CN117474263A - Execution equipment scheduling method based on gas load prediction - Google Patents
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Abstract
The invention discloses an execution equipment scheduling method based on gas load prediction, which comprises the following steps: designing a mathematical model module for compressor scheduling, wherein the mathematical model module is used for completing the mathematical model design of fuel cost, maintenance cost, starting cost, closing cost and supply and demand cost of the operation of the gas compressor, and forming an objective function for compressor scheduling; and designing a compressor scheduling optimization method module, wherein the module designs a compressor scheduling algorithm by utilizing a predicted value of the gas load and a genetic algorithm, and finally outputs a scheduling scheme of the compressor for reference of staff. The invention can provide a more reasonable compressor scheduling scheme according to the designed mathematical model of compressor scheduling and the optimized scheduling method on the basis of more accurate natural gas load prediction, and can reduce the compressor scheduling cost and improve the profit of gas companies under the condition that the compressor scheduling scheme is unreasonable and the cost and the customer satisfaction degree cannot be considered.
Description
Technical Field
The invention belongs to the technical field of energy management, and particularly relates to an execution device scheduling method based on gas load prediction.
Background
Natural gas is one of the world-accepted clean fuels, and the consumption of the natural gas is gradually increased along with the continuous improvement of environmental awareness and requirements. In recent years, urban fuel gas consumption in China generally has a trend of rapid increase, and the requirement on the pressure regulating capability of fuel gas companies is higher and higher. When the natural gas consumption of the user is small and the requirement on the braking horsepower is low, the pressure regulating station can maintain the pipeline pressure by only starting a small number of compressors, and the compressors are required to be closed to reduce the gas transmission quantity; when the natural gas consumption of the user is large, the brake horsepower requirement rises, the pressure regulating station needs to start more compressors to maintain the pressure stability of the pipe network, and the opening and closing of the compressors and the combination mode can influence the stability of the gas consumption of the user and the operation and maintenance cost of the pipe network. The existing gas company pressure regulating scheduling strategy is an expert system based on the experience of a dispatcher, the scheduling strategy is single, and the compressor scheduling strategy is difficult to consider the satisfaction degree of users and the enterprise cost requirement.
Disclosure of Invention
Aiming at the problems, the invention provides an execution equipment scheduling method based on gas load prediction, which provides a more reasonable compressor scheduling scheme on the basis of more accurate natural gas load prediction, reduces the compressor scheduling cost, reduces the running cost and improves the profit of gas companies.
The method is realized in such a way that the execution equipment scheduling method based on the gas load prediction adopts the following systems:
a compressor scheduling system comprising: the fuel cost module, the compressor starts and closes the cost module, the compressor maintains the cost module, the compressor supplies and asks for the cost module;
a compressor schedule optimization system comprising: the method is characterized by comprising the following steps of:
step 1, designing a mathematical model module for compressor scheduling, wherein the mathematical model module completes the mathematical model design of fuel cost, maintenance cost, starting cost, closing cost and supply and demand cost of the operation of a gas compressor, and forms an objective function of compressor scheduling, and the mathematical model module comprises:
step 1.1, calculating and determining a decision variable, namely the state of the compressor, which indicates whether the compressor is started or closed;
step 1.2, calculating and determining the fuel cost of the compressors, wherein the fuel cost is all the fuel cost consumed by running each compressor in a specified time range;
step 1.3, calculating and determining maintenance cost of the compressors, wherein the maintenance cost refers to the cost of maintaining each compressor;
step 1.4, calculating and determining the starting cost of the compressor, wherein the cost is generated by energy resources spent for restarting the compressor;
step 1.5, calculating and determining the closing cost of the compressor;
step 1.6 calculating and determining the supply and demand cost of the compressor: supply cost refers to unnecessary compressor operation;
step 2, designing a compressor scheduling optimization method module, wherein the module designs a compressor scheduling algorithm by utilizing a predicted value of a gas load and a genetic algorithm, and finally outputs a scheduling scheme of the compressor for reference of staff, and the method comprises the following steps:
step 2.1, obtaining forecast data of a week of natural gas load;
step 2.2, designing and using a genetic algorithm to solve the optimization of compressor assembly scheduling; the objective function of the genetic algorithm is the sum of costs of fuel, start-up, maintenance and shut-down;
and step 3, outputting a compressor dispatching optimization scheme.
Further, the step 1.2 calculates and determines the fuel cost of the compressorCalculated with the following formula:
wherein the method comprises the steps ofFuel cost of electric compressor or natural gas compressor, +.>Is the total number of compressors, +.>Is the number of operation cycles->Indicate->The number of compressors is->A state at the time of the operation cycle;
further, the maintenance cost of the step 1.3 compressorThe following formula is used for calculation:
wherein the method comprises the steps ofIs the maintenance cost of the electric compressor or the natural gas compressor;
further, the step 1.4 calculates and determines the starting cost of the compressor: the energy expended to restart the compressor can incur costs, the cost of starting the natural gas compressor and the electric compressorThe mathematical expression of (2) is as follows:
wherein the method comprises the steps ofThe starting cost of the electric compressor or the natural gas compressor;
further, the start-up or shut-down costs of the compressor are given as penalty costs to be added to the total operating costs, one operating cycle being 7 hours, the shut-down costs of the step 1.5 compressorThe following are provided:
wherein,is the first 4 hours of a cycle, < >>Is indicative of a change in compressor status within 3 consecutive hours,penalty cost for less than 3 cycles of compressor operation,/->Indicate->The compressor is +.>State at periodic time.
Further, the step 1.6 determines the supply and demand cost of the compressor: supply and demand costs refer to unnecessary compressor operation, supply and demand costsThe expression is as follows:
wherein the method comprises the steps ofPenalty cost indicating compressor supply over demand, < ->Is->Supply of individual compressors, +.>Is the run period->Is a customer requirement of (1).
Further, the operation of selecting wheel disc selection, single-point crossover, single-bit mutation and elite replacement in the genetic algorithm in the step 2.2 includes fuel cost, maintenance cost of each compressor, starting cost of the compressor, penalty cost of discontinuous operation and sum of penalty costs supplied to demand, and the fitness function formula is as follows:
。
further, the method is suitable for natural gas load data of cities, regions and enterprises.
The beneficial effects of the invention are as follows: the dispatching optimization objective function is obtained by designing the dispatching optimization mathematical model of the compressor, the dispatching optimization method is designed, actual verification is carried out according to actual data, the operation cost under the constraint of satisfying the customer satisfaction degree is compared, the cost and the customer satisfaction degree are considered, the dispatching scheme of the compressor is more reasonable, the problem of dispatching optimization of the compressor combination is solved, the dispatching cost of the compressor can be reduced by about 24%, different compression stations in different cities can be slightly different, and the profit of a gas company is improved.
The method is suitable for natural gas load data of cities, regions and enterprises.
In order to more clearly illustrate the technical scheme of the invention, the invention is further explained in detail below with reference to the attached drawings and the detailed description.
Drawings
FIG. 1 is a schematic block diagram of the process steps of the present invention;
fig. 2 is a schematic block diagram of the system structure of the present invention.
Detailed Description
Embodiment one:
as shown in fig. 1 and 2, the method for scheduling the execution device based on the gas load prediction adopts the following system, which comprises the following steps: the system, compressor scheduling system 1, includes: the fuel cost module, the compressor starts and closes the cost module, the compressor maintains the cost module, the compressor supplies and asks for the cost module; system two, compressor dispatch optimization system 2, includes: the method is characterized by comprising the following steps of: the method comprises the following steps:
step 1, designing a mathematical model of compressor scheduling, completing the mathematical model design of fuel cost, maintenance cost, starting cost, closing cost and supply and demand cost of the operation of a gas compressor, forming an objective function of compressor scheduling, and comprising:
step 1.1, calculating and determining a decision variable, namely the state of the compressor, which indicates whether the compressor is started or closed;
step 1.2, calculating and determining the fuel cost of the compressors, wherein the fuel cost is all the fuel cost consumed by running each compressor in a specified time range;
step 1.3, calculating and determining maintenance cost of the compressors, wherein the maintenance cost refers to the cost of maintaining each compressor;
step 1.4, calculating and determining the starting cost of the compressor, wherein the cost is generated by energy resources spent for restarting the compressor;
step 1.5, calculating and determining the closing cost of the compressor;
step 1.6 calculating and determining the supply and demand cost of the compressor: supply cost refers to unnecessary compressor operation;
step 2, designing a compressor scheduling optimization method module, wherein the module designs a compressor scheduling algorithm by utilizing a predicted value of a gas load and a genetic algorithm, and finally outputs a scheduling scheme of the compressor for reference of staff, and the method comprises the following steps:
step 2.1, obtaining forecast data of a week of natural gas load;
step 2.2, designing and using a genetic algorithm to solve the optimization of compressor assembly scheduling; the objective function of the genetic algorithm is the sum of costs of fuel, start-up, maintenance and shut-down;
and step 3, outputting a compressor dispatching optimization scheme.
Embodiment two:
on the basis of the first embodiment, this embodiment is a refinement and limitation of the first embodiment, and the method step 1 of this embodiment includes:
step 1.1, decision variables are first determined, i.e. whether the compressor is on or off, decision variablesThe definition is as follows: />
In step 1.2, the fuel costs of the compressors are determined, the fuel costs being all the fuel costs consumed to operate each compressor, including gas, electricity, etc., over a specified time frame. The compressor is an electric compressor or a natural gas compressor, which consumes more energy when turned on or off than the natural gas compressor, and therefore, the electric compressor and fuel costs are typically higher than the natural gas compressor. Fuel cost of compressorCalculated with the following formula:
wherein the method comprises the steps ofFuel cost of electric compressor or natural gas compressor, +.>Is the total number of compressors, +.>Is the number of operation cycles->Indicate->The number of compressors is->A state at the time of the operation cycle;
and 1.3, maintaining cost of the compressors, wherein the maintaining cost refers to the cost of maintaining each compressor. Each compressor can only be run for a limited number of hours, after which it must be shut down for maintenance. Maintenance cost of compressorThe following formula is used for calculation:
wherein the method comprises the steps ofIs the maintenance cost of the electric compressor or the natural gas compressor;
in step 1.4, the starting cost of the compressor is determined, and the energy expended to restart the compressor generates costs. Starting cost of natural gas compressor and electric compressorThe mathematical expression of (2) is as follows:
wherein the method comprises the steps ofThe starting cost of the electric compressor or the natural gas compressor;
and 1.5, determining the closing cost of the compressor, wherein excessive wear is caused to equipment by too frequent switching of the compressor, the switching times are reduced as much as possible, and the compressor is continuously operated for as long as possible after being opened, and the embodiment of the compressor is continuously operated for at least not less than 3 hours. The start-up or shut-down costs of the compressor are given as penalty costs, addingOf the total operating costs, one operating cycle is 7 hours, the cost of compressor shutdownThe following are provided:
wherein,is the first 4 hours of a cycle, < >>Is indicative of a change in compressor status within 3 consecutive hours,penalty cost for less than 3 cycles of compressor operation,/->Indicate->The compressor is +.>State at periodic time.
In step 1.6, the cost of supply and demand of the compressor is determined, the cost of supply and demand refers to unnecessary operation of the compressor, and the total amount of natural gas in the pipeline is used as an index of customer demand in the operation process, and when the compressor is excessively compressed to exceed the demand, the operation generates unnecessary operation cost. Cost over demandThe expression is as follows:
wherein the method comprises the steps ofPenalty cost indicating compressor supply over demand, < ->Is->Supply of individual compressors, +.>Is the run period->Is a customer requirement of (1).
Step 2, designing a mathematical model of a compressor scheduling optimization method, designing a compressor scheduling algorithm by using a predicted value of a gas load and a genetic algorithm, and finally outputting a scheduling scheme of the compressor for reference of staff, wherein the method comprises the following steps:
the design uses a genetic algorithm to address compressor assembly scheduling optimization, the objective function of the genetic algorithm is the sum of costs of fuel, start-up, maintenance, and shut-down. The compressor scheduling algorithm is designed by utilizing the predicted value of the gas load and the genetic algorithm, and finally the scheduling scheme of the compressor is output for the reference of staff, and the method is concretely realized as follows:
step 2.1, obtaining forecast data of one week of natural gas load;
and 2.2, designing and using a genetic algorithm to solve the optimization of the compressor assembly scheduling. The objective function of the genetic algorithm is the sum of costs of fuel, start-up, maintenance and shut-down.
The first step in the design of the genetic algorithm is to encode the problem space of the operation plan. The entire operation plan is encoded in one chromosome. There are five compressors running in eight hours, and the change in compressor state only occurs at the beginning of each hour, thus, there are a total of 5 times 8 total 40 bits in the chromosome. If the compressor is on, corresponding position 1; if the compressor is off, position 0 corresponds. If a chromosome uses a plurality of groupsRepresentation of，/>Representing the status of five compressors in the first hour, +.>Representing the state of the same compressor in the second hour, and so on. Binary encoding reduces the number of constraints to one, and in a pipeline problem, the state of the compressor has a discrete value of 0 or 1, which can be represented as a binary bit in a chromosome.
The genetic algorithm comprises the following operations of wheel selection, single-point crossover, single-bit mutation and elite replacement, wherein the parameters of the genetic algorithm are as follows, the population size is 30 or 50, the crossover rate is 0.6, and the mutation rate is 0.01. The algorithm will stop when one of the following conditions is reached: reach 300 maximum algebra; for sequences of 100 consecutive generations, the objective function is not improved; within 50 second intervals, the objective function did not improve at all.
The fitness function is a mathematical model in the compressor scheduling system 1, including the fuel cost, maintenance cost for each compressor, starting cost for the compressor, penalty cost for discontinuous operation, and the sum of penalty costs for supply and demand, and is formulated as follows:
。
and step 3, outputting a compressor dispatching optimization scheme for dispatcher reference.
The invention discloses an execution equipment scheduling method based on gas load prediction, which comprises a compressor scheduling mathematical model module and a compressor scheduling optimization method module design, wherein a more reasonable compressor scheduling scheme is output by using a predicted value of the gas load and a genetic algorithm, so that the compressor scheduling cost is reduced, and the profit of a gas company is improved.
What is not described in detail in the present specification belongs to the prior art known to those skilled in the art.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (8)
1. The execution equipment scheduling method based on the gas load prediction adopts the following system, which comprises the following steps:
a compressor scheduling system comprising: the fuel cost module, the compressor starts and closes the cost module, the compressor maintains the cost module, the compressor supplies and asks for the cost module;
a compressor schedule optimization system comprising: the method is characterized by comprising the following steps of:
step 1, designing a mathematical model of compressor scheduling, completing the mathematical model design of fuel cost, maintenance cost, starting cost, closing cost and supply and demand cost of the operation of a gas compressor, forming an objective function of compressor scheduling, and comprising:
step 1.1, calculating and determining a decision variable, namely the state of the compressor, which indicates whether the compressor is started or closed;
step 1.2, calculating and determining the fuel cost of the compressors, wherein the fuel cost is all the fuel cost consumed by running each compressor in a specified time range;
step 1.3, calculating and determining maintenance cost of the compressors, wherein the maintenance cost refers to the cost of maintaining each compressor;
step 1.4, calculating and determining the starting cost of the compressor, wherein the cost is generated by energy resources spent for restarting the compressor;
step 1.5, calculating and determining the closing cost of the compressor;
step 1.6 calculating and determining the supply and demand cost of the compressor: supply cost refers to unnecessary compressor operation;
step 2, designing a mathematical model of a compressor scheduling optimization method, designing a compressor scheduling algorithm by using a predicted value of a gas load and a genetic algorithm, and finally outputting a scheduling scheme of the compressor for reference of staff, wherein the method comprises the following steps:
step 2.1, obtaining forecast data of a week of natural gas load;
step 2.2, designing and using a genetic algorithm to solve the optimization of compressor assembly scheduling; the objective function of the genetic algorithm is the sum of costs of fuel, start-up, maintenance and shut-down;
and step 3, outputting a compressor dispatching optimization scheme.
2. The method for scheduling execution equipment based on gas load prediction according to claim 1, wherein said step 1.2 calculates and determines the fuel cost of the compressorCalculated with the following formula:
wherein the method comprises the steps ofFuel cost of electric compressor or natural gas compressor, +.>Is the total number of compressors, +.>Is the number of operation cycles->Indicate->The number of compressors is->State at the time of the operation cycle.
3. The method for scheduling execution equipment based on gas load prediction according to claim 1, wherein the maintenance cost of the compressor in step 1.3 is as followsThe following formula is used for calculation:
wherein the method comprises the steps ofIs the maintenance cost of the electric compressor or the natural gas compressor.
4. The method for scheduling execution equipment based on gas load prediction according to claim 1, wherein in step 1.4, the starting cost of the compressor is determined: the energy expended to restart the compressor can incur costs, the cost of starting the natural gas compressor and the electric compressorThe mathematical expression of (2) is as follows:
wherein the method comprises the steps ofIs the starting cost of the electric compressor or the natural gas compressor.
5. A gas load prediction based method as claimed in claim 1Method for scheduling an execution device, characterized in that the starting or closing costs of the compressor are given as penalty costs to be added to the total operating costs, one operating cycle being 7 hours, said closing costs of the step 1.5 compressorThe following are provided:
wherein,is the first 4 hours of a cycle, < >>Is indicative of a change in compressor status in 3 consecutive hours,/for a period of 3 hours>Penalty cost for less than 3 cycles of compressor operation,/->Indicate->The compressor is +.>State at periodic time.
6. The method for scheduling execution equipment based on gas load prediction according to claim 1, wherein said step 1.6 calculates and determines the supply and demand costs of the compressor: supply and demand costs refer to unnecessary compressor operation, supply and demand costsThe expression is as follows:
wherein the method comprises the steps ofPenalty cost indicating compressor supply over demand, < ->Is->Supply of individual compressors, +.>Is an operation periodIs a customer requirement of (1).
7. The method for scheduling the execution device based on the gas load prediction according to claim 1, wherein the operations of selecting wheel disc selection, single-point crossover, single-bit mutation and elite replacement in the genetic algorithm in the step 2.2 include fuel cost, maintenance cost of each compressor, starting cost of the compressor, penalty cost of discontinuous operation and sum of penalty costs for supply and demand, and the fitness function formula is as follows:
。
8. the method for scheduling execution equipment based on gas load prediction according to claim 1, wherein the method is applicable to natural gas load data of cities, regions and enterprises.
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