CN117879062A - Power consumption scheduling method and device - Google Patents
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Abstract
The application relates to a power consumption scheduling method and a power consumption scheduling device, wherein the power price information and a prediction result of a prediction model are obtained; the prediction result comprises the electricity consumption and the electricity generation of the photovoltaic energy storage system in each future period. And inputting the electricity price information and the prediction result into a scheduling model, selecting an optimal scheduling strategy, and controlling the work of the photovoltaic energy storage system according to the optimal scheduling strategy. Through the cooperation of the prediction model and the scheduling model, the efficiency and the income of the photovoltaic energy storage system are effectively improved, the electricity cost of a user is reduced, and the burden and the loss of the photovoltaic energy storage system are reduced.
Description
Technical Field
The application relates to the technical field of photovoltaic energy storage, in particular to a power utilization scheduling method and device.
Background
The photovoltaic energy storage, also called as photovoltaic energy storage system, combines solar photovoltaic power generation and energy storage technology, and stores electric energy generated by photovoltaic power generation so as to supply electric power when needed. Photovoltaic energy storage systems are typically comprised of two parts, a photovoltaic power generation system and an energy storage system. Meanwhile, in order to improve the use benefit of the photovoltaic energy storage system, the photovoltaic energy storage system is usually integrated into a power grid to form a grid-connected photovoltaic energy storage system. Grid-tied photovoltaic energy storage systems must be connected to a public grid and rely on existing grid systems to operate. The grid-connected photovoltaic energy storage system mainly comprises a solar cell panel and an inverter, wherein the solar cell panel generates electricity, the electricity is converted into alternating current through the inverter and then is supplied to a load for use, and redundant electric quantity can be transmitted to a public power grid. And when the solar energy generating capacity can not meet the use of the load, the solar energy can be automatically supplemented from the public power grid.
Because the current power grid faces the problems of power consumption peak and power price fluctuation, how to perform power consumption scheduling on the photovoltaic energy storage system can obviously influence the power consumption cost of a user. The current power utilization management system of the photovoltaic energy storage system has single function, cannot realize high-efficiency power utilization scheduling, improves the power utilization cost of a user, and simultaneously introduces additional burden and loss to the photovoltaic energy storage system.
Disclosure of Invention
Based on the above, it is necessary to provide a power consumption scheduling method and apparatus for the disadvantage that the displayed residual capacity is prone to have a large error with the actual situation.
The embodiment of the disclosure provides a power consumption scheduling method, which comprises the following steps:
acquiring electricity price information and a prediction result of a prediction model; the prediction result comprises the electricity consumption and the electricity generation of the photovoltaic energy storage system in each future period;
inputting the electricity price information and the prediction result into a scheduling model, and selecting an optimal scheduling strategy;
and controlling the work of the photovoltaic energy storage system according to the optimal scheduling strategy.
According to the electricity utilization scheduling method, the electricity price information and the prediction result of the prediction model are obtained; the prediction result comprises the electricity consumption and the electricity generation of the photovoltaic energy storage system in each future period. And inputting the electricity price information and the prediction result into a scheduling model, selecting an optimal scheduling strategy, and controlling the work of the photovoltaic energy storage system according to the optimal scheduling strategy. Through the cooperation of the prediction model and the scheduling model, the efficiency and the income of the photovoltaic energy storage system are effectively improved, the electricity cost of a user is reduced, and the burden and the loss of the photovoltaic energy storage system are reduced.
As an alternative embodiment, the method further comprises the step of:
acquiring electricity consumption, electricity generation and multi-source heterogeneous data of a history record as training data; wherein the multi-source heterogeneous data includes weather information, user behavior information, or device information.
And establishing a prediction model based on a machine learning algorithm, and training the prediction model according to the training data.
As an optional embodiment, the process of inputting the electricity price information and the prediction result into a scheduling model and selecting an optimal scheduling policy includes the steps of:
generating a plurality of scheduling strategies through the scheduling model; wherein each scheduling strategy corresponds to a predicted expenditure expense according to the electricity price information and the prediction result;
and selecting a scheduling strategy with the minimum predicted expenditure cost as the optimal scheduling strategy.
As an alternative embodiment, the method further comprises the step of:
and obtaining constraint conditions, and correcting the optimal scheduling strategy according to the constraint conditions.
As an alternative embodiment, the scheduling model comprises an AI model.
As an alternative embodiment, the process of controlling the operation of the photovoltaic energy storage system according to the optimal scheduling policy includes the steps of:
and adjusting the working mode, the working power or the charge-discharge switching of the photovoltaic energy storage system according to the scheduling strategy.
As an alternative embodiment, the method further comprises the step of:
and acquiring and displaying the prediction result, the optimal scheduling strategy and the running condition of the photovoltaic energy storage system.
The embodiment of the disclosure provides an electricity consumption scheduling device, which comprises:
the data acquisition module is used for acquiring electricity price information and a prediction result of the prediction model; the prediction result comprises the electricity consumption and the electricity generation of the photovoltaic energy storage system in each future period;
the scheduling generation module is used for inputting the electricity price information and the prediction result into a scheduling model and selecting an optimal scheduling strategy;
and the work scheduling module is used for controlling the work of the photovoltaic energy storage system according to the optimal scheduling strategy.
The electricity utilization scheduling device obtains electricity price information and a prediction result of the prediction model; the prediction result comprises the electricity consumption and the electricity generation of the photovoltaic energy storage system in each future period. And inputting the electricity price information and the prediction result into a scheduling model, selecting an optimal scheduling strategy, and controlling the work of the photovoltaic energy storage system according to the optimal scheduling strategy. Through the cooperation of the prediction model and the scheduling model, the efficiency and the income of the photovoltaic energy storage system are effectively improved, the electricity cost of a user is reduced, and the burden and the loss of the photovoltaic energy storage system are reduced.
At least one embodiment of the present disclosure also provides a data control apparatus, including:
one or more memories non-transitory storing computer-executable instructions;
one or more processors configured to execute computer-executable instructions, wherein the computer-executable instructions, when executed by the one or more processors, implement a power consumption scheduling method according to any embodiment of the present disclosure.
The data control device acquires electricity price information and a prediction result of the prediction model; the prediction result comprises the electricity consumption and the electricity generation of the photovoltaic energy storage system in each future period. And inputting the electricity price information and the prediction result into a scheduling model, selecting an optimal scheduling strategy, and controlling the work of the photovoltaic energy storage system according to the optimal scheduling strategy. Through the cooperation of the prediction model and the scheduling model, the efficiency and the income of the photovoltaic energy storage system are effectively improved, the electricity cost of a user is reduced, and the burden and the loss of the photovoltaic energy storage system are reduced.
At least one embodiment of the present disclosure also provides a non-transitory computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement a power consumption scheduling method according to any embodiment of the present disclosure.
The non-transitory computer readable storage medium is used for obtaining the electricity price information and the prediction result of the prediction model; the prediction result comprises the electricity consumption and the electricity generation of the photovoltaic energy storage system in each future period. And inputting the electricity price information and the prediction result into a scheduling model, selecting an optimal scheduling strategy, and controlling the work of the photovoltaic energy storage system according to the optimal scheduling strategy. Through the cooperation of the prediction model and the scheduling model, the efficiency and the income of the photovoltaic energy storage system are effectively improved, the electricity cost of a user is reduced, and the burden and the loss of the photovoltaic energy storage system are reduced.
Drawings
FIG. 1 is a flow chart of a power consumption scheduling method according to an embodiment of the disclosure;
FIG. 2 is a schematic diagram of electricity price information;
FIG. 3 is a flow chart of a power utilization scheduling method in accordance with another disclosed embodiment;
FIG. 4 is a flowchart of an alternative embodiment power consumption scheduling method;
FIG. 5 is a block diagram of a power consumption scheduling device module according to an embodiment;
FIG. 6 is a schematic block diagram of a data control apparatus provided in accordance with at least one embodiment of the present disclosure;
fig. 7 is a schematic diagram of a non-transitory computer-readable storage medium provided by at least one embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present disclosure. It will be apparent that the described embodiments are some, but not all, of the embodiments of the present disclosure. All other embodiments, which can be made by one of ordinary skill in the art without the need for inventive faculty, are within the scope of the present disclosure, based on the described embodiments of the present disclosure.
Unless defined otherwise, technical or scientific terms used in this disclosure should be given the ordinary meaning as understood by one of ordinary skill in the art to which this disclosure belongs. The terms "first," "second," and the like, as used in this disclosure, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", etc. are used merely to indicate relative positional relationships, which may also be changed when the absolute position of the object to be described is changed.
In order to keep the following description of the embodiments of the present disclosure clear and concise, the present disclosure omits a detailed description of some known functions and known components.
The embodiment of the disclosure provides a power consumption scheduling method.
Fig. 1 is a flowchart of a power consumption scheduling method according to an embodiment of the disclosure, as shown in fig. 1, the power consumption scheduling method according to an embodiment of the disclosure includes steps S100 to S102:
s100, acquiring electricity price information and a prediction result of a prediction model; the prediction result comprises the electricity consumption and the electricity generation of the photovoltaic energy storage system in each future period;
s101, inputting the electricity price information and the prediction result into a scheduling model, and selecting an optimal scheduling strategy;
s102, controlling the work of the photovoltaic energy storage system according to the optimal scheduling strategy.
In embodiments of the present disclosure, the photovoltaic energy storage system is incorporated into the grid for operation. When electricity is purchased from the power grid, the load can be supplied with electricity through the power grid and the photovoltaic energy storage system can be charged, so that electricity consumption expenditure is generated. And when the photovoltaic energy storage system sells electricity to the power grid, selling electricity benefits are generated. The electricity consumption expenditure is equal to the electricity purchase quantity, the electricity purchase price, and the electricity selling income is equal to the electricity selling quantity.
In the process of buying electricity and selling electricity, the buying electricity price and selling electricity price are determined according to the region or the provider of the power grid, and the types of fixed electricity price, time-sharing electricity price and the like exist. The traditional electricity utilization dispatching is based on the prediction of electricity buying price and electricity selling price, and dispatching management is carried out by utilizing an algorithm and combining the prediction of electricity consumption and electricity generation, so that dispatching precision is limited by the algorithm, efficiency is low, and the income of a photovoltaic energy storage system is influenced.
The electricity price information can be obtained and determined according to the predicted electricity price of the management system, or according to the electricity price release information on one side of the power grid, or according to the preset setting of a user of the photovoltaic energy storage system. The electricity price information characterizes electricity buying price and electricity selling price of the power grid predicted in a future period of time.
Fig. 2 is a schematic diagram of electricity price information, and as shown in fig. 2, the electricity price type shown in fig. 2 is time-of-use electricity price, and the electricity prices in different time periods are different. The electricity price includes a buying electricity price or a selling electricity price.
On the other hand, the electricity consumption and the electricity generation amount of each period of the photovoltaic energy storage system are predicted through a prediction model. As one embodiment, a prediction model of an original electricity management system of the photovoltaic energy storage system is adopted, the predicted electricity consumption and the generated energy are obtained, and the predicted calculated amount is reduced by directly obtaining data.
As a preferred implementation manner, fig. 3 is a flowchart of a power consumption scheduling method of another disclosed embodiment, as shown in fig. 3, where the power consumption scheduling method of another disclosed embodiment further includes step S200 and step S201:
s200, acquiring electricity consumption, generated energy and multi-source heterogeneous data of a historical record as training data; wherein the multi-source heterogeneous data includes weather information, user behavior information, or device information.
S201, a prediction model is built based on a machine learning algorithm, and the prediction model is trained according to the training data.
The multi-source heterogeneous data are parameters which can affect the operation of the photovoltaic energy storage system in the operation process of the photovoltaic energy storage system, and the parameters comprise weather information, user behavior information or equipment information and the like. The multi-source heterogeneous data can be expanded according to the prediction precision, the prediction precision is positively correlated with the number of data sources of the multi-source heterogeneous data, and the more the variety of the multi-source heterogeneous data is, the higher the prediction precision is.
The weather information comprises air temperature, illumination time, humidity and the like. The user behavior information includes user electricity usage habits, user load power, and the like. The device information includes the generated power, conversion efficiency, or SOC of the photovoltaic energy storage system, etc.
In the training process of the prediction model, the electricity consumption and the electricity generation amount are used as the output of the prediction model, the multi-source heterogeneous data are used as the input, and corresponding data training is executed. The prediction model after training can predict the power consumption and the power generation of each period according to the multi-source heterogeneous data at the current moment.
As shown in table 1 below, the electricity rate information and the prediction result correspond according to the time period, and a time association is established.
Table 1 scheduling model input data table
And inputting the electricity price information and the prediction result into a scheduling model, simulating the charging and discharging of the photovoltaic energy storage system in each period according to the input working mode of the photovoltaic energy storage system by the scheduling model, and calculating the electricity expenditure and the electricity income of the photovoltaic energy storage system under each simulation path under each simulation power and each simulation power. And according to the difference value of the simulated electricity consumption expense and the simulated electricity consumption income, the predicted expense cost of the photovoltaic energy storage system can be determined. The predicted expenditure cost can be negative, namely, the predicted expenditure cost represents that the photovoltaic energy storage system realizes profit according to electricity selling.
As one embodiment, as shown in fig. 3, the process of inputting the electricity price information and the prediction result into a scheduling model in step S101 and selecting an optimal scheduling policy includes steps S300 and S301:
s300, generating various scheduling strategies through the scheduling model; wherein each scheduling strategy corresponds to a predicted expenditure expense according to the electricity price information and the prediction result;
s301, selecting a scheduling strategy with the minimum predicted expenditure cost as the optimal scheduling strategy.
According to different simulation paths, the method corresponds to different predicted expenditure costs and different scheduling strategies of the photovoltaic energy storage system. And taking the scheduling strategy with the smallest predicted expenditure expense as the optimal scheduling strategy.
In one embodiment, as shown in fig. 3, in step S102, a process of controlling the operation of the photovoltaic energy storage system according to the optimal scheduling policy includes step S400:
and S400, adjusting the working mode, the working power or the charge-discharge switching of the photovoltaic energy storage system according to the scheduling strategy.
The working modes of the photovoltaic energy storage system comprise a time sharing mode, a self-use mode or an off-grid mode and the like. The working modes can be selected differently according to the equipment characteristics of the photovoltaic energy storage system. The working power comprises charging power and discharging power of the photovoltaic energy storage system. The charge-discharge switching represents the switching of the charge mode or the discharge mode of the photovoltaic energy storage system.
Wherein, the determination of the scheduling policy and the related data are shown in the following table 2:
table 2 scheduling policy data table
As shown in table 2, the scheduling policy corresponds to the time period and affects the purchase electricity costs and the respective benefits of different optical energy storage systems, etc.
And the scheduling strategy carries out simulation on the working mode, the working power or the charge-discharge switching according to the simulation of the scheduling model, and calculates the predicted expenditure cost according to the electricity price information and the prediction result.
As an alternative embodiment, the scheduling model includes a decision tree model or an AI (Artificial Intelligence ) model, through which the scheduling policy under each control route is output.
As a preferred implementation mode, the scheduling model adopts an AI model, and the richness and the accuracy of the scheduling strategy are improved through the AI model. Meanwhile, when the electricity price information, the prediction result and the like are input and adjusted, the robustness of the output of the AI model scheduling strategy is ensured, and the scheduling model is convenient to adjust.
According to the selection of the AI model, as an alternative embodiment, fig. 4 is a flowchart of an alternative embodiment power consumption scheduling method, and as shown in fig. 4, the alternative embodiment power consumption scheduling method further includes step S500:
s500, obtaining constraint conditions, and correcting the optimal scheduling strategy according to the constraint conditions.
The constraint condition is used for correcting the optimal scheduling strategy, and comprises correction of specific control of a working mode, working power or charge-discharge switching.
The constraint conditions comprise physical conditions such as power limitation and working condition constraint of the photovoltaic inverter, the constraint conditions are input into a scheduling model, and boundary values of an optimal scheduling strategy are limited, so that the optimal scheduling strategy is closer to a real state.
As an alternative embodiment, the constraint includes SOC limits, and when the SOC limits are below a set value, the optimal scheduling strategy is cancelled and not executed to ensure the health of the energy storage cells in the photovoltaic energy storage system. Wherein the set value is 5-15%, and as a preferred embodiment, the set value is 10%.
As an alternative embodiment, the constraint includes electricity rate information and spontaneous utilization rate, the electricity rate information and spontaneous utilization rate are imported into a battery management system of the photovoltaic energy storage system, and electricity cost calculated by the system is output. And comparing the predicted expenditure cost of the optimal scheduling strategy, and if the difference value between the predicted expenditure cost and the electricity consumption cost is smaller than a set value, canceling and not executing the optimal scheduling strategy. As a preferred embodiment, when the ratio of the difference value to the electricity consumption rate is smaller than the set ratio, the optimal scheduling strategy is cancelled and not executed. Wherein, the setting proportion is 5-15%, and as a preferred embodiment, the setting proportion is 10%.
And the accuracy of the optimal scheduling strategy is improved through constraint conditions. Meanwhile, excessive scheduling and ineffective scheduling of the photovoltaic energy storage system are prevented, and the calculation load of the system is reduced.
As an alternative embodiment, as shown in fig. 4, the power consumption scheduling method of an alternative embodiment further includes step S600:
s600, the prediction result, the optimal scheduling strategy and the running condition of the photovoltaic energy storage system are obtained and displayed.
The prediction result and the optimal scheduling policy can be digitally displayed as shown in table 1 and table 2.
The operation condition of the photovoltaic energy storage system comprises working modes, working power or charge-discharge switching and the like of the photovoltaic energy storage system in each period, and also comprises prediction of expenditure expense, comparison of the prediction of expenditure expense and actual expenditure expense and the like.
As one embodiment, in step S600, the cost of the payment is combined with the saving cost and the time period according to the comparison between the predicted cost of the payment and the actual cost of the payment, etc., and the time period is displayed.
The time period of the embodiment of the disclosure may be set according to requirements, including hours, days, months, or the like.
According to the electricity utilization scheduling method of any embodiment of the disclosure, the electricity price information and the prediction result of the prediction model are obtained; the prediction result comprises the electricity consumption and the electricity generation of the photovoltaic energy storage system in each future period. And inputting the electricity price information and the prediction result into a scheduling model, selecting an optimal scheduling strategy, and controlling the work of the photovoltaic energy storage system according to the optimal scheduling strategy. Through the cooperation of the prediction model and the scheduling model, the efficiency and the income of the photovoltaic energy storage system are effectively improved, the electricity cost of a user is reduced, and the burden and the loss of the photovoltaic energy storage system are reduced.
The embodiment of the disclosure also provides a power utilization scheduling device.
Fig. 5 is a block diagram of an electricity consumption scheduling apparatus according to an embodiment, and as shown in fig. 5, the electricity consumption scheduling apparatus according to an embodiment includes:
the data acquisition module 100 is used for acquiring electricity price information and a prediction result of the prediction model; the prediction result comprises the electricity consumption and the electricity generation of the photovoltaic energy storage system in each future period;
the scheduling generation module 101 is configured to input the electricity price information and the prediction result into a scheduling model, and select an optimal scheduling policy;
and the work scheduling module 102 is used for controlling the work of the photovoltaic energy storage system according to the optimal scheduling strategy.
The electricity utilization scheduling device obtains electricity price information and a prediction result of the prediction model; the prediction result comprises the electricity consumption and the electricity generation of the photovoltaic energy storage system in each future period. And inputting the electricity price information and the prediction result into a scheduling model, selecting an optimal scheduling strategy, and controlling the work of the photovoltaic energy storage system according to the optimal scheduling strategy. Through the cooperation of the prediction model and the scheduling model, the efficiency and the income of the photovoltaic energy storage system are effectively improved, the electricity cost of a user is reduced, and the burden and the loss of the photovoltaic energy storage system are reduced.
At least one embodiment of the present disclosure also provides a data control apparatus. Fig. 6 is a schematic block diagram of a data control apparatus provided in at least one embodiment of the present disclosure. For example, as shown in fig. 6, the data control device 20 may include one or more memories 200 and one or more processors 201. Memory 200 is used to non-transitory store computer-executable instructions; the processor 201 is configured to execute computer-executable instructions that, when executed by the processor 201, may cause the processor 201 to perform one or more steps in a power consumption scheduling method according to any embodiment of the present disclosure.
The specific implementation and the related explanation of each step of the power consumption scheduling method can be referred to the related content in the embodiment of the power consumption scheduling method, which is not described herein. It should be noted that the components of the data control device 20 shown in fig. 6 are only exemplary and not limiting, and that the data control device 20 may have other components as desired for practical applications.
In one embodiment, the processor 201 and the memory 200 may communicate with each other directly or indirectly. For example, the processor 201 and the memory 200 may communicate over a network connection. The network may include a wireless network, a wired network, and/or any combination of wireless and wired networks, the disclosure is not limited in type and function of the network herein. For another example, processor 201 and memory 200 may also communicate via a bus connection. The bus may be a peripheral component interconnect standard (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. For example, the processor 201 and the memory 200 may be disposed at a remote data server (cloud) or a distributed energy system (local), or may be disposed at a client (e.g., a mobile device such as a mobile phone). For example, the processor 201 may be a Central Processing Unit (CPU), tensor Processor (TPU), or graphics processor GPU, among other devices having data processing and/or instruction execution capabilities, and may control other components in the data prediction apparatus 20 to perform desired functions. The Central Processing Unit (CPU) can be an X86 or ARM architecture, etc.
In one embodiment, memory 200 may comprise any combination of one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. Volatile memory can include, for example, random Access Memory (RAM) and/or cache memory (cache) and the like. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, erasable programmable read-only memory (EPROM), portable compact disc read-only memory (CD-ROM), USB memory, flash memory, and the like. One or more computer-executable instructions may be stored on the computer-readable storage medium and the processor 201 may execute the computer-executable instructions to implement the various functions of the data prediction device 20. Various applications and various data, as well as various data used and/or generated by the applications, etc., may also be stored in the memory 200.
It should be noted that, the data control device 20 may achieve similar technical effects as the foregoing power consumption scheduling method, and the repetition is omitted.
At least one embodiment of the present disclosure also provides a non-transitory computer-readable storage medium. Fig. 7 is a schematic diagram of a non-transitory computer-readable storage medium provided by at least one embodiment of the present disclosure. For example, as shown in FIG. 7, one or more computer-executable instructions 301 may be non-transitory stored on the non-transitory computer-readable storage medium 30. For example, the computer-executable instructions 301, when executed by a computer, may cause the computer to perform one or more steps in a power consumption scheduling method according to any embodiment of the present disclosure.
In one embodiment, the non-transitory computer readable storage medium 30 may be applied to the data control device 20 described above, which may be, for example, the memory 200 in the data control device 20.
In one embodiment, the description of the non-transitory computer readable storage medium 30 may refer to the description of the memory 200 in the embodiment of the data control device 20, and the repetition is omitted.
It should be noted that the memory 200 stores different non-transitory computer executable instructions that, when executed by the processor 201, may cause the processor 201 to perform one or more steps in a power consumption scheduling method according to any of the embodiments of the present disclosure, the data control apparatus 20 corresponds to a firmware upgrade apparatus.
For the purposes of this disclosure, the following points are also noted:
(1) The drawings of the embodiments of the present disclosure relate only to the structures to which the embodiments of the present disclosure relate, and reference may be made to the general design for other structures.
(2) In the drawings for describing embodiments of the present invention, thicknesses and dimensions of layers or structures are exaggerated for clarity. It will be understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, it can be "directly on" or "under" the other element or intervening elements may be present.
(3) The embodiments of the present disclosure and features in the embodiments may be combined with each other to arrive at a new embodiment without conflict. The above is only a specific embodiment of the present disclosure, but the protection scope of the present disclosure is not limited thereto, and the protection scope of the present disclosure should be subject to the protection scope of the claims
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples represent only a few embodiments of the present application, which are described in more detail and are not thereby to be construed as limiting the scope of the claims. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (10)
1. The power utilization scheduling method is characterized by comprising the following steps:
acquiring electricity price information and a prediction result of a prediction model; the prediction result comprises the electricity consumption and the electricity generation of the photovoltaic energy storage system in each future period;
inputting the electricity price information and the prediction result into a scheduling model, and selecting an optimal scheduling strategy;
and controlling the work of the photovoltaic energy storage system according to the optimal scheduling strategy.
2. The power consumption scheduling method according to claim 1, further comprising the step of:
acquiring electricity consumption, electricity generation and multi-source heterogeneous data of a history record as training data; the multi-source heterogeneous data comprises weather information, user behavior information or equipment information;
and establishing a prediction model based on a machine learning algorithm, and training the prediction model according to the training data.
3. The power consumption scheduling method according to claim 1, wherein the process of inputting the electricity price information and the prediction result into a scheduling model and selecting an optimal scheduling policy includes the steps of:
generating a plurality of scheduling strategies through the scheduling model; wherein each scheduling strategy corresponds to a predicted expenditure expense according to the electricity price information and the prediction result;
and selecting a scheduling strategy with the minimum predicted expenditure cost as the optimal scheduling strategy.
4. The power consumption scheduling method according to claim 1, further comprising the step of:
and obtaining constraint conditions, and correcting the optimal scheduling strategy according to the constraint conditions.
5. The power usage scheduling method of claim 1, wherein the scheduling model comprises an AI model.
6. The power consumption scheduling method according to claim 1, wherein the process of controlling the operation of the photovoltaic energy storage system according to the optimal scheduling policy comprises the steps of:
and adjusting the working mode, the working power or the charge-discharge switching of the photovoltaic energy storage system according to the scheduling strategy.
7. The power consumption scheduling method according to claim 1, further comprising the step of:
and acquiring and displaying the prediction result, the optimal scheduling strategy and the running condition of the photovoltaic energy storage system.
8. An electricity consumption scheduling apparatus, comprising:
the data acquisition module is used for acquiring electricity price information and a prediction result of the prediction model; the prediction result comprises the electricity consumption and the electricity generation of the photovoltaic energy storage system in each future period;
the scheduling generation module is used for inputting the electricity price information and the prediction result into a scheduling model and selecting an optimal scheduling strategy;
and the work scheduling module is used for controlling the work of the photovoltaic energy storage system according to the optimal scheduling strategy.
9. A non-transitory computer-readable storage medium storing computer-executable instructions which, when executed by a processor, implement the power consumption scheduling method of any one of claims 1 to 7.
10. A data control apparatus, comprising:
one or more memories non-transitory storing computer-executable instructions;
one or more processors configured to execute computer-executable instructions, wherein the computer-executable instructions, when executed by the one or more processors, implement the power consumption scheduling method of any one of claims 1 to 7.
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| WO2020143104A1 (en) * | 2019-01-08 | 2020-07-16 | 南京工程学院 | Power grid mixing and rolling scheduling method that considers clogging and energy-storing time-of-use price |
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| CN116111656A (en) * | 2022-12-28 | 2023-05-12 | 杭州中恒电气股份有限公司 | Micro-grid dispatching method and device |
| CN117176079A (en) * | 2023-11-02 | 2023-12-05 | 中科华辰(山东)实业股份有限公司 | Photovoltaic module information management system |
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| WO2020143104A1 (en) * | 2019-01-08 | 2020-07-16 | 南京工程学院 | Power grid mixing and rolling scheduling method that considers clogging and energy-storing time-of-use price |
| WO2021114849A1 (en) * | 2019-12-12 | 2021-06-17 | 国网浙江省电力有限公司台州供电公司 | Island power grid energy storage system hierarchical control method for ameliorating new energy power generation fluctuation |
| CN116111656A (en) * | 2022-12-28 | 2023-05-12 | 杭州中恒电气股份有限公司 | Micro-grid dispatching method and device |
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