CN113568307B - Control strategy optimization method for storage and charging station and terminal - Google Patents
Control strategy optimization method for storage and charging station and terminal Download PDFInfo
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- CN113568307B CN113568307B CN202110749527.4A CN202110749527A CN113568307B CN 113568307 B CN113568307 B CN 113568307B CN 202110749527 A CN202110749527 A CN 202110749527A CN 113568307 B CN113568307 B CN 113568307B
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
- G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/7072—Electromobility specific charging systems or methods for batteries, ultracapacitors, supercapacitors or double-layer capacitors
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02T90/10—Technologies relating to charging of electric vehicles
- Y02T90/12—Electric charging stations
Abstract
The invention discloses a control strategy optimization method and a terminal of a storage and charging station, which are used for determining control parameters of a control strategy and setting an initial value of each control parameter, and can be used for carrying out simulation on the basis of the control parameters to obtain a corresponding target value; judging whether the control parameters meet preset requirements or not according to the target values, if so, saving the control parameters, and if not, adjusting the control parameters by using the gradient of the simulation function until a group of control parameters meet the preset requirements or the optimization fails; each control parameter is adjusted in sequence based on the gradient direction, and the control parameters can be optimized accurately, so that the economic benefit of operation of the storage and charging station can be improved.
Description
Technical Field
The invention relates to the technical field of new energy, in particular to a control strategy optimization method and a control strategy optimization terminal for a storage and charging station.
Background
Due to the continuous decrease of the conventional energy and the pollution to the environment, the utilization and development of new energy are being advanced to a new level. The energy storage charging station contains a set of energy storage battery, can be when the charging station has idle with partly electric energy in the energy storage battery in advance, release when the electric automobile has the power demand after reserving for to improve the output of charging station in a period, and reduced the charges of electricity of charging station through the mode of filling the millet in the peak clipping.
Due to the uncertainty of the charging and discharging requirements of the electric vehicle, the change of the service volume of the charging station, the change of the electric charge scheme, the depreciation cost of charging and discharging of the energy storage battery, the energy conversion efficiency and the operation safety of each electric appliance of the power station under different working conditions and the like, and the performance of different equipment in the using process is possibly changed, the excellent energy management strategy is very difficult to make, and the fixed energy management strategy made manually is difficult to keep the optimization all the time.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: the method and the terminal for optimizing the control strategy of the storage and charging station can improve the economic benefit of the operation of the storage and charging station.
In order to solve the technical problems, the invention adopts the technical scheme that:
a control strategy optimization method for a storage and charging station comprises the following steps:
determining control parameters of a control strategy, and setting an initial value of each control parameter;
simulating all the control parameters to obtain corresponding target values;
and judging whether all the control parameters meet preset requirements or not according to the target values, if so, storing all the control parameters, otherwise, acquiring the gradient of a simulation function of the control strategy, and sequentially adjusting each control parameter based on the direction of the gradient until a group of the control parameters meet the preset requirements or the optimization fails.
In order to solve the technical problem, the invention adopts another technical scheme as follows:
a control strategy optimization terminal of a storage and charging station, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
determining control parameters of a control strategy, and setting an initial value of each control parameter;
simulating all the control parameters to obtain corresponding target values;
and judging whether all the control parameters meet preset requirements or not according to the target values, if so, storing all the control parameters, otherwise, acquiring the gradient of a simulation function of the control strategy, and sequentially adjusting each control parameter based on the direction of the gradient until a group of the control parameters meet the preset requirements or the optimization fails.
The invention has the beneficial effects that: determining control parameters of a control strategy, setting an initial value of each control parameter, and performing simulation based on the control parameters to obtain a corresponding target value; judging whether the control parameters meet preset requirements or not according to the target values, if so, saving the control parameters, and if not, adjusting the control parameters by using the gradient of the simulation function until a group of control parameters meet the preset requirements or the optimization fails; each control parameter is adjusted in sequence based on the gradient direction, and the control parameters can be optimized accurately, so that the economic benefit of operation of the storage and charging station can be improved.
Drawings
Fig. 1 is a flowchart of a control strategy optimization method for a storage and charging station according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a terminal for optimizing a control strategy of a storage and charging station according to an embodiment of the present invention;
fig. 3 is a flowchart illustrating specific steps of a method for optimizing a control strategy of a storage and charging station according to an embodiment of the present invention.
Detailed Description
In order to explain technical contents, achieved objects, and effects of the present invention in detail, the following description is made with reference to the accompanying drawings in combination with the embodiments.
Referring to fig. 1 and fig. 3, an embodiment of the present invention provides a method for optimizing a control strategy of a storage and charging station, including the steps of:
determining control parameters of a control strategy, and setting an initial value of each control parameter;
simulating all the control parameters to obtain corresponding target values;
and judging whether all the control parameters meet preset requirements or not according to the target values, if so, storing all the control parameters, otherwise, acquiring the gradient of a simulation function of the control strategy, and sequentially adjusting each control parameter based on the direction of the gradient until a group of the control parameters meet the preset requirements or the optimization fails.
From the above description, the beneficial effects of the present invention are: determining control parameters of a control strategy, setting an initial value of each control parameter, and performing simulation based on the control parameters to obtain a corresponding target value; judging whether the control parameters meet preset requirements or not according to the target values, if so, saving the control parameters, and if not, adjusting the control parameters by using the gradient of the simulation function until a group of control parameters meet the preset requirements or the optimization fails; each control parameter is adjusted in sequence based on the gradient direction, and the control parameters can be optimized accurately, so that the economic benefit of operation of the storage and charging station can be improved.
Further, simulating all the control parameters to obtain corresponding target values includes:
inputting all the control parameters into a simulation model of the storage and charging station;
calculating the target value Tn in the simulation model:
Tn＝F(X1，X2，……，Xn)；
in the formula, F denotes a simulation function, and X1 to Xn denote control parameters.
According to the description, all the control parameters are input into the simulation model of the storage station for simulation, so that the target values corresponding to the parameters of the control strategy are calculated based on the simulation function, the subsequent parameter adjustment based on the target values is facilitated, and the manual maintenance cost is reduced.
Further, adjusting each of the control parameters in turn based on the direction of the gradient comprises:
sequentially acquiring one of the control parameters for adjustment, and calculating adjustment amplitude according to the precision requirement, the calculation capacity, the data distribution characteristics and the training time of the simulation model;
and adjusting the control parameter by combining the adjusting amplitude and the direction of the gradient.
According to the description, the adjustment range of the control parameters is calculated according to the precision requirement, the calculation capacity, the data distribution characteristics and the training time of the simulation model, the control parameters are adjusted by combining the adjustment range and the gradient direction, the control parameters can be accurately adjusted, and the economic benefit of the operation of the storage and charging station is improved.
Further, whether all the control parameters meet preset requirements is judged according to the target values, if yes, all the control parameters are stored, and if not, the step of obtaining the gradient of the simulation function of the control strategy comprises the following steps:
obtaining the total conversion rate of the storage and charging station according to the target value, if the total conversion rate is greater than a preset conversion rate, enabling all the control parameters of the group after the adjustment to meet preset requirements, and storing all the control parameters of the group after the adjustment;
otherwise, all the control parameters do not meet the preset requirements, and the gradient of the simulation function of the control strategy is obtained.
According to the description, the total conversion rate of the storage and charging station is obtained through the target value, and whether the control parameter meets the preset requirement or not is judged according to the total conversion rate, so that the control parameter is optimized conveniently.
Further, the failure of the control parameter optimization comprises:
when the target value is the maximum value of all control parameter combinations or when the maximum change rate of the target value is smaller than a preset change rate, the target value still does not meet the preset requirement, and then the control parameter optimization is marked to fail.
As can be seen from the above description, when the target value is the maximum value of all the control parameter combinations or when the maximum change rate of the target value is smaller than the preset change rate, if the target value does not satisfy the preset requirement, it indicates that the control parameters will continue to be optimized and still will not satisfy the preset requirement, and the control parameter optimization failure is directly marked, so that unnecessary optimization steps can be reduced, and the economic benefit of the operation of the storage and charging station is improved.
Referring to fig. 2, another embodiment of the present invention provides a control policy optimization terminal for a storage and charging station, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement the following steps:
determining control parameters of a control strategy, and setting an initial value of each control parameter;
simulating all the control parameters to obtain corresponding target values;
and judging whether all the control parameters meet preset requirements or not according to the target values, if so, storing all the control parameters, otherwise, acquiring the gradient of a simulation function of the control strategy, and sequentially adjusting each control parameter based on the direction of the gradient until a group of the control parameters meet the preset requirements or the optimization fails.
As can be seen from the above description, determining the control parameters of the control strategy and setting the initial value of each control parameter can be simulated based on the control parameters to obtain the corresponding target value; judging whether the control parameters meet preset requirements or not according to the target values, if so, saving the control parameters, and if not, adjusting the control parameters by using the gradient of the simulation function until a group of control parameters meet the preset requirements or the optimization fails; each control parameter is adjusted in sequence based on the gradient direction, and the control parameters can be optimized accurately, so that the economic benefit of operation of the storage and charging station can be improved.
Further, simulating all the control parameters to obtain corresponding target values includes:
inputting all the control parameters into a simulation model of the storage and charging station;
calculating the target value Tn in the simulation model:
Tn＝F(X1，X2，……，Xn)；
in the formula, F denotes a simulation function, and X1 to Xn denote control parameters.
According to the description, all the control parameters are input into the simulation model of the storage station for simulation, so that the target values corresponding to the parameters of the control strategy are calculated based on the simulation function, the subsequent parameter adjustment based on the target values is facilitated, and the manual maintenance cost is reduced.
Further, adjusting each of the control parameters in turn based on the direction of the gradient comprises:
sequentially acquiring one of the control parameters for adjustment, and calculating adjustment amplitude according to the precision requirement, the calculation capacity, the data distribution characteristics and the training time of the simulation model;
and adjusting the control parameter by combining the adjusting amplitude and the direction of the gradient.
According to the description, the adjustment range of the control parameters is calculated according to the precision requirement, the calculation capacity, the data distribution characteristics and the training time of the simulation model, the control parameters are adjusted by combining the adjustment range and the gradient direction, the control parameters can be accurately adjusted, and the economic benefit of the operation of the storage and charging station is improved.
Further, whether all the control parameters meet preset requirements is judged according to the target values, if yes, all the control parameters are stored, and if not, the step of obtaining the gradient of the simulation function of the control strategy comprises the following steps:
obtaining the total conversion rate of the storage and charging station according to the target value, if the total conversion rate is greater than a preset conversion rate, enabling all the control parameters of the group after the adjustment to meet preset requirements, and storing all the control parameters of the group after the adjustment;
otherwise, all the control parameters do not meet the preset requirements, and the gradient of the simulation function of the control strategy is obtained.
According to the description, the total conversion rate of the storage and charging station is obtained through the target value, and whether the control parameter meets the preset requirement or not is judged according to the total conversion rate, so that the control parameter is optimized conveniently.
Further, the failure of the control parameter optimization comprises:
when the target value is the maximum value of all control parameter combinations or when the maximum change rate of the target value is smaller than a preset change rate, the target value still does not meet the preset requirement, and then the control parameter optimization is marked to fail.
As can be seen from the above description, when the target value is the maximum value of all the control parameter combinations or when the maximum change rate of the target value is smaller than the preset change rate, if the target value does not satisfy the preset requirement, it indicates that the control parameters will continue to be optimized and still will not satisfy the preset requirement, and the control parameter optimization failure is directly marked, so that unnecessary optimization steps can be reduced, and the economic benefit of the operation of the storage and charging station is improved.
The control strategy optimization method and the terminal for the storage and charging station are suitable for automatically optimizing the control strategy regularly by utilizing a computer program, so that the economic benefit of power station operation is improved, the cost of manual maintenance is reduced, and the following description is given by a specific implementation mode:
example one
Referring to fig. 1 and 3, a method for optimizing a control strategy of a storage and charging station includes the steps of:
and S1, determining control parameters of the control strategy, and setting an initial value of each control parameter.
Specifically, in the present embodiment, according to the functional requirements of the storage and charging station control software, the control parameters X1 to Xn of the storage and charging station control strategy are determined, and initial values are set. Control parameters include, but are not limited to: the control parameters comprise effective starting time, effective ending time, an energy storage battery SOC target value, a charging power maximum value and a discharging power maximum value.
And S2, simulating all the control parameters to obtain corresponding target values.
Specifically, the target value is set according to the demand of model optimization, for example, the average daily total conversion rate of the past 30 days may be obtained by the following calculation method: total charge/ac usage over 24 hours, where the energy storage battery should remain fully charged at the beginning of each calculation cycle.
Inputting all the control parameters into a simulation model of the storage and charging station;
calculating the target value Tn in the simulation model:
Tn＝F(X1，X2，……，Xn)；
in the formula, F denotes a simulation function, and X1 to Xn denote control parameters.
Specifically, the parameters X1 to Xn are set in the simulation model, and the storage and charging station model simulation function is called to calculate the target value Tn ═ F (X1, X2, … …, Xn).
S3, judging whether all the control parameters meet preset requirements or not according to the target values, if so, storing all the control parameters, otherwise, acquiring the gradient of the simulation function of the control strategy, and sequentially adjusting each control parameter based on the direction of the gradient until a group of the control parameters meet the preset requirements or the optimization fails.
Judging whether all the control parameters meet preset requirements or not according to the target values, if so, saving all the control parameters, and if not, acquiring the gradient of the simulation function of the control strategy comprises the following steps:
obtaining the total conversion rate of the storage and charging station according to the target value, if the total conversion rate is greater than a preset conversion rate, enabling all the control parameters of the group after the adjustment to meet preset requirements, and storing all the control parameters of the group after the adjustment;
otherwise, all the control parameters do not meet the preset requirements, and the gradient of the simulation function of the control strategy is obtained.
Specifically, whether the set of strategy control parameters meets preset requirements is judged according to Tn, for example, whether the total conversion rate of the station is greater than 85% is judged, if Tn meets the requirements, the training is successful, all the control parameters of the set after the adjustment are stored, if Tn does not meet the requirements, the gradient of the simulation function of the control strategy is obtained, and the values of the control parameters X1 to Xn are adjusted by using the gradient.
Wherein the failure to optimize the control parameter comprises:
when the target value is the maximum value of all control parameter combinations or when the maximum change rate of the target value is smaller than a preset change rate, the target value still does not meet the preset requirement, and then the control parameter optimization is marked to fail.
Specifically, when Tn is the maximum value, i.e., the global optimum value, among all the control parameter combinations, Tn still does not meet the criteria for optimizing the model by the user; or in the latest m-round parameter adjustment, the maximum change rate of Tn is smaller than the set value E, but Tn still does not meet the standard of the user for model optimization, the control parameters are explained to be continuously optimized under the two conditions, the preset requirements can not be met, the control parameter optimization failure is directly marked, unnecessary optimization steps can be reduced, and the economic benefit of the operation of the storage and charging station is improved.
Example two
The difference between this embodiment and the first embodiment is that how to adjust the control parameter is further defined, specifically:
sequentially adjusting each of the control parameters based on the direction of the gradient comprises:
sequentially acquiring one of the control parameters for adjustment, and calculating adjustment amplitude according to the precision requirement, the calculation capacity, the data distribution characteristics and the training time of the simulation model;
and adjusting the control parameter by combining the adjusting amplitude and the direction of the gradient.
In this embodiment, a control parameter is sequentially obtained for adjustment, the amplitude of the adjustment parameter is determined by parameters such as model accuracy requirement, calculation capability, data distribution characteristics, training time requirement and the like, and the lower the accuracy requirement is, the larger the adjustment amplitude is; the weaker the calculation capability is, the larger the adjustment amplitude is; the more gradual the data change is, the larger the adjustment amplitude is; the shorter the training time, the larger the amplitude. In a specific practice, training may be performed several times first, and the amplitude is continuously adjusted, and if the target value converges within an acceptable time and the convergence limit meets the requirement, for example, when the target function is the total conversion rate, the requirement is that the total conversion rate is greater than 85%, the adjustment amplitude is considered to be better.
And adjusting the control parameters by combining the adjustment amplitude obtained by calculation and the gradient direction of the simulation function. For example, assume that the target value function is a binary quadratic function:
Tn＝F(X1，X2)＝X1 2+X2 2，
the gradient function is then:
assuming that the adjustment range is 0.1 and the current value of the control parameter (X1, X2) is (5, 6), the control parameter will be adjusted to:
(X1，X2)＝(5–0.1*10，3–0.1*12)＝(4，1.8)。
EXAMPLE III
Referring to fig. 2, a terminal for optimizing a control strategy of a storage and charging station includes a memory, a processor, and a computer program stored in the memory and running on the processor, where the processor executes the computer program to implement the steps of the method for optimizing a control strategy of a storage and charging station according to the first embodiment or the second embodiment.
In summary, according to the control strategy optimization method and the terminal for the storage and charging station provided by the invention, the control parameters of the control strategy are determined, the initial value of each control parameter is set, and the corresponding target value can be obtained by performing simulation based on the control parameters; judging whether the control parameters meet preset requirements or not according to the target values, if so, saving the control parameters, and if not, adjusting the control parameters by using the gradient of the simulation function until a group of control parameters meet the preset requirements or the optimization fails; the amplitude of the control parameters needs to be calculated and adjusted according to data such as precision requirements, computing power, data distribution characteristics, training time and the like of the simulation model, and the control parameters are adjusted by combining the adjustment amplitude and the function gradient; and when the control parameters meet the preset requirements or the optimization fails, the optimization is stopped, so that unnecessary optimization steps and the cost of manual maintenance can be reduced, and the economic benefit of the operation of the storage and charging station is improved.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all equivalent changes made by using the contents of the present specification and the drawings, or applied directly or indirectly to the related technical fields, are included in the scope of the present invention.
Claims (10)
1. A control strategy optimization method for a storage and charging station is characterized by comprising the following steps:
determining control parameters of a control strategy, and setting an initial value of each control parameter, wherein the control parameters comprise but are not limited to the effective starting time, the effective ending time, the SOC target value of the energy storage battery, the maximum charging power value and the maximum discharging power value of the control parameters;
simulating all the control parameters to obtain corresponding target values, wherein the target values are average daily total conversion rates of the past 30 days, and the calculation method of the daily total conversion rates is total charge/alternating current consumption in 24 hours;
and judging whether all the control parameters meet preset requirements or not according to the target values, if so, storing all the control parameters, otherwise, acquiring the gradient of a simulation function of the control strategy, and sequentially adjusting each control parameter based on the direction of the gradient until a group of the control parameters meet the preset requirements or the optimization fails.
2. The method for optimizing the control strategy of the storage and charging station according to claim 1, wherein simulating all the control parameters to obtain the corresponding target values comprises:
inputting all the control parameters into a simulation model of the storage and charging station;
calculating the target value Tn in the simulation model:
Tn＝F(X1，X2，……，Xn)；
in the formula, F denotes a simulation function, and X1 to Xn denote control parameters.
3. The method of claim 2, wherein adjusting each of the control parameters in turn based on the direction of the gradient comprises:
sequentially acquiring one of the control parameters for adjustment, and calculating adjustment amplitude according to the precision requirement, the calculation capacity, the data distribution characteristics and the training time of the simulation model;
and adjusting the control parameter by combining the adjusting amplitude and the direction of the gradient.
4. The method for optimizing the control strategy of the storage and charging station according to claim 1, wherein whether all the control parameters meet preset requirements is judged according to the target value, if yes, all the control parameters are saved, and if not, acquiring the gradient of the simulation function of the control strategy comprises:
obtaining the total conversion rate of the storage and charging station according to the target value, if the total conversion rate is greater than a preset conversion rate, enabling all the control parameters of the group after the adjustment to meet preset requirements, and storing all the control parameters of the group after the adjustment;
otherwise, all the control parameters do not meet the preset requirements, and the gradient of the simulation function of the control strategy is obtained.
5. The method for optimizing the control strategy of the storage and charging station according to claim 1, wherein the failure of the optimization of the control parameters comprises:
when the target value is the maximum value of all control parameter combinations or when the maximum change rate of the target value is smaller than a preset change rate, the target value still does not meet the preset requirement, and then the control parameter optimization is marked to fail.
6. A control strategy optimization terminal for a storage and charging station, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the following steps when executing the computer program:
determining control parameters of a control strategy, and setting an initial value of each control parameter, wherein the control parameters comprise but are not limited to the effective starting time, the effective ending time, the SOC target value of the energy storage battery, the maximum charging power value and the maximum discharging power value of the control parameters;
simulating all the control parameters to obtain corresponding target values, wherein the target values are average daily total conversion rates of the past 30 days, and the calculation method of the daily total conversion rates is total charge/alternating current consumption in 24 hours;
and judging whether all the control parameters meet preset requirements or not according to the target values, if so, storing all the control parameters, otherwise, acquiring the gradient of a simulation function of the control strategy, and sequentially adjusting each control parameter based on the direction of the gradient until a group of the control parameters meet the preset requirements or the optimization fails.
7. The terminal for optimizing the control strategy of the storage and charging station according to claim 6, wherein simulating all the control parameters to obtain the corresponding target values comprises:
inputting all the control parameters into a simulation model of the storage and charging station;
calculating the target value Tn in the simulation model:
Tn＝F(X1，X2，……，Xn)；
in the formula, F denotes a simulation function, and X1 to Xn denote control parameters.
8. The terminal for optimizing the control strategy of the storage and charging station according to claim 7, wherein adjusting each of the control parameters in turn based on the direction of the gradient comprises:
sequentially acquiring one of the control parameters for adjustment, and calculating adjustment amplitude according to the precision requirement, the calculation capacity, the data distribution characteristics and the training time of the simulation model;
and adjusting the control parameter by combining the adjusting amplitude and the direction of the gradient.
9. The terminal of claim 6, wherein the step of determining whether all the control parameters meet preset requirements according to the target values is performed, if so, all the control parameters are saved, and if not, the step of obtaining the gradient of the simulation function of the control strategy comprises:
obtaining the total conversion rate of the storage and charging station according to the target value, if the total conversion rate is greater than a preset conversion rate, enabling all the control parameters of the group after the adjustment to meet preset requirements, and storing all the control parameters of the group after the adjustment;
otherwise, all the control parameters do not meet the preset requirements, and the gradient of the simulation function of the control strategy is obtained.
10. The terminal for optimizing the control strategy of the storage and charging station according to claim 6, wherein the failure of the optimization of the control parameters comprises:
when the target value is the maximum value of all control parameter combinations or when the maximum change rate of the target value is smaller than a preset change rate, the target value still does not meet the preset requirement, and then the control parameter optimization is marked to fail.
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