CN115811096A - Intelligent photovoltaic scheduling method and system based on OSS - Google Patents
Intelligent photovoltaic scheduling method and system based on OSS Download PDFInfo
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Abstract
The invention provides an intelligent photovoltaic scheduling method and system based on OSS, and relates to the field of photovoltaic scheduling. The method includes the steps that terminal prediction data are obtained based on an OSS system, a profit value is determined according to the terminal prediction data, scheduling requirements and historical electricity prices, a minimum operation cost value of equipment scheduling is determined through a photovoltaic intelligent scheduling model algorithm, whether the minimum operation cost value meets the profit value or not is judged, and if the minimum operation cost value meets the profit value, a scheduling command is issued to the equipment, so that the equipment obtains an optimal operation strategy in a scheduling process, the scheduling value of the equipment in the scheduling process is improved, and meanwhile the pressure problem caused by mis-scheduling of a power grid side is avoided. In addition, the invention also provides a photovoltaic intelligent scheduling system which comprises an acquisition module, a data processing module and a judgment module.
Description
Technical Field
The invention relates to the field of photovoltaic scheduling, in particular to an intelligent photovoltaic scheduling method and system based on OSS.
Background
China, as a big energy producing and consuming country, pays high attention to environmental protection, and encourages the utilization of new technologies and new energy sources to reduce greenhouse effect and promote ecological virtuous cycle. In this context, clean energy represented by photovoltaic has good economic and environmental benefits and is supported by the state government. However, in the prior art, the underlying logic is based on the consumption condition of user data, the uncertain factors of the operation condition of actual equipment are too many, errors exist in the simulation and prediction of the ideal condition and the actual condition of user and equipment data, the photovoltaic scheduling result is different from the ideal condition, and energy waste and economic loss can be caused.
Disclosure of Invention
The invention aims to provide an intelligent photovoltaic scheduling method and system based on OSS, which can enable equipment to obtain an optimal operation strategy in a scheduling process and improve the scheduling value of the equipment in the scheduling process.
The embodiment of the invention is realized by the following steps:
in a first aspect, an embodiment of the present application provides an intelligent photovoltaic scheduling method based on OSS, including the following steps:
acquiring terminal prediction data based on an OSS system; acquiring scheduling requirements and historical electricity prices; determining a profit value according to the terminal prediction data, the scheduling demand and the historical electricity price; calculating a device scheduling total cost based on the following formula:
in the formula: minC VPP Scheduling a total cost for the device;
a cost factor for scheduling the device; p t,i ES Power of the nth device for t periods; lambda [ alpha ] + A penalty factor for scheduling when the scheduling is not completed as required; f (P) t VPP ) Power when scheduling is not completed according to requirements for the power of the nth device in t time periods; lambda - Scheduling the reward coefficients when completed as required; f (-P) t VPP ) Completing the power of scheduling for the power of the nth device in t time intervals according to requirements; and judging whether the scheduling total cost of the equipment meets the profit value or not, and if so, issuing a scheduling command to the equipment.
Based on the first aspect, in some embodiments of the present invention, the step of obtaining the terminal prediction data includes:
aiming at different photovoltaic equipment, establishing a LightGBM power generation amount prediction model; inputting historical power generation data and weather condition information of the current day in a model; carrying out normalization processing and characteristic correlation analysis on historical power generation data and weather condition information; and predicting the data of the generated energy, the power consumption and the stored energy of the equipment according to the weather forecast.
According to a first aspect, some embodiments of the present invention comprise the step of obtaining device monitoring data.
Based on the first aspect, in some embodiments of the present invention, the device monitoring data includes load power, photovoltaic power generation power, energy storage power, grid power, and energy storage SOC.
Based on the first aspect, in some embodiments of the present invention, the issuing a scheduling command to a device includes the following steps:
and issuing a scheduling command to the equipment through an API (application programming interface) method based on the cloud server.
In a second aspect, an embodiment of the present application provides an intelligent photovoltaic scheduling system based on OSS, which includes:
the acquisition module is used for acquiring terminal prediction data; the data processing module is used for determining a profit value according to the terminal prediction data and the simulation historical electricity price and determining a device scheduling operation cost value based on a photovoltaic intelligent scheduling model algorithm; and the judging module is used for judging whether the scheduling cost value meets the profit value or not, and if so, issuing a scheduling command to the equipment.
In a third aspect, an embodiment of the present application provides an electronic device, including at least one processor, at least one memory, and a data bus; wherein: the processor and the memory complete mutual communication through the data bus; the memory stores program instructions for execution by the processor which are invoked to perform a method as described above in relation to implementing any one of the first aspects described above.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program is executed by a processor to implement the method as described in any one of the first aspect above.
Compared with the prior art, the embodiment of the invention has at least the following advantages or beneficial effects:
on one hand, the embodiment of the invention provides an intelligent photovoltaic scheduling method based on OSS, which comprises the following steps:
acquiring terminal prediction data based on an OSS system; acquiring scheduling requirements and historical electricity prices; determining a profit value according to the terminal prediction data, the scheduling demand and the historical electricity price; calculating a device scheduling total cost based on the following formula:
in the formula: minC VPP Scheduling a total cost for the device;
a cost factor for scheduling the device; p is t,i ES Power of the nth device for t periods; lambda + A penalty factor when the scheduling is not completed according to requirements; f (P) t VPP ) Power when scheduling is not completed according to requirements for the power of the nth device in t time periods; lambda [ alpha ] - Scheduling the reward coefficients when completed as required; f (-P) t VPP ) Completing the power of scheduling for the power of the nth device in t time intervals according to requirements; and judging whether the scheduling total cost of the equipment meets the profit value or not, and if so, issuing a scheduling command to the equipment.
The method includes the steps that terminal prediction data are obtained based on an OSS system, a profit value is determined according to the terminal prediction data and simulated historical electricity prices, a minimum operation cost value of equipment scheduling is determined through a photovoltaic intelligent scheduling model algorithm, whether the minimum operation cost value meets the profit value or not is judged, if yes, a scheduling command is issued to the equipment, the equipment is enabled to obtain an optimal operation strategy in the scheduling process, the scheduling value of the equipment in the scheduling process is improved, and meanwhile the pressure problem caused by mis-scheduling on the side of a power grid is avoided.
On the other hand, the embodiment of the invention provides an intelligent photovoltaic scheduling system based on OSS, which comprises an acquisition module, a data processing module and a judgment module, wherein the acquisition module is used for acquiring terminal prediction data; the data processing module is used for determining a profit value according to the terminal prediction data and the simulated historical electricity price and determining a minimum operation cost value of equipment scheduling based on a photovoltaic intelligent scheduling model algorithm; the judging module is used for judging whether the minimum scheduling cost value meets the profit value or not, and if so, a scheduling command is issued to the equipment.
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 embodiment of an intelligent photovoltaic scheduling method based on OSS according to the present invention;
FIG. 2 is a block diagram of an embodiment of an intelligent photovoltaic dispatch system based on OSS according to the present invention;
fig. 3 is a block diagram of an electronic device according to an embodiment of the present invention.
Icon: 1. an acquisition module; 2. a data processing module; 3. a judgment module; 101. a processor; 102. a memory; 103. a data bus.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, as generally described and illustrated in the figures herein, could be arranged and designed in a wide variety of different configurations.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the individual features of the embodiments can be combined with one another without conflict.
Example 1
Referring to fig. 1, an embodiment of the present application provides an intelligent photovoltaic scheduling method based on OSS, which enables a device to obtain an optimal operation strategy in a scheduling process, and improves the efficiency of the device in the scheduling process
And an optimal operation strategy is obtained, and the scheduling value of the equipment in the scheduling process is improved. The intelligent photovoltaic scheduling method based on the OSS comprises the following steps:
acquiring terminal prediction data based on an OSS system; acquiring scheduling requirements and historical electricity prices; determining a profit value according to the terminal prediction data, the scheduling demand and the historical electricity price; calculating a device scheduling total cost based on the following formula:
in the formula: minC VPP Scheduling a total cost for the device;
a cost factor for scheduling the device; p is t,i ES Power of the nth device for t periods; lambda + A penalty factor for scheduling when the scheduling is not completed as required; f (P) t VPP ) Power when the nth device does not complete scheduling according to requirements in t time intervals; lambda [ alpha ] - Scheduling the reward coefficients when completed as required; f (-P) t VPP ) Completing the power of scheduling for the power of the nth device in t periods according to requirements; and judging whether the scheduling total cost of the equipment meets the profit value or not, and if so, issuing a scheduling command to the equipment.
The method includes the steps that terminal prediction data are obtained based on an OSS system, a profit value is determined according to the terminal prediction data and known scheduling requirements and historical electricity prices, a minimum operation cost value of equipment scheduling is determined through a photovoltaic intelligent scheduling model algorithm, whether the minimum operation cost value meets the profit value or not is judged, and if the minimum operation cost value meets the profit value, a scheduling command is issued to the equipment, so that the equipment obtains an optimal operation strategy in a scheduling process, the scheduling value of the equipment in the scheduling process is improved, and meanwhile the pressure problem caused by mis-scheduling of a power grid side is avoided. For example, when the power generation amount predicted by the terminal is 200, the scheduling demand is 150, the power generation amount is known to satisfy the scheduling demand, the power rate is simulated to be 1 by the historical power rate, the profit value is set to (150 × 1)/130%, if the operation cost value of the equipment needing scheduling is calculated by the above formula to be 100, the scheduling plan is satisfied because of 100< (150 × 1)/130%, if the scheduling demand is 70, the scheduling plan is satisfied 100> (70 × 1)/130%, and the equipment does not execute the scheduling command.
In some embodiments of the present invention, the step of obtaining the terminal prediction data includes:
aiming at different photovoltaic equipment, establishing a LightGBM power generation amount prediction model; inputting historical power generation data and weather condition information of the current day in a model; carrying out normalization processing and characteristic correlation analysis on historical power generation data and weather condition information; and predicting the data of the generated energy, the power consumption and the stored energy of the equipment according to the weather forecast.
The step of obtaining the terminal prediction data comprises: the method comprises the steps of establishing a LightGBM power generation amount prediction model aiming at different photovoltaic devices based on an OSS system, inputting historical power generation data and weather condition information corresponding to the current day into the model, then carrying out abnormal value processing and data normalization operation on the input data, carrying out data integration and characteristic correlation analysis, determining future weather information by combining with weather forecast, comparing information in the weather forecast with the input historical weather information by the model, and predicting the power generation amount data at a certain time in a certain day in the future. For example, the data input into the model includes weather condition information of a clear day without cloud cover, a cloudy day with short-time illumination, and the like, and equipment power generation data under corresponding weather, and if the weather forecast shows that the open day is a clear day without cloud cover, the model compares the input weather data with the weather data to find out equipment power generation conditions corresponding to the weather information of the input data which is also a clear day without cloud cover, thereby deducing the power generation amount of the open-day equipment. The prediction mode can predict the generated energy of equipment, and also can predict the load consumption, the power consumption, the energy storage and the like of a user.
In some embodiments of the invention, a step of obtaining device monitoring data is included.
In consideration of the safety problem of power dispatching, the intelligent photovoltaic dispatching method based on the OSS also comprises the steps of acquiring equipment monitoring data, realizing comprehensive supervision and control on a power system, carrying out timely early warning and treatment on potential safety hazards possibly occurring in the power dispatching process, strictly controlling the power dispatching process, carrying out strict protection, supervision and control on the whole system, avoiding the influence of a plurality of external factors and ensuring the power dispatching safety.
In some embodiments of the present invention, the device monitoring data includes load power, photovoltaic power generation power, energy storage power, grid power, and energy storage SOC.
The equipment monitoring data comprise load power, photovoltaic power generation power, energy storage power, power grid power and energy storage SOC, the working condition of the equipment can be monitored in real time through the data, and the data can also be used as supplementary data for acquiring terminal prediction data, so that perfect data support is provided for acquiring the terminal prediction data.
In some embodiments of the present invention, the issuing a scheduling command to a device includes the following steps:
and issuing a scheduling command to the equipment through an API (application programming interface) method based on the cloud server.
The intelligent photovoltaic scheduling method based on the OSS is based on the fact that the cloud server issues the scheduling command to the equipment through the API method, the scheduling command can be intelligently and automatically issued to the equipment through the API method, manual scheduling operation of operators is not needed, and labor cost is saved.
Example 2
Referring to fig. 2, an embodiment of the present application provides an intelligent photovoltaic scheduling system based on OSS, which includes:
the acquisition module 1 is used for acquiring terminal prediction data; the data processing module 2 is used for determining a profit value according to terminal prediction data, the scheduling demand of a power demand side and historical electricity prices, and determining a minimum operation cost value of equipment scheduling on the basis of a photovoltaic intelligent scheduling model algorithm; and the judging module 3 is used for judging whether the scheduling minimum cost value meets the profit value or not, and if so, issuing a scheduling command to the equipment.
The intelligent photovoltaic scheduling system based on the OSS comprises an acquisition module 1, a data processing module 2 and a judgment module 3, wherein the acquisition module 1 is used for acquiring terminal prediction data, the data processing module 2 determines a profit value according to the terminal prediction data, scheduling requirements and historical electricity prices, determines a minimum operation cost value for equipment scheduling based on a photovoltaic intelligent scheduling model algorithm, judges whether the minimum cost value for scheduling meets the profit value or not through the judgment module 3, and if so, a scheduling command is issued to the equipment.
Example 3
Referring to fig. 3, an electronic device according to an embodiment of the present disclosure includes at least one processor 101, at least one memory 102, and a data bus 103; wherein: the processor 101 and the memory 102 communicate with each other through a data bus 103; the memory 102 stores program instructions executable by the processor 101, and the processor 101 calls the program instructions to execute an OSS-based intelligent photovoltaic scheduling method. For example, the following steps are realized:
acquiring terminal prediction data based on an OSS system; acquiring a scheduling requirement and a historical electricity price; determining a profit value according to the terminal prediction data, the scheduling demand and the historical electricity price; calculating a device scheduling total cost based on the following formula:
in the formula: minC VPP Scheduling a total cost for the device;
a cost factor for scheduling the device; p is t,i ES Power of the nth device for t periods; lambda + A penalty factor for scheduling when the scheduling is not completed as required; f (P) t VPP ) Power when scheduling is not completed according to requirements for the power of the nth device in t time periods; lambda [ alpha ] - Scheduling the reward coefficients when completed as required; f (-P) t VPP ) Completing the power of scheduling for the power of the nth device in t periods according to requirements; and judging whether the scheduling total cost of the equipment meets the profit value or not, and if so, issuing a scheduling command to the equipment.
The Memory 102 may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
The processor 101 may be an integrated circuit chip having signal processing capabilities. The Processor 101 may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
It will be appreciated that the configuration shown in fig. 3 is merely illustrative and that the electronic device may include more or fewer components than shown in fig. 3 or have a different configuration than shown in fig. 3. The components shown in fig. 3 may be implemented in hardware, software, or a combination thereof.
Example 4
The present invention provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor 101, implements an OSS-based intelligent photovoltaic scheduling method. For example, to realize:
acquiring terminal prediction data based on an OSS system; acquiring a scheduling requirement and a historical electricity price; determining a profit value according to the terminal prediction data, the scheduling demand and the historical electricity price; calculating a device scheduling total cost based on the following formula:
in the formula: minC VPP Scheduling a total cost for the device;
a cost factor for scheduling the device; p t,i ES Power of the nth device for t periods; lambda [ alpha ] + A penalty factor when the scheduling is not completed according to requirements; f (P) t VPP ) Power when scheduling is not completed according to requirements for the power of the nth device in t time periods; lambda [ alpha ] - Scheduling the reward coefficients when completed as required; f (-P) t VPP ) Completing the power of scheduling for the power of the nth device in t time intervals according to requirements; and judging whether the scheduling total cost of the equipment meets the profit value or not, and if so, issuing a scheduling command to the equipment.
The above-described functions, if implemented in the form of software functional modules and sold or used as a separate product, may be stored in a computer-readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (8)
1. An intelligent photovoltaic scheduling method based on OSS is characterized by comprising the following steps:
acquiring terminal prediction data based on an OSS system;
acquiring scheduling requirements and historical electricity prices;
determining a profit value according to the terminal prediction data, the scheduling demand and the historical electricity price;
calculating a device scheduling total cost based on the following formula:
in the formula: minC VPP Scheduling a total cost for the device;
a cost factor for scheduling the device; p t,i ES Power of the nth device for t periods; lambda [ alpha ] + A penalty factor when the scheduling is not completed according to requirements; f (P) t VPP ) Power when scheduling is not completed according to requirements for the power of the nth device in t time periods; lambda - Scheduling the reward coefficients when completed as required; f (-P) t VPP ) Work in scheduling for power of nth device in t time periods as requiredRate;
and judging whether the scheduling total cost of the equipment meets the profit value or not, and if so, issuing a scheduling command to the equipment.
2. The intelligent photovoltaic scheduling method based on OSS of claim 1, wherein the step of obtaining terminal prediction data comprises:
aiming at different photovoltaic equipment, establishing a LightGBM power generation amount prediction model;
inputting historical power generation data and corresponding weather condition information in a model;
carrying out normalization processing and characteristic correlation analysis on historical power generation data and weather condition information to obtain a processing analysis result;
and predicting the data of the generated energy, the power consumption and the stored energy of the equipment according to the weather forecast.
3. The OSS-based intelligent photovoltaic scheduling method of claim 1, comprising the step of obtaining equipment monitoring data.
4. The OSS-based intelligent photovoltaic scheduling method according to claim 3, wherein the equipment monitoring data comprises load power, photovoltaic power generation power, energy storage power, grid power and energy storage SOC.
5. The OSS-based intelligent photovoltaic scheduling method of claim 1, wherein the issuing of the scheduling command to the device comprises the steps of:
and issuing a scheduling command to the equipment through an API (application programming interface) method based on the cloud server.
6. An intelligent photovoltaic dispatch system based on OSS, comprising:
the acquisition module is used for acquiring terminal prediction data;
the data processing module is used for determining a profit value according to the terminal prediction data, the scheduling demand of the power demand side and the historical electricity price, and determining an equipment scheduling operation cost value based on a photovoltaic intelligent scheduling model algorithm;
and the judging module is used for judging whether the scheduling operation cost value meets the profit value or not, and if so, issuing a scheduling command to the equipment.
7. An electronic device comprising at least one processor, at least one memory, and a data bus; wherein: the processor and the memory complete mutual communication through the data bus; the memory stores program instructions for execution by the processor, the processor calling the program instructions to perform the method of any of claims 1-5.
8. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-5.
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