CN119134511B

CN119134511B - Photovoltaic power generation and commercial power cooperative power supply method, equipment and storage medium

Info

Publication number: CN119134511B
Application number: CN202411613801.5A
Authority: CN
Inventors: 刘文军; 马启值; 林烨; 刘士涛
Original assignee: Guangzhou Zheminghui Technology Co ltd
Current assignee: Guangzhou Zheminghui Technology Co ltd
Priority date: 2024-11-13
Filing date: 2024-11-13
Publication date: 2025-02-07
Anticipated expiration: 2044-11-13
Also published as: CN119134511A

Abstract

The application discloses a photovoltaic power generation and commercial power cooperative power supply method, equipment and a storage medium, which relate to the technical field of general control systems, wherein the photovoltaic power generation and commercial power cooperative power supply method comprises the steps of determining the expected power of a photovoltaic power station according to the characteristic parameters of each photovoltaic power station, the illumination intensity and the temperature of an area where the photovoltaic power station is positioned, mapping to a time sequence according to the expected power, line data and real-time power consumption, and determining power fluctuation data; determining electric quantity fluctuation data lower than a preset threshold value as a first period and electric quantity fluctuation data not lower than the preset threshold value as a second period, determining expected electric quantity according to the ratio of adjacent first period and second period, and controlling mains supply access at a time point when the expected electric quantity is larger than an access threshold value. The technical problem that the mains supply cannot be effectively utilized for supplementing when the photovoltaic power generation is insufficient is solved, the grid-connected accuracy of the mains supply is improved, and the technical effect of stable power utilization is ensured.

Description

Photovoltaic power generation and commercial power cooperative power supply method, equipment and storage medium

Technical Field

The application relates to the technical field of general control systems, in particular to a photovoltaic power generation and commercial power cooperative power supply method, equipment and a storage medium.

Background

With the continuous progress of renewable energy technology and the enhancement of environmental awareness, photovoltaic power generation is increasingly used as a clean energy source. However, photovoltaic power generation is greatly affected by weather conditions, and the generated energy has uncertainty and fluctuation, which brings challenges to stable power supply of an electric power system.

In the related art, the photovoltaic power generation system has a certain limitation on power distribution, and the actual power consumption condition of electrical equipment and the bearing capacity of a circuit cannot be fully considered, so that the utility power cannot be effectively utilized for supplementing when the photovoltaic power generation is insufficient.

Disclosure of Invention

The application mainly aims to provide a photovoltaic power generation and commercial power cooperative power supply method, equipment and a storage medium, and aims to solve the technical problem that commercial power cannot be effectively utilized for supplementing when the photovoltaic power generation is insufficient due to insufficient consideration of actual power utilization conditions of electrical equipment and bearing capacity of circuits.

In order to achieve the above object, the present application provides a method for cooperatively supplying power to a photovoltaic power generation and a utility power, the method for cooperatively supplying power to a photovoltaic power generation and a utility power comprising:

Determining the expected power of each photovoltaic power station according to the characteristic parameters of each photovoltaic power station, namely the illumination intensity and the temperature of the area where the photovoltaic power station is located, wherein the characteristic parameters comprise at least one of photovoltaic panel layout information, inclination angle data and a maximum power point;

acquiring line data of each photovoltaic power station and a mains supply interface;

acquiring real-time electricity consumption of each electric device based on the intelligent socket;

According to the expected power, the line data and the real-time electricity consumption, mapping the expected power, the line data and the real-time electricity consumption to a time sequence, and determining electricity fluctuation data;

Determining the electric quantity fluctuation data which is lower than a preset threshold value as a first period, and determining the electric quantity fluctuation data which is not lower than the preset threshold value as a second period;

determining an expected electric quantity according to the ratio between the adjacent first time period and the adjacent second time period;

and controlling the mains supply to be connected at the time point when the expected electric quantity is larger than the access threshold.

In an embodiment, the step of determining the expected power of the photovoltaic power plant according to the illumination intensity and the temperature of the area where the photovoltaic power plant is located according to the characteristic parameters of each photovoltaic power plant includes:

Determining the layout information of the photovoltaic panels according to the coordinates of the photovoltaic panels of the photovoltaic power stations and the intervals of the photovoltaic panels;

acquiring an average value of inclination angles of the photovoltaic panel as the inclination angle data;

Determining the maximum power point according to the historical power generation amount of the photovoltaic power station;

And determining the expected power according to the photovoltaic panel layout information, the inclination angle data, the maximum power point, the illumination intensity and the temperature.

In an embodiment, the step of determining the expected power from the photovoltaic panel layout information, the tilt angle data, the maximum power point, the illumination intensity, and the temperature comprises:

determining expected illumination energy of the photovoltaic power plant according to the photovoltaic panel layout information, the inclination angle data and the illumination intensity;

updating the expected illumination energy according to the correction coefficient determined by the temperature;

and planning according to the updated expected illumination energy serving as a positive factor and the maximum power point serving as a negative factor, and determining the expected power.

In an embodiment, the step of obtaining line data of each of the photovoltaic power plants and the mains interface includes:

Acquiring first line data between the photovoltaic power stations;

Acquiring second line data between each photovoltaic power station and energy storage equipment;

Acquiring third line data between the energy storage equipment and the electric equipment;

acquiring fourth line data between the mains supply interface and the energy storage equipment;

And determining the line data according to the first line data, the second line data, the third line data and the fourth line data.

In an embodiment, the step of obtaining the real-time power consumption of each electric device based on the smart socket includes:

acquiring first real-time electricity consumption of each intelligent socket through cloud service;

Determining additional real-time electricity consumption according to the position information of the working area of each intelligent socket;

and determining the real-time power consumption according to the first real-time power consumption and the additional real-time power consumption.

In one embodiment, the step of determining the power fluctuation data according to the expected power, the line data and the real-time power consumption map to a time sequence includes:

acquiring the expected power and the scheduling strategy determined by the real-time electricity consumption;

determining a power difference value according to the scheduling strategy and the line data;

and mapping the power difference value to the time sequence, and determining the electric quantity fluctuation data.

In an embodiment, before the step of determining that the power fluctuation data below the preset threshold is the first period, and the power fluctuation data not below the preset threshold is the second period, the method includes:

and determining the preset threshold according to a preset value or historical electric quantity gap data of the position information corresponding to the mains supply interface.

In one embodiment, the step of determining the expected power according to the ratio between the adjacent first period and second period includes:

determining the average value of the electric quantity of each first period and each second period;

determining a ratio sequence according to the ratio of the average values of the electric quantity adjacent to the first time period and the second time period;

determining a first expected power based on the sequence of ratios processed by the time domain sliding filter;

generating an initial population based on the first expected power, executing cross mutation operation on the initial population, and determining the expected electric quantity corresponding to a time sequence.

In addition, in order to achieve the aim, the application also provides a photovoltaic power generation and commercial power cooperative power supply device, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the computer program is configured to realize the steps of the photovoltaic power generation and commercial power cooperative power supply method.

In addition, in order to achieve the above object, the present application also provides a storage medium, which is a computer-readable storage medium, on which a program for implementing a photovoltaic power generation and commercial power co-power supply method is stored, the program for implementing the photovoltaic power generation and commercial power co-power supply method being executed by a processor to implement the steps of the photovoltaic power generation and commercial power co-power supply method as described above.

The application provides a photovoltaic power generation and mains supply collaborative power supply method, which comprises the steps of firstly determining expected power of a photovoltaic power station according to characteristic parameters of the photovoltaic power station, wherein the characteristic parameters comprise at least one of photovoltaic panel layout information, inclination angle data and a maximum power point, obtaining line data of interfaces of the photovoltaic power station and mains supply, obtaining real-time power consumption of electric equipment based on an intelligent socket, mapping the expected power, the line data and the real-time power consumption to a time sequence, determining power fluctuation data, determining the power fluctuation data lower than a preset threshold value as a first period, determining the power fluctuation data not lower than the preset threshold value as a second period, determining expected power according to the ratio of adjacent first period and second period, and controlling mains supply access at a time point when the expected power is larger than an access threshold value. The technical problem that the utility power cannot be effectively utilized to supplement when the photovoltaic power generation is insufficient due to the fact that the actual power utilization condition of the electrical equipment and the bearing capacity of the circuit are not fully considered in the related technology is solved, the grid-connected accuracy of the utility power is improved, and the technical effect of ensuring stable power utilization is achieved.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.

In order to more clearly illustrate the embodiments of the application or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, and it will be obvious to a person skilled in the art that other drawings can be obtained from these drawings without inventive effort.

FIG. 1 is a schematic flow chart of a photovoltaic power generation and commercial power collaborative power supply method according to an embodiment of the present application;

fig. 2 is a schematic flow chart of a photovoltaic power generation and commercial power collaborative power supply method according to a second embodiment of the present application;

fig. 3 is a schematic flow chart of a photovoltaic power generation and commercial power collaborative power supply method according to a third embodiment of the present application;

fig. 4 is a schematic diagram of a hardware structure related to an embodiment of the photovoltaic power generation and commercial power cooperative power supply device of the present application.

The achievement of the objects, functional features and advantages of the present application will be further described with reference to the accompanying drawings, in conjunction with the embodiments.

Detailed Description

It should be understood that the specific embodiments described herein are merely illustrative of the technical solution of the present application and are not intended to limit the present application.

For a better understanding of the technical solution of the present application, the following detailed description will be given with reference to the drawings and the specific embodiments.

It should be noted that, the execution body of the embodiment may be a photovoltaic power generation and commercial power cooperative power supply system, or may be a computing service device with functions of data processing, network communication and program running, such as a tablet computer, a personal computer, a mobile phone, or a photovoltaic power generation and commercial power cooperative power supply device capable of implementing the above functions, which is not limited in this embodiment. The present embodiment and the following embodiments will be described below using a photovoltaic power generation and commercial power cooperation power supply system as an execution subject.

Based on this, the present application proposes a method for co-supplying power to a photovoltaic power generation and a commercial power according to a first embodiment, referring to fig. 1, the method for co-supplying power to a photovoltaic power generation and a commercial power includes:

And S110, determining the expected power of each photovoltaic power station according to the characteristic parameters of each photovoltaic power station, namely the illumination intensity and the temperature of the area where the photovoltaic power station is positioned, wherein the characteristic parameters comprise at least one of photovoltaic panel layout information, inclination angle data and a maximum power point.

In this embodiment, the expected power refers to an average power that may be generated in a certain period of time and is calculated according to design parameters of the photovoltaic power station, environmental conditions, and the like.

And collecting characteristic parameters of the photovoltaic power station, wherein the characteristic parameters comprise layout information of the photovoltaic panel, such as row number multiplied by column number and inclination angle data, namely an included angle between the photovoltaic panel and the horizontal plane and a maximum power point, namely the maximum output power of the photovoltaic panel under standard test conditions. The illumination intensity and temperature data are acquired, and the illumination intensity and temperature of the area are monitored in real time by using meteorological data or installing a light sensor and a thermometer. And calculating the expected power, and calculating the expected power of each photovoltaic power station by combining the collected data through a formula or a software tool.

Step S120, obtaining line data of each photovoltaic power station and a mains interface.

In this embodiment, the line data refers to parameters related to the transmission line from the photovoltaic power station to the electric equipment, such as resistance, reactance, etc., which affect the efficiency and loss of the electric energy transmission. And measuring line parameters, namely measuring parameters such as line resistance, reactance and the like from the photovoltaic power station to a mains supply interface by using a professional instrument. Recording data, and recording the measured line data for subsequent analysis.

Step S130, obtaining the real-time electricity consumption of each electric equipment based on the intelligent socket.

In this embodiment, the real-time electricity consumption refers to the electricity consumption of each electric device at the current moment, which can be obtained through real-time monitoring of an intelligent socket or other intelligent electric meters. And installing an intelligent socket or an intelligent ammeter on each electric equipment for monitoring and recording the electricity consumption in real time. And data transmission, namely transmitting the real-time electricity consumption data to a central control system through a wireless communication technology.

And step S140, determining power fluctuation data according to the expected power, the line data and the real-time power consumption map to a time sequence.

In this embodiment, the time series is a chronological series of data points, which refers to the chronological order of the expected power, line data, and real-time electricity usage for analysis. And (3) data arrangement, namely arranging expected power, line data and real-time electricity consumption into a time sequence according to time sequence. And analyzing fluctuation, analyzing the time sequence data, and determining the electric quantity fluctuation condition.

Step S150, determining the power fluctuation data below the preset threshold as a first period, and determining the power fluctuation data not below the preset threshold as a second period.

In this embodiment, the time period is a time period divided according to a comparison result of the power fluctuation data and a preset threshold, and the time period below the threshold is a first time period and the time period not below the threshold is a second time period. Setting a threshold value, and setting a threshold value of electric quantity fluctuation according to actual requirements. And comparing and classifying, namely comparing the electric quantity fluctuation data with a threshold value, and classifying a first period and a second period.

And step S160, determining the expected electric quantity according to the ratio of the adjacent first time period and the adjacent second time period.

In the present embodiment, the expected amount of electricity refers to the expected power demand in a specific period, which is calculated here by the ratio of adjacent periods. And calculating a ratio, namely calculating the ratio of the electric quantity of the adjacent first time period and second time period. The expected electric quantity is determined, and the expected electric quantity is determined according to the ratio.

And step S170, controlling the mains supply to be connected at the time point when the expected electric quantity is larger than the connection threshold value.

In this embodiment, the utility power connection refers to starting the utility power supply to ensure the continuity of the power supply when the photovoltaic power generation cannot meet the power demand. The expected power is monitored, and whether the expected power reaches an access threshold is continuously monitored. And executing access operation, and automatically or manually starting the mains supply access when the expected electric quantity is larger than the access threshold value.

In the embodiment, the effective cooperation of the photovoltaic power generation and the commercial power is realized, the energy use efficiency is optimized, and the stability and the economy of power supply are ensured.

The photovoltaic power station A and the photovoltaic power station B are respectively characterized in that the photovoltaic power station A is 10 multiplied by 10 in terms of photovoltaic panel layout information, the inclination angle is 30 degrees, the maximum power point is 100kW, the photovoltaic power station B is 8 multiplied by 8 in terms of photovoltaic panel layout information, the inclination angle is 45 degrees, and the maximum power point is 80kW. The illumination intensity of the region is 1000W/m2, and the temperature is 25 ℃. From these parameters, the expected power for the a and B stations can be calculated to be 100kW and 80kW, respectively. Next, line data of each photovoltaic power plant and the mains interface are acquired, assuming line resistances of the a and B stations to be 0.1 Ω and 0.2 Ω, respectively. Then, based on the intelligent socket, the real-time electricity consumption of each electric equipment is obtained, and the current electricity consumption is assumed to be 50kW. And according to the expected power, the line data and the real-time power consumption map to the time sequence, determining the power fluctuation data. Suppose at some point, the power fluctuation data of station a is 90kW and the power fluctuation data of station b is 70kW. Comparing these data to a preset threshold, for example 80kW, it may be determined that data below the preset threshold is a first period, for example 90kW for station a, and data not below the preset threshold is a second period, for example 70kW for station B. The expected power is determined from the ratio of adjacent first and second time periods. In this example, the expected electric quantity may be calculated as (90 kw×70 kW)/(90 kw+70 kW) =31.82 kW. And finally, when the expected electric quantity is larger than the time point of the access threshold, controlling the mains supply to be accessed to meet the electric power requirement. In this example, since the expected power is greater than the access threshold, the utility power will be accessed at this point to provide additional power support.

Based on the first embodiment, please refer to fig. 2, a method for providing power by combining photovoltaic power generation and commercial power is provided in the second embodiment of the present application, wherein step S110 includes:

step S111, determining the layout information of the photovoltaic panels according to the coordinates of the photovoltaic panels and the intervals of the photovoltaic panels of each photovoltaic power station;

step S112, acquiring an average value of inclination angles of the photovoltaic panel as the inclination angle data;

step S113, determining the maximum power point according to the historical power generation amount of the photovoltaic power station;

step S114, determining the expected power according to the photovoltaic panel layout information, the inclination data, the maximum power point, the illumination intensity and the temperature.

In this embodiment, the layout information of the photovoltaic panels is determined according to the coordinates and the intervals of the photovoltaic panels, and the layout information of the photovoltaic panels refers to a specific arrangement mode of the photovoltaic panels on the installation site, including the number of rows and columns, and the interval distance between each photovoltaic panel. This information is important for calculating the shielding situation and the light receiving area of the photovoltaic power station. The precise coordinates of each photovoltaic panel are measured using GPS or other positioning equipment. The distance between adjacent photovoltaic panels is measured and recorded. And drawing a layout diagram of the photovoltaic panel according to the coordinate and interval data. The inclination data refers to the angle between the photovoltaic panel and the horizontal plane, and the angle can influence the efficiency of the photovoltaic panel for receiving sunlight. The inclination angle of each photovoltaic panel was measured using an inclinometer. And (5) averaging the inclination angles of all the photovoltaic panels to obtain the average inclination angle of the whole photovoltaic power station. The Maximum Power Point (MPP) refers to the maximum output power of the photovoltaic panel under standard test conditions. By analyzing the historical power generation data, the maximum power point under the current condition can be estimated. And collecting historical power generation data, and collecting the historical power generation data from the data record of the photovoltaic power station. Statistical analysis methods, such as regression analysis, are used to determine the maximum power point under the current lighting and temperature conditions. The expected power refers to the electric power that the photovoltaic power plant is expected to be able to produce under certain environmental conditions. This requires consideration of photovoltaic panel layout, tilt angle, maximum power point, illumination intensity, and temperature. And (3) calculating by applying a formula, and calculating the expected power by using a proper formula or model and combining all the factors. For example:

Expected power = number of photovoltaic panels x single panel area x illumination intensity x temperature correction factor x tilt correction factor x maximum power point.

Based on the photovoltaic panel layout information, the expected power is adjusted to account for possible shadowing and system efficiency losses.

An exemplary photovoltaic power plant is characterized by a number of photovoltaic panels of 100. The area of the single plate is 1.6 square meters. Average tilt angle 30 degrees. The illumination intensity is 1000W/m 2. The temperature was 25 ℃. The maximum power point obtained by analyzing the historical power generation amount data is 250W, and according to the information, the expected power can be calculated, wherein the expected power=100×1.6×1000×1×1×0.25=40000W =40 kW.

Optionally, step S114 includes:

step S1141, determining expected illumination energy of the photovoltaic power station according to the photovoltaic panel layout information, the inclination angle data and the illumination intensity;

step S1142, updating the expected illumination energy according to the correction coefficient determined by the temperature;

And step S1143, planning according to the updated expected illumination energy as a positive factor and the maximum power point as a negative factor, and determining the expected power.

In this embodiment, the expected illumination energy is determined according to the layout information of the photovoltaic panel, the inclination angle data and the illumination intensity, and the expected illumination energy refers to the solar energy that the photovoltaic panel is expected to be able to receive under the specific environmental conditions. This requires consideration of the arrangement of the photovoltaic panels, the inclination angle, and the current illumination intensity. And calculating the effective light receiving area of each photovoltaic panel according to the layout information and the inclination angle data of the photovoltaic panels. The occlusion effect and reflection losses are taken into account. And applying the current illumination intensity to the effective light receiving area to obtain preliminary expected illumination energy. The output efficiency of the photovoltaic panel varies with temperature. In general, the higher the temperature, the lower the output efficiency of the photovoltaic panel. Therefore, the expected illumination energy needs to be corrected according to the temperature. The temperature correction coefficient is determined and calculated from the temperature coefficient (typically negative) of the photovoltaic panel, in combination with the current temperature. And (3) applying temperature correction, and applying a temperature correction coefficient to the preliminary expected illumination energy to obtain updated expected illumination energy. And determining the expected power according to the updated expected illumination energy and the maximum power point. The expected power refers to the electrical power that the photovoltaic power plant is expected to be able to produce under certain environmental conditions. This requires consideration of the updated expected illumination energy and the maximum power point of the photovoltaic panel. And planning a forward factor, wherein the updated expected illumination energy is used as the forward factor to represent the energy received by the photovoltaic panel. And programming a negative factor, wherein the maximum power point is taken as the negative factor, and the maximum output capacity of the photovoltaic panel is represented. And calculating the expected power, and determining the expected power according to the proportional relation between the positive factor and the negative factor. The following formula is used:

Where the maximum possible output refers to the maximum output power of the photovoltaic panel under ideal conditions without any loss being considered.

An exemplary photovoltaic power plant is characterized by the following characteristics, namely the number of photovoltaic panels is 100. The area of the single plate is 1.6 square meters. Average tilt angle 30 degrees. The illumination intensity is 1000W/m 2. The temperature was 25 ℃. The maximum power point obtained by analysis of the historical power generation amount data is 250W, and the temperature coefficient is-0.4%/DEGC.

It is assumed that the effective light receiving area of each photovoltaic panel is 1.4 square meters due to the layout and the inclination angle. Preliminary expected illumination energy = 100 x 1.4 x 1000 = 140000W. Temperature correction coefficient=1- (25-25) × (-0.4%) =1. Updated expected illumination energy = 140000W. Under ideal conditions, the maximum possible output per photovoltaic panel is 250W, without any loss being considered. And thus calculate the expected power.

Based on any of the above embodiments, a third embodiment of the present application provides a method for collaborative power supply of photovoltaic power generation and utility power, referring to fig. 3, step S120 includes:

step S121, obtaining first line data among the photovoltaic power stations;

step S122, obtaining second line data between each photovoltaic power station and energy storage equipment;

Step S123, obtaining third line data between the energy storage equipment and the electric equipment;

Step S124, obtaining fourth line data between the mains supply interface and the energy storage device;

step S125, determining the line data according to the first line data, the second line data, the third line data, and the fourth line data.

In this embodiment, first line data between each photovoltaic power plant is acquired. The first line data refers to transmission line information connecting different photovoltaic power plants. These data are important for understanding the power transfer capability and possible mutual support between photovoltaic power plants. And measuring parameters such as resistance, reactance, capacitance and the like of each power transmission line by using an electric measuring instrument. The actual length of each transmission line is measured and recorded. And drawing a power transmission line diagram between the photovoltaic power stations according to the measurement data. The second line data refers to transmission line information connecting the photovoltaic power plant and the energy storage device. These data are critical to assessing the ability of the photovoltaic power plant to deliver power to the energy storage device. The connection point of each photovoltaic power plant to the energy storage device is identified and marked. And measuring parameters such as resistance, reactance, capacitance and the like of each power transmission line by using an electric measuring instrument. The actual length of each transmission line is measured and recorded. And drawing a power transmission line diagram between the photovoltaic power station and the energy storage equipment according to the measurement data. The third line data refers to transmission line information connecting the energy storage device and the electric equipment. These data are critical to assessing the ability of the energy storage device to deliver power to the powered device. The connection point of each energy storage device to the consumer is identified and marked. And measuring parameters such as resistance, reactance, capacitance and the like of each power transmission line by using an electric measuring instrument. The actual length of each transmission line is measured and recorded. And drawing a power transmission line diagram between the energy storage equipment and the electric equipment according to the measurement data. The fourth line data is the information of the power transmission line connecting the mains interface and the energy storage equipment. These data are critical to assessing the ability of the utility to deliver power to the energy storage device. And identifying and marking the connection point of the mains supply interface and the energy storage equipment. And measuring parameters such as resistance, reactance, capacitance and the like of each power transmission line by using an electric measuring instrument. The actual length of each transmission line is measured and recorded. And drawing a power transmission line diagram between the mains supply interface and the energy storage equipment according to the measurement data. The whole line data refers to comprehensively considering the information of all the power transmission lines so as to evaluate the power transmission capacity and efficiency of the whole system. And integrating the first line data, the second line data, the third line data and the fourth line data. And analyzing the maximum transmission capacity of each transmission line according to the integrated data. And according to the line capacity and the system demand, evaluating the power transmission performance and efficiency of the whole system. And according to the evaluation result, proposing to optimize the circuit layout so as to improve the power transmission efficiency and the reliability of the system.

An exemplary photovoltaic power plant system comprises two photovoltaic power plants (PV 1 and PV 2), an energy storage device (ES) and a consumer (Load). All line data of this system needs to be acquired. And measuring the power transmission line parameters between the PV1 and the PV2, recording the length of the line, and drawing a line diagram. And measuring the parameters of the power transmission line between the PV1 and the ES, recording the length of the power transmission line, and drawing a circuit diagram. And measuring the parameters of the power transmission line between the PV2 and the ES, recording the length of the power transmission line, and drawing a circuit diagram. And measuring transmission line parameters between the ES and the Load, recording the length of the transmission line, and drawing a circuit diagram. And measuring the parameters of the power transmission line between the mains supply interface and the ES, recording the length of the line, and drawing a line diagram. And integrating all line data, and analyzing the maximum transmission capacity of each transmission line. The power transmission performance and efficiency of the entire system were evaluated. And according to the evaluation result, proposing the proposal for optimizing the circuit layout.

In this embodiment, the power transmission loss of the power station interaction is determined by the line data between the photovoltaic power stations, the electricity storage cost is determined by the line data between the photovoltaic power stations and the energy storage equipment, the loss of electricity stored into the electricity storage equipment is determined by the line data between the energy storage equipment and the electric equipment, and the cost of the mains supply access is determined by the line data between the mains supply interface and the energy storage equipment, so that a basis is provided for the design and optimization of the system according to the cost.

Based on any of the above embodiments, in a fourth embodiment of the present application, step S130 includes:

step S131, obtaining first real-time electricity consumption of each intelligent socket through cloud service;

step S132, determining additional real-time electricity consumption according to the position information of the working area of each intelligent socket;

Step S133, determining the real-time power consumption according to the first real-time power consumption and the additional real-time power consumption.

In this embodiment, the first real-time electricity consumption refers to current electricity consumption data directly read from the smart socket. These data are typically measured in real time by an electrical energy metering chip built into the smart jack and uploaded through the cloud service. The smart sockets are configured to ensure that all the smart sockets are properly connected to the home Wi-Fi network and are able to communicate with the cloud service. The cloud service API is accessed, and an existing application programming interface is developed or used to obtain real-time electricity consumption data of the intelligent socket from the cloud service. And through API call, the real-time electricity consumption data of each intelligent socket are read from the cloud service regularly, and the data are stored in a local database or a memory. The additional real-time electricity consumption refers to the increase of electricity consumption caused by factors such as temperature change, equipment aging and the like under specific conditions. These factors may vary from work area to work area. Each smart jack is assigned a unique identifier and is associated with location information of the work area in which it is located. Environmental factors, such as temperature, humidity, etc., which may affect the amount of electricity used, are analyzed based on the location information of the work area. And (5) estimating the additional real-time electricity consumption of each intelligent socket by combining the environmental factors and the historical electricity consumption data. The real-time electricity consumption refers to the final electricity consumption after comprehensively considering the electricity consumption data directly read by the intelligent socket and the additional electricity consumption calculated according to the position information. And adding the first real-time electricity consumption and the additional real-time electricity consumption to obtain the total real-time electricity consumption of each intelligent socket. And updating the integrated real-time electricity consumption data into a local database or a memory for further analysis and processing.

Illustratively, three working areas of living room, bedroom and kitchen are each provided with a smart jack to monitor the electricity consumption. Now, it is necessary to calculate the real-time electricity consumption of the entire home. Smart sockets are configured to ensure that they can communicate with cloud services. Through the API call, the real-time electricity consumption data of each intelligent socket is read from the cloud service, for example, the intelligent socket of a living room is displayed as 50 watts, the intelligent socket of a bedroom is displayed as 30 watts, and the intelligent socket of a kitchen is displayed as 70 watts. The position information of the intelligent socket is collected, for example, a living room is located in the south direction, a bedroom is located in the north direction, and a kitchen is located in the east direction. Environmental factors are analyzed, and the assumption is that the additional real-time electricity consumption of the living room is estimated to be 10 watts because the living room in the south direction is irradiated by more sunlight, the additional real-time electricity consumption of the living room is estimated to be 5 watts because the living room uses less air conditioner, and the additional real-time electricity consumption of the kitchen is estimated to be 15 watts because the kitchen is frequently in cooking activities. The total real-time electricity consumption of the living room is 50 watts (first real-time electricity consumption) +10 watts (additional real-time electricity consumption) =60 watts, the total real-time electricity consumption of the bedroom is 30 watts+5 watts=35 watts, and the total real-time electricity consumption of the kitchen is 70 watts+15 watts=85 watts. Updating the electricity consumption, and updating the integrated real-time electricity consumption data into a local database or a memory.

In this embodiment, the cloud service is used to obtain the first real-time power consumption of the smart socket, so as to realize real-time monitoring of each electric device, and then determine additional real-time power consumption according to the position information of the working area to which the smart socket belongs, so that the calculation accuracy of the power consumption is improved. And then, through acquiring and analyzing the electricity consumption data in real time, a user can identify high-energy-consumption equipment and electricity consumption peak periods, so that an electricity consumption strategy is optimized, and the energy cost is reduced. This has a positive impact on energy management for both the home and the enterprise. Overall, the embodiment effectively improves the efficiency and accuracy of power management, optimizes energy use, enhances user experience and provides support for intelligent decision-making through real-time monitoring, accurate calculation and data integration.

Based on any of the above embodiments, in a fourth embodiment of the present application, step S140 includes:

Step S141, obtaining the expected power and the scheduling strategy determined by the real-time electricity consumption;

step S142, determining a power difference value according to the scheduling strategy and the line data;

and step S143, mapping the power difference value to the time sequence, and determining the electric quantity fluctuation data.

In this embodiment, the step of determining the power fluctuation data is performed according to the expected power, the line data, and the real-time power consumption map to the time series. Scheduling strategies refer to planning the distribution and use of electricity based on the expected power and the real-time electricity usage. This helps to optimise the use of power resources and reduce wastage. The expected power generation or supply data is obtained from a photovoltaic power plant or grid. And collecting real-time electricity consumption data of each electric equipment through the intelligent socket or other monitoring equipment. And analyzing the power supply and demand conditions according to the expected power and the real-time power consumption, and making a reasonable power distribution and use plan. The power difference refers to the difference between the expected power and the actual power at a specific point in time. This difference may help to understand the actual operating state of the power system and whether the scheduling policy needs to be adjusted. And applying the formulated scheduling strategy to the power system to guide the distribution and use of the power. The current actual power is measured by the monitoring device. The expected power is compared with the actual power to calculate a power difference. The power fluctuation data refers to a time sequence formed by arranging power difference values in time sequence. This time series may help analyze the wave conditions of the power system and predict future power demands. A suitable time interval is selected and a time sequence is established. At each time point, the corresponding power difference is recorded. And (3) analyzing the time sequence to know the electric quantity fluctuation condition of the electric power system.

For example, assuming a photovoltaic power plant, the power fluctuation data needs to be determined based on the expected power, line data, and real-time power usage. Expected power data is collected, for example, an expected power generation of a photovoltaic power plant over a period of time is 100 kilowatt-hours. Real-time electricity consumption data is acquired, for example, the current real-time electricity consumption is 80 kilowatt-hours. The data is analyzed and scheduling policies are formulated, such as to prioritize the powering of important devices for a certain period of time. And applying a scheduling strategy to guide the distribution and use of the power. The actual power is measured, for example, the current actual power is 90 kilowatt-hours. The power difference is calculated, the expected power is 100 kwh, the actual power is 90 kwh, and thus the power difference is 10 kwh. A time series is established, for example recording data once per minute. The power difference is recorded, for example, at 1 minute, the power difference is 10 kilowatt-hours, at 2 minutes, the power difference is 5 kilowatt-hours, and so on. And analyzing the electric quantity fluctuation, and observing the power difference change in the time sequence to know the fluctuation condition of the power system and adjust the scheduling strategy accordingly.

In this embodiment, the cloud service obtains the first real-time electricity consumption of the smart socket, and determines additional real-time electricity consumption by combining the position information of the smart socket, thereby improving the accuracy of electricity consumption data. And then, by integrating the first real-time electricity consumption and the additional real-time electricity consumption, more comprehensive electricity consumption data can be provided, meanwhile, the electricity consumption condition is monitored and analyzed in real time, and the electricity fluctuation data based on the expected power, the line data and the real-time electricity consumption are obtained, so that a basis is provided for management and optimization of an electric power system.

Based on any of the foregoing embodiments, in a fifth embodiment of the present application, step S150 includes:

step S151, determining the preset threshold according to a preset value or historical electric quantity gap data of the position information corresponding to the mains supply interface.

In this embodiment, the preset value is a fixed value set in advance as a criterion for judging whether or not the fluctuation of the electric quantity exceeds the normal range. This value may be set based on experience, industry standards, or specific needs. And setting a reasonable preset value according to the characteristics and the operation requirements of the power system. For example, the power fluctuation of the preset value of 5% may be set. In a subsequent analysis, this preset value is used to determine whether the power fluctuation data exceeds the normal range. The historical electricity quantity gap data refers to the difference between the actual electricity quantity and the expected electricity quantity of the corresponding position of the mains supply interface in a specific time period. The data can help to know the supply and demand conditions of the power system and adjust the preset threshold accordingly. Historical electricity quantity gap data is collected, and the historical electricity quantity gap data is obtained from corresponding positions of a mains supply interface, wherein the data may comprise electricity consumption differences in different time periods. And analyzing the historical electric quantity gap data to find out the rule and the characteristics thereof. For example, an index of an average power gap, a maximum power gap, or the like may be calculated. And determining a reasonable preset threshold according to the analysis result of the historical data. For example, if the historical data shows an average power gap of 3%, the preset threshold may be set to 5%.

For example, a photovoltaic power plant needs to determine a preset threshold according to a preset value or historical electric quantity gap data of position information corresponding to a mains interface. The preset value is set to 5% of electric quantity fluctuation. In a subsequent analysis, this preset value is used to determine whether the power fluctuation data exceeds the normal range. Historical power gap data for the corresponding location of the mains interface, such as average power gap for each month over the past year, is collected. Historical data were analyzed and found to have an average power gap of 3% and a maximum power gap of 7%. And setting the preset threshold to be 5% according to the analysis result of the historical data.

In this embodiment, a reasonable preset threshold is determined according to the actual situation, so as to provide a basis for the subsequent analysis of the electric quantity fluctuation data.

Based on any of the above embodiments, in a fifth embodiment of the present application, step S160 includes:

step S161, determining an average value of the electric quantity in each of the first period and the second period;

step S162, determining a ratio sequence according to the ratio of the average values of the electric quantity adjacent to the first time period and the second time period;

Step S163, determining a first expected power based on the ratio sequence processed by the time domain sliding filter;

Step S164, generating an initial population based on the first expected power, and performing a cross mutation operation on the initial population to determine the expected power corresponding to the time sequence.

In this embodiment, the average value of the electric quantity refers to an average value of the electric quantity fluctuation data in a specific period of time. This value may help to understand the power consumption of the power system during various periods. The time series is divided into a plurality of first time periods and second time periods. For each period, an average value of its power fluctuation data is calculated. The ratio sequence is a sequence formed by comparing the average values of the electric quantity of two adjacent time periods and arranging the obtained ratios according to the time sequence. This sequence may help to understand the trend of power fluctuations in the power system. For each pair of adjacent first and second time periods, the ratio of the power averages thereof is calculated. And establishing a ratio sequence, and arranging all calculated ratios according to a time sequence to form the ratio sequence. Time-domain sliding filtering is a signal processing technique for smoothing time-series data. By performing time-domain sliding filtering on the sequence of values, more stable and reliable expected power data can be obtained. Sliding filtering is applied, and a time domain sliding filtering algorithm is applied to the comparison value sequence to eliminate the influence of noise and abnormal values. A first expected power is determined based on the filtered sequence of ratios. Generating an initial population based on the first expected power, executing cross mutation operation on the initial population, determining expected electric quantity corresponding to the time sequence, wherein the genetic algorithm is an optimized search algorithm, and solving the optimization problem by simulating natural selection and genetic mechanism. In this step, a genetic algorithm will be used to optimize the prediction of the expected electrical quantity. An initial population is generated based on the first expected power, wherein each individual represents a possible expected power scenario. And performing crossover and mutation operations on the initial population to generate new individuals. The fitness of each individual, i.e. the difference between the expected and actual power of its corresponding scheme, is calculated. And selecting an optimal solution as a final expected electric quantity prediction according to the adaptability evaluation result.

For example, a photovoltaic power plant may need to determine the expected amount of power based on the ratio of adjacent first and second periods. The time series is divided into a plurality of first time periods and second time periods. And calculating the average value of the electric quantity of each time period. For each pair of adjacent first and second time periods, the ratio of the power averages thereof is calculated. All calculated ratios are arranged in time order to form a ratio sequence. The sequence of comparison values is applied with a time-domain sliding filtering algorithm to eliminate the effects of noise and outliers. A first expected power is determined based on the filtered sequence of ratios. An initial population is generated based on the first expected power. And performing crossover and mutation operations on the initial population to generate new individuals. And calculating the fitness of each individual, and selecting an optimal solution as the final expected electric quantity prediction.

In this embodiment, by calculating the average value of the electric quantity in adjacent time periods and determining the ratio, the trend of the electric quantity change is captured more accurately, and the error caused by fluctuation in a single time period is reduced, so that the accuracy of electric quantity prediction is improved. And a ratio sequence is processed by adopting time domain sliding filtering, so that the prediction model can dynamically adapt to the trend of electric quantity change. And further, the model can still keep higher prediction accuracy when facing different loads or environmental changes. Then generating an initial population, executing a cross mutation operation, exploring a solution space of electric quantity prediction, and simultaneously comparing a value sequence, so that noise and abnormal values can be effectively filtered, the robustness of a model is enhanced, and a prediction result is more stable. In summary, through the technical effects of accurate electric quantity prediction, dynamic adaptability, application of an optimization algorithm, enhancement of model robustness and the like, the accuracy and efficiency of electric quantity prediction are remarkably improved, and powerful support is provided for management and decision-making of an electric power system.

The application provides a photovoltaic power generation and commercial power cooperative power supply device, which comprises at least one processor and a memory in communication connection with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the photovoltaic power generation and commercial power cooperative power supply method in the first embodiment.

Referring now to fig. 4, a schematic diagram of a photovoltaic power generation and utility power cogeneration apparatus suitable for use in implementing an embodiment of the application is shown. The photovoltaic power generation and commercial power co-power supply apparatus in the embodiment of the present application may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (Personal DIGITAL ASSISTANT: personal digital assistants), PADs (Portable Application Description: tablet computers), PMPs (Portable MEDIA PLAYER: portable multimedia players), vehicle-mounted terminals (e.g., vehicle-mounted navigation terminals), and the like, and fixed terminals such as digital TVs, desktop computers, and the like. The photovoltaic power generation and commercial power co-generation apparatus shown in fig. 4 is merely an example, and should not impose any limitation on the functions and the scope of use of the embodiments of the present application.

As shown in fig. 4, the photovoltaic power generation and commercial power co-feeding apparatus may include a processing device 1001 (e.g., a central processor, a graphic processor, etc.) that may perform various appropriate actions and processes according to a program stored in a Read Only Memory 1002 or a program loaded from a storage device 1003 into a random access Memory (RAM: random Access Memory) 1004. Also stored in RAM1004 are various programs and data required for the operation of the thermal management devices of the PCS energy storage system. The processing device 1001, the ROM1002, and the RAM1004 are connected to each other by a bus 1005. An input/output (I/O) interface 1006 is also connected to the bus. In general, a system including an input device 1007 such as a touch screen, a touch pad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc., an output device 1008 including a Liquid crystal display (LCD: liquid CRYSTAL DISPLAY), a speaker, a vibrator, etc., a storage device 1003 including a magnetic tape, a hard disk, etc., and a communication device 1009 may be connected to the I/O interface 1006. The communication means 1009 may allow the photovoltaic power generation and mains co-powered device to communicate wirelessly or wired with other devices to exchange data. While photovoltaic power generation and utility power cogeneration devices having various systems are shown, it should be understood that not all of the illustrated systems are required to be implemented or provided. More or fewer systems may alternatively be implemented or provided.

In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through a communication device, or installed from the storage device 1003, or installed from the ROM 1002. The above-described functions defined in the method of the disclosed embodiment of the application are performed when the computer program is executed by the processing device 1001.

The photovoltaic power generation and commercial power cooperative power supply equipment provided by the application adopts the photovoltaic power generation and commercial power cooperative power supply method in the embodiment, and aims to solve the technical problem that the commercial power cannot be effectively utilized for supplementing when the photovoltaic power generation is insufficient due to insufficient consideration of the actual power utilization condition of the electrical equipment and the bearing capacity of a circuit. Compared with the prior art, the photovoltaic power generation and commercial power cooperative power supply equipment has the same beneficial effects as those of the photovoltaic power generation and commercial power cooperative power supply equipment provided by the embodiment, and other technical features in the photovoltaic power generation and commercial power cooperative power supply equipment are the same as those disclosed by the method of the previous embodiment, and are not repeated herein.

It is to be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the description of the above embodiments, particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.

The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

The present application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon for performing the photovoltaic power generation and utility power co-generation method in the above-described embodiments.

The computer readable storage medium provided by the present application may be, for example, a U disk, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any of the foregoing. More specific examples of a computer-readable storage medium may include, but are not limited to, an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access Memory (RAM: random Access Memory), a Read-Only Memory (ROM), an erasable programmable Read-Only Memory (EPROM: erasable Programmable Read Only Memory or flash Memory), an optical fiber, a portable compact disc Read-Only Memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this embodiment, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to electrical wiring, fiber optic cable, RF (Radio Frequency) and the like, or any suitable combination of the foregoing.

The computer readable storage medium can be contained in the photovoltaic power generation and commercial power cooperative power supply equipment or can exist alone and is not assembled into the photovoltaic power generation and commercial power cooperative power supply equipment.

The computer readable storage medium carries one or more programs, when the one or more programs are executed by the photovoltaic power generation and commercial power cooperative power supply equipment, the photovoltaic power generation and commercial power cooperative power supply equipment determines expected power of the photovoltaic power stations according to the characteristic parameters of the photovoltaic power stations, including at least one of photovoltaic panel layout information, inclination angle data and a maximum power point, obtains line data of interfaces of the photovoltaic power stations and the commercial power, obtains real-time power consumption of electric equipment based on an intelligent socket, maps the real-time power consumption to a time sequence, determines electric quantity fluctuation data according to the expected power, the line data and the real-time power consumption, determines the electric quantity fluctuation data lower than a preset threshold value as a first time period, determines the electric quantity fluctuation data not lower than the preset threshold value as a second time period, determines expected electric quantity according to the ratio of the adjacent first time period and the second time period, and controls the access time of the electric quantity of the electric equipment to the commercial power.

Computer program code for carrying out operations of the present application may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of remote computers, the remote computer may be connected to the user's computer through any kind of network, including a local area network (LAN: local Area Network) or a wide area network (WAN: wide Area Network), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The modules involved in the embodiments of the present application may be implemented in software or in hardware. Wherein the name of the module does not constitute a limitation of the unit itself in some cases.

The readable storage medium provided by the application is a computer readable storage medium, and the computer readable storage medium stores computer readable program instructions (namely computer programs) for executing the photovoltaic power generation and mains supply collaborative power supply method, so that the technical problem that the mains supply cannot be effectively utilized for supplementing when the photovoltaic power generation is insufficient due to insufficient actual power utilization condition of electrical equipment and bearing capacity of circuits can be solved. Compared with the prior art, the beneficial effects of the computer readable storage medium provided by the application are the same as those of the photovoltaic power generation and commercial power cooperative power supply method provided by the embodiment, and the description is omitted here.

An embodiment of the present application provides a computer program product, including a computer program, where the computer program when executed by a processor implements the steps of the above-mentioned method for co-operating photovoltaic power generation and utility power.

The computer program product provided by the application can solve the technical problem that the commercial power cannot be effectively utilized for supplementing when the photovoltaic power generation is insufficient due to insufficient consideration of the actual power utilization condition of the electrical equipment and the bearing capacity of the circuit. Compared with the prior art, the beneficial effects of the computer program product provided by the embodiment of the application are the same as those of the photovoltaic power generation and commercial power cooperative power supply method provided by the embodiment, and the description thereof is omitted herein.

The foregoing description is only of the preferred embodiments of the present application, and is not intended to limit the scope of the application, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein, or any application, directly or indirectly, within the scope of the application.

Claims

1. The method for cooperatively supplying power to the photovoltaic power generation and the commercial power is characterized by comprising the following steps of:

Generating an initial population based on the first expected power, executing cross mutation operation on the initial population, and determining expected electric quantity corresponding to a time sequence;

2. The method for co-operating power generation and utility power according to claim 1, wherein the step of determining the expected power of the photovoltaic power plant according to the illumination intensity and the temperature of the area where the photovoltaic power plant is located according to the characteristic parameters of each photovoltaic power plant comprises:

3. The method of co-operating power generation and utility power according to claim 2, wherein the step of determining the expected power from the photovoltaic panel layout information, the tilt angle data, the maximum power point, the illumination intensity, and the temperature comprises:

4. The method for co-operating power generation and utility power according to claim 1, wherein the step of obtaining line data of each of the photovoltaic power stations and the utility power interface comprises:

Acquiring first line data between the photovoltaic power stations;

5. The method for cooperatively supplying power to photovoltaic power generation and commercial power according to claim 1, wherein the step of obtaining the real-time power consumption of each electric device based on the smart jack comprises the following steps:

6. The method of claim 1, wherein the step of determining power fluctuation data from the expected power, the line data, and the real-time power usage map to a time series comprises:

7. The method for co-power generation and utility power according to claim 1, wherein the step of determining that the power fluctuation data below a preset threshold is a first period and the power fluctuation data not below the preset threshold is a second period includes:

8. A photovoltaic power generation and mains co-power supply apparatus comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the photovoltaic power generation and mains co-power supply method of any one of claims 1 to 7.

9. A storage medium, characterized in that the storage medium is a computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, realizes the steps of the photovoltaic power generation and mains co-power supply method according to any one of claims 1 to 7.