CN115936222A - Energy scheduling method, device, equipment and storage medium - Google Patents
Energy scheduling method, device, equipment and storage medium Download PDFInfo
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Abstract
The invention discloses an energy scheduling method, device, equipment and storage medium. The method comprises the following steps: acquiring a first data set, wherein the first data set comprises: at least one data set, the data set comprising: fuel consumption and electricity purchase cost for each device in the set of devices; acquiring target data corresponding to each data group in a first data set, wherein the target data is the sum of first data and second data, the first data is the product of an operation cost and a first weight corresponding to the operation cost, and the second data is the product of a load loss probability and a second weight corresponding to the load loss probability; the fuel consumption of each device in the data group with the minimum target data in the first data set is determined as the target fuel consumption of each device in the device set.
Description
Technical Field
The embodiment of the invention relates to the technical field of computers, in particular to an energy scheduling method, device, equipment and storage medium.
Background
Renewable energy sources such as photovoltaic energy, wind power and the like have the advantages of cleanness, no pollution, environmental friendliness, no greenhouse gas emission, sustainability and the like in the power generation process, but along with the improvement of the permeability of a renewable energy system in a power grid, the defects of low energy density, uncertainty and inaccuracy are increasingly prominent, and the characteristics and the uncertainty of system load bring more uncertainty to the power system, so that negative effects are generated on the scheduling operation, protection control and the like of the power grid, and the problems of wind abandonment, light abandonment and the like are caused.
Energy scheduling methods in the prior art all aim at optimizing economy, safety is ignored, and potential safety hazards exist in the energy scheduling process.
Disclosure of Invention
The embodiment of the invention provides an energy scheduling method, device, equipment and storage medium, which can improve the safety and economy of energy scheduling.
According to an aspect of the present invention, there is provided an energy scheduling method, including:
acquiring a first data set, wherein the first data set comprises: at least one data set, the data set comprising: fuel consumption and electricity purchase cost for each device in the set of devices;
acquiring target data corresponding to each data group in a first data set, wherein the target data is the sum of first data and second data, the first data is the product of an operation cost and a first weight corresponding to the operation cost, and the second data is the product of a load loss probability and a second weight corresponding to the load loss probability;
and determining the fuel consumption of each device in the data group with the minimum corresponding target data in the first data set as the target fuel consumption of each device in the device set.
According to another aspect of the present invention, there is provided an energy scheduling apparatus, including:
a first obtaining module configured to obtain a first data set, wherein the first data set includes: at least one data set, the data set comprising: fuel consumption and electricity purchase cost for each device in the set of devices;
the second obtaining module is used for obtaining target data corresponding to each data group in the first data set, wherein the target data is the sum of first data and second data, the first data is the product of an operation cost and a first weight corresponding to the operation cost, and the second data is the product of a load loss probability and a second weight corresponding to the load loss probability;
and the determining module is used for determining the fuel consumption of each device in the data group with the minimum target data in the first data set as the target fuel consumption of each device in the device set.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor, the computer program being executable by the at least one processor to enable the at least one processor to perform the energy scheduling method according to any of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the method for energy scheduling according to any embodiment of the present invention when the computer instructions are executed.
The embodiment of the invention obtains a first data set, wherein the first data set comprises: at least one data set, the data set comprising: fuel consumption and electricity purchase cost for each device in the set of devices; acquiring target data corresponding to each data group in a first data set, wherein the target data is the sum of first data and second data, the first data is the product of an operation cost and a first weight corresponding to the operation cost, and the second data is the product of a load loss probability and a second weight corresponding to the load loss probability; and determining the fuel consumption of each device in the data group with the minimum target data in the first data set as the target fuel consumption of each device in the device set, so that the safety and the economy of energy scheduling can be improved.
It should be understood that the statements in this section do not necessarily identify key or critical features of the embodiments of the present invention, nor do they necessarily limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 is a flowchart of an energy scheduling method according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of an energy dispatching device in an embodiment of the present invention;
fig. 3 is a schematic structural diagram of an electronic device in an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It is understood that before the technical solutions disclosed in the embodiments of the present disclosure are used, the type, the use range, the use scene, etc. of the personal information related to the present disclosure should be informed to the user and obtain the authorization of the user through a proper manner according to the relevant laws and regulations.
Example one
Fig. 1 is a flowchart of an energy scheduling method according to an embodiment of the present invention, where the embodiment is applicable to energy scheduling, and the method may be executed by an energy scheduling apparatus according to an embodiment of the present invention, where the apparatus may be implemented in a software and/or hardware manner, as shown in fig. 1, the method specifically includes the following steps:
s110, obtaining a first data set, where the first data set includes: at least one data set, the data set comprising: fuel consumption and electricity purchase cost for each device in the set of devices.
Wherein the set of devices comprises: power supply unit, heating equipment, refrigeration plant and coupling equipment. The fuel consumption of each device in the set of devices includes: the fuel consumption of the power supply device, the fuel consumption of the heating device, the fuel consumption of the cooling device and the fuel consumption of the coupling device.
The electricity purchasing cost can be the product of the on-line electricity price and the on-line electric power, and can also be the product of the off-line electricity price and the off-line electric power.
Specifically, the first data set includes a plurality of data sets, and each data set includes: the fuel consumption of the power supply device, the fuel consumption of the heating device, the fuel consumption of the cooling device, the fuel consumption of the coupling device, and the electricity purchase cost may be, for example, that each data set may include: fuel consumption of the power supply device, fuel consumption of the heating device, fuel consumption of the refrigeration device, fuel consumption of the coupling device, and on-grid electric power; each data set may also include: fuel consumption of the power supply device, fuel consumption of the heating device, fuel consumption of the cooling device, fuel consumption of the coupling device, and off-grid electric power. The power supply equipment can be a gas turbine, a fuel cell, photovoltaic equipment, wind power equipment and the like, the heat supply equipment can be a gas boiler, a heat exchanger, a heat pump and the like, and the refrigeration equipment can be absorption refrigeration equipment, electric refrigeration equipment and the like.
And S120, acquiring target data corresponding to each data group in the first data set, wherein the target data is the sum of first data and second data, the first data is the product of the operation cost and a first weight corresponding to the operation cost, and the second data is the product of the load loss probability and a second weight corresponding to the load loss probability.
Wherein the operation cost is the sum of the electricity purchasing cost and the gas purchasing cost. The operation cost can be obtained by the following steps: and determining the operation cost corresponding to the data set according to the fuel consumption, the natural gas price and the electricity purchase cost of each device in the device set.
The method for acquiring the load loss probability may be as follows: and determining the load loss probability according to the electric load loss capacity, the heat load loss capacity, the cold load loss capacity, the annual maximum electric load, the annual maximum heat load and the annual maximum cold load.
Specifically, target data corresponding to each data group in the first data set is obtained. For example, the target data may be calculated based on the following formula: minf = λ e f e +λ s f s Where f is the target data, λ e A first weight corresponding to the running cost, f e For operating costs, λ s A second weight corresponding to the probability of loss of load, f s Is the probability of loss of load.
And S130, determining the fuel consumption of each device in the data group with the minimum target data in the first data set as the target fuel consumption of each device in the device set.
Specifically, the fuel consumption of each device in the data group with the minimum target data in the first data set is determined as the target fuel consumption of each device in the device set, for example, if the first data set includes: data set a, data set B, and data set C, if the target data corresponding to data set a < the target data corresponding to data set B < the target data corresponding to data set C. Data set a includes: the fuel consumption x of the power supply equipment, the fuel consumption y of the heating equipment, the fuel consumption z of the cooling equipment and the fuel consumption n of the coupling equipment. The fuel consumption x of the power supply apparatus, the fuel consumption y of the heat supply apparatus, the fuel consumption z of the cooling apparatus, and the fuel consumption n of the coupling apparatus are determined as the target fuel consumption of the power supply apparatus, the target fuel consumption of the heat supply apparatus, the target fuel consumption of the cooling apparatus, and the target fuel consumption of the coupling apparatus.
Optionally, the obtaining target data corresponding to each data group in the first data set includes:
determining the operation cost corresponding to the data set according to the fuel consumption, the natural gas price and the electricity purchase cost of each device in the device set;
determining the load loss probability corresponding to the data set according to the fuel consumption, the annual maximum electric load, the annual maximum heat load and the annual maximum cold load of each device in the device set;
determining the product of the running cost corresponding to the data group and a first weight corresponding to the running cost as first data corresponding to the data group;
determining the product of the load loss probability corresponding to the data group and a second weight corresponding to the load loss probability as second data corresponding to the data group;
and determining the sum of the first data corresponding to the data group and the second data corresponding to the data group as the target data corresponding to the data group.
Specifically, the method for determining the operation cost corresponding to the data set according to the fuel consumption, the natural gas price and the electricity purchase cost of each device in the device set may be: calculating the operation cost based on the following formula:
wherein, f e For the running cost, T is the total scheduling period, N e Number of plants consuming natural gas, F i (t) fuel consumption of the i-th plant at time t, C gas In order to achieve the price of the natural gas,
on-line electricity price for t time period>
Based on the electric power on the Internet for a period t>
For off-grid electricity prices, on or off>
Is the off-grid electrical power. It should be noted that, if there is an online electric power, the offline electric power is zero, and if there is an offline electric power, the online electric power is zero.
Specifically, the manner of determining the load shedding probability corresponding to the data set according to the fuel consumption, the annual maximum electrical load, the annual maximum thermal load, and the annual maximum cooling load of each device in the device set may be: acquiring time type information and a numerical weather forecast; determining the electric load loss capacity, the heat load loss capacity and the cold load loss capacity according to the fuel consumption, the time type information and the numerical weather forecast of each device in the device set; and determining the load loss probability corresponding to the data set according to the electric load loss, the heat load loss, the cold load loss, the annual maximum electric load, the annual maximum heat load and the annual maximum cold load.
The first weight corresponding to the operation cost may be an economics index coefficient, and the second weight corresponding to the load loss probability may be a safety index coefficient. The first weight corresponding to the operation cost and the second weight corresponding to the load loss probability may be weight values selected by a user.
Optionally, determining an operating cost corresponding to the data set according to the fuel consumption, the natural gas price, and the electricity purchase cost of each device in the device set includes:
determining gas purchase cost according to the fuel consumption and the natural gas price of each device in the device set;
and determining the sum of the gas purchasing cost and the electricity purchasing cost as the operation cost.
Specifically, the method for determining the gas purchase cost according to the fuel consumption and the natural gas price of each device in the device set may be: the gas purchase cost is calculated based on the following formula:
whereinT is the total scheduling period, N e Number of plants consuming natural gas, F i (t) fuel consumption of the i-th plant at time t, C gas Is the natural gas price.
Optionally, determining the load shedding probability corresponding to the data group according to the fuel consumption, the annual maximum electrical load, the annual maximum thermal load and the annual maximum cooling load of each device in the device set, including:
acquiring time type information and a numerical weather forecast;
determining the electric load loss capacity, the heat load loss capacity and the cold load loss capacity according to the fuel consumption, the time type information and the numerical weather forecast of each device in the device set;
and determining the load loss probability corresponding to the data set according to the electric load loss, the heat load loss, the cold load loss, the annual maximum electric load, the annual maximum heat load and the annual maximum cold load.
Wherein the time type information may be: the time with more electricity consumption can also be the time with less electricity consumption, wherein the time with more electricity consumption can be the time with great influence on electricity consumption, such as holidays and the like.
Specifically, the method for determining the electric load loss amount, the heat load loss amount and the cooling load loss amount according to the fuel consumption amount, the time type information and the numerical weather forecast of each device in the device set may be: the method comprises the steps of obtaining a power supply characteristic model of power supply equipment, a heat supply characteristic model of heat supply equipment, a refrigeration characteristic model of refrigeration equipment, a target characteristic model of coupling equipment, a power generation power prediction model of renewable energy power generation equipment, a cold load prediction model, a heat load prediction model and an electric load prediction model, wherein the target characteristic model of the coupling equipment comprises the following steps: at least two of a power supply characteristic model, a heat supply characteristic model, and a refrigeration characteristic model; determining the generated power of the power supply equipment according to the power supply characteristic model of the power supply equipment and the fuel consumption of the power supply equipment; determining the power generation power of the heating equipment according to the heating characteristic model of the heating equipment and the fuel consumption of the heating equipment; determining the power generation power of the refrigeration equipment according to the refrigeration characteristic model of the refrigeration equipment and the fuel consumption of the refrigeration equipment; determining the target power of the coupling equipment according to the target power supply characteristic model of the coupling equipment; inputting the numerical weather forecast into a power generation power prediction model of the renewable energy power generation equipment to obtain the power generation power of the renewable energy power generation equipment; inputting the numerical weather forecast and the time type information into a cold load prediction model to obtain a cold load; inputting the numerical weather forecast and the time type information into a heat load prediction model to obtain a heat load; inputting the numerical weather forecast and the time type information into an electric load prediction model to obtain an electric load; and determining the electric load loss capacity, the heat load loss capacity and the cold load loss capacity according to the cold load, the heat load, the electric load, the power generation power of the power supply equipment, the power generation power of the renewable energy power generation equipment, the power generation power of the heat supply equipment, the power generation power of the refrigeration equipment and the target power of the coupling equipment.
Specifically, the method for determining the load shedding probability corresponding to the data set according to the electric load shedding amount, the heat load shedding amount, the cold load shedding amount, the annual maximum electric load, the annual maximum heat load and the annual maximum cold load may be: calculating the load loss probability based on the following formula:
therein, loss e (t) Loss of load of the electrical load at time t, loss h (t) Heat load Loss at time t, loss c (t) is the cooling load loss at time t,
for the annual maximum electrical load, is selected>
For the annual maximum heat load>
The annual maximum cooling load.
Optionally, the fuel consumption of each device in the set of devices: the fuel consumption of the power supply device, the fuel consumption of the heating device, the fuel consumption of the cooling device and the fuel consumption of the coupling device;
determining the electric load loss capacity, the heat load loss capacity and the cold load loss capacity according to the fuel consumption, the time type information and the numerical weather forecast of each device in the device set, wherein the method comprises the following steps:
the method comprises the steps of obtaining a power supply characteristic model of power supply equipment, a heat supply characteristic model of heat supply equipment, a refrigeration characteristic model of refrigeration equipment, a target characteristic model of coupling equipment, a power generation power prediction model of renewable energy power generation equipment, a cold load prediction model, a heat load prediction model and an electric load prediction model, wherein the target characteristic model of the coupling equipment comprises the following steps: at least two of the power supply characteristic model, the heat supply characteristic model and the refrigeration characteristic model;
determining the generated power of the power supply equipment according to the power supply characteristic model of the power supply equipment and the fuel consumption of the power supply equipment;
determining the power generation power of the heating equipment according to the heating characteristic model of the heating equipment and the fuel consumption of the heating equipment;
determining the power generation power of the refrigeration equipment according to the refrigeration characteristic model of the refrigeration equipment and the fuel consumption of the refrigeration equipment;
determining the target power of the coupling equipment according to the target power supply characteristic model of the coupling equipment;
inputting the numerical weather forecast into a power generation power prediction model of the renewable energy power generation equipment to obtain the power generation power of the renewable energy power generation equipment;
inputting the numerical weather forecast and the time type information into a cold load prediction model to obtain a cold load;
inputting the numerical weather forecast and the time type information into a heat load prediction model to obtain a heat load;
inputting the numerical weather forecast and the time type information into an electric load prediction model to obtain an electric load;
and determining the electric load loss capacity, the heat load loss capacity and the cold load loss capacity according to the cold load, the heat load, the electric load, the power generation power of the power supply equipment, the power generation power of the renewable energy power generation equipment, the power generation power of the heat supply equipment, the power generation power of the refrigeration equipment and the target power of the coupling equipment.
Wherein the numerical weather forecast may include: temperature, humidity, wind speed and other weather information.
The generated power prediction model of the renewable energy power generation equipment is obtained by iteratively training a neural network model through a first target sample set, wherein the first target sample set comprises: the numerical weather forecast sample and the generated power of the renewable energy power generation equipment corresponding to the numerical weather forecast sample. The way of iteratively training the neural network model by the first set of target samples may be: inputting the numerical weather forecast samples in the first target sample set into a neural network model to obtain predicted power generation power; and training parameters of the neural network model according to a first target function formed by the predicted power generation power and the power generation power of the renewable energy power generation equipment corresponding to the numerical weather forecast sample, and returning to execute the operation of inputting the numerical weather forecast sample in the first target sample set into the neural network model to obtain the predicted power generation power until obtaining the power generation power prediction model of the renewable energy power generation equipment.
The cold load prediction model is obtained by iteratively training a neural network model through a second target sample set, wherein the second target sample set comprises: the system comprises a numerical weather forecast sample, a time type information sample and a cold load corresponding to the numerical weather forecast sample and the time type information sample; the way of iteratively training the neural network model by the second target sample set may be: inputting the numerical weather forecast samples and the time type information samples in the second target sample set into a neural network model to obtain a predicted cold load; and training parameters of the neural network model according to a second target function formed by the predicted cold and the cold load corresponding to the numerical weather forecast sample and the time type information sample, returning to execute the operation of inputting the numerical weather forecast sample and the time type information sample in the second target sample set into the neural network model to obtain the predicted cold load until obtaining the cold load prediction model.
The heat load prediction model is obtained by iteratively training a neural network model through a third target sample set, wherein the third target sample set comprises: the numerical weather forecast samples, the time type information samples and the heat loads corresponding to the numerical weather forecast samples and the time type information samples; the way of iteratively training the neural network model by the third target sample set may be: inputting the numerical weather forecast samples and the time type information samples in the third target sample set into a neural network model to obtain a predicted heat load; and training parameters of the neural network model according to a third target function formed by the predicted heat and the heat load corresponding to the numerical weather forecast samples and the time type information samples, and returning to execute the operation of inputting the numerical weather forecast samples and the time type information samples collected by the third target sample into the neural network model to obtain the operation of predicting the heat load until obtaining the heat load prediction model.
The electric load prediction model is obtained by iteratively training a neural network model through a fourth target sample set, wherein the fourth target sample set comprises: the system comprises numerical weather forecast samples, time type information samples and electric loads corresponding to the numerical weather forecast samples and the time type information samples; the way of iteratively training the neural network model by the fourth target sample set may be: inputting the numerical weather forecast samples and the time type information samples in the fourth target sample set into a neural network model to obtain a predicted electric load; and training parameters of the neural network model according to a fourth target function formed by the prediction electricity and the electricity load corresponding to the numerical weather forecast sample and the time type information sample, returning to execute the operation of inputting the numerical weather forecast sample and the time type information sample concentrated in the fourth target sample into the neural network model to obtain the predicted electricity load until obtaining the electricity load prediction model.
Optionally, the target characteristic model of the coupling device includes: a power supply characteristic model, a heat supply characteristic model and a refrigeration characteristic model;
determining a target power of the coupling device from a target power supply characteristic model of the coupling device, comprising:
determining the power generation power of the coupling equipment according to the power supply characteristic model of the coupling equipment;
determining the heat supply power of the coupling equipment according to the heat supply characteristic model of the coupling equipment;
and determining the refrigeration power of the coupling equipment according to the refrigeration characteristic model of the coupling equipment.
Optionally, determining an electric load off-load amount, a thermal load off-load amount, and a cold load off-load amount according to the cold load, the thermal load, the electric load, the power generated by the power supply device, the power generated by the renewable energy power generation device, the power generated by the heat supply device, the power generated by the refrigeration device, and the target power of the coupling device, includes:
determining the load loss amount of the electric load according to the electric load, the power generation power of the power supply equipment, the power generation power of the coupling equipment and the power generation power of the renewable energy power generation equipment;
determining the load loss amount of the heat load according to the heat load, the power generation power of the heat supply equipment and the heat supply power of the coupling equipment;
and determining the load loss amount of the cold load according to the cold load, the power generation power of the refrigeration equipment and the refrigeration power of the coupling equipment.
In one specific example, if the electrical load is greater than X Supplying power * Power supply characteristic model + X Coupling of * Model of the characteristics of the power supply, loss e (t) = electrical load-X Power supply * Power supply characteristic model-X Coupling of * Power supply characteristic model-power generation power of renewable energy power generation equipment; if the thermal load is greater than X Heating of * Heating characteristic model + X Coupling of * Model of heat supply characteristics, loss h (t) = Heat load-X Heating of * Heat supply characteristic model-X Coupling of * A heat supply characteristic model; if the cold load is greater than X Refrigeration system * Refrigeration characteristic model + X Coupling of * Refrigeration characteristic model, loss c (t) = cooling load-X Refrigeration system * Refrigeration characteristic model-X Coupling of * A refrigeration characteristic model. Wherein, X Supplying power For the fuel consumption of the power supply apparatus, X Heating of For fuel consumption of heating plants, X Refrigeration For the fuel consumption of refrigeration plants, X Coupling of Is the fuel consumption of the coupling device.
In a specific example, a power generation power prediction model and uncertainty distribution thereof of renewable energy power generation equipment are established according to historical data of renewable energy power generation equipment (photovoltaic, wind power and the like) and historical data of numerical weather forecast (temperature, humidity, wind speed and the like) in an integrated energy system; and establishing a cold load prediction model, a heat load prediction model, an electric load prediction model and uncertainty distribution thereof according to the cold load historical data, the heat load historical data, the electric load historical data, the numerical weather forecast historical data and the time type information (whether the system is on holidays, whether the system is in a state with more electricity and the like) historical data of the comprehensive energy system. Aiming at a generated power prediction model, a cold load prediction model, a heat load prediction model and an electric load prediction model of renewable energy power generation equipment, a Monte Carlo sampling method is adopted to sample the generated power prediction model, a plurality of scenes are established according to Monte Carlo sampling, and an objective function is constructed:
minf＝λ e f e +λ s f s ；
and based on random planning, taking the expectation of the objective function under a plurality of scenes as the objective function, and solving the comprehensive energy system scheduling scheme.
According to the technical scheme of the embodiment, a first data set is obtained; acquiring target data corresponding to each data group in the first data set; the fuel consumption of each device in the data group with the minimum target data in the first data set is determined as the target fuel consumption of each device in the device set.
Example two
Fig. 2 is a schematic structural diagram of an energy scheduling apparatus according to an embodiment of the present invention. The embodiment may be applicable to the case of energy scheduling, the apparatus may be implemented in a software and/or hardware manner, and the apparatus may be integrated in any device providing an energy scheduling function, as shown in fig. 2, where the energy scheduling apparatus specifically includes: a first acquisition module 210, a second acquisition module 220, and a determination module 230.
The first obtaining module is configured to obtain a first data set, where the first data set includes: at least one data set, the data set comprising: fuel consumption and electricity purchase cost for each device in the set of devices;
the second obtaining module is used for obtaining target data corresponding to each data group in the first data set, wherein the target data is the sum of first data and second data, the first data is the product of an operation cost and a first weight corresponding to the operation cost, and the second data is the product of a load loss probability and a second weight corresponding to the load loss probability;
and the determining module is used for determining the fuel consumption of each device in the data group with the minimum target data in the first data set as the target fuel consumption of each device in the device set.
The product can execute the method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
EXAMPLE III
FIG. 3 illustrates a schematic diagram of an electronic device 10 that may be used to implement an embodiment of the present invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 3, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.
A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
In some embodiments, the energy scheduling method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the energy scheduling method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the energy scheduling method by any other suitable means (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on a machine, as a stand-alone software package partly on a machine and partly on a remote machine or entirely on a remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage 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. Alternatively, the computer readable storage medium may be a machine readable signal medium. 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 compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user may provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the Internet.
The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.
The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. An energy scheduling method, comprising:
acquiring a first data set, wherein the first data set comprises: at least one data set, the data set comprising: fuel consumption and electricity purchase cost for each device in the set of devices;
acquiring target data corresponding to each data group in a first data set, wherein the target data is the sum of first data and second data, the first data is the product of an operation cost and a first weight corresponding to the operation cost, and the second data is the product of a load loss probability and a second weight corresponding to the load loss probability;
and determining the fuel consumption of each device in the data group with the minimum corresponding target data in the first data set as the target fuel consumption of each device in the device set.
2. The method of claim 1, wherein obtaining target data corresponding to each data set in the first data set comprises:
determining the operation cost corresponding to the data set according to the fuel consumption, the natural gas price and the electricity purchasing cost of each device in the device set;
determining the load loss probability corresponding to the data set according to the fuel consumption, the annual maximum electric load, the annual maximum heat load and the annual maximum cold load of each device in the device set;
determining the product of the running cost corresponding to the data group and a first weight corresponding to the running cost as first data corresponding to the data group;
determining the product of the load loss probability corresponding to the data group and a second weight corresponding to the load loss probability as second data corresponding to the data group;
and determining the sum of the first data corresponding to the data group and the second data corresponding to the data group as the target data corresponding to the data group.
3. The method of claim 2, wherein determining the operating cost for the data set based on the fuel consumption, the natural gas price, and the electricity purchase cost for each of the set of equipment comprises:
determining the gas purchase cost according to the fuel consumption and the natural gas price of each device in the device set;
and determining the sum of the gas purchasing cost and the electricity purchasing cost as the operation cost.
4. The method of claim 2, wherein determining the probability of the data set corresponding to the loss of load based on the fuel consumption, the annual maximum electrical load, the annual maximum thermal load, and the annual maximum cooling load for each device in the set of devices comprises:
acquiring time type information and a numerical weather forecast;
determining the electric load loss capacity, the heat load loss capacity and the cold load loss capacity according to the fuel consumption, the time type information and the numerical weather forecast of each device in the device set;
and determining the load loss probability corresponding to the data set according to the electric load loss, the heat load loss, the cold load loss, the annual maximum electric load, the annual maximum heat load and the annual maximum cold load.
5. The method of claim 4, wherein the fuel consumption of each device in the set of devices is: the fuel consumption of the power supply device, the fuel consumption of the heating device, the fuel consumption of the cooling device and the fuel consumption of the coupling device;
determining the electric load loss capacity, the heat load loss capacity and the cold load loss capacity according to the fuel consumption, the time type information and the numerical weather forecast of each device in the device set, wherein the method comprises the following steps:
the method comprises the steps of obtaining a power supply characteristic model of power supply equipment, a heat supply characteristic model of heat supply equipment, a refrigeration characteristic model of refrigeration equipment, a target characteristic model of coupling equipment, a power generation power prediction model of renewable energy power generation equipment, a cold load prediction model, a heat load prediction model and an electric load prediction model, wherein the target characteristic model of the coupling equipment comprises the following steps: at least two of the power supply characteristic model, the heat supply characteristic model and the refrigeration characteristic model;
determining the generated power of the power supply equipment according to the power supply characteristic model of the power supply equipment and the fuel consumption of the power supply equipment;
determining the power generation power of the heating equipment according to the heating characteristic model of the heating equipment and the fuel consumption of the heating equipment;
determining the power generation power of the refrigeration equipment according to the refrigeration characteristic model of the refrigeration equipment and the fuel consumption of the refrigeration equipment;
determining the target power of the coupling equipment according to the target power supply characteristic model of the coupling equipment;
inputting the numerical weather forecast into a power generation power prediction model of the renewable energy power generation equipment to obtain the power generation power of the renewable energy power generation equipment;
inputting the numerical weather forecast and the time type information into a cold load prediction model to obtain a cold load;
inputting the numerical weather forecast and the time type information into a heat load prediction model to obtain a heat load;
inputting the numerical weather forecast and the time type information into an electric load prediction model to obtain an electric load;
and determining the electric load loss capacity, the heat load loss capacity and the cold load loss capacity according to the cold load, the heat load, the electric load, the power generation power of the power supply equipment, the power generation power of the renewable energy power generation equipment, the power generation power of the heat supply equipment, the power generation power of the refrigeration equipment and the target power of the coupling equipment.
6. The method of claim 5, wherein the target characteristic model of the coupling device comprises: a power supply characteristic model, a heat supply characteristic model and a refrigeration characteristic model;
determining a target power of the coupling device from a target power supply characteristic model of the coupling device, comprising:
determining the power generation power of the coupling equipment according to the power supply characteristic model of the coupling equipment;
determining the heat supply power of the coupling equipment according to the heat supply characteristic model of the coupling equipment;
and determining the refrigeration power of the coupling equipment according to the refrigeration characteristic model of the coupling equipment.
7. The method of claim 6, wherein determining the electrical load shed, the thermal load shed, and the thermal load shed according to the cold load, the thermal load, the electrical load, the power generated by the power supply device, the power generated by the renewable energy power generation device, the power generated by the heat supply device, the power generated by the refrigeration device, and the target power of the coupling device comprises:
determining the load loss amount of the electric load according to the electric load, the power generation power of the power supply equipment, the power generation power of the coupling equipment and the power generation power of the renewable energy power generation equipment;
determining the load loss amount of the heat load according to the heat load, the power generation power of the heat supply equipment and the heat supply power of the coupling equipment;
and determining the load loss amount of the cold load according to the cold load, the power generation power of the refrigeration equipment and the refrigeration power of the coupling equipment.
8. An energy scheduling apparatus, comprising:
a first obtaining module configured to obtain a first data set, wherein the first data set includes: at least one data set, the data set comprising: fuel consumption and electricity purchase cost for each device in the set of devices;
the second obtaining module is used for obtaining target data corresponding to each data group in the first data set, wherein the target data is the sum of first data and second data, the first data is the product of an operation cost and a first weight corresponding to the operation cost, and the second data is the product of a load loss probability and a second weight corresponding to the load loss probability;
and the determining module is used for determining the fuel consumption of each device in the data group with the minimum target data in the first data set as the target fuel consumption of each device in the device set.
9. An electronic device, characterized in that the electronic device comprises:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the energy scheduling method of any one of claims 1-7.
10. A computer-readable storage medium, having stored thereon computer instructions for causing a processor to execute a method for energy scheduling according to any one of claims 1-7.
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