CN117856339B - Micro-grid electric energy data control system and method based on big data - Google Patents
Micro-grid electric energy data control system and method based on big data Download PDFInfo
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Abstract
The invention discloses a micro-grid electric energy data control system and method based on big data, which relate to the technical field of micro-grid electric energy data control and comprise the following steps: s10: according to real-time meteorological data of the position of the micro-grid, a three-dimensional model is built, and the generated energy of the photovoltaic power generation equipment in any period of time is predicted; s20: predicting the real-time power supply quantity of each distributed power generation device; s30: determining a real-time power supply mode of the micro-grid; s40: and controlling the operation conditions of the output circuit breaker and the input circuit breaker of each distributed power generation device in real time according to the determined real-time power supply mode of the micro-grid. The method determines the real-time power supply mode of the micro-grid, ensures the stable operation of the micro-grid, reduces the damage to the energy storage batteries of the distributed power generation equipment, and further improves the overall operation effect of the micro-grid.
Description
Technical Field
The invention relates to the technical field of micro-grid electric energy data control, in particular to a micro-grid electric energy data control system and method based on big data.
Background
The micro-grid is an organic whole composed of a plurality of parts such as a distributed power supply, an electric load, an energy management system and the like. Based on renewable energy sources such as distributed photovoltaic, distributed wind power and the like, the comprehensive utilization of electric power and electricity is realized through various energy forms such as a gas turbine, electrochemical energy storage, a super capacitor and the like.
The existing micro-grid control system can not predict the power supply condition of the micro-grid in a period of time in the future when controlling the running condition of the micro-grid, the use effect of the system is reduced, and in the process of predicting the power supply condition of the micro-grid, the prediction of the power supply quantity is realized only through simple meteorological data, the influence of the use condition of each distributed power generation device and other meteorological data is not generated, the prediction effect of the system on the electric energy data of the micro-grid is reduced, and the use efficiency of an energy storage battery is not considered when the existing system manages and controls the power supply mode of the micro-grid, so that the integral running effect of the micro-grid is poor.
Therefore, a micro-grid electric energy data control system and method based on big data are urgently needed to solve the technical problems.
Disclosure of Invention
The invention aims to provide a micro-grid electric energy data control system and method based on big data, which are used for solving the problems in the prior art.
In order to achieve the above purpose, the present invention provides the following technical solutions: a micro-grid electrical energy data control method based on big data, the method comprising:
S10: according to real-time meteorological data of the position of the micro-grid, a three-dimensional model is built, and the generated energy of the photovoltaic power generation equipment in any period of time is predicted;
S20: predicting the real-time power supply quantity of each distributed power generation device according to the real-time meteorological data of the position of the micro-grid and the service condition of each distributed power generation device, wherein each distributed power generation device comprises a photovoltaic power generation device and a wind power generation device;
s30: determining a real-time power supply mode of the micro-grid according to a fixed power supply object and an emergency power supply object of the micro-grid, the predicted real-time power supply quantity of each distributed power generation device and the charging threshold value of an energy storage battery in each distributed power generation device;
s40: and controlling the operation conditions of the output circuit breaker and the input circuit breaker of each distributed power generation device in real time according to the determined real-time power supply mode of the micro-grid.
Further, the step S10 includes:
S101: collecting real-time meteorological data of the position of the micro-grid, wherein the meteorological data comprise solar radiation intensity, cloud layer distribution condition, cloud layer thickness and wind speed;
S102: constructing a three-dimensional model, carrying out three-dimensional simulation on the distribution condition of cloud layers and the placement condition of a photovoltaic panel in the three-dimensional model, mapping the cloud layers onto the photovoltaic panel according to the irradiation angle of the sun and the photovoltaic panel according to the three-dimensional simulation result, and determining the shielding coefficient theta of the photovoltaic panel, wherein theta = the area of the cloud layers mapped onto the front surface of the photovoltaic panel/the total area of the front surface of the photovoltaic panel;
S103: the cloud layer thickness and the solar radiation intensity are combined, the generated energy of the photovoltaic power generation equipment in any period of time is predicted, and a specific prediction formula is as follows:
wherein t=1, 2, …, n represents the number corresponding to the number of the collection times of the meteorological data, n represents the collection times of the meteorological data, θ t represents the shielding coefficient of the photovoltaic panel determined according to the meteorological data collected at the T time, β represents the power generation efficiency of the photovoltaic panel, T represents the collection interval time of the meteorological data, S represents the total area of the photovoltaic panel, D t represents the solar average radiation intensity determined according to the meteorological data collected at the T time, w represents the variable corresponding to the solar radiation intensity when the cloud layer thickness increases by 1 meter, D t represents the cloud layer average thickness determined according to the meteorological data collected at the T time, Y t represents the electric quantity value generated by scattered light and reflected light of the photovoltaic power generation equipment in the time T, a t represents the power generation amount predicted according to the meteorological data collected at the T time, and the photovoltaic power generation amount of the photovoltaic power generation equipment between the time point of the T time of the meteorological data collection and the time point of the t+1.
Further, the step S20 includes:
S201: determining the generated energy of the wind power generation equipment in any period of time according to the acquired wind speed value, Wherein ρ represents the air density, L represents the wind turbine blade rotation area, v t represents the average wind speed value of the wind power generation device determined from the meteorological data collected at the t-th time, B t represents the power generation amount of the wind power generation device between the time point of the t-th meteorological data collection and the time point of the t+1th meteorological data collection predicted from the meteorological data collected at the t-th time;
s202: according to the ageing condition of the photovoltaic power generation equipment and the pollution condition of the photovoltaic panel, the real-time power supply quantity of the photovoltaic power generation equipment is predicted, and a specific prediction formula is as follows:
When R is more than or equal to 0 and less than or equal to T:
When R > T:
wherein R represents the time length value from the first meteorological data acquisition time point, F 1 represents initial energy storage of an energy storage battery in the photovoltaic power generation equipment, e is a constant, e is more than 0 and less than 1, x t represents an aging coefficient corresponding to the photovoltaic power generation equipment when the meteorological data is acquired for the t time, x t≤1,ut is more than or equal to 0 and less than or equal to the pollution coefficient corresponding to the photovoltaic power generation equipment when the meteorological data is acquired for the t time, and u t＜1,ER is more than or equal to 0 and represents the power supply quantity corresponding to the photovoltaic power generation equipment when the time length value from the first meteorological data acquisition time point is R;
s203: according to the ageing condition of the wind power generation equipment, predicting the real-time power supply quantity of the wind power generation equipment, wherein a specific prediction formula is as follows:
When R is more than or equal to 0 and less than or equal to T:
When R > T:
wherein F 2 represents the initial energy storage capacity of an energy storage battery in the wind power generation equipment, y t represents the aging coefficient corresponding to the wind power generation equipment when the meteorological data is acquired for the t time, and y t≤1,GR is more than or equal to 0 and is equal to the power supply capacity corresponding to the wind power generation equipment when the time length value from the time point of the first meteorological data acquisition is R.
Further, the step S30 includes:
S301: under the condition that external power is not supplied, comparing the predicted real-time power supply quantity of each distributed power generation device with the electric energy corresponding to the energy storage battery when the energy storage battery reaches the full power state, and determining the time point when the energy storage battery in each distributed power generation device reaches the full power state for the first time if the predicted real-time power supply quantity of each distributed power generation device is equal to the electric energy corresponding to the energy storage battery when the energy storage battery reaches the full power state;
Determining a first power supply time period according to the magnitude relation between a time point H 1 when the energy storage battery in the photovoltaic power generation equipment reaches the full power state for the first time and a time point H 1 when the energy storage battery in the wind power generation equipment reaches the full power state for the first time, wherein the time length of the first power supply time period=c-t 0, and c=min { H 1,h1},t0 represents an acquisition time point of initial energy storage energy of the energy storage battery;
S302: according to historical power supply data of the micro-grid on the emergency power supply object and the fixed power supply object, constructing a linear model of time-power supply data, and according to the trend condition of the constructed linear model, determining the power supply quantity K 1 of the micro-grid on the emergency power supply object and the fixed power supply object in a first power supply time period;
If (W-U) is more than or equal to K 1, the power supply mode of the micro-grid in the first power supply time period depends on power generation equipment corresponding to min { H 1,h1 }, if (W-U) is less than K 1, a corresponding time point t 1' when the power supply quantity of the corresponding power supply object and the fixed power supply object of the micro-grid is W-U is determined, the first power supply time period is adjusted according to a determination result, the time length of the adjusted first power supply time period=t 1′-t0, the power supply mode of the micro-grid in the adjusted first power supply time period depends on power generation equipment corresponding to min { H 1,h1 }, W represents the corresponding electric energy when the energy storage battery reaches a full power state, and U represents the corresponding electric energy when the energy storage battery reaches a charging prompt state;
S303: and switching the power supply modes of the emergency power supply object and the fixed power supply object of the micro-grid at the power supply ending point of the adjusted first power supply time period.
Further, the specific method for controlling the operation conditions of the output circuit breaker and the input circuit breaker of each distributed power generation device in real time in S40 is as follows:
When the real-time power supply mode of the micro-grid supplies power for the photovoltaic power generation equipment, controlling the output circuit breaker and the input circuit breaker of the photovoltaic power generation equipment to be in a working state, controlling the output circuit breaker of the wind power generation equipment to be in a closing state and controlling the input circuit breaker of the wind power generation equipment to be in a working state;
When the real-time power supply mode of the micro-grid supplies power for the wind power generation equipment, the output breaker and the input breaker of the wind power generation equipment are controlled to be in a working state, the output breaker of the photovoltaic power generation equipment is controlled to be in a closing state, and the input breaker of the photovoltaic power generation equipment is controlled to be in a working state.
The micro-grid electric energy data control system based on big data comprises a photovoltaic equipment generating capacity prediction module, a distributed power generation equipment power supply capacity prediction module, a micro-grid power supply mode determination module and a control module;
the photovoltaic equipment power generation amount prediction module is used for predicting the power generation amount of the photovoltaic power generation equipment in any period of time;
the distributed power generation equipment power supply quantity prediction module is used for predicting the real-time power supply quantity of each distributed power generation equipment;
the micro-grid power supply mode determining module is used for determining a real-time power supply mode of the micro-grid;
the control module is used for controlling the running conditions of the output circuit breaker and the input circuit breaker of each distributed power generation device in real time.
Further, the photovoltaic equipment power generation amount prediction module comprises a meteorological data acquisition unit, a three-dimensional simulation unit and a photovoltaic equipment power generation amount prediction unit;
The meteorological data acquisition unit acquires real-time meteorological data of the position of the micro-grid, transmits acquired cloud layer distribution conditions to the three-dimensional simulation unit, transmits acquired solar radiation intensity and cloud layer thickness to the photovoltaic equipment generating capacity prediction unit, and transmits acquired wind speed to the distributed power generation equipment power supply amount prediction module;
The three-dimensional simulation unit receives the cloud layer distribution condition transmitted by the meteorological data acquisition unit, simulates the received cloud layer distribution condition and the placement condition of the photovoltaic panel in a three-dimensional model, determines the shielding coefficient of the photovoltaic panel according to a simulation result, and transmits the determined shielding coefficient to the photovoltaic equipment generating capacity prediction unit;
The photovoltaic equipment power generation amount prediction unit receives the solar radiation intensity and cloud layer thickness transmitted by the meteorological data acquisition unit and the shielding coefficient transmitted by the three-dimensional simulation unit, predicts the power generation amount of the photovoltaic power generation equipment in any period of time based on the receiving information, and transmits a prediction result to the distributed power generation equipment power supply amount prediction module.
Further, the distributed power generation equipment power supply quantity prediction module comprises a wind power generation equipment power generation quantity prediction unit, a photovoltaic power generation equipment real-time power supply quantity prediction unit and a wind power generation equipment real-time power supply quantity prediction unit;
The wind power generation equipment power generation amount prediction unit receives the wind speed transmitted by the meteorological data acquisition unit, predicts the power generation amount of the wind power generation equipment in any period of time based on the received information, and transmits a prediction result to the wind power generation equipment real-time power supply amount prediction unit;
the photovoltaic power generation equipment real-time power supply quantity prediction unit is used for receiving a prediction result transmitted by the photovoltaic power generation equipment power generation quantity prediction unit, predicting the real-time power supply quantity of the photovoltaic power generation equipment by combining the aging condition of the photovoltaic power generation equipment and the pollution condition of the photovoltaic panel, and transmitting the prediction result to the micro-grid power supply mode determination module;
The wind power generation equipment real-time power supply quantity prediction unit receives the prediction result transmitted by the wind power generation equipment power generation quantity prediction unit, predicts the real-time power supply quantity of the wind power generation equipment by combining the aging condition of the wind power generation equipment, and transmits the prediction result to the micro-grid power supply mode determination module.
Further, the micro-grid power supply mode determining module comprises a power supply time determining unit, a power supply time adjusting unit and a power supply mode determining unit;
The power supply time determining unit is used for receiving prediction results transmitted by the photovoltaic power generation equipment real-time power supply quantity predicting unit and the wind power generation equipment real-time power supply quantity predicting unit respectively, determining a time point when the energy storage battery in each distributed power generation equipment reaches the full power state for the first time if the received prediction results are equal to the corresponding electric energy when the energy storage battery reaches the full power state respectively, determining a first power supply time period based on the determination results, and transmitting the determination results to the power supply time adjusting unit;
The power supply time adjustment unit receives the determination result transmitted by the power supply time determination unit, adjusts the first power supply time period by combining the power supply amounts of the emergency power supply object and the fixed power supply object in the first power supply time period, and transmits the adjusted first power supply time period to the power supply mode determination unit;
The power supply mode determining unit receives the adjustment result transmitted by the power supply time adjusting unit, switches the power supply modes of the corresponding power supply object and the fixed power supply object of the micro-grid at the power supply end point of the adjusted first power supply time period based on the receiving information, and transmits the switching result to the control module.
Further, the control module receives the switching result transmitted by the power supply mode determining unit, and controls the operation conditions of the output circuit breaker and the input circuit breaker of each distributed power generation device in real time based on the receiving information.
Compared with the prior art, the invention has the beneficial effects that:
1. When the theoretical power generation amount of the photovoltaic power generation equipment is predicted, the three-dimensional model is utilized to simulate the sun illumination direction and the placing condition of the photovoltaic panel, and errors existing in the prediction process of the theoretical power generation amount are eliminated based on simulation results and cloud layer change conditions, so that the prediction precision is improved.
2. In the process of calculating the real-time power supply quantity of each distributed power generation device, the influence of the aging condition and the pollution condition of each distributed power generation device on the theoretical power generation quantity is considered, and the prediction accuracy of the electric energy data of the micro-grid is improved.
3. According to the method, the first power supply time period is determined according to the predicted real-time power supply quantity of each distributed power generation device, the first power supply time period is adjusted by combining the power supply quantity of the corresponding power supply object and the fixed power supply object in the first power supply time period of the micro-grid, the real-time power supply mode of the micro-grid is determined based on the adjustment result, the stable operation of the micro-grid is ensured, the damage to the energy storage batteries of each distributed power generation device is reduced, and the integral operation effect of the micro-grid is further improved.
Drawings
FIG. 1 is a schematic diagram of a workflow of a micro-grid power data control system and method based on big data according to the present invention;
Fig. 2 is a schematic structural diagram of the working principle of the micro-grid electric energy data control system and method based on big data.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1 and fig. 2, the present invention provides the following technical solutions, and a micro-grid electric energy data control method based on big data, where the method includes:
S10: according to real-time meteorological data of the position of the micro-grid, a three-dimensional model is built, and the generated energy of the photovoltaic power generation equipment in any period of time is predicted;
S10 comprises the following steps:
S101: collecting real-time meteorological data of the position of the micro-grid, wherein the meteorological data comprise solar radiation intensity, cloud layer distribution condition, cloud layer thickness and wind speed;
S102: constructing a three-dimensional model, carrying out three-dimensional simulation on the distribution condition of cloud layers and the placement condition of a photovoltaic panel in the three-dimensional model, mapping the cloud layers onto the photovoltaic panel according to the irradiation angle of the sun to the photovoltaic panel according to the three-dimensional simulation result, determining the shielding coefficient theta of the photovoltaic panel, wherein theta = the area of the cloud layers mapped to the front surface of the photovoltaic panel/the total area of the front surface of the photovoltaic panel, and the front surface of the photovoltaic panel refers to the surface capable of receiving solar rays and generating electricity;
S103: the cloud layer thickness and the solar radiation intensity are combined, the generated energy of the photovoltaic power generation equipment in any period of time is predicted, and a specific prediction formula is as follows:
wherein t=1, 2, …, n represents the number corresponding to the number of the meteorological data collection times, n represents the total number of the meteorological data collection times, θ t represents the shielding coefficient of the photovoltaic panel determined according to the meteorological data collected at the T time, β represents the power generation efficiency of the photovoltaic panel, T represents the collection interval time of the meteorological data, p represents the value of the unit time length, the unit time length represents 1 hour, S represents the total area of the photovoltaic panel, D t represents the solar average radiation intensity determined according to the meteorological data collected at the T time, w represents the variable corresponding to the solar radiation intensity when the thickness of the cloud layer increases by 1 meter, D t represents the average thickness of the cloud layer determined according to the meteorological data collected at the T time, Y t represents the electric quantity value generated by scattered light and reflected light of the photovoltaic power generation device in the time T, a t represents the power generation amount predicted according to the meteorological data collected at the T time, and the photovoltaic power generation amount of the photovoltaic power generation device between the time point of the meteorological data collected at the T time and the (t+1) th meteorological data collection time point;
S20: predicting the real-time power supply quantity of each distributed power generation device according to the real-time meteorological data of the position of the micro-grid and the service condition of each distributed power generation device, wherein each distributed power generation device comprises a photovoltaic power generation device and a wind power generation device;
S20 includes:
S201: determining the generated energy of the wind power generation equipment in any period of time according to the acquired wind speed value, Wherein ρ represents the air density, L represents the wind turbine blade rotation area, v t represents the average wind speed value of the wind power generation device determined from the meteorological data collected at the t-th time, B t represents the power generation amount of the wind power generation device between the time point of the t-th meteorological data collection and the time point of the t+1th meteorological data collection predicted from the meteorological data collected at the t-th time;
s202: according to the ageing condition of the photovoltaic power generation equipment and the pollution condition of the photovoltaic panel, the real-time power supply quantity of the photovoltaic power generation equipment is predicted, and a specific prediction formula is as follows:
When R is more than or equal to 0 and less than or equal to T:
When R > T:
wherein R represents the time length value from the first meteorological data acquisition time point, F 1 represents initial energy storage of an energy storage battery in the photovoltaic power generation equipment, e is a constant, e is more than 0 and less than 1, x t represents an aging coefficient corresponding to the photovoltaic power generation equipment when the meteorological data is acquired for the t time, x t≤1,ut is more than or equal to 0 and less than or equal to the pollution coefficient corresponding to the photovoltaic power generation equipment when the meteorological data is acquired for the t time, and u t＜1,ER is more than or equal to 0 and represents the power supply quantity corresponding to the photovoltaic power generation equipment when the time length value from the first meteorological data acquisition time point is R;
For example, when the first meteorological data acquisition time point is 2022, 12, 11, 17 and 08 minutes, and the predicted time point of the power supply amount of the photovoltaic power generation device is 2022, 12, 11, 17 and 58 minutes, then r=2022, 12, 11, 17 and 58 minutes-2022, 12, 11, 17 and 08 minutes=50 minutes;
s203: according to the ageing condition of the wind power generation equipment, predicting the real-time power supply quantity of the wind power generation equipment, wherein a specific prediction formula is as follows:
When R is more than or equal to 0 and less than or equal to T:
When R > T:
Wherein F 2 represents initial energy storage capacity of an energy storage battery in the wind power generation equipment, the initial energy storage capacity refers to electric quantity storage capacity corresponding to the energy storage battery when the meteorological data is acquired for the 1 st time, namely, when the meteorological data is acquired for the t time at the time point when the meteorological data is acquired for the 1 st time, aging coefficient corresponding to the wind power generation equipment is represented by t 0,yt, and y t≤1,GR is more than or equal to 0 and is equal to power supply capacity corresponding to the wind power generation equipment when a time length value from the first meteorological data acquisition time point is R;
In the process of calculating the real-time power supply quantity of each distributed power generation device, when the theoretical power generation quantity of the photovoltaic power generation device is predicted, the influence of the solar irradiation direction, the photovoltaic panel placement direction and the cloud layer change condition on the power generation quantity is considered, and in the process of predicting the real-time power supply quantity of each distributed power generation device, the influence of the aging condition and the pollution condition of each distributed power generation device on the theoretical power generation quantity is considered, so that the error between a prediction result and an actual result is reduced, and the prediction precision is improved;
s30: determining a real-time power supply mode of the micro-grid according to a fixed power supply object and an emergency power supply object of the micro-grid, the predicted real-time power supply quantity of each distributed power generation device and the charging threshold value of an energy storage battery in each distributed power generation device;
S30 includes:
S301: under the condition that external power is not supplied, comparing the predicted real-time power supply quantity of each distributed power generation device with the electric energy corresponding to the energy storage battery when the energy storage battery reaches the full power state, and determining the time point when the energy storage battery in each distributed power generation device reaches the full power state for the first time if the predicted real-time power supply quantity of each distributed power generation device is equal to the electric energy corresponding to the energy storage battery when the energy storage battery reaches the full power state;
Determining a first power supply time period according to the magnitude relation between a time point H 1 when the energy storage battery in the photovoltaic power generation equipment reaches the full power state for the first time and a time point H 1 when the energy storage battery in the wind power generation equipment reaches the full power state for the first time, wherein the time length of the first power supply time period=c-t 0, and c=min { H 1,h1},t0 represents an acquisition time point of initial energy storage energy of the energy storage battery;
S302: according to historical power supply data of the micro-grid on the emergency power supply object and the fixed power supply object, constructing a linear model of time-power supply data, and according to the trend condition of the constructed linear model, determining the power supply quantity K 1 of the micro-grid on the emergency power supply object and the fixed power supply object in a first power supply time period;
if (W-U) is greater than or equal to K 1, the power supply mode of the micro-grid in the first power supply period depends on the power generation device corresponding to min { H 1,h1 }, if (W-U) is less than K 1, determining a corresponding time point t 1' when the power supply amounts of the micro-grid to the corresponding power supply object and the fixed power supply object are W-U, adjusting the first power supply period according to the determination result, and the adjusted time length=t 1′-t0 of the first power supply period, where the power supply mode of the micro-grid in the adjusted first power supply period depends on the power generation device corresponding to min { H 1,h1 }, W represents the electric energy corresponding to the energy storage battery when the energy storage battery reaches the full power state, and U represents the electric energy corresponding to the energy storage battery when the energy storage battery reaches the charging prompt state, for example, when the electric energy of the energy storage battery remains 40%, the energy storage battery prompts to charge, and 40% W represents the electric energy corresponding to the energy storage battery when the energy storage battery reaches the charging prompt state;
S303: switching power supply modes of the emergency power supply object and the fixed power supply object of the micro-grid at a power supply end point of the adjusted first power supply time period;
s40: according to the determined real-time power supply mode of the micro-grid, the operation conditions of the output circuit breaker and the input circuit breaker of each distributed power generation device are controlled in real time;
s40, the specific method for controlling the running conditions of the output circuit breaker and the input circuit breaker of each distributed power generation device in real time comprises the following steps:
When the real-time power supply mode of the micro-grid supplies power for the photovoltaic power generation equipment, controlling the output circuit breaker and the input circuit breaker of the photovoltaic power generation equipment to be in a working state, controlling the output circuit breaker of the wind power generation equipment to be in a closing state and controlling the input circuit breaker of the wind power generation equipment to be in a working state;
When the real-time power supply mode of the micro-grid supplies power for the wind power generation equipment, the output breaker and the input breaker of the wind power generation equipment are controlled to be in a working state, the output breaker of the photovoltaic power generation equipment is controlled to be in a closing state, and the input breaker of the photovoltaic power generation equipment is controlled to be in a working state.
The micro-grid electric energy data control system based on big data comprises a photovoltaic equipment generating capacity prediction module, a distributed power generation equipment power supply capacity prediction module, a micro-grid power supply mode determination module and a control module;
The photovoltaic equipment power generation amount prediction module is used for predicting the power generation amount of the photovoltaic power generation equipment in any period of time;
the photovoltaic equipment power generation amount prediction module comprises a meteorological data acquisition unit, a three-dimensional simulation unit and a photovoltaic equipment power generation amount prediction unit;
the meteorological data acquisition unit acquires real-time meteorological data of the position of the micro-grid, transmits the acquired cloud layer distribution condition to the three-dimensional simulation unit, transmits the acquired solar radiation intensity and cloud layer thickness to the photovoltaic equipment generating capacity prediction unit, and transmits the acquired wind speed to the distributed power generation equipment power supply amount prediction module;
The three-dimensional simulation unit receives the cloud layer distribution condition transmitted by the meteorological data acquisition unit, simulates the received cloud layer distribution condition and the placement condition of the photovoltaic panel in a three-dimensional model, determines the shielding coefficient of the photovoltaic panel according to a simulation result, and transmits the determined shielding coefficient to the photovoltaic equipment power generation amount prediction unit;
The photovoltaic equipment power generation amount prediction unit receives the solar radiation intensity and the cloud layer thickness transmitted by the meteorological data acquisition unit and the shielding coefficient transmitted by the three-dimensional simulation unit, predicts the power generation amount of the photovoltaic power generation equipment in any period of time based on the receiving information, and transmits a prediction result to the distributed power generation equipment power supply amount prediction module;
the distributed power generation equipment power supply quantity prediction module is used for predicting the real-time power supply quantity of each distributed power generation equipment;
The distributed power generation equipment power supply quantity prediction module comprises a wind power generation equipment power generation quantity prediction unit, a photovoltaic power generation equipment real-time power supply quantity prediction unit and a wind power generation equipment real-time power supply quantity prediction unit;
The wind power generation equipment power generation amount prediction unit receives the wind speed transmitted by the meteorological data acquisition unit, predicts the power generation amount of the wind power generation equipment in any period of time based on the received information, and transmits a prediction result to the wind power generation equipment real-time power supply amount prediction unit;
The photovoltaic power generation equipment real-time power supply quantity prediction unit is used for receiving the prediction result transmitted by the photovoltaic power generation equipment power generation quantity prediction unit, predicting the real-time power supply quantity of the photovoltaic power generation equipment by combining the aging condition of the photovoltaic power generation equipment and the pollution condition of the photovoltaic panel, and transmitting the prediction result to the micro-grid power supply mode determination module;
The wind power generation equipment real-time power supply quantity prediction unit receives the prediction result transmitted by the wind power generation equipment power generation quantity prediction unit, predicts the real-time power supply quantity of the wind power generation equipment by combining the aging condition of the wind power generation equipment, and transmits the prediction result to the micro-grid power supply mode determination module;
the micro-grid power supply mode determining module is used for determining a real-time power supply mode of the micro-grid;
The micro-grid power supply mode determining module comprises a power supply time determining unit, a power supply time adjusting unit and a power supply mode determining unit;
The power supply time determining unit is used for receiving the prediction results respectively transmitted by the photovoltaic power generation equipment real-time power supply quantity predicting unit and the wind power generation equipment real-time power supply quantity predicting unit, determining the time point when the energy storage battery in each distributed power generation equipment reaches the full power state for the first time if the received prediction results are respectively equal to the corresponding electric energy when the energy storage battery reaches the full power state, determining the first power supply time period based on the determination results, and transmitting the determination results to the power supply time adjusting unit;
The power supply time adjusting unit receives the determination result transmitted by the power supply time determining unit, adjusts the first power supply time period by combining the power supply amounts of the corresponding power supply object and the fixed power supply object in the first power supply time period by the micro-grid, and transmits the adjusted first power supply time period to the power supply mode determining unit;
The power supply mode determining unit receives the adjustment result transmitted by the power supply time adjusting unit, switches the power supply modes of the corresponding power supply object and the fixed power supply object of the micro-grid at the power supply end point of the adjusted first power supply time period based on the receiving information, and transmits the switching result to the control module;
The control module is used for controlling the running conditions of the output circuit breaker and the input circuit breaker of each distributed power generation device in real time;
the control module receives the switching result transmitted by the power supply mode determining unit, and controls the operation conditions of the output circuit breaker and the input circuit breaker of each distributed power generation device in real time based on the receiving information.
Example 1: let the photovoltaic panel generating efficiency be β=0.9, the total area of photovoltaic panel is s=1000 square meter, t=30 minutes, p=1 hour, the corresponding change rate w=1w/square meter of solar radiation intensity when the cloud layer thickness increases by 1 meter, the shading coefficient θ 1 =0.3 of photovoltaic panel according to the meteorological data collected for the first time, the solar average radiation intensity D 1 =1200W/square meter according to the meteorological data collected for the first time, the cloud layer average thickness D 1 =400 m according to the meteorological data collected for the first time, the electric quantity value Y 1 =100 degree electricity generated by scattered light and reflected light by the photovoltaic power generation equipment within 30 minutes is predicted according to the meteorological data collected for the first time, the generated energy of photovoltaic power generation equipment between the meteorological data collection time point 1 st time point and the meteorological data collection time point 2 nd time point is:
A degree;
Therefore, the power generation amount of the photovoltaic power generation device between the 1 st time of the meteorological data collection and the 2 nd time of the meteorological data collection is 486.1 DEG electricity according to the forecast of the meteorological data collected for the first time.
Example 2: let a 1 = 972.1 degrees, a 1 =900 degrees, r=40 minutes, e=0.5, when collecting meteorological data for the 1 st and 2 nd times, the ageing coefficient corresponding to the photovoltaic power generation equipment is x 1＝0.05、x2 =0.05 respectively, the initial energy storage capacity F 1 =300 degrees of the energy storage battery in the photovoltaic power generation equipment, when collecting meteorological data for the 1 st and 2 nd times, the pollution coefficient corresponding to the photovoltaic power generation equipment is u 1＝0.04、u2 =0.05 respectively, when the time length value from the first meteorological data collection time point is 40 minutes, the power supply corresponding to the photovoltaic power generation equipment is as follows:
since r=40 > t=30, it can be seen that Then:
A degree;
therefore, when the time length value from the first meteorological data acquisition time point is 40 minutes, the corresponding power supply amount of the photovoltaic power generation equipment is 1176.72 DEG electricity.
Example 3: if E 60＝W,G65 =w, if the energy storage battery of the photovoltaic power generation device reaches the time point H 1＝60+t0 of the full power state for the first time without external power supply, and if the energy storage battery of the wind power generation device reaches the time point H 1＝65+t0 of the full power state for the first time, c=min { H 1,h1}＝min{65+t0,60+t0}＝60+t0, therefore, the time length of the first power supply period=c-t 0＝60+t0-t0 =60;
Assuming that the power supply amounts K 1 =0.8w and u=0.4W of the micro grid to the emergency power supply object and the fixed power supply object in the first power supply period, since (W-U) = (W-0.4W) =0.6W < 0.8W, the corresponding time point t 1' when the power supply amounts of the micro grid to the emergency power supply object and the fixed power supply object are W-U is determined, the adjusted time length=t 1′-t0 of the first power supply period, and the power supply mode of the micro grid in the adjusted first power supply period is photovoltaic power generation.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (4)
1. A micro-grid electric energy data control method based on big data is characterized in that: the method comprises the following steps:
S10: according to real-time meteorological data of the position of the micro-grid, a three-dimensional model is built, and the generated energy of the photovoltaic power generation equipment in any period of time is predicted;
the S10 includes:
S101: collecting real-time meteorological data of the position of the micro-grid, wherein the meteorological data comprise solar radiation intensity, cloud layer distribution condition, cloud layer thickness and wind speed;
S102: constructing a three-dimensional model, carrying out three-dimensional simulation on the distribution condition of cloud layers and the placement condition of a photovoltaic panel in the three-dimensional model, mapping the cloud layers onto the photovoltaic panel according to the irradiation angle of the sun and the photovoltaic panel according to the three-dimensional simulation result, and determining the shielding coefficient theta of the photovoltaic panel, wherein theta = the area of the cloud layers mapped onto the front surface of the photovoltaic panel/the total area of the front surface of the photovoltaic panel;
S103: the cloud layer thickness and the solar radiation intensity are combined, the generated energy of the photovoltaic power generation equipment in any period of time is predicted, and a specific prediction formula is as follows:
Wherein t=1, 2, …, n represents the number corresponding to the number of the meteorological data collection times, n represents the total number of the meteorological data collection times, θ t represents the shielding coefficient of the photovoltaic panel determined according to the meteorological data collected at the T-th time, β represents the power generation efficiency of the photovoltaic panel, T represents the collection interval time of the meteorological data, p represents the value of unit time length, S represents the total area of the photovoltaic panel, D t represents the solar average radiation intensity determined according to the meteorological data collected at the T-th time, w represents the variable corresponding to the solar radiation intensity when the cloud layer thickness increases by 1 meter, D t represents the cloud layer average thickness determined according to the meteorological data collected at the T-th time, Y t represents the electric quantity value generated by scattered light and reflected light of the photovoltaic power generation equipment within the time T, a t represents the power generation amount predicted according to the meteorological data collected at the T-th time, and the photovoltaic power generation amount of the photovoltaic power generation equipment between the T-th meteorological data collection time point and the t+1th meteorological data collection time point;
S20: predicting the real-time power supply quantity of each distributed power generation device according to the real-time meteorological data of the position of the micro-grid and the service condition of each distributed power generation device, wherein each distributed power generation device comprises a photovoltaic power generation device and a wind power generation device;
the S20 includes:
S201: determining the generated energy of the wind power generation equipment in any period of time according to the acquired wind speed value, Wherein ρ represents the air density, L represents the wind turbine blade rotation area, v t represents the average wind speed value of the wind power generation device determined from the meteorological data collected at the t-th time, B t represents the power generation amount of the wind power generation device between the time point of the t-th meteorological data collection and the time point of the t+1th meteorological data collection predicted from the meteorological data collected at the t-th time;
s202: according to the ageing condition of the photovoltaic power generation equipment and the pollution condition of the photovoltaic panel, the real-time power supply quantity of the photovoltaic power generation equipment is predicted, and a specific prediction formula is as follows:
When R is more than or equal to 0 and less than or equal to T:
When R > T:
wherein R represents the time length value from the first meteorological data acquisition time point, F 1 represents initial energy storage of an energy storage battery in the photovoltaic power generation equipment, e is a constant, e is more than 0 and less than 1, x t represents an aging coefficient corresponding to the photovoltaic power generation equipment when the meteorological data is acquired for the t time, x t≤1,ut is more than or equal to 0 and less than or equal to the pollution coefficient corresponding to the photovoltaic power generation equipment when the meteorological data is acquired for the t time, and u t＜1,ER is more than or equal to 0 and represents the power supply quantity corresponding to the photovoltaic power generation equipment when the time length value from the first meteorological data acquisition time point is R;
s203: according to the ageing condition of the wind power generation equipment, predicting the real-time power supply quantity of the wind power generation equipment, wherein a specific prediction formula is as follows:
When R is more than or equal to 0 and less than or equal to T:
When R > T:
Wherein F 2 represents the initial energy storage capacity of an energy storage battery in the wind power generation equipment, y t represents the aging coefficient corresponding to the wind power generation equipment when the meteorological data is acquired for the t time, and y t≤1,GR is more than or equal to 0 and is equal to the power supply capacity corresponding to the wind power generation equipment when the time length value from the time point of the first meteorological data acquisition is R;
s30: determining a real-time power supply mode of the micro-grid according to a fixed power supply object and an emergency power supply object of the micro-grid, the predicted real-time power supply quantity of each distributed power generation device and the charging threshold value of an energy storage battery in each distributed power generation device;
the S30 includes:
S301: under the condition that external power is not supplied, comparing the predicted real-time power supply quantity of each distributed power generation device with the electric energy corresponding to the energy storage battery when the energy storage battery reaches the full power state, and determining the time point when the energy storage battery in each distributed power generation device reaches the full power state for the first time if the predicted real-time power supply quantity of each distributed power generation device is equal to the electric energy corresponding to the energy storage battery when the energy storage battery reaches the full power state;
Determining a first power supply time period according to the magnitude relation between a time point H 1 when the energy storage battery in the photovoltaic power generation equipment reaches the full power state for the first time and a time point H 1 when the energy storage battery in the wind power generation equipment reaches the full power state for the first time, wherein the time length of the first power supply time period=c-t 0, and c=min { H 1,h1},t0 represents an acquisition time point of initial energy storage energy of the energy storage battery;
S302: according to historical power supply data of the micro-grid on the emergency power supply object and the fixed power supply object, constructing a linear model of time-power supply data, and according to the trend condition of the constructed linear model, determining the power supply quantity K 1 of the micro-grid on the emergency power supply object and the fixed power supply object in a first power supply time period;
If (W-U) is more than or equal to K 1, the power supply mode of the micro-grid in the first power supply time period depends on power generation equipment corresponding to min { H 1,h1 }, if (W-U) is less than K 1, a corresponding time point t 1' when the power supply quantity of the corresponding power supply object and the fixed power supply object of the micro-grid is W-U is determined, the first power supply time period is adjusted according to a determination result, the time length of the adjusted first power supply time period=t 1′-t0, the power supply mode of the micro-grid in the adjusted first power supply time period depends on power generation equipment corresponding to min { H 1,h1 }, W represents the corresponding electric energy when the energy storage battery reaches a full power state, and U represents the corresponding electric energy when the energy storage battery reaches a charging prompt state;
S303: switching power supply modes of the emergency power supply object and the fixed power supply object of the micro-grid at a power supply end point of the adjusted first power supply time period;
s40: and controlling the operation conditions of the output circuit breaker and the input circuit breaker of each distributed power generation device in real time according to the determined real-time power supply mode of the micro-grid.
2. The micro-grid power data control method based on big data according to claim 1, wherein: the specific method for controlling the running conditions of the output circuit breaker and the input circuit breaker of each distributed power generation device in real time in the S40 is as follows:
When the real-time power supply mode of the micro-grid supplies power for the photovoltaic power generation equipment, controlling the output circuit breaker and the input circuit breaker of the photovoltaic power generation equipment to be in a working state, controlling the output circuit breaker of the wind power generation equipment to be in a closing state and controlling the input circuit breaker of the wind power generation equipment to be in a working state;
When the real-time power supply mode of the micro-grid supplies power for the wind power generation equipment, the output breaker and the input breaker of the wind power generation equipment are controlled to be in a working state, the output breaker of the photovoltaic power generation equipment is controlled to be in a closing state, and the input breaker of the photovoltaic power generation equipment is controlled to be in a working state.
3. A big data based micro grid power data control system applied to the big data based micro grid power data control method of any one of claims 1-2, characterized in that: the system comprises a photovoltaic equipment power generation amount prediction module, a distributed power generation equipment power supply amount prediction module, a micro-grid power supply mode determination module and a control module;
the photovoltaic equipment power generation amount prediction module is used for predicting the power generation amount of the photovoltaic power generation equipment in any period of time;
the photovoltaic equipment power generation amount prediction module comprises a meteorological data acquisition unit, a three-dimensional simulation unit and a photovoltaic equipment power generation amount prediction unit;
The meteorological data acquisition unit acquires real-time meteorological data of the position of the micro-grid, transmits acquired cloud layer distribution conditions to the three-dimensional simulation unit, transmits acquired solar radiation intensity and cloud layer thickness to the photovoltaic equipment generating capacity prediction unit, and transmits acquired wind speed to the distributed power generation equipment power supply amount prediction module;
The three-dimensional simulation unit receives the cloud layer distribution condition transmitted by the meteorological data acquisition unit, simulates the received cloud layer distribution condition and the placement condition of the photovoltaic panel in a three-dimensional model, determines the shielding coefficient of the photovoltaic panel according to a simulation result, and transmits the determined shielding coefficient to the photovoltaic equipment generating capacity prediction unit;
The photovoltaic equipment power generation amount prediction unit receives the solar radiation intensity and the cloud layer thickness transmitted by the meteorological data acquisition unit and the shielding coefficient transmitted by the three-dimensional simulation unit, predicts the power generation amount of the photovoltaic power generation equipment in any period of time based on the receiving information, and transmits a prediction result to the distributed power generation equipment power supply amount prediction module;
the distributed power generation equipment power supply quantity prediction module is used for predicting the real-time power supply quantity of each distributed power generation equipment;
The distributed power generation equipment power supply quantity prediction module comprises a wind power generation equipment power generation quantity prediction unit, a photovoltaic power generation equipment real-time power supply quantity prediction unit and a wind power generation equipment real-time power supply quantity prediction unit;
The wind power generation equipment power generation amount prediction unit receives the wind speed transmitted by the meteorological data acquisition unit, predicts the power generation amount of the wind power generation equipment in any period of time based on the received information, and transmits a prediction result to the wind power generation equipment real-time power supply amount prediction unit;
the photovoltaic power generation equipment real-time power supply quantity prediction unit is used for receiving a prediction result transmitted by the photovoltaic power generation equipment power generation quantity prediction unit, predicting the real-time power supply quantity of the photovoltaic power generation equipment by combining the aging condition of the photovoltaic power generation equipment and the pollution condition of the photovoltaic panel, and transmitting the prediction result to the micro-grid power supply mode determination module;
The wind power generation equipment real-time power supply quantity prediction unit receives a prediction result transmitted by the wind power generation equipment power generation quantity prediction unit, predicts the real-time power supply quantity of the wind power generation equipment by combining the ageing condition of the wind power generation equipment, and transmits the prediction result to the micro-grid power supply mode determination module;
the micro-grid power supply mode determining module is used for determining a real-time power supply mode of the micro-grid;
the micro-grid power supply mode determining module comprises a power supply time determining unit, a power supply time adjusting unit and a power supply mode determining unit;
The power supply time determining unit is used for receiving prediction results transmitted by the photovoltaic power generation equipment real-time power supply quantity predicting unit and the wind power generation equipment real-time power supply quantity predicting unit respectively, determining a time point when the energy storage battery in each distributed power generation equipment reaches the full power state for the first time if the received prediction results are equal to the corresponding electric energy when the energy storage battery reaches the full power state respectively, determining a first power supply time period based on the determination results, and transmitting the determination results to the power supply time adjusting unit;
The power supply time adjustment unit receives the determination result transmitted by the power supply time determination unit, adjusts the first power supply time period by combining the power supply amounts of the emergency power supply object and the fixed power supply object in the first power supply time period, and transmits the adjusted first power supply time period to the power supply mode determination unit;
The power supply mode determining unit receives the adjustment result transmitted by the power supply time adjusting unit, switches the power supply modes of the corresponding power supply object and the fixed power supply object of the micro-grid at the power supply end point of the adjusted first power supply time period based on the receiving information, and transmits the switching result to the control module;
the control module is used for controlling the running conditions of the output circuit breaker and the input circuit breaker of each distributed power generation device in real time.
4. A micro-grid power data control system based on big data according to claim 3, wherein: the control module receives the switching result transmitted by the power supply mode determining unit, and controls the operation conditions of the output circuit breaker and the input circuit breaker of each distributed power generation device in real time based on the receiving information.
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