CN117424261A - Light storage and charge cooperative control heavy overload treatment method and system based on energy router - Google Patents
Light storage and charge cooperative control heavy overload treatment method and system based on energy router Download PDFInfo
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- H—ELECTRICITY
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- H02J3/381—Dispersed generators
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Abstract
The invention discloses an optical storage and charge cooperative control heavy overload treatment method and system based on an energy router, comprising the following steps: collecting data of an energy router and an optical storage and charging device; performing abnormality judgment processing on the data to obtain the operation condition of a heavy overload treatment method; selecting different control strategies according to the load rate of the platform area and the load rate threshold value, calculating the control power of the optical storage and charging equipment, and issuing a control instruction to the optical storage and charging equipment; and regulating the optical storage and charging equipment to execute startup and shutdown and adjust power by using the control instruction. The method can prevent the power grid from frequently generating heavy overload events through monitoring and adjusting the charge and discharge process, effectively reduce the operation risk of the power system, improve the reliability and stability of the power system and promote the sustainable development of the power system.
Description
Technical Field
The invention relates to the technical field of optical storage and charge cooperative control, in particular to an optical storage and charge cooperative control heavy overload treatment method based on an energy router.
Background
The energy router is a novel power electronic device, is an intelligent body with functions of calculation, communication, accurate control, remote coordination, autonomy, plug and play and the like, adopts a fully flexible architecture design, has functions of a traditional transformer, a circuit breaker, a power flow control device and an electric energy quality control device, and can realize autonomous distributed operation control and energy management.
The photo-storage charging refers to short for photovoltaic, energy storage and charging piles. In recent years, with the increasing consumption of energy and the instability of energy supply, the demand for the utilization and storage of renewable energy has been increasing. The light storage and charge cooperative control technology has been developed, and the photovoltaic power generation, the energy storage system and the electric pile charging technology are combined, so that the high-efficiency utilization and storage of electric energy can be realized. However, in practical applications, the optical storage and charge cooperative control system faces a problem of heavy overload. The heavy overload means that the electric energy generated in the optical storage and charge cooperative control system exceeds the bearing capacity of the system, so that the stability and the safety of the system are threatened. Heavy overload may not only damage equipment, but may also cause serious consequences such as fire, so that it is highly desirable to solve the problem.
Disclosure of Invention
The present invention has been made in view of the above-described problems.
Therefore, the technical problems solved by the invention are as follows: how to effectively reduce the occurrence of heavy overload events, reduce the influence on the operation of the power grid and improve the reliability and stability of the power system.
In order to solve the technical problems, the invention provides the following technical scheme: an optical storage and charge cooperative control heavy overload treatment method based on an energy router comprises the following steps: collecting data of an energy router and an optical storage and charging device; performing abnormality judgment processing on the data to obtain the operation condition of a heavy overload treatment method; selecting different control strategies according to the load rate of the platform area and the load rate threshold value, calculating the control power of the optical storage and charging equipment, and issuing a control instruction to the optical storage and charging equipment; and regulating the optical storage and charging equipment to execute startup and shutdown and adjust power by using the control instruction.
As a preferable scheme of the light storage and charge cooperative control heavy overload treatment method based on the energy router, the invention comprises the following steps: the data of the energy router and the optical storage and charging equipment comprise data of the energy router, data of the photovoltaic equipment, data of the energy storage equipment and data of the charging pile equipment;
the data of the energy router comprises the number of devices, the running state, the running mode, the network side voltage, the network side current, the network side power and the direct current bus voltage; the data of the photovoltaic equipment comprises the equipment quantity, the running state, the generation power, the grid-connected point voltage and the grid-connected point current; the energy storage equipment data comprise equipment quantity, running state, running mode, SOC, charge and discharge power, grid-connected point voltage and grid-connected point current; the charging pile equipment data comprises equipment quantity, running state, charging power, voltage and current.
As a preferable scheme of the light storage and charge cooperative control heavy overload treatment method based on the energy router, the invention comprises the following steps: the abnormality judgment processing includes, in a first step,
the collected data is normalized, and abnormal values are detected by using a multi-dimensional Z score, which is expressed as follows:
wherein Z represents an abnormality detection index; n represents the number of features; x is X i A value representing an ith feature; u (u) i Representing the mean of the ith feature; sigma (sigma) i Standard deviation representing the ith feature;
establishing a random forest model, extracting data characteristics, dividing a training set and a testing set, and training the random forest model by using the training set; samples are randomly selected from the dataset, feature subsets are randomly selected at each decision node, and the information gain splitting nodes are used to represent:
wherein I is g Representing the information gain; i represents information entropy; d (D) p A dataset representing a parent node; d (D) j A dataset representing a j-th child node; n (N) j A sample number representing a parent node; n (N) p Representing the number of samples of child nodes; and carrying out anomaly judgment processing on input data by using a trained random forest model, predicting the input data of each decision tree, carrying out outlier detection on the data according to Z scores, and carrying out voting decision on a final result by all decision trees by a decision of random forest output.
As a preferable scheme of the light storage and charge cooperative control heavy overload treatment method based on the energy router, the invention comprises the following steps: the operating conditions of the heavy overload management method include,
classifying the detected abnormal data by using a random forest model; determining the possible impact of the outlier data point on the system; determining the operation conditions of the system according to the classification and influence of the abnormal data, wherein the operation conditions comprise adjustment of the photovoltaic power generation amount and the charging and discharging strategy of the energy storage equipment, and adjustment of the charging power of the charging pile; and according to the determined operation conditions, setting corresponding coping strategies to adjust the operation power of the equipment and reallocating resources to reduce and prevent heavy overload.
As a preferable scheme of the light storage and charge cooperative control heavy overload treatment method based on the energy router, the invention comprises the following steps: the control strategy comprises the steps of selecting different control strategies according to whether the load rate of the platform area exceeds a load rate threshold value;
dynamically adjusting parameters of a control strategy according to real-time response and historical performance data of the system, and adjusting a load rate threshold and controlling response sensitivity; calculating the control power of the optical storage and filling equipment, and sending a control instruction to the optical storage and filling equipment;
judging whether the load rate of the platform area exceeds a load rate threshold value: if the load rate threshold is not exceeded, the power of the optical storage and charging equipment is not regulated and controlled;
if the load rate threshold value is exceeded, calculating the power variation quantity of the energy router network side and the total regulation capacity of the optical storage and filling equipment according to the target value reached by the load rate of the transformer area and the control load rate at the moment;
comparing the total regulation capacity of the optical storage and filling equipment with the port power limit value of the energy router, and if the total regulation capacity of the optical storage and filling equipment is larger than the port power limit value of the energy router, assigning the port power limit value of the energy router as the total regulation capacity of the optical storage and filling equipment;
wherein, the load rate threshold value of the platform area is set as theta; the optical storage and charging equipment executes startup and shutdown and adjusts power according to the received control instruction; the energy router is connected with a 10kV line and a direct current bus, and the optical storage and charging equipment is connected to the direct current bus to support control of power and on-off.
As a preferable scheme of the light storage and charge cooperative control heavy overload treatment method based on the energy router, the invention comprises the following steps: the calculation of the control power of the optical storage and charging equipment comprises the steps of constructing a control strategy selection function, calculating to obtain a comprehensive power adjustment factor and calculating a control power value;
control strategy selection function F:
integrated power adjustment factor G:
calculating a power control value P, expressed as:
wherein k is an adjustment parameter; f represents the sensitivity of the adjustment control strategy according to the load factor and the threshold; g represents a power adjustment factor; p represents a control power for giving an operation instruction; λ represents the zone load factor; θ represents a load factor threshold; p (P) g Representing network side power; p (P) v Representing photovoltaic power generation; p (P) c Representing the charging power of the charging pile; p (P) s Charging and discharging power of the energy storage device; v (V) g Representing the net side voltage; i g Representing the net side current.
As a preferable scheme of the light storage and charge cooperative control heavy overload treatment method based on the energy router, the invention comprises the following steps: the control command regulation comprises the following steps of,
calculating a photovoltaic power generation power control value according to the total regulation capacity of the photovoltaic storage charging equipment and the photovoltaic power generation limit value, so as to ensure that the power of photovoltaic power generation is in a controllable range and avoid overload and deficiency;
if the control photovoltaic power reaches the maximum, the load rate of the platform area still exceeds the threshold value, and an energy storage discharge power control value is calculated; if the control energy storage power reaches the maximum, the load rate of the district still exceeds a threshold value, and a discharge power control value of the charging pile is calculated;
determining the type of a control instruction according to the power control value P and the current power state of the equipment; if P is a positive value and the equipment is in a shutdown state, the instruction type is startup, and the output power of the equipment is adjusted to P; if P is zero or negative and the device is running, the instruction type is shutdown; if P is a positive value and the output power of the device needs to be adjusted, the instruction type is to adjust the power to P.
In a second aspect, the invention also provides an optical storage and charge cooperative control heavy overload control system based on the energy router, which comprises a data acquisition module: monitoring the states of all the devices in real time, and periodically collecting the operation data of the energy router, the photovoltaic device, the energy storage device and the charging pile;
the abnormality judgment processing module: analyzing the acquired data, and detecting abnormal conditions of the data by using a random forest algorithm; determining the operating conditions of the system to avoid overload conditions;
a control strategy selection module: selecting a proper control strategy to adjust the operation of the optical storage and filling equipment according to the load rate of the platform area and the load rate threshold;
the control instruction execution module: receiving a control instruction issued by a control strategy module; controlling the photovoltaic equipment, the energy storage equipment and the charging pile to start and shut down or adjust power according to the instruction; the execution is monitored to ensure proper execution of the instructions.
In a third aspect, the present invention also provides a computing device comprising: a memory and a processor;
the memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions, and the computer executable instructions realize the steps of the method for controlling and managing the heavy overload by the light storage and charge cooperative control based on the energy router when being executed by the processor.
In a fourth aspect, the present invention also provides a computer readable storage medium storing computer executable instructions which when executed by a processor implement the steps of the method for controlling and managing heavy overload based on optical storage and charge coordination of an energy router.
The invention has the beneficial effects that: according to the method for controlling and managing the heavy overload by the light storage and charging cooperation based on the energy router, the system can monitor the load condition of the power grid in real time through accurate data acquisition and analysis, and corresponding light storage and charging equipment cooperation scheduling and intelligent control can be carried out according to the power grid requirements, so that the tight matching among the light storage and charging equipment can be realized, and the control and management problems of the light storage and charging system under the heavy overload condition can be effectively solved. When the power grid load suddenly increases or fails, the system can timely adjust the running state of the light storage and charging equipment so as to avoid overload of the system. Meanwhile, the system can also prevent the power grid from frequently generating heavy overload events through monitoring and adjusting the charging and discharging process, effectively reduce the running risk of the power system, improve the reliability and stability of the power system and promote the sustainable development of the power system.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
FIG. 1 is a general flow chart of a method for controlling and managing heavy overload based on optical storage and inflation cooperative control of an energy router according to an embodiment of the present invention;
FIG. 2 is a diagram of an overall architecture of a method for controlling and managing heavy overload based on optical storage and charge coordination control of an energy router according to a first embodiment of the present invention;
fig. 3 is a control flow chart of a method for controlling and managing heavy overload based on optical storage and inflation cooperative control of an energy router according to a first embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
Example 1
Referring to fig. 1-3, for one embodiment of the present invention, there is provided a method for controlling and managing heavy overload based on optical storage and charge coordination of an energy router, including:
s1: data of an energy router and an optical storage and charging device are collected.
Further, the data of the energy collecting router and the optical storage and charging equipment comprise data of the energy router, data of photovoltaic equipment, data of energy storage equipment and data of charging pile equipment;
further, the data of the energy router comprises the number of devices, the running state, the running mode, the network side voltage, the network side current, the network side power and the direct current bus voltage; the data of the photovoltaic equipment comprises the equipment quantity, the running state, the generation power, the grid-connected point voltage and the grid-connected point current; the energy storage equipment data comprise equipment quantity, running state, running mode, SOC, charge and discharge power, grid-connected point voltage and grid-connected point current; the charging pile equipment data comprises equipment quantity, running state, charging power, voltage and current.
Wherein X represents the original data; x is X max Representing a maximum value in the dataset; x is X min Representing the minimum in the dataset. By scaling all features to between 0 and 1 by normalization, the effect of dimension is eliminated. The convergence of the learning algorithm is facilitated, and certain features are prevented from being dominant in model training due to the large numerical range.
It should be noted that through these preprocessing steps, the quality and consistency of the data can be ensured, laying a solid foundation for subsequent analysis or model training. The normalization process allows for comparisons between features, while outlier detection helps identify and process outlier data points that may distort the analysis results. These steps are particularly important in the monitoring and control of complex systems such as optical storage and filling devices.
S2: and obtaining the operating conditions of the heavy overload treatment method.
Further, the collected data is normalized, and abnormal values are detected by using a multi-dimensional Z score, which is expressed as:
wherein Z represents an abnormality detection index; n represents the number of features; x is X i A value representing an ith feature; u (u) i Representing the mean of the ith feature; sigma (sigma) i Representing the standard deviation of the ith feature.
Further, a random forest model is established, data characteristics are extracted, a training set and a testing set are divided, and the random forest model is trained by the training set; samples are randomly selected from the dataset, feature subsets are randomly selected at each decision node, and the information gain splitting nodes are used to represent:
wherein I is g Representing the information gain; i represents information entropy; d (D) p A dataset representing a parent node; d (D) j A dataset representing a j-th child node; n (N) j A sample number representing a parent node; n (N) p Representing the number of samples of child nodes; and carrying out anomaly judgment processing on input data by using a trained random forest model, predicting the input data of each decision tree, carrying out outlier detection on the data according to Z scores, and carrying out voting decision on a final result by all decision trees by a decision of random forest output.
Further, for a new data point, each tree generates a prediction (normal or abnormal). Using a majority voting mechanism to determine the final predicted outcome: y=mode ({ Y) 1 ,y 2 ,...,y n Y) wherein y i Is the prediction result of the ith tree. The output probability of the model is used to set the dynamic threshold, instead of relying solely on majority voting, to improve the sensitivity and accuracy of anomaly detection.
Further, classifying the detected abnormal data by using a random forest model; determining the possible impact of the outlier data point on the system; determining the operation conditions of the system according to the classification and influence of the abnormal data, wherein the operation conditions comprise adjustment of the photovoltaic power generation amount and the charging and discharging strategy of the energy storage equipment, and adjustment of the charging power of the charging pile; and according to the determined operation conditions, setting corresponding coping strategies to adjust the operation power of the equipment and reallocating resources to reduce and prevent heavy overload.
It should be noted that through the above steps, the random forest algorithm can be effectively used to identify abnormal patterns in the data. The innovative application of feature importance analysis and adaptive threshold adjustment enhances the interpretation and flexibility of the model, so that the model is more suitable for complex real-time data environments, such as anomaly detection in an optical storage and inflation cooperative control system.
S3: and calculating the control power of the optical storage and filling device.
Further, different control strategies are selected according to whether the load rate of the station area exceeds a load rate threshold.
Further, according to the real-time response and the historical performance data of the system, parameters of a control strategy, load rate threshold adjustment and control response sensitivity are dynamically adjusted; and calculating the control power of the light storage and charging equipment, and sending a control instruction to the light storage and charging equipment.
Further, whether the load rate of the platform area exceeds a load rate threshold value is judged: and if the load rate threshold is not exceeded, not regulating and controlling the power of the optical storage and charging equipment. If the load rate threshold value is exceeded, calculating the power change quantity of the energy router network side and the total regulation capacity of the optical storage and filling equipment according to the target value reached by the load rate of the area and the control load rate at the moment.
Further, comparing the total regulation capacity of the optical storage and filling device with the port power limit value of the energy router, and if the total regulation capacity of the optical storage and filling device is larger than the port power limit value of the energy router, assigning the port power limit value of the energy router as the total regulation capacity of the optical storage and filling device.
Wherein, the load rate threshold value of the platform area is set as theta; the optical storage and charging equipment executes startup and shutdown and adjusts power according to the received control instruction; the energy router is connected with a 10kV line and a direct current bus, and the optical storage and charging equipment is connected to the direct current bus to support control of power and on-off. The calculation of the control power of the optical storage and charging equipment comprises the steps of constructing a control strategy selection function, calculating to obtain a comprehensive power adjustment factor and calculating a control power value.
Control strategy selection function F:
integrated power adjustment factor G:
calculating a power control value P, expressed as:
wherein k is an adjustment parameter; f represents the sensitivity of the adjustment control strategy according to the load factor and the threshold; g represents a power adjustment factor; p represents a control power for giving an operation instruction; λ represents the zone load factor; θ represents a load factor threshold; p (P) g Representing network side power; p (P) v Representing photovoltaic power generation; p (P) c Representing the charging power of the charging pile; p (P) s Charging and discharging power of the energy storage device; v (V) g Representing the net side voltage; i g Representing the net side current.
It should be noted that this process allows for precise control of the optical storage and filling device by taking into account the load factor and the power conditions of each device. The selection of the control strategy relies on a comparison of the load factor to a threshold value to ensure that the output of the energy source is reduced at high loads and increased or maintained at low loads, thereby optimizing the performance and stability of the overall system.
S4: and regulating the optical storage and charging equipment to execute startup and shutdown and adjust power by using the control instruction.
Further, according to the total regulation capacity of the light storage charging equipment and the photovoltaic power generation limit value, the photovoltaic power generation power control value is calculated, the power of photovoltaic power generation is ensured to be in a controllable range, and overload and insufficient conditions are avoided.
Further, if the control photovoltaic power reaches the maximum, the load rate of the district still exceeds the threshold value, and an energy storage discharge power control value is calculated; and if the control energy storage power reaches the maximum, the load rate of the district still exceeds the threshold value, and the discharge power control value of the charging pile is calculated.
Further, determining the type of the control instruction according to the power control value P and the current power state of the equipment; if P is a positive value and the equipment is in a shutdown state, the instruction type is startup; if P is zero or negative and the device is running, the instruction type is shutdown; if P is a positive value and the output power of the device needs to be adjusted, the instruction type is to adjust the power to P.
It should be noted that the above steps enable smooth switching of the control strategy and accurate calculation of the power adjustment. The light storage and charging equipment can be managed more accurately, the performance and stability of the whole system are optimized, the heavy overload phenomenon of the light storage equipment is effectively reduced, and the damage caused by heavy overload is reduced.
An optical storage and charge cooperative control heavy overload control system based on an energy router is characterized by comprising,
and a data acquisition module: and monitoring the states of all the devices in real time, and periodically collecting the operation data of the energy router, the photovoltaic device, the energy storage device and the charging pile.
The abnormality judgment processing module: analyzing the acquired data, and detecting abnormal conditions of the data by using a random forest algorithm; the operating conditions of the system are determined to avoid overload conditions.
A control strategy selection module: and selecting a proper control strategy to adjust the operation of the optical storage and charging equipment according to the load rate of the platform area and the load rate threshold value.
The control instruction execution module: receiving a control instruction issued by a control strategy module; controlling the photovoltaic equipment, the energy storage equipment and the charging pile to start and shut down or adjust power according to the instruction; the execution is monitored to ensure proper execution of the instructions.
The present embodiment also provides a computing device comprising, a memory and a processor; the memory is used for storing computer executable instructions, and the processor is used for executing the computer executable instructions to realize the method for controlling and managing the heavy overload based on the optical storage and charge cooperative control of the energy router according to the embodiment.
The present embodiment also provides a storage medium, on which a computer program is stored, which when executed by a processor implements the method for controlling light storage and charge cooperative control and heavy overload based on the energy router as set forth in the above embodiment.
The storage medium proposed in this embodiment belongs to the same inventive concept as the method for controlling and managing heavy overload based on optical storage and charge cooperative control of an energy router proposed in the above embodiment, and technical details not described in detail in this embodiment can be seen in the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.
From the above description of embodiments, it will be clear to a person skilled in the art that the present invention may be implemented by means of software and necessary general purpose hardware, but of course also by means of hardware, although in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a computer readable storage medium, such as a floppy disk, a Read Only Memory (ROM), a random access Memory (RandomAccessMemory, RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, etc., including several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to execute the method of the embodiments of the present invention.
Example 2
The following provides an optical storage and charging cooperative control heavy overload treatment method based on an energy router, and in order to verify the beneficial effects of the invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments. As shown in Table 1, the theoretical experiment comparison table of the light storage and charge cooperative control heavy overload treatment method of the invention is shown, and part of data adopts grading indexes.
TABLE 1 theoretical experiment comparison table for light storage and charge cooperative control heavy overload treatment method
| Performance index | Prior art solution | The method | Percentage improvement |
| Efficiency of energy utilization | 85％ | 95％ | +11.76％ |
| Incidence of overload | 10％ | 2％ | -80％ |
| Response time | 15s | 5s | -66.67％ |
| Cost of operation | High height | Low and low | —— |
| Reliability of | In (a) | High height | —— |
| Environmental impact | Larger size | Smaller size | —— |
Compared with the prior art, the energy utilization rate is improved by 11.76%, the energy waste is reduced, the overload condition is reduced, and the stability of the whole system is enhanced.
Regarding the identification of abnormal data, comparative verification was employed as shown in table 2.
Table 2 abnormal data identification efficiency comparison table
The method realizes the abnormal recognition rate of 98 percent, and compared with 90 percent of the prior art, the method improves the abnormal recognition rate by 8.89 percent. The method is more effective in identifying abnormal data, and can more accurately detect abnormal conditions in the system. The system can identify and respond to abnormal conditions more rapidly, the processing capacity of the system is improved, the invention is more efficient when processing data, and the timeliness of the real-time monitoring and control system is greatly improved.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (10)
1. The method for controlling and treating the heavy overload by the light storage and charge coordination based on the energy router is characterized by comprising the following steps of:
collecting data of an energy router and an optical storage and charging device;
performing abnormality judgment processing on the data to obtain the operation condition of a heavy overload treatment method;
selecting different control strategies according to the load rate of the platform area and the load rate threshold value, calculating the control power of the optical storage and charging equipment, and issuing a control instruction to the optical storage and charging equipment;
and regulating the optical storage and charging equipment to execute startup and shutdown and adjust power by using the control instruction.
2. The energy router-based optical storage and filling cooperative control heavy overload treatment method as claimed in claim 1, wherein the method comprises the following steps: the data of the energy router and the optical storage and charging equipment comprise data of the energy router, data of the photovoltaic equipment, data of the energy storage equipment and data of the charging pile equipment;
the data of the energy router comprises the number of devices, the running state, the running mode, the network side voltage, the network side current, the network side power and the direct current bus voltage; the data of the photovoltaic equipment comprises the equipment quantity, the running state, the generation power, the grid-connected point voltage and the grid-connected point current; the energy storage equipment data comprise equipment quantity, running state, running mode, SOC, charge and discharge power, grid-connected point voltage and grid-connected point current; the charging pile equipment data comprises equipment quantity, running state, charging power, voltage and current.
3. The method for controlling and managing heavy overload based on the optical storage and charge coordination control of the energy router as claimed in claim 2, wherein the method comprises the following steps: the abnormality judgment processing includes, in a first step,
the collected data is normalized, and abnormal values are detected by using a multi-dimensional Z score, which is expressed as follows:
wherein Z represents an abnormality detection index; n represents the number of features; x is X i A value representing an ith feature; u (u) i Representing the mean of the ith feature; sigma (sigma) i Standard deviation representing the ith feature;
establishing a random forest model, extracting data characteristics, dividing a training set and a testing set, and training the random forest model by using the training set; samples are randomly selected from the dataset, feature subsets are randomly selected at each decision node, and the information gain splitting nodes are used to represent:
wherein I is g Representing the information gain; i represents information entropy; d (D) p A dataset representing a parent node; d (D) j A dataset representing a j-th child node; n (N) j A sample number representing a parent node; n (N) p Representing the number of samples of child nodes; and carrying out anomaly judgment processing on input data by using a trained random forest model, predicting the input data of each decision tree, carrying out outlier detection on the data according to Z scores, and carrying out voting decision on a final result by all decision trees by a decision of random forest output.
4. The method for controlling and managing heavy overload based on the optical storage and charge coordination control of the energy router as claimed in claim 3, wherein the method comprises the following steps: the operating conditions of the heavy overload management method include,
classifying the detected abnormal data by using a random forest model; determining the possible impact of the outlier data point on the system; determining the operation conditions of the system according to the classification and influence of the abnormal data, wherein the operation conditions comprise adjustment of the photovoltaic power generation amount and the charging and discharging strategy of the energy storage equipment, and adjustment of the charging power of the charging pile; and according to the determined operation conditions, setting corresponding coping strategies to adjust the operation power of the equipment and reallocating resources to reduce and prevent heavy overload.
5. The method for controlling and managing heavy overload based on the optical storage and charge coordination control of the energy router as claimed in claim 4, wherein the method comprises the following steps: the control strategy comprises the steps of selecting different control strategies according to whether the load rate of the platform area exceeds a load rate threshold value;
dynamically adjusting parameters of a control strategy according to real-time response and historical performance data of the system, and adjusting a load rate threshold and controlling response sensitivity; calculating the control power of the optical storage and filling equipment, and sending a control instruction to the optical storage and filling equipment;
judging whether the load rate of the platform area exceeds a load rate threshold value: if the load rate threshold is not exceeded, the power of the optical storage and charging equipment is not regulated and controlled;
if the load rate threshold value is exceeded, calculating the power variation quantity of the energy router network side and the total regulation capacity of the optical storage and filling equipment according to the target value reached by the load rate of the transformer area and the control load rate at the moment;
comparing the total regulation capacity of the optical storage and filling equipment with the port power limit value of the energy router, and if the total regulation capacity of the optical storage and filling equipment is larger than the port power limit value of the energy router, assigning the port power limit value of the energy router as the total regulation capacity of the optical storage and filling equipment;
wherein, the load rate threshold value of the platform area is set as theta; the optical storage and charging equipment executes startup and shutdown and adjusts power according to the received control instruction; the energy router is connected with a 10kV line and a direct current bus, and the optical storage and charging equipment is connected to the direct current bus to support control of power and on-off.
6. The method for controlling and managing heavy overload based on the optical storage and charge coordination control of the energy router as claimed in claim 5, wherein the method comprises the following steps: the calculation of the control power of the optical storage and charging equipment comprises the steps of constructing a control strategy selection function, calculating to obtain a comprehensive power adjustment factor and calculating a control power value;
control strategy selection function F:
integrated power adjustment factor G:
calculating a power control value P, expressed as:
wherein k represents the adjustment parameter; f represents the sensitivity of the adjustment control strategy according to the load factor and the threshold; g represents a power adjustment factor; p represents a control power for giving an operation instruction; λ represents the zone load factor; θ represents a load factor threshold; p (P) g Representing network side power; p (P) v Representing photovoltaic power generation; p (P) c Representing the charging power of the charging pile; p (P) s Charging and discharging power of the energy storage device; v (V) g Representing the net side voltage; i g Representing the net side current.
7. The energy router-based optical storage and filling cooperative control heavy overload treatment method as claimed in claim 6, wherein: the control command regulation comprises the following steps of,
calculating a photovoltaic power generation power control value according to the total regulation capacity of the photovoltaic storage charging equipment and the photovoltaic power generation limit value, so as to ensure that the power of photovoltaic power generation is in a controllable range and avoid overload and deficiency;
if the control photovoltaic power reaches the maximum, the load rate of the platform area still exceeds the threshold value, and an energy storage discharge power control value is calculated; if the control energy storage power reaches the maximum, the load rate of the district still exceeds a threshold value, and a discharge power control value of the charging pile is calculated;
determining the type of a control instruction according to the power control value P and the current power state of the equipment; if P is a positive value and the equipment is in a shutdown state, the instruction type is startup; if P is zero or negative and the device is running, the instruction type is shutdown; if P is positive value and the output power of the equipment needs to be adjusted, the instruction type is the adjustment power, the current power is larger than P, the output is reduced, the current power is smaller than P, the output power is increased until the output power is adjusted to be P, adjustment is completed, the adjustment information is fed back, a new power control value is calculated, and a control instruction is generated.
8. An energy router-based optical storage and charge cooperative control heavy overload control system adopting the method as claimed in any one of claims 1 to 7, which is characterized by comprising,
and a data acquisition module: monitoring the states of all the devices in real time, and periodically collecting the operation data of the energy router, the photovoltaic device, the energy storage device and the charging pile;
the abnormality judgment processing module: analyzing the acquired data, and detecting abnormal conditions of the data by using a random forest algorithm; determining the operating conditions of the system to avoid overload conditions;
a control strategy selection module: selecting a proper control strategy to adjust the operation of the optical storage and filling equipment according to the load rate of the platform area and the load rate threshold;
the control instruction execution module: receiving a control instruction issued by a control strategy module; controlling the photovoltaic equipment, the energy storage equipment and the charging pile to start and shut down or adjust power according to the instruction; the execution is monitored to ensure proper execution of the instructions.
9. A computing device, comprising: a memory and a processor;
the memory is for storing computer executable instructions, the processor being for executing the computer executable instructions which when executed by the processor implement the steps of the method of any one of claims 1 to 7.
10. A computer readable storage medium storing computer executable instructions which when executed by a processor implement the steps of the method of any one of claims 1 to 7.
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