a processor containing a computer program, and the work of each part can be completed in a pure software form.
Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A boiler group scheduling optimization method is characterized by comprising the following steps:
setting an optimization target of a boiler group according to the gas consumption of the boilers in the boiler group in a specified time period;
setting a constraint condition for optimizing the optimization target according to the steam quantity generated by the boilers in the boiler group in a specified time period;
establishing a differential evolution algorithm model for optimizing the optimization target;
and at least calculating an optimization target association parameter when the optimization target meets a preset condition according to the constraint condition and the differential evolution algorithm model so as to obtain a load distribution plan of the boiler group and complete the scheduling optimization of the boiler group.
2. The boiler group scheduling optimization method of claim 1, wherein the optimization objective is: the total gas consumption of the boiler group in a specified time period is minimum, namely:
wherein, i is 1, 2,…, n, n is the total number of boilers participating in optimization, t is unit time, XitAnd T is the gas consumption of the boiler corresponding to the number i in the unit time T, and is the designated optimizing time period.
3. The boiler group scheduling optimization method of claim 1, wherein the optimization constraints comprise:
the total steam production load of the boiler group in unit time is equal to the total steam load actually required by a user;
the actual load of each boiler in the boiler group cannot exceed the rated load of the boiler;
and the load lifting rate of each boiler in the boiler group is provided with a limit value.
4. The boiler group scheduling optimization method of claim 3, wherein the total steam production load per unit time of the boiler group is: the total steam production of the boiler group in unit time t is as follows:
wherein, YtI.e. the total load of steam production per unit time of the boiler group, YitAnd n is the total number of the boilers participating in optimization, wherein n is the steam production amount of the boiler corresponding to the number i in the unit time t.
5. The method of boiler group scheduling optimization of claim 1, wherein building a differential evolution algorithm model that optimizes the optimization objective comprises:
setting a population scale in the population setting of the differential evolution algorithm model;
and setting a variation scaling factor and a cross probability of differential evolution in the algorithm setting of the differential evolution algorithm model.
6. The boiler group scheduling optimization method of claim 5, wherein the scaling factor and the cross probability of the differential evolution variance are set according to the distribution of the actual solution set in the calculation result and the algorithm exploration efficiency.
7. The boiler group scheduling optimization method of claim 1, wherein the optimization objective association parameters comprise: the gas consumption of the corresponding boiler in the corresponding time period and the steam production of the corresponding boiler in the corresponding time period.
8. A boiler group scheduling optimization apparatus, comprising:
the target setting module is used for setting an optimization target of the boiler group according to the gas consumption of the boilers in the boiler group in a specified time period;
the condition setting module is used for setting a constraint condition for optimizing the optimization target according to the steam quantity generated by the boilers in the boiler group in a specified time period;
the model establishing module is used for establishing a differential evolution algorithm model for optimizing the optimization target;
and the optimizing solving module is used for at least calculating an optimization target association parameter when the optimization target meets a preset condition according to the constraint condition and the differential evolution algorithm model so as to obtain a load distribution plan of the boiler group and finish the scheduling optimization of the boiler group.
9. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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