CN112884221A - Local area network multi-energy mutual-aid optimization scheduling method and device - Google Patents
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Abstract
Embodiments of the present disclosure provide a local area network multi-energy mutual aid optimization scheduling method, apparatus, device, and computer-readable storage medium. The method comprises the steps of calculating predicted values of distributed power sources and electricity/cold/heat loads in a local area network; establishing a local area network multi-energy mutual-aid optimization scheduling model taking the operation cost as a target based on the distributed power supply and the electricity/cold/heat load predicted value; and optimally solving the energy cost and energy consumption of the local area network based on the local area network multi-energy mutual-aid optimization scheduling model. In this way, the distributed source-storage-load matching scheduling in the building can be realized under the conditions of less training sample requirements, shorter training convergence time and lower computational power requirements.
Description
Technical Field
Embodiments of the present disclosure relate generally to the field of grid power supply technologies, and more particularly, to a local area network multi-energy mutual aid optimization scheduling method, apparatus, device, and computer-readable storage medium.
Background
The comprehensive energy service is an energy service innovation mode formed by arranging and combining multiple energy supply modes of an energy supply side and multiple demand responses of an energy utilization side, and covers the aspects of energy planning design, engineering investment construction, multi-energy operation service, investment and financing service and the like. The comprehensive energy service is essentially energy industry structure remodeling caused by new technology revolution, green development and new energy rising, so that new business states, business modes and service modes are promoted to be continuously innovated. The comprehensive energy service has the characteristics of integration, interconnection, sharing, high efficiency and friendliness.
However, in the existing comprehensive energy service, the dispatching of electricity, gas, cold and hot energy is automatically executed based on manual work or simple rules, the energy use structure cannot be optimized, the comprehensive energy use efficiency in an energy network is not high, and the outsourcing energy cost cannot be reduced.
Disclosure of Invention
According to an embodiment of the present disclosure, a local area network multi-capability mutual-aid optimized scheduling scheme is provided.
In a first aspect of the disclosure, a local area network multi-energy mutual aid optimized scheduling method is provided. The method comprises the following steps: calculating a predicted value of a distributed power supply and an electricity/cold/heat load in a local area network; establishing a local area network multi-energy mutual-aid optimization scheduling model taking the operation cost as a target based on the distributed power supply and the electricity/cold/heat load predicted value; and optimally solving the energy cost and energy consumption of the local area network based on the local area network multi-energy mutual-aid optimization scheduling model.
The above aspects and any possible implementations further provide an implementation in which the local area network includes a distributed power supply and an energy storage battery, and the integrated operation of the electric, gas and thermal energy sources provides an electric load, a cold load and a thermal load.
The above-described aspect and any possible implementation manner further provide an implementation manner, wherein calculating the predicted values of the distributed power sources and the electric/cold/heat loads in the local area network further includes: establishing a training sample of a neural network model according to operation historical data, outsourcing electricity historical data, refrigeration historical data, heat supply historical data, corresponding time and weather forecast data of a distributed power supply in a local area network; training a neural network model according to the training samples; and inputting the date to be predicted and weather forecast data into the trained neural network model to obtain the predicted values of the distributed power supply and the electricity/cold/heat load.
The above aspects and any possible implementation manners further provide an implementation manner, wherein the neural network model is obtained by training according to operation historical data, outsourcing electricity historical data, refrigeration historical data and heating historical data of distributed power supplies in a large number of local area networks, and corresponding time and weather forecast data; and carrying out transfer training on the neural network model according to the training samples.
The above-described aspects and any possible implementation further provide an implementation, wherein the establishing of the run-cost-targeted local area network multi-energy mutual-aid optimized scheduling model comprises: and establishing a mathematical model and a constraint function model of each device in the local area network, and establishing a local area network multi-energy mutual-aid optimization scheduling model based on the mathematical model and the constraint function model of each device.
The above-described aspects and any possible implementations further provide an implementation in which the local area network mutual-economic optimization scheduling model is as follows: and the local area network operation cost in a certain time is equal to electricity price, purchased electric power, gas price, purchased airflow rate, heat price, purchased heat flow rate, distributed power supply operation cost and energy storage battery operation cost.
The above-described aspects and any possible implementation further provide an implementation in which the optimal solution for energy costs and energy usage of the local area network includes: establishing a constraint condition matrix through the constraint function model of each device, and performing constraint truncation on the output result of each model; acquiring energy used within a certain time which meets the constraint condition matrix, and determining energy cost according to the energy price; and optimally solving the energy cost and the energy consumption.
In a second aspect of the disclosure, a local area network multi-energy mutual optimization scheduler. The device includes: the prediction module is used for calculating the predicted values of the distributed power supply and the electricity/cold/heat load in the local area network; the model establishing module is used for establishing a local area network multi-energy mutual-aid optimized scheduling model taking the running cost as a target based on the distributed power supply and the electricity/cold/heat load predicted value; and the optimal solving module is used for optimally solving the energy cost and the energy consumption of the local area network based on the local area network multi-energy mutual-aid optimal scheduling model.
In a third aspect of the disclosure, an electronic device is provided. The electronic device includes: a memory having a computer program stored thereon and a processor implementing the method as described above when executing the program.
In a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored which, when being executed by a processor, carries out the method as according to the first and/or second aspect of the present disclosure.
It should be understood that the statements herein reciting aspects are not intended to limit the critical or essential features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like or similar reference characters designate like or similar elements, and wherein:
FIG. 1 is a flow chart illustrating a local area network multi-capability mutual aid optimization scheduling method according to an embodiment of the disclosure;
FIG. 2 illustrates a block diagram of a local area network in accordance with an embodiment of the present disclosure;
FIG. 3 is a diagram illustrating a local area network multi-energy mutual aid optimized scheduling model according to an embodiment of the disclosure;
FIG. 4 is a block diagram of a local area network multi-capability mutual aid optimized scheduler in accordance with an embodiment of the present disclosure;
FIG. 5 illustrates a block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are some, but not all embodiments of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
In the context of the present disclosure, it is,
the energy use structure is optimized, the outsourcing energy cost is reduced, and the comprehensive energy use efficiency in the energy network is improved.
Fig. 1 is a flowchart illustrating a local area network multi-capability mutual aid optimization scheduling method according to an embodiment of the disclosure, as shown in fig. 1, including the following steps:
at block 102, calculating distributed power and electricity/cold/heat load predictions in a local area network;
in some embodiments, the local area network generally has a large power consumption, includes various controllable loads, has distributed energy facilities such as distributed power sources and energy storage, and comprehensively operates energy types such as electricity, gas and heat, and is an important carrier of an energy internet. As shown in fig. 2 of the accompanying drawings, in the local area network, an electric load can be provided by purchasing electricity from outside, generating electricity by a distributed power supply and an energy storage battery; the distributed power generation comprises a micro-combustion engine, a photovoltaic, a fan and the like. The power supply and refrigeration of the air conditioner and the power supply of the electric boiler can be realized by purchasing electricity from outside; the energy storage battery can also be used for supplying power for the air conditioner through external electricity purchase and distributed power generation; the gas can be supplied to the gas boiler for heat supply through the purchased gas; heat supply can be carried out through purchased heat; wherein, the air conditioner refrigerates through the ventilating duct to provide cold load; the electric boiler and the gas boiler supply heat through the ventilation pipeline to provide heat load.
In some embodiments, the local area network performs comprehensive regulation on a microgrid controller, a distributed power supply control device, an energy storage control device, an air conditioner control device, a boiler control device, a line control device and a pipe network magnetic control valve through a local edge gateway.
In some embodiments, a training sample of a neural network model is established according to operation historical data, outsourcing electricity historical data, refrigeration historical data, heating historical data, corresponding time and weather forecast data of the distributed power supply in the local area network; training a neural network model according to the training samples; and inputting the date to be predicted and weather forecast data into the trained neural network model to obtain the predicted values of the distributed power supply and the electricity/cold/heat load. At different time (season, working day, rest day, working time and the like), refrigeration, heat supply and the like in the local area network are in different working conditions, and basis and premise can be provided for subsequent multi-energy mutual-aid optimal scheduling by calculating a distributed power supply and predicted values of electricity/cold/heat loads.
In some embodiments, the distributed power source and the electrical/cold/heat load predictions are output via different neural network models, respectively.
In some embodiments, the predicted value of the distributed power supply is the power that the distributed power supply can provide due to seasonal, meteorological conditions.
In some embodiments, the distributed power source and the electrical/cold/heat load are predicted at fixed time intervals, for example, hourly intervals, to derive short term prediction curves thereof.
In some embodiments, the neural network model is obtained by training a cloud to generate a first training sample from data uploaded by a large number of local area networks through the edge computing gateway. Generating a first training sample according to historical input parameters and output parameters of a large number of local area networks, and training a deep neural network by using the first training sample to generate a first neural network model. In some embodiments, according to the obtained historical input parameters and output parameters of the local area network, the output parameters are used as labels of the input parameters to generate a first training sample; wherein the input parameters include: general parameters of the local area network (date, time, area, type of local area network, area of local area network, construction time), equipment characteristics of the local area network (equipment type, key indexes, calibration parameters, working mode and the like), meteorological conditions (temperature, illumination and the like) and personnel-related conditions (such as specific instructions of users). The output parameters are distributed power supplies and electric/cold/heat load values.
In some embodiments, the first neural network model is deployed into an edge computing gateway of a second local area network, and input parameters of the second local area network are input into the first neural network model, so as to obtain output parameters output by the first neural network model. And generating a second training sample according to the input parameters and the actual output parameters of the second local area network. And performing transfer learning on the first neural network model by using the second training sample to generate a second neural network model, and deploying the second neural network model in an edge computing gateway of a second local area network.
At block 104, establishing a local area network multi-energy mutual-aid optimized scheduling model with the operation cost as the target based on the distributed power supply and the electricity/cold/heat load predicted value;
in some embodiments, the most economical energy supply mode can be selected from multiple energy sources such as external electricity, external gas, external heat, distributed power supply, energy storage battery power supply and the like by aiming at the operation cost.
In some embodiments, a mathematical model and a constraint function model of each device in the local area network are established, and a local area network multi-energy mutual-aid optimization scheduling model is established based on the mathematical model and the constraint function model of each device. For example, an electric power load model, an energy storage battery model, an air conditioner model, a cold accumulation model, a boiler model, a secondary heat exchange model and an air pipe model are established, wherein outsourcing electric power depends on the electric power load model, the energy storage battery model and the air conditioner model; the outsourcing gas flow rate depends on the boiler model; the outsourcing heat power depends on a secondary heat exchange model; the air-conditioning model influences the cold accumulation model and the air duct model; the boiler model influences the secondary heat exchange model, and the secondary heat exchange model influences the air pipe model.
In some embodiments, the local area network mutual-benefit optimization scheduling model is as follows:
the local area network operation cost within a certain time is equal to the electricity price, the purchased electric power, the gas price, the purchased airflow rate, the heat price, the purchased heat flow rate, the distributed power supply operation cost and the energy storage battery operation cost
The local area network multi-energy mutual-aid optimization scheduling model obtains local electricity prices, gas prices and heat prices at specific moments from an energy price library.
In some embodiments, it is also desirable to consider the case of bi-directional power flow, such as the selling of electricity from a distributed power source to a utility.
At block 106, energy costs and energy usage of the local area network are optimally solved based on the local area network multi-energy mutual-aid optimization scheduling model.
In some embodiments, the local area network multi-energy mutual-aid optimized scheduling model is as shown in fig. 3, a constraint condition matrix is established through the constraint function models of the above devices, and the output results of the models are constrained and truncated; acquiring energy used within a certain time which meets the constraint condition matrix, and determining energy cost according to the energy price; and optimally solving the energy cost and the energy consumption, for example, optimally solving the energy cost and the energy consumption by adopting a genetic algorithm or a particle swarm algorithm to obtain an optimal energy consumption matrix, and further obtaining the optimal outsourcing electric power, the air flow, the thermal power and the optimal distribution of the electric power, the air flow and the thermal power by utilizing the optimal energy consumption matrix.
In some embodiments, optimally solving for energy costs and energy usage using a particle swarm algorithm comprises:
the method comprises the following steps: determining values of parameters such as iteration times, particle quantity, learning factors and weight coefficients of a particle swarm algorithm, initializing the speed and the position of particles, and inputting a predicted power value of a load and other equipment parameters;
step two: calculating a fitness value, selecting the individual optimum and the group optimum of the particles, and sequencing the non-dominated solution;
step three: respectively updating the speed and the position of the particle, and simultaneously updating the non-dominated solution set of the particle;
step four: constructing a redundancy set, performing variation on the positions of the group particles by respectively using a difference equation and a Gaussian equation, and returning the varied particles to a non-dominated solution set of the particles for comparison to obtain an elite archive set;
step five: updating the individual optimal position and the global optimal position, updating the learning factor of the particle, and updating the inertia weight of the particle;
step six: and judging whether a termination condition is met, if so, outputting a non-dominant solution set of the particles, and if not, returning to the step three.
In some embodiments, based on the local area network multi-energy mutual-aid optimization scheduling model, a microgrid controller, a distributed power supply controller, an energy storage controller, an air conditioner controller, a boiler controller, a line controller and a pipe network magnetic control valve in a local area network are controlled, so that the comprehensive utilization effect of energy is improved, and the most economical energy supply mode is realized.
In some embodiments, a user is provided with a specific quantized optimized scheduling suggestion and controlled according to user instructions.
In some embodiments, the local area network is optimally solved based on the local area network multi-energy mutual-aid optimization scheduling model according to different working conditions at different times (seasons, working days, rest days and the like).
According to the embodiment of the disclosure, the following technical effects are achieved:
the optimal outsourcing electric power, the air flow, the thermal power and the optimal distribution of the electric power, the air flow and the thermal power can be obtained;
the energy use structure is optimized, the outsourcing energy cost is reduced, and the comprehensive energy use efficiency in the energy network is improved.
It is noted that while for simplicity of explanation, the foregoing method embodiments have been described as a series of acts or combination of acts, it will be appreciated by those skilled in the art that the present disclosure is not limited by the order of acts, as some steps may, in accordance with the present disclosure, occur in other orders and concurrently. Further, those skilled in the art should also appreciate that the embodiments described in the specification are exemplary embodiments and that acts and modules referred to are not necessarily required by the disclosure.
The above is a description of embodiments of the method, and the embodiments of the apparatus are further described below.
Fig. 4 illustrates a block diagram of a distributed source-store-load matching apparatus 400 based on migratory learning, in accordance with an embodiment of the present disclosure. As shown in fig. 4, the apparatus 400 includes:
it can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the described module may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
FIG. 5 shows a schematic block diagram of an electronic device 500 that may be used to implement embodiments of the present disclosure. As shown, device 500 includes a Central Processing Unit (CPU)501 that may perform various appropriate actions and processes in accordance with computer program instructions stored in a Read Only Memory (ROM)502 or loaded from a storage unit 508 into a Random Access Memory (RAM) 503. In the RAM 503, various programs and data required for the operation of the device 500 can also be stored. The CPU501, ROM 502, and RAM 503 are connected to each other via a bus 504. An input/output (I/O) interface 505 is also connected to bus 504.
A number of components in the device 500 are connected to the I/O interface 505, including: an input unit 506 such as a keyboard, a mouse, or the like; an output unit 507 such as various types of displays, speakers, and the like; a storage unit 508, such as a magnetic disk, optical disk, or the like; and a communication unit 509 such as a network card, modem, wireless communication transceiver, etc. The communication unit 509 allows the device 500 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The processing unit 501 performs the various methods and processes described above, such as the methods 100, 200. For example, in some embodiments, the methods 100, 200 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 508. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 500 via the ROM 502 and/or the communication unit 509. When the computer program is loaded into the RAM 503 and executed by the CPU501, one or more steps of the methods 100, 200 described above may be performed. Alternatively, in other embodiments, the CPU501 may be configured to perform the methods 100, 200 by any other suitable means (e.g., by way of firmware).
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a load programmable logic device (CPLD), and the like.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Further, while operations are depicted in a particular order, this should be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
Claims (10)
1. A local area network multi-energy mutual aid optimization scheduling method is characterized by comprising the following steps:
calculating a predicted value of a distributed power supply and an electricity/cold/heat load in a local area network;
establishing a local area network multi-energy mutual-aid optimization scheduling model taking the operation cost as a target based on the distributed power supply and the electricity/cold/heat load predicted value;
and optimally solving the energy cost and energy consumption of the local area network based on the local area network multi-energy mutual-aid optimization scheduling model.
2. The method of claim 1,
the local area network comprises a distributed power supply and an energy storage battery, and electric, gas and heat energy sources are comprehensively operated to provide electric load, cold load and heat load.
3. The method of claim 2, wherein calculating the predicted values of distributed power and electrical/cold/heat load in the local area network further comprises:
establishing a training sample of a neural network model according to operation historical data, outsourcing electricity historical data, refrigeration historical data, heat supply historical data, corresponding time and weather forecast data of a distributed power supply in a local area network;
training a neural network model according to the training samples;
and inputting the date to be predicted and weather forecast data into the trained neural network model to obtain the predicted values of the distributed power supply and the electricity/cold/heat load.
4. The method of claim 3,
the neural network model is obtained by training according to operation historical data, outsourcing electricity historical data, refrigeration historical data, heating historical data, corresponding time and weather forecast data of distributed power supplies in a large number of local area networks;
and carrying out transfer training on the neural network model according to the training samples.
5. The method of claim 2, wherein establishing a cost-effective optimized scheduling model for the local area network comprises:
and establishing a mathematical model and a constraint function model of each device in the local area network, and establishing a local area network multi-energy mutual-aid optimization scheduling model based on the mathematical model and the constraint function model of each device.
6. The method of claim 5, wherein the local area network mutual-aid optimization scheduling model is as follows:
and the local area network operation cost in a certain time is equal to electricity price, purchased electric power, gas price, purchased airflow rate, heat price, purchased heat flow rate, distributed power supply operation cost and energy storage battery operation cost.
7. The method of claim 6, wherein optimally solving for energy costs and energy usage of the local area network comprises:
establishing a constraint condition matrix through the constraint function model of each device, and performing constraint truncation on the output result of each model;
acquiring energy used within a certain time which meets the constraint condition matrix, and determining energy cost according to the energy price;
and optimally solving the energy cost and the energy consumption.
8. A local area network multi-energy mutual aid optimization scheduling apparatus, comprising:
the prediction module is used for calculating the predicted values of the distributed power supply and the electricity/cold/heat load in the local area network;
the model establishing module is used for establishing a local area network multi-energy mutual-aid optimized scheduling model taking the running cost as a target based on the distributed power supply and the electricity/cold/heat load predicted value;
and the optimal solving module is used for optimally solving the energy cost and the energy consumption of the local area network based on the local area network multi-energy mutual-aid optimal scheduling model.
9. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program, wherein the processor, when executing the program, implements the method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the method according to any one of claims 1 to 7.
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