CN109858787B - Energy scheduling management method and device, readable medium and electronic equipment - Google Patents
Energy scheduling management method and device, readable medium and electronic equipment Download PDFInfo
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Abstract
The invention discloses an energy scheduling management method, an energy scheduling management device, a readable medium and electronic equipment, wherein the method comprises the following steps: acquiring schedulable gas quantity of each gas source in a transport network, minimum gas supply quantity and maximum gas supply quantity between each gas source and each gas station and required gas quantity of each gas station; acquiring vehicle scheduling data of a carrier corresponding to a transport network; forming an energy efficiency model corresponding to the transport network according to the vehicle scheduling data, the schedulable air volume of each air source, the minimum air supply volume and the maximum air supply volume between each air source and each air station and the required air volume of each air station; the energy efficiency model is optimized to determine energy scheduling data for the transport network. By the technical scheme, natural gas meeting user requirements can be timely and accurately carried to the gas station and transport capacity waste is avoided when the natural gas scheduling condition is managed, so that the natural gas scheduling condition can be managed more reasonably.
Description
Technical Field
The invention relates to the field of energy, in particular to an energy scheduling management method, an energy scheduling management device, a readable medium and electronic equipment.
Background
Natural gas is a clean energy source and is a common fuel in life and production. In the natural gas industry, a user needs to purchase liquefied natural gas from a gas source, and the gas source conveys the natural gas of the gas source to a gas station corresponding to a corresponding user through a vehicle of a carrier according to the gas purchase demand of the user so as to realize the scheduling of the natural gas.
Therefore, it can be seen that the basis for managing the scheduling condition of the natural gas is only the gas purchase demand of the user, and the actual operation condition in the scheduling process is not considered, for example, the schedulable gas volume of each gas source, the demand gas volume of the gas station, the minimum gas supply volume meeting the production requirement, the maximum gas supply volume exceeding the load capacity, the carrying capacity of the carrier and the like are not considered, which may result in that the natural gas meeting the demand of the user cannot be carried to the gas station timely and accurately and cause the transportation capacity waste, therefore, how to realize more reasonable management of the scheduling condition of the natural gas becomes the technical problem to be solved urgently.
Disclosure of Invention
The invention provides an energy scheduling management method, an energy scheduling management device, a readable medium and electronic equipment, which can timely and accurately carry natural gas meeting user requirements to a gas station, avoid transport capacity waste and realize more reasonable management on the scheduling condition of the natural gas.
In a first aspect, the present invention provides an energy scheduling management method, including:
acquiring schedulable gas volume of each gas source in a transport network, minimum gas supply volume and maximum gas supply volume between each gas source and each gas station, and required gas volume of each gas station;
acquiring vehicle scheduling data of a carrier corresponding to the transportation network;
forming an energy efficiency model corresponding to the transportation network according to the vehicle scheduling data, the schedulable gas volume of each gas source, the minimum gas supply volume and the maximum gas supply volume between each gas source and each gas station and the required gas volume of each gas station;
optimizing the energy efficiency model to determine energy scheduling data for the transportation network.
Preferably, the first and second electrodes are formed of a metal,
the vehicle scheduling data comprising: the transportation unit price of each energy transport vehicle, the maximum air-carrying capacity of each energy transport vehicle, the travel distance required by each energy transport vehicle to travel when each energy transport vehicle executes the transportation task between each gas station and each gas source, and the delay time;
then, the forming an energy efficiency model corresponding to the transportation network includes: forming an energy efficiency model which is composed of an objective function and a constraint condition and corresponds to the transportation network; wherein,
the objective function includes:
the constraint conditions include:
wherein Z represents the energy efficiency value, N represents the total amount of gas stations in the transportation network, M represents the total amount of gas sources in the transportation network, and F represents the total amount of the carrier energy transport vehicles;
cij(Hij) The recommended traffic between the ith gas source and the jth gas station is represented as HijThe unit profit between the ith gas source and the jth gas station;
Hijcharacterizing a recommended traffic volume between an ith gas source and a jth gas station;
xijcharacterizing a recommended sales volume between an ith gas source and a jth gas station;
Tfijtaking the value 0 or 1, TfijWhen the value is 1, the f energy transport vehicle is distributed to execute the transport task between the ith air source and the jth air station, TfijWhen the value is 0, the f energy transport vehicle is not distributed to execute the transport task between the ith air source and the jth air station;
Pfcharacterizing a transportation unit price of the f energy transportation vehicle;
Dfijcharacterizing a travel distance required to be traveled by the f-th energy source transport vehicle when the f-th energy source transport vehicle is allocated to perform a transport task between the ith gas source and the jth gas station;
yfijcharacterizing a corresponding energy transportation volume when the f energy transportation vehicle is allocated to execute a transportation task between the ith gas source and the jth gas station;
Cfrepresenting the maximum air capacity of the f energy transport vehicle;
Kfijcharacterizing a delay time required for the f-th energy source carrier to be assigned to perform a transport task between the ith gas source and the jth gas station;
aicharacterise schedulable gas quantity of ith gas source, bjCharacterizing the required gas quantity of a jth gas station;
Vaijcharacterizing a minimum supply volume, V, between an ith gas source and a jth gas stationbijCharacterizing a maximum air supply between an ith air supply and a jth air station;
w is a preset constant;
the optimizing the energy efficiency model to determine energy scheduling data for the transportation network includes:
and solving the optimal solution of the objective function according to the constraint conditions to obtain the recommended sales volume between each air source and each air station, the transportation task allocation of each energy transport vehicle and the energy transportation volume corresponding to each allocated transportation task.
Preferably, the first and second electrodes are formed of a metal,
the constraint further comprises:
wherein, P1、P2、P3、d1、d2Are all constants.
Preferably, the first and second electrodes are formed of a metal,
the solving of the optimal solution of the objective function according to the constraint condition comprises:
a1, initializing a population comprising a plurality of individuals according to the objective function and each independent variable in the constraint condition, wherein each individual comprises a first gene, a second gene and a third gene, the first gene comprises candidate sales volume between each air source and each air station, the second gene comprises candidate task allocation condition of each energy transport vehicle, and the third gene comprises energy transport volume corresponding to each transport task allocated to each energy transport vehicle;
a2, calculating the fitness value corresponding to each individual in the population, and recording the global optimal individual according to the fitness value corresponding to each individual;
a3, selecting and deleting a plurality of individuals in the population by adopting an optimal retention method according to the fitness value corresponding to each individual;
a4, randomly selecting the crossing positions of the first gene, the second gene and the third gene for each individual in the population, and pairwise crossing the three selected crossing positions to form a new individual in the population;
a5, carrying out mutation operation on the individuals of the population in a mode of randomly appointing mutation positions with uniform probability, and selecting uniformly distributed random numbers to replace the original genes of the individuals according to the mutation operation result;
a6, calculating the fitness value corresponding to each individual in the population, and updating the recorded global optimal individual according to the fitness value corresponding to each individual;
and A7, judging whether a preset termination condition is reached, if so, determining the global optimal individual as the optimal solution of the objective function, otherwise, executing A3.
In a second aspect, an embodiment of the present invention provides an energy scheduling management apparatus, including:
the operation data acquisition module is used for acquiring schedulable gas volume of each gas source in the transport network, minimum gas supply volume and maximum gas supply volume between each gas source and each gas station and required gas volume of each gas station;
the scheduling data acquisition module is used for acquiring vehicle scheduling data of a carrier corresponding to the transportation network;
the model building module is used for forming an energy efficiency model corresponding to the transportation network according to the vehicle scheduling data, the schedulable gas quantity of each gas source, the minimum gas supply quantity and the maximum gas supply quantity between each gas source and each gas station and the required gas quantity of each gas station;
and the optimization processing module is used for optimizing the energy efficiency model to determine energy scheduling data of the transportation network.
Preferably, the first and second electrodes are formed of a metal,
the vehicle scheduling data comprising: the transportation unit price of each energy transport vehicle, the maximum air-carrying capacity of each energy transport vehicle, the travel distance required by each energy transport vehicle to travel when each energy transport vehicle executes the transportation task between each gas station and each gas source, and the delay time;
the model construction module is used for forming an energy efficiency model which is composed of an objective function and a constraint condition and corresponds to the transportation network; wherein,
the objective function includes:
the constraint conditions include:
wherein Z represents the energy efficiency value, N represents the total amount of gas stations in the transportation network, M represents the total amount of gas sources in the transportation network, and F represents the total amount of the carrier energy transport vehicles;
cij(Hij) The recommended traffic between the ith gas source and the jth gas station is represented as HijThe unit profit between the ith gas source and the jth gas station;
Hijcharacterizing a recommended traffic volume between an ith gas source and a jth gas station;
xijcharacterizing a recommended sales volume between an ith gas source and a jth gas station;
Tfijtaking the value 0 or 1, TfijWhen the value is 1, the f energy transport vehicle is distributed to execute the transport task between the ith air source and the jth air station, TfijWhen the value is 0, the f energy transport vehicle is not distributed to execute the transport task between the ith air source and the jth air station;
Pfcharacterizing a transportation unit price of the f energy transportation vehicle;
Dfijcharacterizing that the f-th energy transporter is assigned to perform the i-th air source and the hThe travel distance required to travel during the transportation task among the j gas stations;
yfijcharacterizing a corresponding energy transportation volume when the f energy transportation vehicle is allocated to execute a transportation task between the ith gas source and the jth gas station;
Cfrepresenting the maximum air capacity of the f energy transport vehicle;
Kfijcharacterizing a delay time required for the f-th energy source carrier to be assigned to perform a transport task between the ith gas source and the jth gas station;
aicharacterise schedulable gas quantity of ith gas source, bjCharacterizing the required gas quantity of a jth gas station;
Vaijcharacterizing a minimum supply volume, V, between an ith gas source and a jth gas stationbijCharacterizing a maximum air supply between an ith air supply and a jth air station;
w is a preset constant;
and the optimization processing module is used for solving the optimal solution of the objective function according to the constraint condition to obtain the recommended sales volume between each air source and each air station, the transportation task allocation of each energy transport vehicle and the energy transportation volume corresponding to each allocated transportation task.
Preferably, the first and second electrodes are formed of a metal,
the constraint further comprises:
wherein, P1、P2、P3、d1、d2Are all constants.
Preferably, the first and second electrodes are formed of a metal,
the optimization processing module is used for executing the following steps A1-A7:
a1, initializing a population comprising a plurality of individuals according to the objective function and each independent variable in the constraint condition, wherein each individual comprises a first gene, a second gene and a third gene, the first gene comprises candidate sales volume between each air source and each air station, the second gene comprises candidate task allocation condition of each energy transport vehicle, and the third gene comprises energy transport volume corresponding to each transport task allocated to each energy transport vehicle;
a2, calculating the fitness value corresponding to each individual in the population, and recording the global optimal individual according to the fitness value corresponding to each individual;
a3, selecting and deleting a plurality of individuals in the population by adopting an optimal retention method according to the fitness value corresponding to each individual;
a4, randomly selecting the crossing positions of the first gene, the second gene and the third gene for each individual in the population, and pairwise crossing the three selected crossing positions to form a new individual in the population;
a5, carrying out mutation operation on the individuals of the population in a mode of randomly appointing mutation positions with uniform probability, and selecting uniformly distributed random numbers to replace the original genes of the individuals according to the mutation operation result;
a6, calculating the fitness value corresponding to each individual in the population, and updating the recorded global optimal individual according to the fitness value corresponding to each individual;
and A7, judging whether a preset termination condition is reached, if so, determining the global optimal individual as the optimal solution of the objective function, and otherwise, executing A3.
In a third aspect, the invention provides a readable medium comprising executable instructions, which when executed by a processor of an electronic device, perform the method according to any of the first aspect.
In a fourth aspect, the present invention provides an electronic device, comprising a processor and a memory storing execution instructions, wherein when the processor executes the execution instructions stored in the memory, the processor performs the method according to any one of the first aspect.
The invention provides an energy scheduling management method, an energy scheduling management device, a readable medium and electronic equipment, wherein the method comprises the steps of obtaining schedulable air volume of each air source in a transport network, minimum air volume and maximum air volume between each air source and each air station, required air volume of each air station and vehicle scheduling data of carriers corresponding to the transport network, forming an energy efficiency model corresponding to the transport network according to the obtained vehicle scheduling data, the schedulable air volume of each air source, the minimum air volume and the maximum air volume between each air source and each air station and the required air volume of each air station, and then optimizing the energy efficiency model to determine the energy scheduling data of the transport network; in the subsequent process, the energy transport vehicles of the carriers can be reasonably dispatched according to the obtained energy dispatching data to execute the energy transport tasks between each air source and each gas station, so that the natural gas meeting the user requirements can be accurately carried to the gas stations in time, and the transport capacity waste is avoided, thereby realizing more reasonable management on the dispatching condition of the natural gas.
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In order to more clearly illustrate the embodiments or the prior art solutions of the present invention, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments described in the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive labor.
Fig. 1 is a schematic flowchart illustrating an energy scheduling management method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an energy scheduling management apparatus according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail and completely with reference to the following embodiments and accompanying drawings. It is to be understood that the described embodiments are merely exemplary of the invention, and not restrictive of the full scope of the invention. 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.
As shown in fig. 1, an embodiment of the present invention provides an energy scheduling management method, including the following steps:
102, acquiring vehicle scheduling data of a carrier corresponding to the transportation network;
103, forming an energy efficiency model corresponding to the transportation network according to the vehicle scheduling data, the schedulable air volume of each air source, the minimum air supply volume and the maximum air supply volume between each air source and each air station and the required air volume of each air station;
and 104, optimizing the energy efficiency model to determine energy scheduling data of the transportation network.
As shown in fig. 1, in the method, schedulable gas volumes of each gas source in a transportation network, a minimum gas volume and a maximum gas volume between each gas source and each gas station, and a required gas volume of each gas station are obtained, vehicle scheduling data of a carrier corresponding to the transportation network is obtained, an energy efficiency model corresponding to the transportation network is formed according to the obtained vehicle scheduling data, the schedulable gas volumes of each gas source, the minimum gas volume and the maximum gas volume between each gas source and each gas station, and the required gas volume of each gas station, and then energy scheduling data of the transportation network can be determined by optimizing the energy efficiency model; in the subsequent process, the energy transport vehicles of the carriers can be reasonably dispatched according to the obtained energy dispatching data to execute the energy transport tasks between each air source and each gas station, so that the natural gas meeting the user requirements can be accurately carried to the gas stations in time, and the transport capacity waste is avoided, thereby realizing more reasonable management on the dispatching condition of the natural gas.
As will be appreciated by those skilled in the art, a transport network may be comprised of a plurality of gas sources belonging to a natural gas supplier and a plurality of gas stations belonging to a natural gas user (i.e., a customer) for transporting natural gas provided by the gas sources to the respective gas stations by their energy carriers. The carrier may be the natural gas supplier itself and/or a third party shipping service contractor.
It should be noted that the users belonging to each gas station may include merchants, industrial users, power plants, and other types of customers, and the business criticality and the natural gas demand amount respectively corresponding to different users are different. Therefore, a large number of users can be divided into different categories according to the business criticality of the users and the natural gas demand, for example, the large number of users are divided into a plurality of categories such as a guarantee customer, a special supply customer, a stable customer and a protocol customer, and correspondingly, under the condition that the required gas volume required for meeting the maximum production demand corresponding to each user is known, the minimum gas supply volume and the maximum gas supply volume corresponding to each gas source and each gas station can be set according to the category of the user to which each gas station belongs.
In a specific service scenario, the natural gas provided by the gas source can completely meet the gas demand of the gas station, but the carrying capacity of the carrier is not enough to support the transportation of all the natural gas meeting the user demand to the corresponding gas station, i.e. the carrying capacity of the carrier cannot meet the natural gas scheduling demand of the transportation network, at this time, in order to avoid the waste of the carrying capacity of the carrier on the premise of ensuring the maximization of the profit of the supplier corresponding to the transportation network, respectively transport the natural gas not less than the natural gas meeting the minimum production demand of each gas station, realize the reasonable scheduling of the energy carrier and reduce the resource consumption, in one embodiment of the invention,
the vehicle scheduling data comprising: the transportation unit price of each energy transport vehicle, the maximum air-carrying capacity of each energy transport vehicle, the travel distance required by each energy transport vehicle to travel when each energy transport vehicle executes the transportation task between each gas station and each gas source, and the delay time;
then, the forming an energy efficiency model corresponding to the transportation network includes: forming an energy efficiency model which is composed of an objective function and a constraint condition and corresponds to the transportation network; wherein,
the objective function includes:
the constraint conditions include:
wherein Z represents the energy efficiency value, N represents the total amount of gas stations in the transportation network, M represents the total amount of gas sources in the transportation network, and F represents the total amount of the carrier energy transport vehicles;
cij(Hij) The recommended traffic between the ith gas source and the jth gas station is represented as HijThe unit profit between the ith gas source and the jth gas station;
Hijcharacterizing a recommended traffic volume between an ith gas source and a jth gas station;
xijcharacterizing a recommended sales volume between an ith gas source and a jth gas station;
Tfijtaking the value 0 or 1, TfijWhen the value is 1, the f energy transport vehicle is distributed to execute the transport task between the ith air source and the jth air station, TfijWhen the value is 0, the f energy transport vehicle is not distributed to execute the transport task between the ith air source and the jth air station;
Pfcharacterizing a transportation unit price of the f energy transportation vehicle;
Dfijcharacterization ofThe f energy transport vehicle is allocated to the travel distance required to travel when the transport task between the ith air source and the jth air station is executed;
yfijcharacterizing a corresponding energy transportation volume when the f energy transportation vehicle is allocated to execute a transportation task between the ith gas source and the jth gas station;
Cfrepresenting the maximum air capacity of the f energy transport vehicle;
Kfijcharacterizing a delay time required for the f-th energy source carrier to be assigned to perform a transport task between the ith gas source and the jth gas station;
aicharacterise schedulable gas quantity of ith gas source, bjCharacterizing the required gas quantity of a jth gas station;
Vaijcharacterizing a minimum supply volume, V, between an ith gas source and a jth gas stationbijCharacterizing a maximum air supply between an ith air supply and a jth air station;
w is a preset constant;
the optimizing the energy efficiency model to determine energy scheduling data for the transportation network includes:
and solving the optimal solution of the objective function according to the constraint conditions to obtain the recommended sales volume between each air source and each air station, the transportation task allocation of each energy transport vehicle and the energy transportation volume corresponding to each allocated transportation task.
In this embodiment, the constraint specifically refers to:
(1) the carrier can provide one or more energy transport vehicles to simultaneously execute the transport task between the ith air source and the jth air station, namely, under the condition that other conditions are met between the ith air source and the jth air station, the recommended transport quantity between the ith air source and the jth air station is equal to the sum of the energy transport quantities corresponding to the energy transport vehicles which are allocated to execute the transport task between the ith air source and the jth air station when the transport tasks between the ith air source and the jth air station are respectively executed.
(2) The recommended transportation volume between the ith gas source and the jth gas station is not less than the minimum gas supply volume between the ith gas source and the jth gas station, so that the natural gas actually transported to each gas station can meet the minimum production requirement of a user corresponding to the gas station when the energy scheduling condition is managed according to energy scheduling data in the following process; meanwhile, the recommended sales volume of the gas station recommending that the gas station purchase natural gas from the ith gas source can be slightly larger than the corresponding minimum gas supply volume but not larger than the schedulable gas volume of the ith gas source.
(3) The ith gas source may sell natural gas to multiple gas stations, but the total amount of natural gas actually sold by the ith gas source to each gas station should not be greater than the schedulable amount of gas for the ith gas source.
(4) The plurality of gas sources can simultaneously provide natural gas for the jth gas station, but the total amount of the natural gas provided by each gas source for the jth gas station is not more than the required gas amount corresponding to the jth gas station, and the required gas amount specifically refers to the total amount of the natural gas required to be consumed for meeting the highest production requirement corresponding to the jth gas station within a set time period.
(5) The f-th energy source transport vehicle can be distributed to execute the transport tasks between the ith air source and the jth air station, but can only execute the direct transport tasks between one air source and one air station at the same time, and cannot execute a plurality of transport tasks between one air source and a plurality of air stations or between a plurality of air sources and one air station at the same time.
(6) The f energy source transport vehicle can be distributed to execute the transport task between the ith gas source and the jth gas station, but the corresponding energy source transport amount when the f energy source transport vehicle executes the transport task between the ith gas source and the jth gas station is not more than the maximum gas carrying amount.
For example, the predetermined constant w is used as a penalty factor, and may be an empirical value obtained by gradually adjusting according to the historical scheduling management situation.
For example, the delay time KfijSpecifically, when the f-th energy source transport vehicle executes a transport task between the position of the ith gas source and the jth gas station, the sum of the predicted time intervals of the ith gas source and the jth gas station which arrive earlier or later is respectively calculated. In one possible implementation, the data may be obtained byDistance D traveledfijEstimating a first estimated time of arriving at the ith air source when the ith energy transport vehicle executes a transport task between the position of the ith air source and the jth air station and estimating a second estimated time of arriving at the jth air station, comparing the first estimated time of arriving at the ith air source with a preset limited time of arriving at the ith air source to obtain a first predicted time interval, comparing the second estimated time of arriving at the jth air station with a preset limited time of arriving at the jth air station to obtain a second predicted time interval, wherein the sum of the first predicted time interval and the second predicted time interval is delay time Kfij(ii) a For example, the limited arrival time of the ith gas source is time TiaAnd TibIn between, the arrival time defined for the jth station is time TjaAnd TjbAssume that the f-th energy transport vehicle is earlier than time TiaOr later than the time TibA total of t1The time interval reaches the ith gas source and is earlier than the time TjaOr later than the time TjbA total of t2A time interval arrives, then Kfij＝t1+t2。
In addition, D isfijSpecifically, when the f-th energy source transport vehicle is assigned to perform a transport task between the ith gas source and the jth gas station, the sum of a first travel distance from the position of the f-th energy source transport vehicle to the ith gas source and a second travel distance from the ith gas source to the jth gas station.
It is apparent that in this energy efficiency model, HijAnd yfijThere is a direct correlation, i.e. the variables present in the energy efficiency model include only xij、TfijAnd yfijWhen optimizing the energy efficiency model, only x needs to be obtainedij、TfijAnd yfijAnd obtaining the recommended sales volume between each air source and each air station, the transportation task allocation of each energy transportation vehicle and the energy transportation volume corresponding to each allocated transportation task.
The unit profit between the ith gas source and the jth gas station in the objective function can be realized by at least one of the following two implementation manners.
In the implementation mode 1, the gas purchasing unit price and the gas supply unit price of the ith gas source are directly obtained, and the difference value between the gas supply unit price and the gas purchasing unit price is used as the unit profit.
Implementation 2, associating the recommended transportation amount between the ith gas source and the jth gas station, forming a piecewise function with the recommended transportation amount between the ith gas source and the jth gas station as an independent variable, and using the piecewise function as a constraint condition.
For implementation 2, the piecewise function is specifically:
wherein, P1、P2、P3、d1、d2Are all constants.
Solving the optimal solution of the objective function according to the constraint conditions may specifically include the following steps a1 to a 7.
A1, initializing a population comprising a plurality of individuals according to the objective function and each independent variable in the constraint condition, wherein each individual comprises a first gene, a second gene and a third gene, the first gene comprises candidate sales volume between each air source and each air station, the second gene comprises candidate task allocation condition of each energy transport vehicle, and the third gene comprises energy transport volume corresponding to each transport task allocated to each energy transport vehicle;
a2, calculating the fitness value corresponding to each individual in the population, and recording the global optimal individual according to the fitness value corresponding to each individual;
a3, selecting and deleting a plurality of individuals in the population by adopting an optimal retention method according to the fitness value corresponding to each individual;
a4, randomly selecting the crossing positions of the first gene, the second gene and the third gene for each individual in the population, and pairwise crossing the three selected crossing positions to form a new individual in the population;
a5, carrying out mutation operation on the individuals of the population in a mode of randomly appointing mutation positions with uniform probability, and selecting uniformly distributed random numbers to replace the original genes of the individuals according to the mutation operation result;
a6, calculating the fitness value corresponding to each individual in the population, and updating the recorded global optimal individual according to the fitness value corresponding to each individual;
and A7, judging whether a preset termination condition is reached, if so, determining the global optimal individual as the optimal solution of the objective function, otherwise, executing A3.
It should be understood by those skilled in the art that the termination condition may be that the number of iterations of the population reaches the maximum number of iterations, i.e., by determining whether the number of loop executions A3-a 6 (i.e., the number of iterations) reaches the maximum number of iterations, and if so, determining the globally optimal individual as the optimal solution of the objective function, otherwise, executing A3. The termination condition may be that the change between the optimum individuals of two consecutive updates satisfies a preset condition.
It should be noted that the optimal solution of the objective function may also be determined through other algorithms.
For example,
the first gene may be recorded as: [ x ] of00…xij…xMN]；
The second gene can be recorded as: [ T ]000…Tfij…TFMN]；
The third gene can be recorded as: [ y ]000…yfij…yFMN]；
For each individual in the population, each element in each gene constituting the individual should be represented separately using a binary code.
Referring to fig. 2, based on the same concept as the method embodiment of the present invention, an embodiment of the present invention further provides an energy scheduling management apparatus, including:
an operation data obtaining module 201, configured to obtain an schedulable gas volume of each gas source in a transportation network, a minimum gas supply volume and a maximum gas supply volume between each gas source and each gas station, and a required gas volume of each gas station;
the scheduling data acquisition module 202 is configured to acquire vehicle scheduling data of a carrier corresponding to the transportation network;
the model building module 203 is configured to form an energy efficiency model corresponding to the transportation network according to the vehicle scheduling data, the schedulable gas volume of each gas source, a minimum gas supply volume and a maximum gas supply volume between each gas source and each gas station, and a required gas volume of each gas station;
an optimization processing module 204, configured to optimize the energy efficiency model to determine energy scheduling data of the transportation network.
In one embodiment of the present invention, the vehicle scheduling data includes: the transportation unit price of each energy transport vehicle, the maximum air-carrying capacity of each energy transport vehicle, the travel distance required by each energy transport vehicle to travel when each energy transport vehicle executes the transportation task between each gas station and each gas source, and the delay time;
then, the model building module 203 is configured to form an energy efficiency model corresponding to the transportation network, where the energy efficiency model is composed of an objective function and a constraint condition; wherein,
the objective function includes:
the constraint conditions include:
wherein Z represents the energy efficiency value, N represents the total amount of gas stations in the transportation network, M represents the total amount of gas sources in the transportation network, and F represents the total amount of the carrier energy transport vehicles;
cij(Hij) The recommended traffic between the ith gas source and the jth gas station is represented as HijThe unit profit between the ith gas source and the jth gas station;
Hijcharacterizing a recommended traffic volume between an ith gas source and a jth gas station;
xijcharacterizing a recommended sales volume between an ith gas source and a jth gas station;
Tfijtaking the value 0 or 1, TfijWhen the value is 1, the f energy transport vehicle is distributed to execute the transport task between the ith air source and the jth air station, TfijWhen the value is 0, the f energy transport vehicle is not distributed to execute the transport task between the ith air source and the jth air station;
Pfcharacterizing a transportation unit price of the f energy transportation vehicle;
Dfijcharacterizing a travel distance required to be traveled by the f-th energy source transport vehicle when the f-th energy source transport vehicle is allocated to perform a transport task between the ith gas source and the jth gas station;
yfijcharacterizing a corresponding energy transportation volume when the f energy transportation vehicle is allocated to execute a transportation task between the ith gas source and the jth gas station;
Cfrepresenting the maximum air capacity of the f energy transport vehicle;
Kfijcharacterizing a delay time required for the f-th energy source carrier to be assigned to perform a transport task between the ith gas source and the jth gas station;
aicharacterise schedulable gas quantity of ith gas source, bjCharacterizing the required gas quantity of a jth gas station;
Vaijcharacterizing a minimum supply volume, V, between an ith gas source and a jth gas stationbijCharacterizing a maximum air supply between an ith air supply and a jth air station;
w is a preset constant;
the optimization processing module 204 is configured to solve the optimal solution of the objective function according to the constraint condition to obtain a recommended sales volume between each gas source and each gas station, a transportation task allocation of each energy transport vehicle, and an energy transportation volume corresponding to each allocated transportation task.
In an embodiment of the present invention, the constraint further includes:
wherein, P1、P2、P3、d1、d2Are all constants.
In an embodiment of the present invention, the optimization processing module 204 is configured to perform the following steps a1 to a 7:
a1, initializing a population comprising a plurality of individuals according to the objective function and each independent variable in the constraint condition, wherein each individual comprises a first gene, a second gene and a third gene, the first gene comprises candidate sales volume between each air source and each air station, the second gene comprises candidate task allocation condition of each energy transport vehicle, and the third gene comprises energy transport volume corresponding to each transport task allocated to each energy transport vehicle;
a2, calculating the fitness value corresponding to each individual in the population, and recording the global optimal individual according to the fitness value corresponding to each individual;
a3, selecting and deleting a plurality of individuals in the population by adopting an optimal retention method according to the fitness value corresponding to each individual;
a4, randomly selecting the crossing positions of the first gene, the second gene and the third gene for each individual in the population, and pairwise crossing the three selected crossing positions to form a new individual in the population;
a5, carrying out mutation operation on the individuals of the population in a mode of randomly appointing mutation positions with uniform probability, and selecting uniformly distributed random numbers to replace the original genes of the individuals according to the mutation operation result;
a6, calculating the fitness value corresponding to each individual in the population, and updating the recorded global optimal individual according to the fitness value corresponding to each individual;
a7, judging whether a preset termination condition is reached, if so, determining the global optimal individual as the optimal solution of the objective function, otherwise, executing A3
For convenience of description, the above device embodiments are described with functions divided into various units or modules, and the functions of the units or modules may be implemented in one or more software and/or hardware when implementing the present invention.
Fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention. On the hardware level, the electronic device comprises a processor and optionally an internal bus, a network interface and a memory. The Memory may include a Memory, such as a Random-Access Memory (RAM), and may further include a non-volatile Memory, such as at least 1 disk Memory. Of course, the electronic device may also include hardware required for other services.
The processor, the network interface, and the memory may be connected to each other via an internal bus, which may be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, an EISA (Extended Industry Standard Architecture) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one double-headed arrow is shown in FIG. 3, but this does not indicate only one bus or one type of bus.
And the memory is used for storing the execution instruction. In particular, a computer program that can be executed by executing instructions. The memory may include both memory and non-volatile storage and provides execution instructions and data to the processor.
In a possible implementation manner, the processor reads the corresponding execution instruction from the nonvolatile memory to the memory and then runs the corresponding execution instruction, and may also obtain the corresponding execution instruction from other devices to form the energy scheduling management apparatus on a logical level. The processor executes the execution instruction stored in the memory, so as to implement the energy scheduling management method provided by any embodiment of the invention through the executed execution instruction.
The method executed by the energy scheduling management apparatus according to the embodiment of the invention shown in fig. 2 can be applied to a processor, or implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The steps of the method disclosed in connection with the embodiments of the present invention may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software module may be located in ram, flash memory, rom, prom, or eprom, registers, etc. storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor.
An embodiment of the present invention further provides a readable storage medium, where the readable storage medium stores an execution instruction, and when the stored execution instruction is executed by a processor of an electronic device, the electronic device can be caused to perform the energy scheduling management method provided in any embodiment of the present invention, and is specifically configured to perform the method shown in fig. 1.
The electronic device described in the foregoing embodiments may be a computer.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects.
The embodiments of the present invention are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, as for the apparatus embodiment, since it is substantially similar to the method embodiment, the description is relatively simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present invention, and is not intended to limit the present invention. Various modifications and alterations to this invention will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the scope of the claims of the present invention.
Claims (4)
1. An energy scheduling management method, comprising:
acquiring schedulable gas volume of each gas source in a transport network, minimum gas supply volume and maximum gas supply volume between each gas source and each gas station, and required gas volume of each gas station;
acquiring vehicle scheduling data of a carrier corresponding to the transportation network;
forming an energy efficiency model corresponding to the transportation network according to the vehicle scheduling data, the schedulable gas volume of each gas source, the minimum gas supply volume and the maximum gas supply volume between each gas source and each gas station and the required gas volume of each gas station;
optimizing the energy efficiency model to determine energy scheduling data for the transportation network;
the vehicle scheduling data comprising: the transportation unit price of each energy transport vehicle, the maximum air-carrying capacity of each energy transport vehicle, the travel distance required by each energy transport vehicle to travel when each energy transport vehicle executes the transportation task between each gas station and each gas source, and the delay time;
then, the forming an energy efficiency model corresponding to the transportation network includes: forming an energy efficiency model which is composed of an objective function and a constraint condition and corresponds to the transportation network; wherein,
the objective function includes:
the constraint conditions include:
wherein Z represents the energy efficiency value, N represents the total amount of gas stations in the transportation network, M represents the total amount of gas sources in the transportation network, and F represents the total amount of the carrier energy transport vehicles;
cij(Hij) The recommended traffic between the ith gas source and the jth gas station is represented as HijThe unit profit between the ith gas source and the jth gas station;
Hijcharacterizing a recommended traffic volume between an ith gas source and a jth gas station;
xijcharacterizing a recommended sales volume between an ith gas source and a jth gas station;
Tfijtaking the value 0 or 1, TfijWhen the value is 1, the f energy transport vehicle is distributed to execute the transport task between the ith air source and the jth air station, TfijWhen the value is 0, the f energy transport vehicle is not distributed to execute the transport task between the ith air source and the jth air station;
Pfcharacterizing a transportation unit price of the f energy transportation vehicle;
Dfijcharacterizing a travel distance required to be traveled by the f-th energy source transport vehicle when the f-th energy source transport vehicle is allocated to perform a transport task between the ith gas source and the jth gas station;
yfijcharacterizing a corresponding energy transportation volume when the f energy transportation vehicle is allocated to execute a transportation task between the ith gas source and the jth gas station;
Cfrepresenting the maximum air capacity of the f energy transport vehicle;
Kfijcharacterizing a delay time required for the f-th energy source carrier to be assigned to perform a transport task between the ith gas source and the jth gas station;
aicharacterise schedulable gas quantity of ith gas source, bjCharacterizing the required gas quantity of a jth gas station;
Vaijcharacterizing a minimum supply volume, V, between an ith gas source and a jth gas stationbijCharacterizing a maximum air supply between an ith air supply and a jth air station;
w is a preset constant;
the optimizing the energy efficiency model to determine energy scheduling data for the transportation network includes:
solving the optimal solution of the objective function according to the constraint conditions to obtain the recommended sales volume between each air source and each air station, the transportation task allocation of each energy transport vehicle and the energy transportation volume corresponding to each allocated transportation task;
the constraint further comprises:
wherein, P1、P2、P3、d1、d2Are all constants;
the solving of the optimal solution of the objective function according to the constraint condition comprises:
a1, initializing a population comprising a plurality of individuals according to the objective function and each independent variable in the constraint condition, wherein each individual comprises a first gene, a second gene and a third gene, the first gene comprises candidate sales volume between each air source and each air station, the second gene comprises candidate task allocation condition of each energy transport vehicle, and the third gene comprises energy transport volume corresponding to each transport task allocated to each energy transport vehicle;
a2, calculating the fitness value corresponding to each individual in the population, and recording the global optimal individual according to the fitness value corresponding to each individual;
a3, selecting and deleting a plurality of individuals in the population by adopting an optimal retention method according to the fitness value corresponding to each individual;
a4, randomly selecting the crossing positions of the first gene, the second gene and the third gene for each individual in the population, and pairwise crossing the three selected crossing positions to form a new individual in the population;
a5, carrying out mutation operation on the individuals of the population in a mode of randomly appointing mutation positions with uniform probability, and selecting uniformly distributed random numbers to replace the original genes of the individuals according to the mutation operation result;
a6, calculating the fitness value corresponding to each individual in the population, and updating the recorded global optimal individual according to the fitness value corresponding to each individual;
and A7, judging whether a preset termination condition is reached, if so, determining the global optimal individual as the optimal solution of the objective function, otherwise, executing A3.
2. An energy scheduling management apparatus, comprising:
the operation data acquisition module is used for acquiring schedulable gas volume of each gas source in the transport network, minimum gas supply volume and maximum gas supply volume between each gas source and each gas station and required gas volume of each gas station;
the scheduling data acquisition module is used for acquiring vehicle scheduling data of a carrier corresponding to the transportation network;
the model building module is used for forming an energy efficiency model corresponding to the transportation network according to the vehicle scheduling data, the schedulable gas quantity of each gas source, the minimum gas supply quantity and the maximum gas supply quantity between each gas source and each gas station and the required gas quantity of each gas station;
an optimization processing module for optimizing the energy efficiency model to determine energy scheduling data for the transportation network;
the vehicle scheduling data comprising: the transportation unit price of each energy transport vehicle, the maximum air-carrying capacity of each energy transport vehicle, the travel distance required by each energy transport vehicle to travel when each energy transport vehicle executes the transportation task between each gas station and each gas source, and the delay time;
the model construction module is used for forming an energy efficiency model which is composed of an objective function and a constraint condition and corresponds to the transportation network; wherein,
the objective function includes:
the constraint conditions include:
wherein Z represents the energy efficiency value, N represents the total amount of gas stations in the transportation network, M represents the total amount of gas sources in the transportation network, and F represents the total amount of the carrier energy transport vehicles;
cij(Hij) The recommended traffic between the ith gas source and the jth gas station is represented as HijThe unit profit between the ith gas source and the jth gas station;
Hijcharacterizing a recommended traffic volume between an ith gas source and a jth gas station;
xijcharacterizing a recommended sales volume between an ith gas source and a jth gas station;
Tfijtaking the value 0 or 1, TfijWhen the value is 1, the f energy transport vehicle is distributed to execute the transport task between the ith air source and the jth air station, TfijWhen the value is 0, the f energy transport vehicle is not distributed to execute the transport task between the ith air source and the jth air station;
Pfcharacterizing a transportation unit price of the f energy transportation vehicle;
Dfijcharacterizing a travel distance required to be traveled by the f-th energy source transport vehicle when the f-th energy source transport vehicle is allocated to perform a transport task between the ith gas source and the jth gas station;
yfijcharacterizing a corresponding energy transportation volume when the f energy transportation vehicle is allocated to execute a transportation task between the ith gas source and the jth gas station;
Cfrepresenting the maximum air capacity of the f energy transport vehicle;
Kfijcharacterizing a delay time required for the f-th energy source carrier to be assigned to perform a transport task between the ith gas source and the jth gas station;
aicharacterise schedulable gas quantity of ith gas source, bjCharacterizing the required gas quantity of a jth gas station;
Vaijcharacterizing a minimum supply volume, V, between an ith gas source and a jth gas stationbijCharacterizing a maximum air supply between an ith air supply and a jth air station;
w is a preset constant;
the optimization processing module is used for solving the optimal solution of the objective function according to the constraint condition to obtain the recommended sales volume between each air source and each air station, the transportation task allocation of each energy transport vehicle and the energy transportation volume corresponding to each allocated transportation task;
the constraint further comprises:
wherein, P1、P2、P3、d1、d2Are all constants;
the optimization processing module is used for executing the following steps A1-A7:
a1, initializing a population comprising a plurality of individuals according to the objective function and each independent variable in the constraint condition, wherein each individual comprises a first gene, a second gene and a third gene, the first gene comprises candidate sales volume between each air source and each air station, the second gene comprises candidate task allocation condition of each energy transport vehicle, and the third gene comprises energy transport volume corresponding to each transport task allocated to each energy transport vehicle;
a2, calculating the fitness value corresponding to each individual in the population, and recording the global optimal individual according to the fitness value corresponding to each individual;
a3, selecting and deleting a plurality of individuals in the population by adopting an optimal retention method according to the fitness value corresponding to each individual;
a4, randomly selecting the crossing positions of the first gene, the second gene and the third gene for each individual in the population, and pairwise crossing the three selected crossing positions to form a new individual in the population;
a5, carrying out mutation operation on the individuals of the population in a mode of randomly appointing mutation positions with uniform probability, and selecting uniformly distributed random numbers to replace the original genes of the individuals according to the mutation operation result;
a6, calculating the fitness value corresponding to each individual in the population, and updating the recorded global optimal individual according to the fitness value corresponding to each individual;
and A7, judging whether a preset termination condition is reached, if so, determining the global optimal individual as the optimal solution of the objective function, and otherwise, executing A3.
3. A readable medium comprising executable instructions that, when executed by a processor of an electronic device, cause the electronic device to perform the method of claim 1.
4. An electronic device comprising a processor and a memory storing execution instructions, the processor performing the method of claim 1 when the processor executes the execution instructions stored by the memory.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103971174A (en) * | 2014-05-06 | 2014-08-06 | 大连理工大学 | Hydropower station group optimized dispatching method based on improved quantum-behaved particle swarm algorithm |
| CN104408589A (en) * | 2014-10-24 | 2015-03-11 | 陕西科技大学 | AGV optimization scheduling method based on mixed particle swarm optimization |
| CN106447524A (en) * | 2016-07-12 | 2017-02-22 | 广东电网有限责任公司电力科学研究院 | User energy center operation energy consumption cost control method and system |
| CN106707778A (en) * | 2016-12-06 | 2017-05-24 | 长沙理工大学 | Model predictive control-based home integrated energy intelligent optimization and management system |
| CN107578119A (en) * | 2017-08-09 | 2018-01-12 | 泉州装备制造研究所 | A kind of resource allocation global optimization method of intelligent dispatching system |
| CN107609681A (en) * | 2017-08-22 | 2018-01-19 | 西安建筑科技大学 | A kind of more metal multiple target ore-proportioning methods based on APSO algorithm |
-
2019
- 2019-01-17 CN CN201910042631.2A patent/CN109858787B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103971174A (en) * | 2014-05-06 | 2014-08-06 | 大连理工大学 | Hydropower station group optimized dispatching method based on improved quantum-behaved particle swarm algorithm |
| CN104408589A (en) * | 2014-10-24 | 2015-03-11 | 陕西科技大学 | AGV optimization scheduling method based on mixed particle swarm optimization |
| CN106447524A (en) * | 2016-07-12 | 2017-02-22 | 广东电网有限责任公司电力科学研究院 | User energy center operation energy consumption cost control method and system |
| CN106707778A (en) * | 2016-12-06 | 2017-05-24 | 长沙理工大学 | Model predictive control-based home integrated energy intelligent optimization and management system |
| CN107578119A (en) * | 2017-08-09 | 2018-01-12 | 泉州装备制造研究所 | A kind of resource allocation global optimization method of intelligent dispatching system |
| CN107609681A (en) * | 2017-08-22 | 2018-01-19 | 西安建筑科技大学 | A kind of more metal multiple target ore-proportioning methods based on APSO algorithm |
Non-Patent Citations (2)
| Title |
|---|
| LNG车辆运输调度优化模型研究;刘振玮;《中国优秀硕士学位论文全文数据库》;20130315(第3期);第1-4章 * |
| 天然气管网系统输配气运行方案优化;李波;《石油规划设计》;20011031;第12卷(第5期);第22～25页 * |
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