CN119239361A - A photovoltaic storage and charging integrated charging station management system based on energy control algorithm - Google Patents
A photovoltaic storage and charging integrated charging station management system based on energy control algorithm Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/50—Charging stations characterised by energy-storage or power-generation means
- B60L53/51—Photovoltaic means
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/50—Charging stations characterised by energy-storage or power-generation means
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
- B60L53/60—Monitoring or controlling charging stations
- B60L53/62—Monitoring or controlling charging stations in response to charging parameters, e.g. current, voltage or electrical charge
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Abstract
The invention discloses an optical storage and charging integrated charging station management system based on an energy regulation algorithm, and belongs to the technical field of charging station energy management; the energy management system comprises a photovoltaic power generation device, an energy storage device, a charging device and an energy management model, wherein the energy management model comprises a data acquisition module, a data analysis module, an energy control module and an energy scheduling execution module, the photovoltaic power generation device is in bidirectional connection with the energy management model, the energy storage device is in bidirectional connection with the energy management model, and the charging device is in bidirectional connection with the energy management model. The invention can greatly improve the charging efficiency and the energy utilization rate of the light storage and charging integrated charging station.
Description
Technical Field
The invention relates to the technical field of charging station energy management, in particular to an optical storage and charging integrated charging station management system based on an energy regulation algorithm.
Background
With the popularization of electric vehicles and the development of renewable energy sources, an optical storage and charging integrated charging station is used as an important infrastructure for connecting the electric vehicles and the renewable energy sources, and the optimization and the intellectualization of an energy management system become a hot spot of current research. The conventional charging station management system has the following disadvantages:
The energy utilization efficiency is low, and the photovoltaic power generation amount and the electric vehicle charging requirement cannot be accurately predicted, so that the energy configuration is unreasonable, and the energy utilization efficiency is low;
the traditional control strategy is often based on fixed rules or simple threshold judgment, cannot be optimally adjusted according to real-time data and future prediction results, and further limits the improvement of energy utilization efficiency;
The uncertainty in handling the uncertainty in the ability to handle, photovoltaic power generation and electric vehicle charging requirements presents challenges to the stable operation of the charging station system.
Therefore, the invention provides an optical storage and charging integrated charging station management system based on an energy regulation algorithm, which effectively improves the energy utilization rate and the stability.
Disclosure of Invention
Aiming at the defects of the technology, the invention discloses an optical storage and charging integrated charging station management system based on an energy regulation algorithm, which has low energy utilization rate and poor stability, and can effectively improve the energy utilization rate and stability.
An optical storage and charging integrated charging station management system based on an energy regulation algorithm, comprising:
the photovoltaic power generation device converts solar energy into electric energy through a photovoltaic cell panel;
The energy storage device is used for storing the electric energy generated by the photovoltaic power generation device or the electric energy of the power grid and managing the charging and discharging processes of the energy storage device through a battery management system BMS;
The charging device provides charging service for the electric automobile and comprises a direct current charging terminal and an alternating current charging terminal;
the energy management model realizes cooperative control of the photovoltaic power generation device, the energy storage device and the charging device through an energy regulation algorithm, wherein the energy management model comprises a data acquisition module, a data analysis module, an energy control module and an energy scheduling execution module;
The data acquisition module acquires the operation data information of the device in real time through a sensor network model, wherein the operation data information at least comprises current, voltage, power and the operation state of the device;
The system comprises a data analysis module, an improved support vector machine, a training optimization unit and a prediction evaluation unit, wherein the data analysis module is used for carrying out data analysis, predicting energy demands and optimizing energy distribution through the improved support vector machine, the improved support vector machine comprises a data preprocessing unit, a feature selection extraction unit, a model construction unit, a classification unit, a training optimization unit and a prediction evaluation unit, the signal output end of the data preprocessing unit is connected with the signal input end of the feature selection extraction unit, the signal output end of the feature selection extraction unit is connected with the signal input end of the model construction unit, the signal output end of the model construction unit is connected with the signal input end of the classification unit, the signal output end of the classification unit is connected with the signal input end of the training optimization unit, and the signal output end of the training optimization unit is connected with the signal input end of the prediction evaluation unit;
The energy control module carries out secondary prediction on the data analysis result in real time through model prediction control according to the analysis result of the data analysis module, and formulates an optimal charge-discharge strategy according to the prediction result;
The energy scheduling execution module is used for executing energy scheduling tasks according to the strategy formulated by the energy control module;
The photovoltaic power generation device is in bidirectional connection with the energy management model, the energy storage device is in bidirectional connection with the energy management model, the charging device is in bidirectional connection with the energy management model, the signal output end of the data acquisition module is connected with the signal input end of the data analysis module, the signal output end of the data analysis module is connected with the signal input end of the energy control module, and the signal output end of the energy control module is connected with the signal input end of the energy scheduling execution module.
The data acquisition module comprises a first data acquisition unit, a second data acquisition unit and a third data acquisition unit, wherein the first data acquisition unit acquires data information of the photovoltaic power generation device through a first sensor network, the data information at least comprises photovoltaic power generation power, generated energy, photovoltaic panel temperature and illumination intensity, the first sensor network at least comprises a voltage sensor, a current sensor, a temperature sensor and a photoresistor, the second data acquisition unit acquires data information of the energy storage device through a second sensor network, the data information at least comprises a charge state, a charge-discharge current, a charge-discharge voltage and temperature, the third data acquisition unit acquires data information of the charging device through a third sensor network, the data information at least comprises charging power, charging time, charging current and working state of charging equipment, and the first sensor network, the second sensor network and the third sensor network adopt a network topology structure and form a star-shaped network model. As a further embodiment of the present invention, the input samples of the model building unit are:
The optimal problem of classifying the hyperplane in the classifying unit is expressed as follows:
In equation (4), f is the optimal decision. As a further embodiment of the present invention, the improved support vector machine classification process is that SVM1 is used as a root node of a binary tree, test samples of the 1 st class of data are decided out, and samples not belonging to the 1 st class are classified by SVM2 until SVM And deciding the q-th sample. In the formula (3) of the present invention,In order to be a lagrange multiplier,Is an optimal objective function;
As a further embodiment of the invention, the model predictive control comprises a prediction unit, an optimization unit, a control unit, a feedback adjustment unit and a man-machine interaction unit; the signal output end of the prediction unit is connected with the signal input end of the optimization unit, the signal output end of the optimization unit is connected with the signal input end of the control unit, the signal output end of the control unit is connected with the signal input end of the feedback adjustment unit, and the signal output end of the control unit is connected with the signal input end of the man-machine interaction unit; the system comprises a prediction unit, a feedback adjustment unit, a man-machine interaction unit, a display screen and a keyboard, wherein the prediction unit is used for carrying out long-term prediction and short-term prediction on the future according to data acquired in real time, the long-term prediction time is 1-7 days, the short-term prediction time is 0.5-24 hours, a prediction result comprises photovoltaic power generation capacity, electric quantity change of an energy storage battery and load demand of a charging pile, the optimization unit is used for formulating an optimal charging and discharging strategy based on the prediction data, the control unit is used for selecting a first element from an optimal control sequence obtained through optimization as a current control input and applying the first element to the model, the output of a photovoltaic power generation system, the charging and discharging power of the energy storage system and the load distribution of the charging pile are adjusted according to the current control input, the feedback adjustment unit is used for monitoring the state of the system again when the next control period starts, new data are acquired, and a new round of prediction and optimization is carried out, and the man-machine interaction unit is used for monitoring the running state, checking the prediction result and the optimization strategy through the display screen and the keyboard.
As a further embodiment of the present invention, the control unit controls the energy flow direction of the photovoltaic power generation device, the energy storage device and the charging device;
the energy scheduling of the photovoltaic power generation device meets the following conditions:
The method comprises the steps of providing the total power for charging the energy storage device, storing surplus electric energy to the energy storage device or grid connection when the photovoltaic power generation power of the photovoltaic power generation device is larger than the total requirements of the charging device and the energy storage device, supplying the electric energy of the energy storage device to the charging device preferentially when the photovoltaic power generation power of the photovoltaic power generation device is insufficient, supplementing the insufficient part by a power grid, and carrying out intelligent scheduling by the charging device according to the charging requirement of the electric automobile and the energy state of a current system, wherein the electric energy of the photovoltaic power generation and the energy storage device is preferentially used for charging the electric automobile.
According to the invention, the energy flow direction is that the photovoltaic power generation device converts sunlight into electric energy, the generated direct current is sent to the photovoltaic inverter, the photovoltaic inverter converts the direct current into alternating current, the converted alternating current is sent to the alternating current bus, the electric energy is sent to the power grid through the transformer, the power grid and the alternating current bus are in bidirectional transmission, the electric energy is transmitted to the energy storage device through the energy storage converter, the alternating current bus and the energy storage converter are in bidirectional transmission, the energy storage converter and the energy storage device are in bidirectional transmission, and the electric energy directly supplies power for the charging pile.
Positive beneficial effects
An optical storage and charging integrated charging station management system based on an energy regulation algorithm constructs an accurate energy prediction model through an improved support vector machine, realizes accurate prediction of photovoltaic power generation amount and electric vehicle charging requirements, provides an important reference basis for model prediction control, calculates an optimal control strategy based on the current system state and future prediction results by the model prediction control, realizes coordination optimization among photovoltaic power generation, energy storage and charging, improves energy utilization efficiency, and enhances system stability and reliability.
Drawings
For a clearer description of embodiments of the invention or of solutions in the prior art, the drawings that are necessary for the description of the embodiments or of the prior art will be briefly described, it being apparent that the drawings in the description below are only some embodiments of the invention, from which, without inventive faculty, other drawings can be obtained for a person skilled in the art, in which:
fig. 1 is a schematic diagram of the overall architecture of an optical storage and charging integrated charging station management system based on an energy regulation algorithm;
FIG. 2 is a schematic diagram of an energy management model of an integrated optical storage and charging station management system based on an energy regulation algorithm;
FIG. 3 is a schematic diagram of the working principle of an improved support vector machine of the optical storage and charging integrated charging station management system based on an energy regulation algorithm;
FIG. 4 is a schematic diagram of the operation of the improved support vector machine classification of the light storage and charging integrated charging station management system based on the energy regulation algorithm of the present invention;
FIG. 5 is a schematic diagram of the operation of model predictive control of an optical storage and charging integrated charging station management system based on an energy regulation algorithm;
fig. 6 is an energy flow chart of an optical storage and charging integrated charging station management system based on an energy regulation algorithm.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
An optical storage and charging integrated charging station management system based on an energy regulation algorithm, comprising:
the photovoltaic power generation device converts solar energy into electric energy through a photovoltaic cell panel;
The energy storage device is used for storing the electric energy generated by the photovoltaic power generation device or the electric energy of the power grid and managing the charging and discharging processes of the energy storage device through a battery management system BMS;
The charging device provides charging service for the electric automobile and comprises a direct current charging terminal and an alternating current charging terminal;
the energy management model realizes cooperative control of the photovoltaic power generation device, the energy storage device and the charging device through an energy regulation algorithm, wherein the energy management model comprises a data acquisition module, a data analysis module, an energy control module and an energy scheduling execution module;
The data acquisition module acquires the operation data information of the device in real time through a sensor network model, wherein the operation data information at least comprises current, voltage, power and the operation state of the device;
The system comprises a data analysis module, an improved support vector machine, a training optimization unit and a prediction evaluation unit, wherein the data analysis module is used for carrying out data analysis, predicting energy demands and optimizing energy distribution through the improved support vector machine, the improved support vector machine comprises a data preprocessing unit, a feature selection extraction unit, a model construction unit, a classification unit, a training optimization unit and a prediction evaluation unit, the signal output end of the data preprocessing unit is connected with the signal input end of the feature selection extraction unit, the signal output end of the feature selection extraction unit is connected with the signal input end of the model construction unit, the signal output end of the model construction unit is connected with the signal input end of the classification unit, the signal output end of the classification unit is connected with the signal input end of the training optimization unit, and the signal output end of the training optimization unit is connected with the signal input end of the prediction evaluation unit;
The energy control module carries out secondary prediction on the data analysis result in real time through model prediction control according to the analysis result of the data analysis module, and formulates an optimal charge-discharge strategy according to the prediction result;
The energy scheduling execution module is used for executing energy scheduling tasks according to the strategy formulated by the energy control module;
The photovoltaic power generation device is in bidirectional connection with the energy management model, the energy storage device is in bidirectional connection with the energy management model, the charging device is in bidirectional connection with the energy management model, the signal output end of the data acquisition module is connected with the signal input end of the data analysis module, the signal output end of the data analysis module is connected with the signal input end of the energy control module, and the signal output end of the energy control module is connected with the signal input end of the energy scheduling execution module.
The data acquisition module comprises a first data acquisition unit, a second data acquisition unit and a third data acquisition unit, wherein the first data acquisition unit acquires data information of the photovoltaic power generation device through a first sensor network, the data information at least comprises photovoltaic power generation power, power generation capacity, photovoltaic panel temperature and illumination intensity, the first sensor network at least comprises a voltage sensor, a current sensor, a temperature sensor and a photoresistor, the second data acquisition unit acquires data information of the energy storage device through a second sensor network, the data information at least comprises a charge state, a charge-discharge current, a charge-discharge voltage and temperature, the third data acquisition unit acquires data information of the charging device through a third sensor network, the data information at least comprises charging power, charging time, charging current and working state of charging equipment, and the first sensor network, the second sensor network and the third sensor network adopt a mesh network topology structure and form a star-shaped sensor network model through the star-shaped network topology structure.
In a specific embodiment, the sensor network model performs data fusion on the data acquired by the first data acquisition unit, the second data acquisition unit and the third data acquisition unit, and provides data support for an energy regulation algorithm. The nodes in the mesh network topology structure are connected with each other through transmission paths, each node is connected with at least two other nodes, local faults do not affect the whole network due to the fact that paths among the nodes are relatively large, multiple communication channels exist among the nodes, an optimal path can be selected for information transmission to enable delay to be minimum, any node of the star network topology structure is only connected with a central node, therefore, a medium access control method is simple, an access protocol is very simple, network monitoring and management are easy, the central node can isolate connection lines one by one to detect and position faults, faults of a single connection point only affect one device, the whole network cannot be affected, and the central node provides service and network reconfiguration for each station. In the present invention, the input samples of the model building unit are:
The optimal problem of classifying the hyperplane in the classifying unit is expressed as follows:
Selecting a kernel function And penalty parameter A, constructing and solving an optimization problem:
solving to obtain an optimal solution SelectingIs less than the positive component of AFinally solving a decision function:
。
In a specific embodiment, the original expression of the optimal problem of classifying the hyperplane in the classification unit is:
The original expression for solving the optimization problem is:
the original expression of the final solving decision function is:
。
in a particular embodiment, input data is used to train a modified support vector machine, solve for the optimal weight vector m, bias terms a and b, and kernel function The method comprises the steps of calculating similarity between data points so as to find an optimal classification hyperplane, predicting the type of a new data point after the training of the improved support vector machine is completed, taking energy requirements as labels and training the improved support vector machine by using related feature vectors, formulating an energy distribution strategy based on the predicted energy requirements, adjusting energy supply in advance to increase power generation capacity or adjusting power grid dispatching to meet the requirements if the predicted energy requirements are higher in a certain time period, correspondingly reducing the energy supply to save cost if the predicted energy requirements are lower, and adjusting based on real-time prediction results to realize more efficient and sustainable energy management. In a specific embodiment, the improved support vector machine solves the machine learning problem under the condition of large samples, simplifies the common classification and regression problems, and solves the problems of dimension disasters and nonlinear separability by adopting a kernel function method, so that the computational complexity is not increased when mapping to a high-dimensional space. Since the final decision function of the support vector calculation method is determined by only a few support vectors, the complexity of the calculation depends on the number of support vectors, not the dimension of the sample space. The support vector machine algorithm uses relaxation variables to allow the distances of points to the classification plane to be insufficient to meet the original requirements, thereby avoiding the impact of the points on model learning.
In a specific embodiment, the workflow of the improved support vector machine is as follows:
the data preprocessing unit is used for collecting and arranging data information from the sensor network model, cleaning the collected data, removing abnormal values and noise, ensuring the accuracy and reliability of the data, and carrying out standardized or normalized processing on the data so as to improve the efficiency of subsequent data analysis;
Extracting key characteristics related to energy demand prediction and energy distribution optimization from the preprocessed data, wherein the characteristics at least comprise photovoltaic power generation power, energy storage device charge state, charging station historical load mode and weather forecast information;
Constructing a model for energy demand prediction and energy distribution optimization;
The class unit divides the energy demand into different classes by utilizing the classification capability of the improved support vector machine so as to carry out more refined energy distribution and management subsequently;
The model prediction method comprises the steps of training a preliminarily constructed improved support vector machine by using historical data, and optimizing the prediction performance of the model by adjusting model parameters, wherein in the training process, cross verification can be adopted to evaluate the generalization capability of the model, so as to avoid the occurrence of over-fitting or under-fitting phenomena;
The method comprises the steps of predicting new data by using a trained improved support vector machine, evaluating the prediction accuracy and stability of a model, comparing a prediction result with an actual situation, analyzing error sources and possible improvement directions of the model, and formulating an energy distribution strategy according to the prediction result to optimize the operation efficiency of the light storage and charging integrated charging station.
In a specific embodiment, the impact of different penalty parameters on the charging stake is shown in Table 1.
As can be seen from table 1, when the penalty coefficient has a value of 10.001, the energy utilization rate is the highest, and the charging efficiency is the highest.
In the invention, the improved support vector machine classification process is that SVM1 is used as the root node of a binary tree, test samples of the 1 st class of data are decided out, and samples which do not belong to the 1 st class are classified by SVM2 until the SVMAnd deciding the q-th sample. In a specific embodiment, the improved support vector machine is a support vector machine, a decision tree algorithm is added on the basis of the original support vector machine, the decision tree algorithm comprises a root node, a plurality of internal nodes and a plurality of leaf nodes, the leaf nodes correspond to decision results, each other node corresponds to an attribute test, a sample set contained in each node is divided into sub-nodes according to the results of the attribute test, the root node comprises a sample complete set, a determined test sequence corresponds to a path from the root node to each leaf node, the decision tree algorithm has strong learning capacity, the decision tree algorithm is used for carrying out decision making, namely, the root node is used for testing corresponding characteristic attributes in items to be classified, an output branch is selected according to the values of the characteristic attributes until the leaf nodes are reached, and the category stored in the leaf nodes is used as the decision result. The pruning of the decision tree algorithm is to prevent or reduce the occurrence of the over fitting phenomenon, the specific method of pruning is to remove leaf nodes which are too finely divided, enable the leaf nodes to fall back to a father node and change the father node into new leaf nodes, the pruning comprises pre-pruning and post-pruning, the pre-pruning is to prune the nodes when the decision tree is constructed, the nodes are evaluated in the decision tree construction process, if the nodes cannot be divided and can not be divided in a verification concentration mode any more to improve accuracy, the current nodes are taken as the leaf nodes, the post-pruning is to prune the nodes after the decision tree is generated, each node is evaluated from the leaf nodes of the decision tree in a layer-by-layer mode, and if the node is pruned, the node can be replaced by the leaf nodes if the verification concentration accuracy difference brought by pruning is not large or is obviously improved.
The model prediction control method comprises a prediction unit, an optimization unit, a control unit, a feedback adjustment unit and a man-machine interaction unit, wherein a signal output end of the prediction unit is connected with a signal input end of the optimization unit, a signal output end of the optimization unit is connected with a signal input end of the control unit, a signal output end of the control unit is connected with a signal input end of the feedback adjustment unit, a signal output end of the control unit is connected with a signal input end of the man-machine interaction unit, the prediction unit predicts the future for a long term and a short term according to data acquired in real time, the long term prediction time is 1-7 days, the short term prediction time is 0.5-24 h, the prediction result comprises photovoltaic power generation capacity, electric quantity change of an energy storage battery and load demand of a charging pile, the optimization unit makes an optimal charging and discharging strategy based on prediction data, the control unit selects a first element from an optimal control sequence obtained through optimization as a current control input, the current control input is applied to a model, the control input is adjusted according to the current control input, the output of a photovoltaic power generation system, the energy storage power storage system and the load of the charging pile is adjusted in a new state of the optimal system is displayed through the optimal charging system, and the monitoring system is updated, and the state of the monitoring system is adjusted in a new state is displayed when the monitoring system is in a monitor and the interaction state is updated, and the monitor is started, and the state of the monitoring system is adjusted. In a specific embodiment, the workflow of the model predictive control is:
the prediction unit collects real-time data of the photo-electricity storage and charging integrated charging station, wherein the real-time data at least comprises photovoltaic power generation amount, charging and discharging states of an energy storage system, charging requirements of a charging pile and load conditions of a power grid;
The optimization unit is used for formulating an optimal charge-discharge strategy according to the prediction result provided by the prediction unit, and solving the optimal charge-discharge strategy by an improved support vector machine and model prediction control in consideration of the fluctuation of photovoltaic power generation, the capacity limit of an energy storage system, the charging efficiency of a charging pile and the load balance of a power grid so as to realize the maximum utilization of energy and the minimization of cost;
Receiving an optimal charging and discharging strategy provided by an optimizing unit, converting the strategy into control instructions of a photovoltaic power generation device, an energy storage device and a charging device, and monitoring the running state of a system in real time to ensure the accurate execution of the control instructions;
The control strategy is adjusted in a necessary way according to the difference between the actual running state and the prediction result, and the adjusted strategy is fed back to the optimizing unit and the control unit so as to realize continuous optimization and control of the system;
the man-machine interaction unit displays real-time state information of the system, such as photovoltaic power generation amount, charging and discharging states of the energy storage system and charging states of the charging piles, receives control instructions or parameter settings input by a user, provides a user-friendly interface and interaction mode, and facilitates the user to monitor and manage the operation of the system.
In the invention, the control unit controls the energy flow directions of the photovoltaic power generation device, the energy storage device and the charging device;
the energy scheduling of the photovoltaic power generation device meets the following conditions:
The method comprises the steps of providing the total power for charging the energy storage device, storing surplus electric energy to the energy storage device or grid connection when the photovoltaic power generation power of the photovoltaic power generation device is larger than the total requirements of the charging device and the energy storage device, supplying the electric energy of the energy storage device to the charging device preferentially when the photovoltaic power generation power of the photovoltaic power generation device is insufficient, supplementing the insufficient part by a power grid, and carrying out intelligent scheduling by the charging device according to the charging requirement of the electric automobile and the energy state of a current system, wherein the electric energy of the photovoltaic power generation and the energy storage device is preferentially used for charging the electric automobile.
In a specific embodiment, the impact of the energy scheduling algorithm on the charge pile with integrated light storage and charging is shown in table 2.
As can be seen from table 2, the light storage and charging integrated charging pile optimized by the energy scheduling algorithm greatly improves the energy utilization rate and the charging efficiency.
In the invention, the energy flow direction is that the photovoltaic power generation device converts sunlight into electric energy, the generated direct current is sent to the photovoltaic inverter, the photovoltaic inverter converts the direct current into alternating current, the converted alternating current is sent to the alternating current bus, the electric energy is sent to the power grid through the transformer, the power grid and the alternating current bus are in bidirectional transmission, the electric energy is transmitted to the energy storage device through the energy storage converter, the alternating current bus and the energy storage converter are in bidirectional transmission, the energy storage converter and the energy storage device are in bidirectional transmission, and the electric energy directly supplies power for the charging pile.
While specific embodiments of the present invention have been described above, it will be understood by those skilled in the art that these specific embodiments are by way of example only, and that various omissions, substitutions, and changes in the form and details of the methods and systems described above may be made by those skilled in the art without departing from the spirit and scope of the invention. For example, it is within the scope of the present invention to combine the above-described method steps to perform substantially the same function in substantially the same way to achieve substantially the same result. Accordingly, the scope of the invention is limited only by the following claims.
Claims (7)
1. An optical storage and charging integrated charging station management system based on an energy regulation algorithm is characterized by comprising:
the photovoltaic power generation device converts solar energy into electric energy through a photovoltaic cell panel;
The energy storage device is used for storing the electric energy generated by the photovoltaic power generation device or the electric energy of the power grid and managing the charging and discharging processes of the energy storage device through a battery management system BMS;
The charging device provides charging service for the electric automobile and comprises a direct current charging terminal and an alternating current charging terminal;
the energy management model realizes cooperative control of the photovoltaic power generation device, the energy storage device and the charging device through an energy regulation algorithm, wherein the energy management model comprises a data acquisition module, a data analysis module, an energy control module and an energy scheduling execution module;
The data acquisition module acquires the operation data information of the device in real time through a sensor network model, wherein the operation data information at least comprises current, voltage, power and the operation state of the device;
The system comprises a data analysis module, an improved support vector machine, a training optimization unit and a prediction evaluation unit, wherein the data analysis module is used for carrying out data analysis, predicting energy demands and optimizing energy distribution through the improved support vector machine, the improved support vector machine comprises a data preprocessing unit, a feature selection extraction unit, a model construction unit, a classification unit, a training optimization unit and a prediction evaluation unit, the signal output end of the data preprocessing unit is connected with the signal input end of the feature selection extraction unit, the signal output end of the feature selection extraction unit is connected with the signal input end of the model construction unit, the signal output end of the model construction unit is connected with the signal input end of the classification unit, the signal output end of the classification unit is connected with the signal input end of the training optimization unit, and the signal output end of the training optimization unit is connected with the signal input end of the prediction evaluation unit;
The energy control module carries out secondary prediction on the data analysis result in real time through model prediction control according to the analysis result of the data analysis module, and formulates an optimal charge-discharge strategy according to the prediction result;
The energy scheduling execution module is used for executing energy scheduling tasks according to the strategy formulated by the energy control module;
The photovoltaic power generation device is in bidirectional connection with the energy management model, the energy storage device is in bidirectional connection with the energy management model, the charging device is in bidirectional connection with the energy management model, the signal output end of the data acquisition module is connected with the signal input end of the data analysis module, the signal output end of the data analysis module is connected with the signal input end of the energy control module, and the signal output end of the energy control module is connected with the signal input end of the energy scheduling execution module.
2. The optical storage and charging integrated charging station management system based on the energy regulation algorithm is characterized in that the data acquisition module comprises a first data acquisition unit, a second data acquisition unit and a third data acquisition unit, the first data acquisition unit acquires data information of the photovoltaic power generation device through a first sensor network, the data information at least comprises photovoltaic power generation power, power generation capacity, photovoltaic panel temperature and illumination intensity, the first sensor network at least comprises a voltage sensor, a current sensor, a temperature sensor and a photoresistor, the second data acquisition unit acquires data information of the energy storage device through a second sensor network, the data information at least comprises a charge state, a charge-discharge current, a charge-discharge voltage and a temperature, the third data acquisition unit acquires data information of the charging device through a third sensor network, the data information at least comprises charging power, charging time, a charge current and a working state of charging equipment, and the first sensor network, the second sensor network and the third sensor network adopt a topological structure and form a topological model through a star network.
3. The light storage and charging integrated charging station management system based on the energy regulation algorithm of claim 1, wherein the input samples of the model building unit are:
The optimal problem of classifying the hyperplane in the classifying unit is expressed as follows:
In the formula (3) of the present invention, In order to be a lagrange multiplier,Is an optimal objective function;
In the formula (4) of the present invention, Is an optimal decision.
4. The light storage and charging integrated charging station management system based on the energy regulation algorithm of claim 1, wherein the improved support vector machine classification process is characterized in that SVM1 is used as a root node of a binary tree, test samples of data class 1 are decided out, and samples which do not belong to class 1 are classified by SVM2 until SVMAnd deciding the q-th sample.
5. The light storage and charging integrated charging station management system based on the energy regulation algorithm of claim 1, wherein the model prediction control comprises a prediction unit, an optimization unit, a control unit, a feedback adjustment unit and a human-computer interaction unit; the signal output end of the prediction unit is connected with the signal input end of the optimization unit, the signal output end of the optimization unit is connected with the signal input end of the control unit, the signal output end of the control unit is connected with the signal input end of the feedback adjustment unit, and the signal output end of the control unit is connected with the signal input end of the man-machine interaction unit; the system comprises a prediction unit, a feedback adjustment unit, a man-machine interaction unit, a display screen and a keyboard, wherein the prediction unit is used for carrying out long-term prediction and short-term prediction on the future according to data acquired in real time, the long-term prediction time is 1-7 days, the short-term prediction time is 0.5-24 hours, a prediction result comprises photovoltaic power generation capacity, electric quantity change of an energy storage battery and load demand of a charging pile, the optimization unit is used for formulating an optimal charging and discharging strategy based on the prediction data, the control unit is used for selecting a first element from an optimal control sequence obtained through optimization as a current control input and applying the first element to the model, the output of a photovoltaic power generation system, the charging and discharging power of the energy storage system and the load distribution of the charging pile are adjusted according to the current control input, the feedback adjustment unit is used for monitoring the state of the system again when the next control period starts, new data are acquired, and a new round of prediction and optimization is carried out, and the man-machine interaction unit is used for monitoring the running state, checking the prediction result and the optimization strategy through the display screen and the keyboard.
6. The light-storage-charging integrated charging station management system based on the energy regulation algorithm of claim 1, wherein the control unit controls the energy flow directions of the photovoltaic power generation device, the energy storage device and the charging device;
the energy scheduling of the photovoltaic power generation device meets the following conditions:
The method comprises the steps of providing the total power for charging the energy storage device, storing surplus electric energy to the energy storage device or grid connection when the photovoltaic power generation power of the photovoltaic power generation device is larger than the total requirements of the charging device and the energy storage device, supplying the electric energy of the energy storage device to the charging device preferentially when the photovoltaic power generation power of the photovoltaic power generation device is insufficient, supplementing the insufficient part by a power grid, and carrying out intelligent scheduling by the charging device according to the charging requirement of the electric automobile and the energy state of a current system, wherein the electric energy of the photovoltaic power generation and the energy storage device is preferentially used for charging the electric automobile.
7. The light-storage and charge integrated charging station management system based on the energy regulation algorithm of claim 6, wherein the energy flow direction is that sunlight is converted into electric energy by the photovoltaic power generation device, the generated direct current is sent to the photovoltaic inverter, the photovoltaic inverter converts the direct current into alternating current, the converted alternating current is sent to an alternating current bus, the electric energy is transmitted to a power grid through a transformer, the power grid and the alternating current bus are in bidirectional transmission, the electric energy is transmitted to the energy storage device through an energy storage converter, the alternating current bus and the energy storage converter are in bidirectional transmission, the energy storage converter and the energy storage device are in bidirectional transmission, and the electric energy directly supplies power for the charging pile.
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