Auxiliary control method, system and storage medium of photovoltaic-storage-direct-flexible-direct-current microgrid

Abstract

The present invention relates to the field of photovoltaic technology, and in particular to an auxiliary control method, system and storage medium for a photovoltaic-storage-direct-flexible-direct-current microgrid. The present invention predicts photovoltaic power generation through weather forecast information on the target prediction day and photovoltaic power generation component information in the building where the target photovoltaic-storage-direct-flexible-direct-current microgrid is located, and obtains power prediction data; determines the power load information in the building where the target photovoltaic-storage-direct-flexible-direct-current microgrid is located, obtains necessary energy consumption data through non-adjustable load information, obtains multi-level adjustable energy consumption data of adjustable loads according to the adjustable load information, flexibly adjusts the load level according to photovoltaic power generation through necessary energy consumption data and multi-level adjustable energy consumption data, and controls the power consumption of the target photovoltaic-storage-direct-flexible-direct-current microgrid through power prediction data, necessary energy consumption data and multi-level adjustable energy consumption data; the present invention achieves the technical effect of flexibly controlling the power consumption of the DC microgrid and reducing the impact on the power grid through the prediction of the generated power and the detailed classification of the load.

Description

Technical Field

[0001] The present invention relates to the field of photovoltaic technology, and in particular to an auxiliary control method, system, device and computer storage medium for a photovoltaic-storage-direct-flexible-direct-current microgrid. Background Art

[0002] In recent years, with the development of smart grids, DC microgrids have gradually increased their share in smart grids because they do not need to control the frequency and phase of the voltage and are more controllable than AC microgrids. However, renewable energy such as solar energy is unstable and random, and the power consumption of equipment in microgrids also has peaks and valleys, which is volatile.

[0003] Therefore, the interaction between the photovoltaic storage direct-flexible DC microgrid and the large power grid will cause a certain impact on the large power grid, affecting the safety and stability of the power grid. For example, the current DC microgrid control adopts a single method of unified power dispatching. During the instantaneous changes such as large-scale instantaneous access, grid connection or disconnection, the DC bus voltage will rise and fall due to large load changes. Therefore, the existing control technology of the DC microgrid has the technical problems of single control and large impact on the power grid. Summary of the invention

[0004] Therefore, the technical problem to be solved by the present invention is to overcome the problems of single control and great impact on the power grid in the prior art.

[0005] In order to solve the above technical problems, the present invention provides an auxiliary control method for a photovoltaic-storage-direct-flexible-direct-current microgrid, comprising:

[0006] Obtain weather forecast information for the target forecast day from the weather forecast management system;

[0007] Collect information on photovoltaic power generation components in the building where the target photovoltaic storage direct-flexible direct-current microgrid is located;

[0008] Predicting the photovoltaic power generation power on the target prediction day according to the photovoltaic power generation component information and the weather forecast information to obtain power prediction data;

[0009] Determine the power load information in the building where the target PV-storage-DC-flexible microgrid is located, and classify the power load information according to load attributes to obtain adjustable load information and non-adjustable load information;

[0010] Acquire necessary energy consumption data according to the non-adjustable load information, and acquire multi-level adjustable energy consumption data of the adjustable load according to the adjustable load information;

[0011] The power consumption of the target PV-storage-direct-flexible-direct-current microgrid is controlled through the power prediction data, the required energy consumption data and the multi-level adjustable energy consumption data.

[0012] Preferably, the photovoltaic power generation power of the target prediction day is predicted according to the photovoltaic power generation component information and the weather forecast information to obtain the power prediction data, including:

[0013] Calculating solar radiation according to the weather forecast information;

[0014] Extracting rooftop crystalline silicon component information and BIPV curtain wall film component information according to the photovoltaic power generation component information, and calculating component conversion rates of rooftop crystalline silicon components and BIPV curtain wall film components according to the rooftop crystalline silicon component information and the BIPV curtain wall film component information;

[0015] Predicting the power of the rooftop crystalline silicon module according to the solar radiation and the conversion rate of the rooftop crystalline silicon module;

[0016] Predicting the power of the BIPV curtain wall thin film assembly according to the solar radiation and the conversion rate of the BIPV curtain wall thin film assembly;

[0017] The power prediction data is obtained by adding the power of the rooftop crystalline silicon module and the power of the BIPV curtain wall thin film module.

[0018] Preferably, after obtaining the power prediction data, the step further includes:

[0019] Obtaining equipment information of the rooftop crystalline silicon module and the BIPV curtain wall thin film module;

[0020] Performing power impact analysis based on the device information;

[0021] The power prediction data is corrected according to the power impact analysis result.

[0022] Preferably, the step of acquiring the multi-level adjustable energy consumption data of the adjustable load according to the adjustable load information includes:

[0023] Acquire the maximum power load data and the minimum power load data of the adjustable load according to the adjustable load information, and obtain the power load range of the adjustable load;

[0024] Dividing the power load interval into equal intervals to obtain a plurality of power load sub-intervals;

[0025] Each power load sub-interval is marked with a multi-level power consumption level to obtain the multi-level adjustable energy consumption data.

[0026] Preferably, the power consumption control of the target PV-storage-DC-flexible microgrid by using the power prediction data, the required energy consumption data and the multi-level adjustable energy consumption data includes:

[0027] The multi-level adjustable energy consumption data and the required energy consumption data are added and calculated to obtain multi-level comprehensive power load data;

[0028] When the power prediction data is not lower than the lowest level of power load data in the multi-level comprehensive power load data, the power prediction data is traversed and compared in the multi-level comprehensive power load data to obtain matching power load data of the corresponding level;

[0029] Acquire a level scheduling threshold, and perform power load level lowering on the matching power load data according to the level scheduling threshold;

[0030] The comprehensive power load data corresponding to the low-profile power load level is used as the actual power load data to control the power consumption of the target photovoltaic storage direct current flexible direct current microgrid.

[0031] Preferably, the method of controlling the power consumption of the target PV-storage-direct-flexible-dc microgrid by using the comprehensive power load data corresponding to the low-profile power load level as the actual power load data includes:

[0032] Calculating a load difference between the actual power load data and the matching power load data;

[0033] According to the load difference, the excess electric energy corresponding to the load difference is stored in the energy storage battery in the building where the target photovoltaic-storage-direct-flexible-direct-current microgrid is located, so as to cope with emergency situations;

[0034] The power load in the building where the target PV-storage-DC-flexible microgrid is located is controlled according to the actual power load data.

[0035] Preferably, when the power forecast data is lower than the lowest level power load data among the multi-level comprehensive power load data:

[0036] Calculating the power difference between the power forecast data and the lowest level power load data;

[0037] The load demand information is sent to the external power grid based on the power difference calculation result to ensure the minimum power demand of the building where the target PV-storage-DC-flexible microgrid is located.

[0038] The present invention also provides an auxiliary control system for a photovoltaic-storage-direct-flexible-direct-current microgrid, comprising:

[0039] A weather information acquisition module is used to obtain weather forecast information for a target forecast day from a weather forecast management system;

[0040] Component information collection module, used to collect photovoltaic power generation component information in the building where the target photovoltaic storage direct-flexible direct-current microgrid is located;

[0041] A power data prediction module, used to predict the photovoltaic power generation power of the target prediction day according to the photovoltaic power generation component information and the weather forecast information, and obtain power prediction data;

[0042] A load information acquisition module is used to determine the power load information in the building where the target PV-storage-DC-flexible microgrid is located, and classify the power load information according to load attributes to obtain adjustable load information and non-adjustable load information;

[0043] An energy consumption data acquisition module, used to acquire necessary energy consumption data according to the non-adjustable load information, and to acquire multi-level adjustable energy consumption data of the adjustable load according to the adjustable load information;

[0044] The power consumption control module is used to control the power consumption of the target photovoltaic energy storage direct current flexible direct current microgrid through the power prediction data, the required energy consumption data and the multi-level adjustable energy consumption data.

[0045] The present invention also provides an auxiliary control device for a photovoltaic-storage-direct-flexible-direct-current microgrid, comprising:

[0046] Memory for storing computer programs;

[0047] A processor is used to implement the above-mentioned auxiliary control method steps of a photovoltaic-storage-direct-flexible-direct-current microgrid when executing the computer program.

[0048] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the auxiliary control method of the above-mentioned photovoltaic-storage-direct-flexible-direct-current microgrid are implemented.

[0049] The above technical solution of the present invention has the following advantages compared with the prior art:

[0050] The auxiliary control method of the photovoltaic-storage-direct-flexible-DC microgrid described in the present invention obtains weather forecast information of the target forecast day by connecting to a weather forecast management system, providing data support for calculating solar radiation. Collect information on photovoltaic power generation components in the building where the target photovoltaic-storage-direct-flexible-DC microgrid is located, providing data support for calculating the solar energy conversion rate of the photovoltaic power generation components; predict the photovoltaic power generation power of the target forecast day based on the photovoltaic power generation component information and the target weather forecast information, obtain power prediction data, and then predict the fluctuation of photovoltaic power generation, reducing the impact of the uncertainty of photovoltaic power generation on the power grid; determine the power load information in the building where the target photovoltaic-storage-direct-flexible-DC microgrid is located, and classify the power load information according to load attributes to obtain adjustable load information and non-adjustable load information, and Classify and then flexibly control the power consumption of the DC microgrid; obtain necessary energy consumption data through non-adjustable load information; obtain multi-level adjustable energy consumption data of the adjustable load according to the adjustable load information, and flexibly adjust the load level according to photovoltaic power generation through the necessary energy consumption data and the multi-level adjustable energy consumption data, so as to improve the flexibility of power consumption of the power grid; control the power consumption of the target photovoltaic storage direct and flexible DC microgrid through the power prediction data, the necessary energy consumption data and the multi-level adjustable energy consumption data, and achieve the technical effect of flexibly controlling the power consumption of the DC microgrid and reducing the impact on the power grid through the prediction of the generated power and the detailed classification of the load. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below according to specific embodiments of the present invention in conjunction with the accompanying drawings, wherein:

[0052] Figure 1 A flowchart of an auxiliary control method for a photovoltaic-storage-direct-flexible-direct-current microgrid provided by the present invention;

[0053] Figure 2 A structural block diagram of an auxiliary control system for a PV-storage-DC-flexible microgrid provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0054] The core of the present invention is to provide an auxiliary control method, system, equipment and computer storage medium for a photovoltaic-storage-direct-flexible-direct-current microgrid, which can flexibly control the power consumption of the direct-current microgrid and reduce the impact on the power grid.

[0055] In order to enable those skilled in the art to better understand the scheme of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0056] Please refer to Figure 1 , Figure 1 This is a flow chart of the implementation of an auxiliary control method for a photovoltaic-storage-flexible-DC microgrid provided by the present invention; the specific operation steps are as follows:

[0057] S101: Acquire weather forecast information for a target forecast day from a weather forecast management system;

[0058] Specifically, the weather forecast management system refers to a system that forecasts the weather in the area where the photovoltaic storage direct-flexible DC microgrid is located. The target forecast day refers to the day on which the power generation of the photovoltaic power generation equipment is predicted, and the forecast period is 24 hours. The weather forecast information refers to the total surface radiation, surface scattered irradiance, surface horizontal direct radiation, ambient temperature, relative humidity, average wind speed, air pressure, etc. of the DC microgrid on the target forecast day.

[0059] The weather forecast management system and the control system use both wireless and wired methods to connect and transmit information. The wired connection uses a dedicated cable, and the wireless connection uses satellite technology and 5G communication technology. When the control system controls the power consumption of the DC microgrid, it initiates a data request to the weather forecast management system to obtain the weather forecast information of the target forecast day, and sends a connection request to the weather forecast management system through a wireless connection. If the wireless connection request fails, a wired connection is used to make a connection request. After the connection is successful, the weather forecast management system collects weather forecast information within 24 hours after the connection is successful, and feeds back the weather information data to the control system according to a predetermined standardized format. Weather factors are one of the main factors that affect photovoltaic power generation and cause uncertainty in photovoltaic power generation systems. For example, haze, thunderstorms, heavy rains, hail, cloudy, high temperatures and other weather conditions will affect power generation. Taking temperature as an example, the ideal operating temperature of photovoltaic power generation components is 25°C. For every 1°C increase in temperature, the output power will decrease by about 0.35%. Therefore, weather forecast information provides data support for predicting photovoltaic power generation and flexible control of power consumption.

[0060] S102: Collecting information on photovoltaic power generation components in the building where the target photovoltaic-storage-direct-flexible-direct-current microgrid is located;

[0061] Specifically, photovoltaic power generation components refer to power generation systems that convert solar energy into direct current, also known as solar panels. Photovoltaic power generation component information refers to the mechanical parameters of photovoltaic power generation components, including solar cell material, number of cells, component size, installation year, real-time temperature, connectors, etc.

[0062] First, a database is established to determine the target control microgrid area, and the photovoltaic power generation component information in the area is retrieved; then, the power generation components are classified according to the cell material, number of cells, and component size, and the same type of power generation components are classified; finally, the statistical power generation component data is transmitted to the control system through Ethernet, WIFI, dedicated cables, etc. in a standard output format to realize the collection of photovoltaic power generation component information. The standard output format includes different component materials, number of cells, component size, component temperature, etc., which provides data support for calculating the solar energy conversion rate of photovoltaic power generation components in the target area.

[0063] S103: predicting the photovoltaic power generation power of the target prediction day according to the photovoltaic power generation component information and the weather forecast information to obtain power prediction data;

[0064] Specifically, power prediction data refers to the use of weather forecast information and photovoltaic power generation component information to predict the output power value of the photovoltaic power generation system within the next 24 hours. First, a power prediction model is established based on the local historical weather conditions and historical photovoltaic power generation data; the clustering grouping method is used to classify the historical photovoltaic power generation data, and the random forest algorithm is used to model the historical weather information. Then, the conversion rate of each type of power generation component to solar energy and the conversion coefficient in the power prediction model are calculated by classifying the historical power generation data, and the weather coefficient in the power prediction model is obtained by modeling the historical weather information. Finally, the solar radiation on the target prediction day is obtained through the radiation calculation formula based on the data and weather coefficient in the weather forecast information, and the conversion rate of the target prediction day of the power generation component in the target area is obtained through the conversion rate formula based on the component information and the conversion coefficient picture. The power prediction data can be obtained through the solar radiation and the conversion rate of the component to solar energy. By predicting the power generation, the fluctuation of photovoltaic power generation is predicted, and then the power consumption plan is specified in a targeted manner to reduce the impact of the microgrid on the large power grid.

[0065] S104: Determine the power load information in the building where the target PV-storage-DC-flexible microgrid is located, and classify the power load information according to load attributes to obtain adjustable load information and non-adjustable load information;

[0066] Specifically, the information of the photovoltaic power generation components includes the information of the connected loads, and the load information to be controlled can be determined by the connection machine of the target photovoltaic component information. First, the local load information, such as frequency, voltage, and current energy information, is collected through the sampling system, and transmitted to the control system through the wired system or the wireless system. The control system divides the power load of the target microgrid into adjustable load information and non-adjustable load information according to the stability principle and economic principle of the target microgrid. The adjustable load information refers to the load that does not cause losses to the DC microgrid and the power grid when the load is operated at the minimum power or a short power outage. The non-adjustable load refers to the load that will cause significant losses when it is operated at low power or the power supply is interrupted. Then, the control system sends the classification results to multiple computers for simultaneous parallel calculation using MPICH through grid technology, simulates the operation of the control target microgrid according to the classification results and the information of the electrical equipment, and performs optimization calculations based on safety and stability analysis and economic operation analysis. Finally, multiple computers feed back the optimized classification results to the control system to obtain optimized adjustable load information and non-adjustable load information.

[0067] S105: acquiring necessary energy consumption data according to the non-adjustable load information, and acquiring multi-level adjustable energy consumption data of the adjustable load according to the adjustable load information;

[0068] Specifically, non-adjustable loads refer to loads that will cause significant losses if they are operated at low power or if power is interrupted. Therefore, the power supply to non-adjustable loads should be uninterrupted and meet the power quality standards. They are the power that must be consumed by the microgrid, such as power lost in line transmission and equipment that cannot be powered off. The voltage, power, frequency and other information of equipment that must consume power to ensure normal operation of the equipment are collected to determine the location of the load, thereby determining the minimum loss of the transmission path, etc. Based on this, the power and power required to ensure the normal operation of the non-adjustable load are calculated and integrated into the necessary energy consumption data, including the power supply voltage, transmission path, power supply frequency, etc. This provides data support for subsequent power consumption control, thereby determining the necessary power consumption for the operation of the DC microgrid.

[0069] The adjustable load information is the load that does not affect the DC microgrid and the power grid when the load is running at the minimum power or a short power outage. First, a database is established to count the adjustable loads, and the loads connected to the target DC microgrid are listed and classified; then, the minimum power and maximum power of each load are limited according to the electrical information of the load and the location of use; finally, the minimum power and maximum power of all adjustable loads are integrated to form the minimum power load data and the maximum power load data. The intervals of the minimum power load data and the maximum power load data are divided into multiple intervals, each interval is a level, and multi-level adjustable energy consumption data is formed. For example, the different power sources, number of light sources, and location of light sources in the lighting system calculate the minimum operating power and maximum operating power of each light source, and superimpose the power classification superposition theorem of all light sources to generate the power consumption interval of the lighting system. The lighting load is divided according to power outage, low power operation, and full power operation, and then adjusted according to the predicted power generation power to provide support for flexible control of microgrid power consumption.

[0070] S106: Control the power consumption of the target PV-storage-DC-flexible microgrid through the power prediction data, the required energy consumption data and the multi-level adjustable energy consumption data.

[0071] Power prediction data predicts the power generation of photovoltaic power generation components. Energy consumption data and multi-level adjustable energy consumption data are required to predict the load power consumption of power-consuming equipment. Match power generation data with power consumption data. If the power generation data meets the power consumption data, the level of the adjustable load equipment in the power-consuming equipment is adjusted according to the power generation. The excess power is stored through the energy storage system to prevent emergencies, improve the robustness of the microgrid, and reduce the impact on the power grid; if the power generation data does not meet the power consumption data, it is necessary to ensure the minimum power consumption of the microgrid. The stored power of the microgrid can be used, or a request can be made to the external power grid to supply power through the external power grid. By predicting the power of photovoltaic power generation, the load of the microgrid is refined and classified, and the load is regulated, the flexibility of power control is improved, the energy flow of the microgrid is mastered, and the impact of the interaction between the microgrid and the large power grid on the power grid is reduced.

[0072] Based on the above embodiments, this embodiment describes step S103 in detail:

[0073] Specifically, the control system issues a collection instruction for the photovoltaic power generation component information, and the control system begins to retrieve the photovoltaic power generation component information installed in the area, including component materials, component sizes, component numbers, etc., and classifies the collected data according to the component materials based on the tree classification method, and feeds back to the control system. Among them, the components include rooftop crystalline silicon components and BIPV curtain wall thin film components. Refers to the use of cluster analysis to cluster and group the historically collected photovoltaic power generation data, and combine the random forest algorithm to predict the power of photovoltaic power generation components, so as to improve the prediction accuracy of photovoltaic power generation power. Among them, the power prediction of crystalline silicon components and thin film components is carried out separately:

[0074] Calculate the solar radiation according to the weather forecast information:

[0075] HA=R a (aT max +bT min +c)

[0076] Among them, HA is the total solar radiation (MJ / ㎡); R a is the daily extraterrestrial solar radiation (MJ / ㎡ / day); T max is the daily maximum temperature (℃); T min is the daily minimum temperature (C); a, b, c are the weather coefficients in the power prediction model.

[0077] The rooftop crystalline silicon component information and the BIPV curtain wall thin film component information are extracted according to the photovoltaic power generation component information, and the component conversion rate of the rooftop crystalline silicon component and the BIPV curtain wall thin film component is calculated according to the rooftop crystalline silicon component information and the BIPV curtain wall thin film component information:

[0078]

[0079] Among them, K1 is the conversion efficiency of the component; P out ——The maximum output power of the component, obtained through component information; P in ——The sunlight intensity received by the component is obtained through component information and solar radiation.

[0080] The power of the rooftop crystalline silicon module is predicted based on the solar radiation and the conversion rate of the rooftop crystalline silicon module, and the power of the BIPV curtain wall thin film module is predicted based on the solar radiation and the conversion rate of the BIPV curtain wall thin film module:

[0081] Ep=HA×S×K1×K2

[0082] Among them, Ep is the total predicted power of photovoltaic power generation, HA is the total solar radiation (MJ / ㎡); S is the total area of ​​​​the components (㎡); K1 is the component conversion efficiency; K2 is the conversion coefficient in the power prediction model.

[0083] The power prediction data is obtained by adding the power of the rooftop crystalline silicon module and the power of the BIPV curtain wall thin film module.

[0084] Obtain equipment information of the rooftop crystalline silicon module and the BIPV curtain wall thin-film module, perform power impact analysis based on the equipment information, and correct the power prediction data based on the power impact analysis result:

[0085] Specifically, the photovoltaic power generation component information includes the rooftop crystalline silicon component and the BIPV curtain wall film component information. The component information is fed back to the control system, and the control system extracts the component equipment information, including the geographical location of the component installation, the inclination angle of the power generation component, the orientation of the power generation component, the attenuation rate of the power generation component, whether the power generation component is clean, etc. This information will be useful. For example, if the power generation component is located in a dusty environment, the cleaning cycle of the power generation component should be checked. Dust coverage will affect the ability of the power generation component to absorb solar energy, and changes in the inclination angle and orientation of the power generation component will result in different conversion rates.

[0086] Taking the attenuation rate as an example, after a photovoltaic module has been running for a period of time, the ratio of the output power to the nominal power under standard test conditions (AM1.5, module temperature 25°C, irradiance 1000W / ㎡) is the attenuation rate of the photovoltaic module.

[0087]

[0088] The above is the attenuation rate calculation method under standard test conditions. For the actual application environment, the environmental coefficient is obtained through nonlinear regression method based on historical power generation component data, and then the actual attenuation rate is obtained to calibrate the predicted power data.

[0089] To evaluate the factors affecting equipment power, first, a power factor impact model under a standard environment is established. Secondly, the environmental coefficient is derived by combining historical photovoltaic power generation data with a nonlinear regression method. Finally, the environmental coefficient is integrated into the power factor impact model under a standard environment to derive the impact of power-influencing factors on power generation. This allows the power forecast data to be corrected, further improving the accuracy of the predicted power, improving the quality of power consumption control, and reducing the impact on the large power grid.

[0090] Based on the above embodiments, this embodiment describes step S105 in detail:

[0091] Acquire the maximum power load data and the minimum power load data of the adjustable load according to the adjustable load information, and obtain the power load range of the adjustable load;

[0092] Dividing the power load interval into equal intervals to obtain a plurality of power load sub-intervals;

[0093] Each power load sub-interval is marked with a multi-level power consumption level to obtain the multi-level adjustable energy consumption data.

[0094] Specifically, the maximum power load data refers to the maximum load value of the adjustable load in a period of time, and the minimum power load data refers to the minimum load value of the adjustable load in a period of time. First, a load database is established, and the historical load data and historical weather data of the adjustable load are retrieved from the power management system and imported into the load database. Then, based on the database data, the linearization method is used to correct the data in the database, and the interval algorithm is used to describe the uncertainty of the data, so as to intervalize the data and update the database. Finally, the power consumption level is identified according to the obtained interval, and it is identified according to Arabic numerals, with 1 being the minimum power load. For the load data intervalization, each interval is a level, and each level can be used as multi-level adjustable energy consumption data according to the load information such as voltage, current, frequency, etc. corresponding to each load in the database, as well as the power supply information, to achieve power control optimization, and achieve the technical effect of flexible control of DC microgrid power consumption.

[0095] Based on the above embodiments, this embodiment describes step S106 in detail:

[0096] The multi-level adjustable energy consumption data and the required energy consumption data are added and calculated to obtain multi-level comprehensive power load data;

[0097] When the power prediction data is not lower than the lowest level of power load data in the multi-level comprehensive power load data, the power prediction data is traversed and compared in the multi-level comprehensive power load data to obtain matching power load data of the corresponding level;

[0098] Acquire a level scheduling threshold, and perform power load level lowering on the matching power load data according to the level scheduling threshold;

[0099] The comprehensive power load data corresponding to the low-profile power load level is used as the actual power load data to control the power consumption of the target photovoltaic storage direct current flexible direct current microgrid.

[0100] Specifically, the power load in the target microgrid includes adjustable power load and non-adjustable power load. The adjustable power load can be controlled at multiple levels of energy consumption, and the energy consumption can be adjusted according to the power grid conditions. The non-adjustable load is the required energy consumption, and the energy consumption remains unchanged. Based on this, multi-level comprehensive power load data can be established. For example, the adjustable power load is divided into three levels of adjustable energy consumption, level 1 is the minimum energy consumption of the adjustable energy consumption, and level 3 is the maximum energy consumption of the adjustable energy consumption. The energy consumption data of each level is integrated with the required energy consumption data according to the superposition theorem to obtain multi-level comprehensive power load data. Comprehensive energy consumption 1 is the minimum energy consumption of comprehensive power consumption, and comprehensive energy consumption 3 is the maximum energy consumption of comprehensive power consumption.

[0101] The power forecast data and comprehensive energy consumption 1 are output in the same standard format and transmitted to the control system to compare the power forecast data with the comprehensive energy consumption 1 to determine whether the predicted photovoltaic power generation power on the target forecast day meets the minimum energy consumption of the target power grid. When the control system outputs a power forecast data greater than the comprehensive energy consumption 1, the proportion of each load to the comprehensive energy consumption 1 is calculated based on the comprehensive energy consumption 1, and the power forecast data and the proportion are traversed and compared with the energy consumption level to obtain the matching power load data of each corresponding level. Each level of the load has a corresponding scheduling threshold, and the load level is adjusted according to the energy consumption level. For example, the predicted photovoltaic power generation is 15KW·h, and the comprehensive energy consumption 1 is 10KW·h. If there is a load whose energy consumption level is divided into three levels, namely 1KW·h, 1.2KW·h, and 2KW·h, at this time, the predicted power generation is greater than the power of the comprehensive energy consumption 1, and the load accounts for 10% of the power. According to the predicted power generation and the proportion, the load can increase the energy consumption level to 1.5KW·h, which is larger than the second level and smaller than the third level. Therefore, the load can be adjusted to the second level of 1.2KW·h, and then the load is controlled with the second level 1.2KW·h as the actual power load data. Through the prediction and calculation of photovoltaic power generation and load power consumption, the power control is optimized, the uncertainty of photovoltaic power grid power generation is mastered, so as to flexibly control the power consumption of DC microgrids, reduce the impact of the volatility of photovoltaic power generation on the power grid, and thus reduce the impact on the large power grid.

[0102] The method of controlling the power consumption of the target PV-storage-direct-flexible-dc microgrid by using the comprehensive power load data corresponding to the low-profile power load level as the actual power load data comprises:

[0103] Calculating a load difference between the actual power load data and the matching power load data;

[0104] According to the load difference, the excess electric energy corresponding to the load difference is stored in the energy storage battery in the building where the target photovoltaic-storage-direct-flexible-direct-current microgrid is located, so as to cope with emergency situations;

[0105] The power load in the building where the target PV-storage-DC-flexible microgrid is located is controlled according to the actual power load data.

[0106] Specifically, different levels of load power consumption can be determined based on multi-level comprehensive power load data, and the power that meets the low level but does not meet the high level load interval can be calculated. This part of the power is stored in the energy storage battery of the photovoltaic storage direct current flexible DC microgrid system to deal with emergencies in the power grid, such as power outage of the first-level load, power grid dispatching power, and daily power supply to make up for the sunshine period. The actual power load data is the power load data corresponding to the adjustable load level determined according to the predicted power generation power. The control system issues control instructions based on the actual power load data to control the power consumption of the power load. For example, there is a load whose energy consumption level is divided into three levels, namely 1KW·h, 1.2KW·h, and 2KW·h, and the predicted power data can reach 1.5KW·h. At this time, based on the second level 1.2KW as the actual power load data, the middle difference is 0.3KW·h, and the difference power is stored in the energy storage device to deal with emergencies in the power grid and microgrid. By predicting power generation and classifying load information, the technical effect of quantitative storage of excess electricity can be achieved, providing support for the subsequent use of stored electricity to respond to emergencies, thereby reducing the impact on the power grid.

[0107] When the power forecast data is lower than the lowest level of power load data among the multi-level comprehensive power load data:

[0108] Calculating the power difference between the power forecast data and the lowest level power load data;

[0109] The load demand information is sent to the external power grid based on the power difference calculation result to ensure the minimum power demand of the building where the target PV-storage-DC-flexible microgrid is located.

[0110] Specifically, the power prediction data and the comprehensive energy consumption 1 are output in the same standard format and transmitted to the control system to compare the power prediction data with the comprehensive energy consumption 1 to determine whether the predicted photovoltaic power generation power on the target prediction day meets the minimum energy consumption of the target power grid. When the power prediction data output by the control system is less than the data of the comprehensive energy consumption 1, the difference between the power prediction data and the data of the comprehensive energy consumption 1 is calculated one by one, and the difference is generated in a standard request format through the control system, and the difference result is sent to the external power grid through satellite communication technology. The difference result is the load demand information. After the external power grid receives the request, according to the load demand information and the loss of the transmission path, the photovoltaic storage direct flexible DC microgrid is powered through a dedicated transmission route under the premise of ensuring the safety and stability of the external power grid, so as to ensure the minimum power demand of the building where the target photovoltaic storage direct flexible DC microgrid is located. For example, the predicted photovoltaic power generation is 6KW·h, and the comprehensive energy consumption 1 is 10KW·h. At this time, the predicted power generation is less than the power of comprehensive energy consumption 1, and the photovoltaic power generation is not enough to meet the minimum power demand of the building where the standard photovoltaic storage direct flexible DC microgrid is located. The control system calculates the difference between the predicted power generation of 6KW·h and the minimum power consumption of 10KW·h, and sends the difference result to the external power grid, which supplies power to the DC microgrid. By predicting the power generation and classifying the load information, the technical effect of ensuring the normal operation of the microgrid during the low period of photovoltaic power generation is achieved, reducing the uncertainty of photovoltaic power generation, and flexibly controlling the power consumption of the DC microgrid is achieved.

[0111] Please refer to Figure 2 , Figure 2 A structural block diagram of an auxiliary control system of a photovoltaic-storage-direct-flexible-direct-current microgrid provided in an embodiment of the present invention; the specific system may include:

[0112] The weather information acquisition module 100 is used to obtain the weather forecast information of the target forecast day from the weather forecast management system;

[0113] The component information collection module 200 is used to collect the photovoltaic power generation component information in the building where the target photovoltaic storage direct-flexible direct-current microgrid is located;

[0114] A power data prediction module 300 is used to predict the photovoltaic power generation power of the target prediction day according to the photovoltaic power generation component information and the weather forecast information to obtain power prediction data;

[0115] The load information acquisition module 400 is used to determine the power load information in the building where the target PV-storage-DC-flexible microgrid is located, and classify the power load information according to load attributes to obtain adjustable load information and non-adjustable load information;

[0116] An energy consumption data acquisition module 500 is used to acquire necessary energy consumption data according to the non-adjustable load information, and to acquire multi-level adjustable energy consumption data of the adjustable load according to the adjustable load information;

[0117] The power consumption control module 600 is used to control the power consumption of the target PV-storage-direct-flexible-direct-current microgrid through the power prediction data, the required energy consumption data and the multi-level adjustable energy consumption data.

[0118] The auxiliary control system of the photovoltaic, storage, direct current and flexible DC microgrid of this embodiment is used to implement the auxiliary control method of the photovoltaic, storage, direct current and flexible DC microgrid mentioned above. Therefore, the specific implementation method of the auxiliary control system of the photovoltaic, storage, direct current and flexible DC microgrid can be seen in the implementation method part of the auxiliary control method of the photovoltaic, storage, direct current and flexible DC microgrid mentioned above. For example, the weather information acquisition module 100, the component information acquisition module 200, the power data prediction module 300, the load information acquisition module 400, the energy consumption data acquisition module 500, and the power consumption control module 600 are respectively used to implement steps S101, S102, S103, S104, S105 and S106 in the auxiliary control method of the photovoltaic, storage, direct current and flexible DC microgrid mentioned above. Therefore, its specific implementation method can refer to the description of the corresponding each part of the implementation method, which will not be repeated here.

[0119] A specific embodiment of the present invention also provides an auxiliary control device for a PV-storage-DC-flexible microgrid, comprising: a memory for storing a computer program; and a processor for implementing the steps of the above-mentioned auxiliary control method for a PV-storage-DC-flexible microgrid when executing the computer program.

[0120] A specific embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the auxiliary control method of the above-mentioned photovoltaic-storage-direct-flexible-direct-current microgrid are implemented.

[0121] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0122] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or boxes.

[0123] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce a product including an instruction system, which is implemented in the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0124] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0125] Obviously, the above embodiments are merely examples for the purpose of clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived therefrom are still within the scope of protection of the present invention.

Claims

1. An auxiliary control method for a photovoltaic-storage-direct-flexible-direct-current microgrid, characterized in that: include: Obtain weather forecast information for the target forecast day from the weather forecast management system; Collect information on photovoltaic power generation components in the building where the target photovoltaic storage direct-flexible direct-current microgrid is located; Predicting the photovoltaic power generation power on the target prediction day according to the photovoltaic power generation component information and the weather forecast information to obtain power prediction data; Determine the power load information in the building where the target PV-storage-DC-flexible microgrid is located, and classify the power load information according to load attributes to obtain adjustable load information and non-adjustable load information; Obtaining necessary energy consumption data according to the non-adjustable load information, and obtaining multi-level adjustable energy consumption data of the adjustable load according to the adjustable load information, including: obtaining maximum power load data and minimum power load data of the adjustable load according to the adjustable load information to obtain the power load interval of the adjustable load; dividing the power load interval into equal distances to obtain multiple power load sub-intervals; and marking each power load sub-interval with multiple levels of power consumption levels to obtain the multi-level adjustable energy consumption data; The power consumption of the target PV-storage-DC-flexible microgrid is controlled by means of the power prediction data, the required energy consumption data and the multi-level adjustable energy consumption data, including: calculating the proportion of each power load to the comprehensive energy consumption, and determining the energy consumption level corresponding to each power load according to the power prediction data and the proportion, and using this as the actual power load data of each power load for power control.

2. The auxiliary control method of the PV-DC-SDC microgrid according to claim 1 is characterized in that: The photovoltaic power generation power of the target prediction day is predicted according to the photovoltaic power generation component information and the weather forecast information to obtain the power prediction data, including: Calculating solar radiation according to the weather forecast information; Extracting rooftop crystalline silicon component information and BIPV curtain wall film component information according to the photovoltaic power generation component information, and calculating component conversion rates of rooftop crystalline silicon components and BIPV curtain wall film components according to the rooftop crystalline silicon component information and the BIPV curtain wall film component information; Predicting the power of the rooftop crystalline silicon module according to the solar radiation and the conversion rate of the rooftop crystalline silicon module; Predicting the power of the BIPV curtain wall thin film assembly according to the solar radiation and the conversion rate of the BIPV curtain wall thin film assembly; The power prediction data is obtained by adding the power of the rooftop crystalline silicon module and the power of the BIPV curtain wall thin film module.

3. The auxiliary control method of the PV-DC-flexible DC microgrid according to claim 2 is characterized in that: After obtaining the power prediction data, the following further comprises: Obtaining equipment information of the rooftop crystalline silicon module and the BIPV curtain wall thin film module; Performing power impact analysis based on the device information; The power prediction data is corrected according to the power impact analysis result.

4. The auxiliary control method of the PV-DC-SDC microgrid according to claim 1 is characterized in that: The power consumption control of the target PV-storage-DC-flexible microgrid by using the power prediction data, the required energy consumption data and the multi-level adjustable energy consumption data includes: The multi-level adjustable energy consumption data and the required energy consumption data are added and calculated to obtain multi-level comprehensive power load data; When the power prediction data is not lower than the lowest level of power load data in the multi-level comprehensive power load data, the power prediction data is traversed and compared in the multi-level comprehensive power load data to obtain matching power load data of the corresponding level; Acquire a level scheduling threshold, and perform power load level lowering on the matching power load data according to the level scheduling threshold; The comprehensive power load data corresponding to the low-profile power load level is used as the actual power load data to control the power consumption of the target photovoltaic storage direct current flexible direct current microgrid.

5. The auxiliary control method of the PV-DC-flexible DC microgrid according to claim 4 is characterized in that: The method of controlling the power consumption of the target PV-storage-direct-flexible-dc microgrid by using the comprehensive power load data corresponding to the power load level after the power load is lowered as the actual power load data comprises: Calculating a load difference between the actual power load data and the matching power load data; According to the load difference, the excess electric energy corresponding to the load difference is stored in the energy storage battery in the building where the target photovoltaic-storage-direct-flexible-direct-current microgrid is located, so as to cope with emergency situations; The power load in the building where the target PV-storage-DC-flexible microgrid is located is controlled according to the actual power load data.

6. The auxiliary control method of the PV-DC-flexible DC microgrid according to claim 4 is characterized in that: When the power forecast data is lower than the lowest level of power load data among the multi-level comprehensive power load data: Calculating the power difference between the power forecast data and the lowest level power load data; The load demand information is sent to the external power grid based on the power difference calculation result to ensure the minimum power demand of the building where the target PV-storage-DC-flexible microgrid is located.

7. An auxiliary control system for a photovoltaic-storage-direct-flexible-direct-current microgrid, characterized in that: include: A weather information acquisition module is used to obtain weather forecast information for a target forecast day from a weather forecast management system; Component information collection module, used to collect photovoltaic power generation component information in the building where the target photovoltaic storage direct-flexible direct-current microgrid is located; A power data prediction module, used to predict the photovoltaic power generation power of the target prediction day according to the photovoltaic power generation component information and the weather forecast information, and obtain power prediction data; A load information acquisition module is used to determine the power load information in the building where the target PV-storage-DC-flexible microgrid is located, and classify the power load information according to load attributes to obtain adjustable load information and non-adjustable load information; An energy consumption data acquisition module is used to acquire necessary energy consumption data according to the non-adjustable load information, and acquire multi-level adjustable energy consumption data of the adjustable load according to the adjustable load information, including: acquiring maximum power load data and minimum power load data of the adjustable load according to the adjustable load information to obtain the power load interval of the adjustable load; dividing the power load interval into equal distances to obtain multiple power load sub-intervals; and marking each power load sub-interval with multiple levels of power consumption to obtain the multi-level adjustable energy consumption data; The power consumption control module is used to control the power consumption of the target photovoltaic, energy-storage, direct-current and flexible DC microgrid through the power prediction data, the required energy consumption data and the multi-level adjustable energy consumption data, including: calculating the proportion of each power load to the comprehensive energy consumption, and determining the energy consumption level corresponding to each power load according to the power prediction data and the proportion, and using the actual power load data of each power load as the actual power load data for power control.

8. An auxiliary control device for a photovoltaic-storage-direct-flexible-direct-current microgrid, characterized in that: include: Memory for storing computer programs; A processor is used to implement the steps of the auxiliary control method of a photovoltaic-storage-direct-flexible-direct-current microgrid as described in any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the auxiliary control method of a PV-storage-DC-flexible-DC microgrid are implemented as described in any one of claims 1 to 6.

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