CN117639040A - Parking lot energy storage system and method based on photovoltaic power generation - Google Patents
Parking lot energy storage system and method based on photovoltaic power generation Download PDFInfo
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Abstract
The invention relates to a parking lot energy storage system and method based on photovoltaic power generation, and belongs to the technical field of photovoltaic energy sources. Wherein the method comprises the following steps: historical weather data and historical power generation amount data of a parking lot are obtained, and the power generation amount per hour is obtained through prediction by a grid model trained by a neural network tool. And acquiring electricity consumption data of the parking lot, and processing the generated energy per hour and the electricity consumption data of the parking lot to obtain a photovoltaic electricity consumption model of the parking lot. And acquiring the day weather data and the storage battery energy storage data, and processing the day weather data and the storage battery energy storage data through a photovoltaic electricity consumption model of the parking lot to obtain a control instruction. According to the control instruction, the control system controls the off-grid switch and the charge and discharge functions of the storage battery. And acquiring real-time energy storage data of the storage battery through the battery management system, uploading the data to the control system, and correcting the control instruction. The operation cost of the parking lot can be reduced, and the energy utilization efficiency is improved.
Description
Technical Field
The invention belongs to the technical field of new energy, and particularly relates to a parking lot energy storage system and method based on photovoltaic power generation.
Background
The traditional parking lot electricity is mainly supplied by a power grid, and most of the power grid is from traditional energy sources such as coal, fuel oil and the like, so that the environment is seriously polluted, and the risk of energy shortage exists. With the continuous development of the technical field of new energy, the photovoltaic power generation energy storage system gradually becomes a research hot spot, the system is applied to a parking lot, the working mode of the parking lot is switched through the prediction of the generated energy and the analysis of the real-time state of a storage battery, the operation cost of the parking lot can be reduced, the dependence on traditional energy is effectively reduced, and the development of renewable energy and energy-saving and environment-friendly technology is promoted.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a parking lot energy storage method based on photovoltaic power generation, which is implemented by the following technical scheme:
s1: setting an initial weight W j Using the formulaAnd->Correcting the initial weight to construct a BP neural network model, wherein eta is a learning step length, E is a square error, K is the number of samples (K=1, 2, …, N), i is a neural output node, historical weather data and parking lot historical generating capacity data are obtained, and the historical weather data and the parking lot historical generating capacity data are predicted through the BP neural network model to obtain generating capacity per hour;
s2: acquiring electricity consumption data of a parking lot, processing the electricity consumption data of the parking lot by using an ARIMA model to obtain electricity consumption of the parking lot per hour, and constructing a photovoltaic electricity consumption model of the parking lot according to the electricity generation amount per hour and the electricity consumption of the parking lot per hour;
s3: acquiring the day weather data through a weather sensor, and using a formulaAnd P t ＝FP t- 1 F T The +Q processes the day weather data to eliminate errors, storage battery energy storage data are obtained, and the day weather data and the storage battery energy storage data are processed through the parking lot photovoltaic electricity consumption model to obtain a control instruction;
s4: according to the control instruction, the control system controls the off-grid switch and the charging and discharging functions of the storage battery;
s5: and acquiring the real-time energy storage data of the storage battery through a battery management system, uploading the data to the control system, and correcting the control instruction.
Specifically, the specific implementation steps of the S1 are as follows:
s101: analyzing the historical weather data to obtain weather meteorological data, temperature data, month data and time data, and carrying out normalization processing on the weather data, the temperature data, the month data and the time data to obtain normalized values;
s102: setting an initial weight W j Correcting the initial weight to construct a BP neural network model, acquiring historical weather data and parking lot historical power generation amount data, and predicting the historical weather data and the parking lot historical power generation amount data through the BP neural network model to obtain power generation amount per hour;
s103: and placing the test set into the training model to obtain the hourly power generation amount.
Specifically, the specific implementation steps of S2 are as follows:
s201: analyzing the parking lot electricity consumption data to obtain parking lot historical electricity consumption data, real-time electricity price and storage battery energy storage data, and processing the parking lot historical electricity consumption data through the ARIMA model to obtain the hourly electricity consumption of the parking lot;
s202: using the formula value=f L -SOC*C-F P +e obtains the charge quantity needed to charge the battery pack, wherein Value is the charge quantity, F L Is the power consumption per hour of the parking lot, the SOC is the residual battery power, F P The power generation amount per hour is the power generation amount per hour, and C is the capacity of an energy storage system;
s203: and constructing a photovoltaic power consumption model of the parking lot according to the charging electric quantity, when the charging electric quantity is smaller than 80%, using a storage battery and a photovoltaic system to supply power to a load, when the charging electric quantity is larger than 80%, judging the working state of the photovoltaic system, if the predicted value of the power generation amount per hour is larger than the predicted value of the power consumption per hour of the parking lot, using the photovoltaic system to supply power to the load and the storage battery, if the predicted value of the power generation amount per hour is smaller than the predicted value of the power consumption per hour of the parking lot, detecting the real-time electricity price, if the real-time electricity price is in a valley stage, using a power grid to supply power to the load, and if the real-time electricity price is in a level stage, only supplying power to the load.
Specifically, the specific implementation steps of S3 are as follows:
s301: acquiring the current day weather data through a weather sensor, and using a formulaAnd P t ＝FP t-1 F T The +Q processes the weather data of the current day to eliminate errors, wherein F is a state transition matrix, B is a control matrix, P is a covariance matrix, and Q is a noise function;
s302: processing the day weather data by using the BP network model to obtain the day power generation amount per hour, obtaining the day parking lot power consumption per hour by using the ARIMA model, and obtaining the storage battery real-time energy storage data by using the battery management system;
s303: and processing the power generation amount per hour of the current day parking lot, the power consumption per hour of the current day parking lot and the real-time energy storage data of the storage battery by using the parking lot photovoltaic power consumption model to obtain a control instruction and uploading the control instruction to a main control system.
Specifically, the specific implementation steps of S5 are as follows:
s501: obtaining the battery residual electric quantity SOC of the storage battery through the battery management system, and uploading the battery residual electric quantity SOC to a control system when the SOC value is smaller than 10%;
s502: the control system sends out a signal to stop the power supply of the storage battery to the load and analyze the real-time electricity price, if the storage battery is in a valley stage, the power grid is used for supplying power to the load and the storage battery, and if the storage battery is in a level stage, only the load is supplied with power.
Specifically, the algorithm used in S1 includes a prediction function, a loss function and a learning rate, where the prediction function isWherein Y is a predictive function value, i is a sample number, W is a model parameter, X is an input value, B is a characteristic term, and the loss function is +.>Where Yi is a predicted value, Y is a standard value, and N is an input number.
Specifically, the function of the ARIMA model used in S2 is defined as:wherein X is t Is the observed value at time t, p is the autoregressive term number, q is the moving average term number, d is the number of differences, L is the hysteresis operator, ε t Is an error phi i Is an autoregressive coefficient, θ i Is the white noise figure, i is the number of samples.
In a second aspect, the present invention provides a parking lot energy storage system based on photovoltaic power generation, operating using a method as described above, characterized in that it comprises the following modules:
the system comprises a data acquisition module, a data processing module, a battery management module and a main control module, wherein the data acquisition module acquires historical weather data, historical power generation data of a parking lot, power utilization data of the parking lot and current day weather data and uploads the data to the data processing module;
the data processing module processes the historical weather data and the historical power generation amount data of the parking lot to obtain power generation amount per hour, a parking lot photovoltaic power utilization model is built according to the power generation amount per hour and the power utilization data per hour of the parking lot, and the current day weather data and the storage battery energy storage data are processed through the parking lot photovoltaic power utilization model to obtain control instructions and uploaded to the main control module;
the main control module controls the off-grid switch and the charging and discharging functions of the storage battery according to the control instruction;
and the battery management module acquires the battery residual capacity of the storage battery and uploads the battery residual capacity to the control system, and when the battery residual capacity is smaller than a preset alarm value, the control instruction is corrected.
The beneficial effects of the invention are as follows:
(1) By using the photovoltaic power generation energy storage technology to carry out intelligent management on the parking lot, the running state of the system can be monitored and controlled in real time, and the efficient management and utilization of energy sources are realized.
(2) By constructing the photovoltaic power consumption model of the parking lot, the corresponding working mode can be quickly switched, and the operation cost of the parking lot is reduced.
Drawings
The present invention is further described below with reference to the accompanying drawings for the convenience of understanding by those skilled in the art.
FIG. 1 is a schematic flow chart of a parking lot energy storage method based on photovoltaic power generation;
fig. 2 is a block diagram of a parking lot energy storage system based on photovoltaic power generation in the present invention.
Detailed Description
In order to further describe the technical means and effects adopted by the invention for achieving the preset aim, the following detailed description is given below of the specific implementation, structure, characteristics and effects according to the invention with reference to the attached drawings and the preferred embodiment.
Referring to fig. 1, a flow chart of a parking lot energy storage method based on photovoltaic power generation includes the following steps:
s1: setting an initial weight W j Using the formulaAnd->Correcting the initial weight to construct a BP neural network model, wherein eta is a learning step length, E is a square error, K is the number of samples (K=1, 2, …, N), i is a neural output node, historical weather data and parking lot historical generating capacity data are obtained, and the historical weather data and the parking lot historical generating capacity data are predicted through the BP neural network model to obtain generating capacity per hour;
s2: acquiring electricity consumption data of a parking lot, processing the electricity consumption data of the parking lot by using an ARIMA model to obtain electricity consumption of the parking lot per hour, and constructing a photovoltaic electricity consumption model of the parking lot according to the electricity generation amount per hour and the electricity consumption of the parking lot per hour;
s3: acquiring the day weather data through a weather sensor, and using a formulaAnd P t ＝FP t- 1 F T The +Q processes the day weather data to eliminate errors, storage battery energy storage data are obtained, and the day weather data and the storage battery energy storage data are processed through the parking lot photovoltaic electricity consumption model to obtain a control instruction;
s4: according to the control instruction, the control system controls the off-grid switch and the charging and discharging functions of the storage battery;
s5: and acquiring the real-time energy storage data of the storage battery through a battery management system, uploading the data to the control system, and correcting the control instruction.
Specifically, the specific implementation steps of the S1 are as follows:
s101: analyzing the historical weather data to obtain weather meteorological data, temperature data, month data and time data, and carrying out normalization processing on the weather data, the temperature data, the month data and the time data to obtain normalized values;
s102: setting an initial weight W j Correcting the initial weight to construct a BP neural network model, acquiring historical weather data and parking lot historical power generation amount data, and predicting the historical weather data and the parking lot historical power generation amount data through the BP neural network model to obtain power generation amount per hour;
s103: and placing the test set into the training model to obtain the hourly power generation amount.
Step S2 and step S3 relate to a data processing module and a control module, the collected data are processed through a BP neural network model and an ARIMA model to obtain the charging electric quantity required to charge a battery pack, and the charging electric quantity is uploaded to a control system to obtain a control instruction, wherein the step S2 specifically comprises the following steps:
s201: analyzing the parking lot electricity consumption data to obtain parking lot historical electricity consumption data, real-time electricity price and storage battery energy storage data, and processing the parking lot historical electricity consumption data through the ARIMA model to obtain the hourly electricity consumption of the parking lot;
s202: using the formula value=f L -SOC*C-F P +e obtains the charge quantity needed to charge the battery pack, wherein Value is the charge quantity, F L Is the power consumption per hour of the parking lot, the SOC is the residual battery power, F P The power generation amount per hour is the power generation amount per hour, and C is the capacity of an energy storage system;
s203: and constructing a photovoltaic power consumption model of the parking lot according to the charging electric quantity, when the charging electric quantity is smaller than 80%, using a storage battery and a photovoltaic system to supply power to a load, when the charging electric quantity is larger than 80%, judging the working state of the photovoltaic system, if the predicted value of the power generation amount per hour is larger than the predicted value of the power consumption per hour of the parking lot, using the photovoltaic system to supply power to the load and the storage battery, if the predicted value of the power generation amount per hour is smaller than the predicted value of the power consumption per hour of the parking lot, detecting the real-time electricity price, if the real-time electricity price is in a valley stage, using a power grid to supply power to the load, and if the real-time electricity price is in a level stage, only supplying power to the load.
Specifically, the specific implementation steps of S3 are as follows:
s301: acquiring the current day weather data through a weather sensor, and using a formulaAnd P t ＝FP t-1 F T The +Q processes the weather data of the current day to eliminate errors, wherein F is a state transition matrix, B is a control matrix, P is a covariance matrix, and Q is a noise function;
s302: processing the day weather data by using the BP network model to obtain the day power generation amount per hour, obtaining the day parking lot power consumption per hour by using the ARIMA model, and obtaining the storage battery real-time energy storage data by using the battery management system;
s303: and processing the power generation amount per hour of the current day parking lot, the power consumption per hour of the current day parking lot and the real-time energy storage data of the storage battery by using the parking lot photovoltaic power consumption model to obtain a control instruction and uploading the control instruction to a main control system.
S501: obtaining the battery residual electric quantity SOC of the storage battery through the battery management system, and uploading the battery residual electric quantity SOC to a control system when the SOC value is smaller than 10%;
s502: the control system sends out a signal to stop the power supply of the storage battery to the load and analyze the real-time electricity price, if the storage battery is in a valley stage, the power grid is used for supplying power to the load and the storage battery, and if the storage battery is in a level stage, only the load is supplied with power.
Specifically, the algorithm used in S1 includes a prediction function, a loss function and a learning rate, where the prediction function isWherein Y is a predictive function value, i is a sample number, and W is a modelThe parameters, X is the input value, B is the characteristic term, the loss function is +.>Where Yi is a predicted value, Y is a standard value, and N is an input number.
Specifically, the function of the ARIMA model used in S2 is defined as:wherein X is t Is the observed value at time t, p is the autoregressive term number, q is the moving average term number, d is the number of differences, L is the hysteresis operator, ε t Is an error phi i Is an autoregressive coefficient, θ i Is the white noise figure, i is the number of samples.
In a second aspect, the present invention provides a parking lot energy storage system based on photovoltaic power generation, operating using a method as described above, characterized in that it comprises the following modules:
the system comprises a data acquisition module, a data processing module, a battery management module and a main control module, wherein the data acquisition module acquires historical weather data, historical power generation data of a parking lot, power utilization data of the parking lot and current day weather data and uploads the data to the data processing module;
the data processing module processes the historical weather data and the historical power generation amount data of the parking lot to obtain power generation amount per hour, a parking lot photovoltaic power utilization model is built according to the power generation amount per hour and the power utilization data per hour of the parking lot, and the current day weather data and the storage battery energy storage data are processed through the parking lot photovoltaic power utilization model to obtain control instructions and uploaded to the main control module;
the main control module controls the off-grid switch and the charging and discharging functions of the storage battery according to the control instruction;
and the battery management module acquires the battery residual capacity of the storage battery and uploads the battery residual capacity to the control system, and when the battery residual capacity is smaller than a preset alarm value, the control instruction is corrected.
The present invention is not limited to the above embodiments, but is capable of modification and variation in detail, and other modifications and variations can be made by those skilled in the art without departing from the scope of the present invention.
Claims (8)
1. The parking lot energy storage method based on photovoltaic power generation is characterized by comprising the following steps of:
s1: setting an initial weight W j Using the formulaAnd->Correcting the initial weight to construct a BP neural network model, wherein eta is a learning step length, E is a square error, K is the number of samples (K=1, 2, …, N), i is a neural output node, historical weather data and parking lot historical generating capacity data are obtained, and the historical weather data and the parking lot historical generating capacity data are predicted through the BP neural network model to obtain generating capacity per hour;
s2: acquiring electricity consumption data of a parking lot, processing the electricity consumption data of the parking lot by using an ARIMA model to obtain electricity consumption of the parking lot per hour, and constructing a photovoltaic electricity consumption model of the parking lot according to the electricity generation amount per hour and the electricity consumption of the parking lot per hour;
s3: acquiring the day weather data through a weather sensor, and using a formulaAnd P t ＝FP t-1 F T +Q versus the current day weather dataProcessing and eliminating errors, obtaining storage battery energy storage data, and processing the current day weather data and the storage battery energy storage data through the parking lot photovoltaic electricity consumption model to obtain a control instruction;
s4: according to the control instruction, the control system controls the off-grid switch and the charging and discharging functions of the storage battery;
s5: and acquiring the real-time energy storage data of the storage battery through a battery management system, uploading the data to the control system, and correcting the control instruction.
2. The method according to claim 1, wherein the specific implementation step of S1 is:
s101: analyzing the historical weather data to obtain weather meteorological data, temperature data, month data and time data, and carrying out normalization processing on the weather data, the temperature data, the month data and the time data to obtain normalized values;
s102: setting an initial weight W j Correcting the initial weight to construct a BP neural network model, acquiring historical weather data and parking lot historical power generation amount data, and predicting the historical weather data and the parking lot historical power generation amount data through the BP neural network model to obtain power generation amount per hour;
s103: and placing the test set into the training model to obtain the hourly power generation amount.
3. The method according to claim 1, wherein the specific implementation step of S2 is:
s201: analyzing the parking lot electricity consumption data to obtain parking lot historical electricity consumption data, real-time electricity price and storage battery energy storage data, and processing the parking lot historical electricity consumption data through the ARIMA model to obtain the hourly electricity consumption of the parking lot;
s202: using the formula value=f L -SOC*C-F P +e obtains the charge quantity needed to charge the battery pack, wherein Value is the charge quantity, F L Is saidThe power consumption of parking lot per hour, SOC is the residual power of battery, F P The power generation amount per hour is the power generation amount per hour, and C is the capacity of an energy storage system;
s203: and constructing a photovoltaic power consumption model of the parking lot according to the charging electric quantity, when the charging electric quantity is smaller than 80%, using a storage battery and a photovoltaic system to supply power to a load, when the charging electric quantity is larger than 80%, judging the working state of the photovoltaic system, if the predicted value of the power generation amount per hour is larger than the predicted value of the power consumption per hour of the parking lot, using the photovoltaic system to supply power to the load and the storage battery, if the predicted value of the power generation amount per hour is smaller than the predicted value of the power consumption per hour of the parking lot, detecting the real-time electricity price, if the real-time electricity price is in a valley stage, using a power grid to supply power to the load, and if the real-time electricity price is in a level stage, only supplying power to the load.
4. The method according to claim 1, wherein the specific implementation step of S3 is:
s301: acquiring the current day weather data through a weather sensor, and using a formulaAnd P t ＝FP t-1 F T The +Q processes the weather data of the current day to eliminate errors, wherein F is a state transition matrix, B is a control matrix, P is a covariance matrix, and Q is a noise function;
s302: processing the day weather data by using the BP network model to obtain the day power generation amount per hour, obtaining the day parking lot power consumption per hour by using the ARIMA model, and obtaining the storage battery real-time energy storage data by using the battery management system;
s303: and processing the power generation amount per hour of the current day parking lot, the power consumption per hour of the current day parking lot and the real-time energy storage data of the storage battery by using the parking lot photovoltaic power consumption model to obtain a control instruction and uploading the control instruction to a main control system.
5. The method according to claim 1, wherein the specific implementation step of S5 is:
s501: obtaining the battery residual electric quantity SOC of the storage battery through the battery management system, and uploading the battery residual electric quantity SOC to a control system when the SOC value is smaller than 10%;
s502: the control system sends out a signal to stop the power supply of the storage battery to the load and analyze the real-time electricity price, if the storage battery is in a valley stage, the power grid is used for supplying power to the load and the storage battery, and if the storage battery is in a level stage, only the load is supplied with power.
6. The method according to claim 2, wherein the algorithm used in S1 includes a prediction function, a loss function, and a learning rate, and the prediction function isWherein Y is a predictive function value, i is a sample number, W is a model parameter, X is an input value, B is a characteristic term, and the loss function is +.>Where Yi is a predicted value, Y is a standard value, and N is an input number.
7. A method according to claim 3, characterized in that the function of the ARIMA model used in S2 is defined as:wherein X is t Is the observed value at time t, p is the autoregressive term number, q is the moving average term number, d is the number of differences, L is the hysteresis operator, ε t Is an error phi i Is an autoregressive coefficient, θ i Is the white noise figure, i is the number of samples.
8. A photovoltaic power generation-based parking lot energy storage system, performed using the method of any one of claims 1-5, comprising the following modules:
the system comprises a data acquisition module, a data processing module, a battery management module and a main control module, wherein the data acquisition module acquires historical weather data, historical power generation data of a parking lot, power utilization data of the parking lot and current day weather data and uploads the data to the data processing module;
the data processing module processes the historical weather data and the historical power generation amount data of the parking lot to obtain power generation amount per hour, a parking lot photovoltaic power utilization model is built according to the power generation amount per hour and the power utilization data per hour of the parking lot, and the current day weather data and the storage battery energy storage data are processed through the parking lot photovoltaic power utilization model to obtain control instructions and uploaded to the main control module;
the main control module controls the off-grid switch and the charging and discharging functions of the storage battery according to the control instruction;
and the battery management module acquires the battery residual capacity of the storage battery and uploads the battery residual capacity to the control system, and when the battery residual capacity is smaller than a preset alarm value, the control instruction is corrected.
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