CN113128790B - Absorption optimization method and device of distributed photovoltaic system and terminal equipment - Google Patents
Absorption optimization method and device of distributed photovoltaic system and terminal equipment Download PDFInfo
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Abstract
The invention is suitable for the technical field of power systems, and provides a method, a device and a terminal device for optimizing the consumption of a distributed photovoltaic system, wherein the method comprises the following steps: acquiring a power generation prediction function of the distributed photovoltaic system in a preset first time period, a load prediction function of a non-charging load and charging load prediction data of a chargeable load; generating a plurality of charging schemes of the distributed photovoltaic system in the first time period according to the generated power prediction function, the load prediction function and the charging load prediction data; calculating the electricity purchasing quantity corresponding to each charging scheme, and taking the charging scheme with the minimum electricity purchasing quantity as a target charging scheme; charging respective charging loads in the first time period according to a target charging scheme. The method provided by the invention can determine the optimal target charging scheme according to the power generation condition, the load condition and the charging load condition of the distributed photovoltaic system in the first time period, thereby reducing the power purchasing amount and improving the photovoltaic absorption capacity of the system.
Description
Technical Field
The invention belongs to the technical field of power systems, and particularly relates to a method and a device for optimizing consumption of a distributed photovoltaic system and terminal equipment.
Background
Renewable energy has the advantages of low emission and environmental friendliness, so that the renewable energy has wider and wider development prospect. Wherein the distributed photovoltaic system is capable of providing convenient auxiliary power supply service for users in remote areas. However, due to the instability of the power generation process of the photovoltaic system, when the distributed photovoltaic system is used, on one hand, partial electric energy of photovoltaic power generation is abandoned, and on the other hand, electricity is purchased to an external power grid, namely, the wasting phenomenon exists in the elimination process of the distributed photovoltaic system.
Disclosure of Invention
In view of this, embodiments of the present invention provide a consumption optimization method and apparatus for a distributed photovoltaic system, and a terminal device, which can reduce power purchase to a power grid and improve photovoltaic consumption capability.
A first aspect of an embodiment of the present invention provides a method for optimizing absorption of a distributed photovoltaic system, including:
acquiring a power generation prediction function of the distributed photovoltaic system in a preset first time period;
acquiring a load prediction function of a non-charging load of the distributed photovoltaic system in the first time period;
acquiring charging load prediction data of a chargeable load of the distributed photovoltaic system in the first time period;
generating a plurality of charging schemes of the distributed photovoltaic system in the first time period according to the generating power prediction function, the load prediction function and the charging load prediction data;
calculating the electricity purchasing quantity corresponding to each charging scheme, and taking the charging scheme with the minimum electricity purchasing quantity as a target charging scheme;
charging respective charging loads within the first time period according to the target charging schedule.
A second aspect of the embodiments of the present invention provides a consumption optimization apparatus for a distributed photovoltaic system, including:
the power generation prediction function acquisition module is used for acquiring a power generation prediction function of the distributed photovoltaic system in a preset first time period;
the load prediction function acquisition module is used for acquiring a load prediction function of a non-charging load of the distributed photovoltaic system in the first time period;
the charging load prediction data acquisition module is used for acquiring charging load prediction data of a chargeable load of the distributed photovoltaic system in the first time period;
a charging scheme generation module, configured to generate a plurality of charging schemes of the distributed photovoltaic system in the first time period according to the generated power prediction function, the load prediction function, and the charging load prediction data;
the target charging scheme determining module is used for calculating the electricity purchasing quantity corresponding to each charging scheme and taking the charging scheme with the minimum electricity purchasing quantity as the target charging scheme;
and the charging module is used for charging each charging load in the first time interval according to the target charging scheme.
A third aspect of the embodiments of the present invention provides a terminal device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the method when executing the computer program.
A fourth aspect of embodiments of the present invention provides a computer-readable storage medium, which stores a computer program that, when executed by a processor, implements the steps of the method as described above.
A fifth aspect of embodiments of the present invention provides a computer program product, which, when run on a terminal device, causes the electronic device to perform the steps of the method according to any one of the first aspect.
Compared with the prior art, the embodiment of the invention has the following beneficial effects: the absorption optimization method of the distributed photovoltaic system provided by the embodiment of the invention comprises the following steps: acquiring a power generation prediction function of the distributed photovoltaic system in a preset first time period, a load prediction function of a non-charging load and charging load prediction data of a chargeable load; generating a plurality of charging schemes of the distributed photovoltaic system in the first time period according to the generated power prediction function, the load prediction function and the charging load prediction data; calculating the electricity purchasing quantity corresponding to each charging scheme, and taking the charging scheme with the minimum electricity purchasing quantity as a target charging scheme; charging respective charging loads in the first time period according to a target charging scheme. The method provided by the embodiment of the invention can determine the optimal target charging scheme according to the power generation condition, the load condition and the charging load condition of the distributed photovoltaic system in the first time period, thereby reducing the power purchasing amount and improving the photovoltaic consumption capacity of the system.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a schematic view of an application scenario of a method for absorption optimization of a distributed photovoltaic system according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of an implementation of a method for absorption optimization of a distributed photovoltaic system according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a digestion optimization device of a distributed photovoltaic system according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a terminal device according to an embodiment of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.
In order to explain the technical means of the present invention, the following description will be given by way of specific examples.
Fig. 1 shows an application scenario of the embodiment of the present invention, and referring to fig. 1, a distributed photovoltaic system applied by the method provided by the embodiment of the present invention includes a power grid 110, at least one photovoltaic power generation apparatus 120, at least one chargeable load 130, at least one non-chargeable load 140, and a bus 150. Alternatively, bus 150 is an ac bus and photovoltaic power plant 120 may include an inverter coupled to bus 150.
The working state of the photovoltaic power generation device 120 is greatly influenced by the natural environment, the power generation power is unstable, and in order to adapt to the power generation condition of the photovoltaic power generation device 120, the embodiment of the invention provides a consumption optimization method of a distributed photovoltaic system.
Fig. 2 shows a schematic implementation flow diagram of a method for absorption optimization of a distributed photovoltaic system according to an embodiment of the present invention, and referring to fig. 2, the method may include S101 to S106.
S101: and acquiring a power generation prediction function of the distributed photovoltaic system in a preset first time period.
In some embodiments, S101 may include: acquiring meteorological prediction data of a first region in the first time period, wherein the first region is a region where the distributed photovoltaic system is located, and the meteorological prediction data comprises illumination intensity data and illumination angle data. And calculating a power generation prediction function of the distributed photovoltaic system in the first time period according to the illumination intensity data and the illumination angle data.
Specifically, irradiance received by the photovoltaic power generation device at each moment is calculated according to the illumination intensity data and the illumination angle data at each moment. And calculating a power generation prediction function of the distributed photovoltaic power generation system according to the irradiance at each moment, the effective area of the photovoltaic power generation device and the energy brick changing efficiency.
Optionally, the abscissa of the power generation prediction function of the distributed photovoltaic system in the first period is time, the ordinate is power generation power, and the power generation prediction function is denoted as P 1 (t)。
In a specific embodiment, the distributed photovoltaic system is optimized for consumption with the sunset time as a starting time. The starting time of the first time interval is the current nearest sunset time, the ending time of the first time interval is the next sunset time, and the duration of the first time interval is 24 hours.
S102: and acquiring a load prediction function of the non-charging load of the distributed photovoltaic system in the first time period.
In some embodiments, S102 comprises: a load prediction function for each non-charging load is obtained. And summing the load prediction functions of the non-charging loads to obtain the load prediction functions of the non-charging loads of the distributed photovoltaic system in the first time period.
Optionally, the load prediction function of the first non-charging load is an average of historical load functions of the first non-charging load. The first non-charging load is any one of the respective non-charging loads.
Optionally, the abscissa of the load prediction function of the distributed photovoltaic system in the first period is time, the ordinate is load power, and the load prediction function is denoted as P 2 (t)。
S103: and acquiring charging load prediction data of a charging load of the distributed photovoltaic system in the first time period.
Specifically, the charging load prediction data includes a preset charging completion time and a preset charging amount.
In some embodiments, S103 may include: acquiring preset charging completion time set by a user;
and calculating the preset charging amount of the chargeable load according to the current electric quantity of the chargeable load.
In a specific application scenario, the chargeable load is an electric vehicle, and the charging completion time set by the user is 13: 00. Calculating a preset charging amount according to the difference value between the capacity of the battery of the electric automobile and the current electric quantity, and recording the preset charging amount as W 1 。
S104: and generating a plurality of charging schemes of the distributed photovoltaic system in the first time period according to the generated power prediction function, the load prediction function and the charging load prediction data.
In some embodiments, S104 may include: and generating a plurality of charging schemes of the distributed photovoltaic system in the first time period according to the generated power prediction function, the load prediction function, the preset charging completion time and the preset charging amount.
Optionally, S104 includes: the first time period is divided into a plurality of subintervals. And calculating the required charging time, namely the number of subintervals required by charging according to the preset charging amount and the rated charging power of the chargeable load. And calculating a difference function of the generating power prediction function and the load prediction function, and acquiring a subinterval which has a mean value larger than the rated charging power and is before the preset charging completion time as a chargeable subinterval.
And if the number of the chargeable subintervals is larger than that of the subintervals required by charging, charging in the range of the chargeable subintervals to generate at least one charging scheme.
And if the number of the chargeable subintervals is equal to the number of the subintervals required by charging, continuously charging in the range of each chargeable subinterval to generate a charging scheme.
And if the number of the chargeable subintervals is less than that of the subintervals required for charging, charging the chargeable subintervals on the premise of containing the chargeable subintervals to generate at least one charging scheme.
In a specific application scenario, the first time period is divided into 24 sub-intervals, denoted as T 1 、T 2 、T 3 …T 24 The duration of each subinterval is one hour, and if the sunset time of the current day is 19:00 and the sunrise time of the next day is 5:00, T is 1 Corresponding time is 19:00 to 20:00, T 2 The corresponding time is 20:00 to 21:00 …, T 24 Corresponding times are 18:00 to 19:00 the next day. According to the preset charge W 1 And rated charging power P, and the number of subintervals required by charging is calculated to be n. Calculating a generating power prediction function P 1 (t) and the load prediction function P 2 (t) difference function P 3 (t) of (d). In each subinterval, the difference function P 3 The subinterval where the mean value of (T) is greater than the rated charging frequency P is T 12 To T 16 And T 18 To T 22 I.e., 6:00 to 11:00 and 12:00 to 17: 00. Acquiring a subinterval before the preset charging completion time 13:00, namely a subinterval T, in the subintervals 12 To T 16 And T 18 There are 6 chargeable subintervals.
If the number n of subintervals required for charging is 5, any 5 subintervals among the 6 chargeable subintervals are selected for charging, and five charging schemes are generated.
If the number n of the subintervals required for charging is 6, the charging is performed in 6 chargeable subintervals to generate one charging scheme.
If the number n of the sub-regions required for charging is 8, charging is performed in 6 chargeable sub-regions, and any 2 sub-regions are selected for charging in the remaining sub-regions before the charging completion time, that is, in the sub-region T 1 To T 11 And T 17 Any 2 sub-intervals are selected for charging, and thirty charging schemes are generated.
In some embodiments, S104 may further include: and under the conditions of dynamic load change constraint, a power flow equation, node voltage constraint and distribution network radiation operation constraint of the distributed photovoltaic system, generating a plurality of charging schemes of the distributed photovoltaic system in the first time period according to the power generation predicted power data, the load preset data and the charging load predicted data.
S105: and calculating the electricity purchasing quantity corresponding to each charging scheme, and taking the charging scheme with the minimum electricity purchasing quantity as a target charging scheme.
In some embodiments, after S105, the method further comprises: and calculating the load fluctuation rate corresponding to the target charging scheme. And if the load fluctuation rate corresponding to the target charging scheme is larger than a preset first threshold value, updating the target charging scheme, and taking the charging scheme with the minimum electricity purchasing cost in the rest charging schemes as the target charging scheme. And repeatedly executing the steps until the load fluctuation rate corresponding to the target charging scheme is less than or equal to the preset first threshold value.
In this embodiment, if the load fluctuation rate corresponding to the target charging scheme is too large, the stable operation of the distributed photovoltaic system is not facilitated, and therefore, a charging scheme with the load fluctuation rate being less than or equal to a first preset threshold needs to be selected as the target charging scheme.
S106: charging respective charging loads within the first time period according to the target charging schedule.
In this embodiment, charging each charging load according to the target charging scheme can determine an optimal target charging scheme according to the power generation condition, the load condition and the charging load condition of the distributed photovoltaic system in the first period of time on the basis of ensuring the stability of the distributed photovoltaic system, so that the power purchasing amount is reduced, and the photovoltaic absorption capacity of the system is improved.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Fig. 3 shows a schematic diagram of a consumption optimization device of a distributed photovoltaic system according to an embodiment of the present invention, and referring to fig. 3, a consumption optimization device 30 of a distributed photovoltaic system according to an embodiment of the present invention may include: a power generation prediction function acquisition module 310, a load prediction function acquisition module 320, a charging load prediction data acquisition module 330, a charging plan generation module 340, a target charging plan determination module 350, and a charging module 360.
A power generation prediction function obtaining module 310, configured to obtain a power generation prediction function of the distributed photovoltaic system in a preset first time period.
A load prediction function obtaining module 320, configured to obtain a load prediction function of a non-charging load of the distributed photovoltaic system in the first time period.
A charging load prediction data obtaining module 330, configured to obtain charging load prediction data of a chargeable load of the distributed photovoltaic system in the first time period.
A charging plan generating module 340, configured to generate a plurality of charging plans of the distributed photovoltaic system in the first time period according to the generated power prediction function, the load prediction function, and the charging load prediction data.
And a target charging scheme determining module 350, configured to calculate power purchase amounts corresponding to the charging schemes, and use the charging scheme with the minimum power purchase amount as the target charging scheme.
A charging module 360, configured to charge each charging load within the first time period according to the target charging scheme.
The consumption optimization device of the distributed photovoltaic system provided by the embodiment of the invention can determine an optimal target charging scheme according to the power generation condition, the load condition and the charging load condition of the distributed photovoltaic system in the first period, thereby reducing the electricity purchasing quantity and improving the photovoltaic consumption capacity of the system.
In some embodiments, the power generation prediction function obtaining module 310 is specifically configured to: acquiring meteorological prediction data of a first region in the first time period, wherein the first region is a region where the distributed photovoltaic system is located, and the meteorological prediction data comprises illumination intensity data and illumination angle data. And calculating a power generation prediction function of the distributed photovoltaic system in the first time period according to the illumination intensity data and the illumination angle data.
In some embodiments, the load prediction function obtaining module 320 is specifically configured to: a load prediction function for each non-charging load is obtained. And summing the load prediction functions of the non-charging loads to obtain the load prediction functions of the non-charging loads of the distributed photovoltaic system in the first time period.
In some embodiments, the charging load prediction data obtaining module 330 is specifically configured to: and acquiring preset charging completion time set by a user. And calculating the preset charging amount of the chargeable load according to the current electric quantity of the chargeable load.
In some embodiments, the charging scheme generation module 340 is specifically configured to: and generating a plurality of charging schemes of the distributed photovoltaic system in the first time period according to the generated power prediction function, the load prediction function, the preset charging completion time and the preset charging amount.
In some embodiments, the charging scheme generation module 340 is specifically configured to: and under the conditions of dynamic load change constraint, a power flow equation, node voltage constraint and distribution network radiation operation constraint of the distributed photovoltaic system, generating a plurality of charging schemes of the distributed photovoltaic system in the first time period according to the power generation predicted power data, the load preset data and the charging load predicted data.
In some embodiments, the target charging schedule determination module 350 is specifically configured to: and calculating the load fluctuation rate corresponding to the target charging scheme. And if the load fluctuation rate corresponding to the target charging scheme is greater than a preset first threshold value, updating the target charging scheme, and taking the charging scheme with the minimum electricity purchasing cost in the rest charging schemes as the target charging scheme. And repeatedly executing the steps until the load fluctuation rate corresponding to the target charging scheme is less than or equal to the preset first threshold value.
Fig. 4 is a schematic diagram of a terminal device according to an embodiment of the present invention. As shown in fig. 4, the terminal device 40 of this embodiment includes: a processor 400, a memory 410 and a computer program 420, such as a consumption optimization program for a distributed photovoltaic system, stored in said memory 410 and executable on said processor 400. The processor 40, when executing the computer program 420, implements the steps in each of the above-described embodiments of the method for optimizing the absorption of a distributed photovoltaic system, such as the steps S101 to S106 shown in fig. 2. Alternatively, the processor 400, when executing the computer program 420, implements the functions of the modules/units in the above-mentioned device embodiments, such as the functions of the modules 310 to 360 shown in fig. 3.
Illustratively, the computer program 420 may be partitioned into one or more modules/units that are stored in the memory 410 and executed by the processor 400 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing certain functions, which are used to describe the execution of the computer program 420 in the terminal device 40. For example, the computer program 420 may be divided into a power generation prediction function acquisition module, a load prediction function acquisition module, a charging load prediction data acquisition module, a charging scheme generation module, a target charging scheme determination module, and a charging module (a module in a virtual device).
The terminal device 40 may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device may include, but is not limited to, a processor 400, a memory 410. Those skilled in the art will appreciate that fig. 4 is merely an example of a terminal device 40 and does not constitute a limitation of terminal device 40 and may include more or fewer components than shown, or some components may be combined, or different components, for example, the terminal device may also include input output devices, network access devices, buses, etc.
The Processor 400 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The storage 410 may be an internal storage unit of the terminal device 40, such as a hard disk or a memory of the terminal device 40. The memory 410 may also be an external storage device of the terminal device 40, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, provided on the terminal device 40. Further, the memory 410 may also include both an internal storage unit and an external storage device of the terminal device 40. The memory 410 is used for storing the computer programs and other programs and data required by the terminal device. The memory 410 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the technical solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. . Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U.S. disk, removable hard disk, magnetic diskette, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signal, telecommunications signal, and software distribution medium, etc. It should be noted that the computer-readable medium may contain suitable additions or subtractions depending on the requirements of legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer-readable media may not include electrical carrier signals or telecommunication signals in accordance with legislation and patent practice.
The above-mentioned embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (9)
1. A method for absorption optimization of a distributed photovoltaic system, comprising:
acquiring a generating power prediction function of the distributed photovoltaic system in a preset first time period;
acquiring a load prediction function of a non-charging load of the distributed photovoltaic system in the first time period;
acquiring charging load prediction data of a chargeable load of the distributed photovoltaic system in the first time period;
generating a plurality of charging schemes of the distributed photovoltaic system in the first time period according to the generating power prediction function, the load prediction function and the charging load prediction data;
calculating the electricity purchasing quantity corresponding to each charging scheme, and taking the charging scheme with the minimum electricity purchasing quantity as a target charging scheme;
charging respective charging loads within the first time period according to the target charging schedule;
the abscissa of a generated power prediction function of the distributed photovoltaic system in a first period is time, and the ordinate is generated power;
the load prediction function of the non-charging load is the average value of the historical load functions of the first non-charging load, and the first non-charging load is any one of the non-charging loads;
the abscissa of a load prediction function of the distributed photovoltaic system in a first period is time, and the ordinate is load power;
wherein the content of the first and second substances,
the charging load prediction data includes a preset charging completion time and a preset charging amount; the generating a plurality of charging schemes of the distributed photovoltaic system in the first time period according to the generated power prediction function, the load prediction function and the charging load prediction data comprises:
generating a plurality of charging schemes of the distributed photovoltaic system in the first time period according to the generated power prediction function, the load prediction function, the preset charging completion time and the preset charging amount;
dividing the first time period into a plurality of subintervals, and calculating the number of the subintervals required by charging according to the preset charging amount and the rated charging power of the chargeable load;
calculating a difference function of the generating power prediction function and the load prediction function, and acquiring a subinterval which has a mean value larger than the rated charging power and is before the preset charging completion time as a chargeable subinterval;
if the number of the chargeable subintervals is larger than that of the subintervals required by charging, charging in the range of the chargeable subintervals to generate at least one charging scheme;
if the number of the chargeable subintervals is equal to the number of the subintervals required by charging, continuously charging in the range of each chargeable subinterval to generate a charging scheme;
and if the number of the chargeable subintervals is less than that of the subintervals required for charging, charging the chargeable subintervals on the premise of containing the chargeable subintervals to generate at least one charging scheme.
2. A method for absorption optimization of a distributed photovoltaic system according to claim 1, wherein said obtaining a prediction function of generated power of the distributed photovoltaic system over a first period of time comprises:
acquiring meteorological prediction data of a first region in the first time period, wherein the first region is a region where the distributed photovoltaic system is located, and the meteorological prediction data comprises illumination intensity data and illumination angle data;
and calculating a generating power prediction function of the distributed photovoltaic system in the first time period according to the illumination intensity data and the illumination angle data.
3. A method for absorption optimization of a distributed photovoltaic system according to claim 1, wherein said obtaining a load prediction function of a non-charging load of said distributed photovoltaic system during said first time period comprises:
acquiring a load prediction function of each non-charging load;
and summing the load prediction functions of the non-charging loads to obtain the load prediction functions of the non-charging loads of the distributed photovoltaic system in the first time period.
4. A method for absorption optimization of a distributed photovoltaic system as recited in claim 1, wherein said obtaining charging load prediction data for a chargeable load of said distributed photovoltaic system during said first time period comprises:
acquiring preset charging completion time set by a user;
and calculating the preset charging amount of the chargeable load according to the current electric quantity of the chargeable load.
5. The method for absorption optimization of a distributed photovoltaic system according to claim 1, wherein said generating a plurality of charging schedules for said distributed photovoltaic system over said first time period based on said generated power prediction function, said load prediction function, and said charging load prediction data comprises:
and under the conditions of dynamic load change constraint, power flow equation, node voltage constraint and distribution network radiation operation constraint of the distributed photovoltaic system, generating a plurality of charging schemes of the distributed photovoltaic system in the first time period according to the generating power prediction function, the load prediction function and the charging load prediction data.
6. A method as claimed in claim 1, wherein the step of calculating the power purchase amount corresponding to each charging scheme, and using the charging scheme with the minimum power purchase amount as the target charging scheme, comprises the steps of:
calculating a load fluctuation rate corresponding to the target charging scheme;
if the load fluctuation rate corresponding to the target charging scheme is larger than a preset first threshold value, updating the target charging scheme, and taking the charging scheme with the minimum electricity purchasing cost in the rest charging schemes as a target charging scheme;
and repeatedly executing the steps until the load fluctuation rate corresponding to the target charging scheme is less than or equal to the preset first threshold value.
7. A device for optimizing the absorption of a distributed photovoltaic system, comprising:
the power generation prediction function acquisition module is used for acquiring a power generation power prediction function of the distributed photovoltaic system in a preset first time period;
the load prediction function acquisition module is used for acquiring a load prediction function of a non-charging load of the distributed photovoltaic system in the first time period;
the charging load prediction data acquisition module is used for acquiring charging load prediction data of a chargeable load of the distributed photovoltaic system in the first time period;
a charging scheme generation module, configured to generate a plurality of charging schemes of the distributed photovoltaic system in the first time period according to the generated power prediction function, the load prediction function, and the charging load prediction data;
the target charging scheme determining module is used for calculating the electricity purchasing quantity corresponding to each charging scheme and taking the charging scheme with the minimum electricity purchasing quantity as the target charging scheme;
the charging module is used for charging each charging load in the first time interval according to the target charging scheme;
the abscissa of a generated power prediction function of the distributed photovoltaic system in a first period is time, and the ordinate is generated power;
the load prediction function of the non-charging load is the average value of the historical load functions of the first non-charging load, and the first non-charging load is any one of the non-charging loads;
the abscissa of a load prediction function of the distributed photovoltaic system in a first period is time, and the ordinate is load power;
wherein the content of the first and second substances,
the charging load prediction data includes a preset charging completion time and a preset charging amount; the generating a plurality of charging schemes of the distributed photovoltaic system in the first time period according to the generated power prediction function, the load prediction function and the charging load prediction data comprises:
generating a plurality of charging schemes of the distributed photovoltaic system in the first time period according to the generated power prediction function, the load prediction function, the preset charging completion time and the preset charging amount;
dividing the first time period into a plurality of subintervals, and calculating the number of the subintervals required by charging according to the preset charging amount and the rated charging power of the chargeable load;
calculating a difference function of the generating power prediction function and the load prediction function, and acquiring a subinterval which has a mean value larger than the rated charging power and is before the preset charging completion time as a chargeable subinterval;
if the number of the chargeable subintervals is larger than that of the subintervals required by charging, charging in the range of the chargeable subintervals to generate at least one charging scheme;
if the number of the chargeable subintervals is equal to the number of the subintervals required by charging, continuously charging in the range of each chargeable subinterval to generate a charging scheme;
and if the number of the chargeable subintervals is less than that of the subintervals required for charging, charging the chargeable subintervals on the premise of containing the chargeable subintervals to generate at least one charging scheme.
8. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1 to 6 when executing the computer program.
9. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 6.
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