CN116093466A

CN116093466A - Ordered charging method, ordered charging device, computer readable storage medium and computer equipment

Info

Publication number: CN116093466A
Application number: CN202310128516.3A
Authority: CN
Inventors: 赵贺; 林志法; 柴志超; 王立永; 李香龙; 潘鸣宇; 王瀚秋; 孙钦斐; 曹昕; 侯宇程
Original assignee: State Grid Corp of China SGCC; State Grid Beijing Electric Power Co Ltd
Current assignee: State Grid Corp of China SGCC; State Grid Beijing Electric Power Co Ltd
Priority date: 2023-02-07
Filing date: 2023-02-07
Publication date: 2023-05-09

Abstract

The invention discloses an ordered charging method, an ordered charging device, a computer readable storage medium and computer equipment. Wherein the method comprises the following steps: determining a predicted charging duration of the target device based on the type of the target device; acquiring charging power of a battery of target equipment along with the change of the charging duration and the state of charge; determining an initial charging strategy based on the charging power and the predicted charging duration; and in the preset iteration times, taking the minimum sum of the running cost and the charging dissatisfaction degree as a target, and carrying out iterative optimization on the initial charging strategy based on a preset constraint condition set according to the self-adaptive inertia weight to obtain a target charging strategy. The invention solves the technical problems of disordered charging behavior, poor charging effect and increased service life loss of the battery caused by the charging behavior when the electric agricultural appliance is charged.

Description

Ordered charging method, ordered charging device, computer readable storage medium and computer equipment

Technical Field

The present invention relates to the field of electric energy, and in particular, to an ordered charging method, an ordered charging device, a computer readable storage medium, and a computer apparatus.

Background

Along with the continuous promotion of the progress of modern construction of agricultural facilities, centralized, refined and intelligent agricultural park planting modes gradually replace traditional low-efficiency and extensive planting modes, meanwhile, the mobile electric power tool has the advantages of environmental protection, portability, economy and the like, and is widely used for replacing traditional fuel oil and manpower tools, such as small and convenient three-wheeled electric vehicle conveying tools, energy-saving and environment-friendly electric pickup, electric forklifts, small electric bulldozers, efficient and portable electric pesticide spraying machines, electric irrigation machines and the like, and high-standard farmland electrified mobile agricultural machine tools and the like are widely applied, and the development direction of national energy green rotation is also met. But the use of large-scale electric agricultural implements is also faced with the problem of efficient management of charging.

Therefore, in the related art, there are technical problems in that the charging behavior is disordered when charging the electric agricultural implement, the charging effect is poor, and the battery life loss is increased due to the charging behavior.

In view of the above problems, no effective solution has been proposed at present.

Disclosure of Invention

The embodiment of the invention provides an ordered charging method, an ordered charging device, a computer readable storage medium and computer equipment, which at least solve the technical problems of disordered charging behavior and poor charging effect when an electric agricultural appliance is charged, and the service life loss of a battery caused by the charging behavior.

According to an aspect of an embodiment of the present invention, there is provided an ordered charging method including: determining a predicted charging duration of the target device based on the type of the target device; acquiring charging power of a battery of target equipment along with the change of the charging duration and the state of charge; determining an initial charging strategy based on the charging power and the predicted charging duration; and in the preset iteration times, taking the minimum sum of the running cost and the charging dissatisfaction degree as a target, and carrying out iterative optimization on the initial charging strategy based on a preset constraint condition set according to the self-adaptive inertia weight to obtain a target charging strategy.

Optionally, determining the predicted charging duration of the target device based on the type of the target device includes: acquiring actual charging data in a third preset time range before the charging date based on the type of the target equipment, wherein the actual charging data comprises actual charging duration and actual charging frequency; determining actual charging probability distribution conditions corresponding to actual charging data; carrying out Gaussian distribution fitting on the actual charging probability distribution condition to obtain a fitting result; and generating a predicted charging duration based on the fitting result.

Optionally, obtaining the charging power of the battery of the target device according to the charging duration and the charging state change includes: acquiring the current state of charge of the battery; under the condition that the current charge state is smaller than the preset charge state, determining that the charging power is first power which changes along with the charging duration in a constant-current charging mode; and under the condition that the current charge state is greater than or equal to the preset charge state, determining that the charging power is the second power which changes along with the charging duration in the constant-voltage charging mode.

Optionally, the set of preset constraints includes at least one of: charging power and state of charge constraints, charge duration constraints, battery charge and discharge life cost constraints.

Optionally, in the preset iteration number, taking the minimum sum of the running cost and the charging dissatisfaction degree as a target, and based on a preset constraint condition set, performing iterative optimization on the initial charging strategy according to the self-adaptive inertia weight to obtain a target charging strategy, where the method includes: determining the operation cost and the charging dissatisfaction degree corresponding to the initial charging strategy; screening the initial charging strategy based on the operation cost and the charging dissatisfaction degree corresponding to the initial charging strategy to obtain a first candidate charging strategy; under the condition that a preset constraint condition set is met, according to a preset updating mode and the self-adaptive inertia weight, iteratively optimizing a first candidate charging strategy to obtain a second candidate charging strategy in a preset iteration number by taking the minimum sum of the running cost and the charging dissatisfaction degree as a target; determining an optimized evaluation factor of the second candidate charging strategy; and determining a target charging strategy based on the second candidate charging strategy and the optimized evaluation factor.

Optionally, the determining means of the operation cost and the charging dissatisfaction degree includes: determining a theoretical charging duration of the target device based on the type of the target device; predicting a photovoltaic power generation power which varies with time in a charging day based on historical illumination data in a first predetermined time range before the charging day; predicting a time-varying base load during the charging day based on a historical load curve during a second predetermined time range prior to the charging day; determining the equivalent cycle life times of the battery in the current charge based on the charge and discharge depth of the battery; determining battery life loss costs based on the number of equivalent cycle life times; based on the photovoltaic power generation power, the base load, the theoretical charging duration, the predicted charging duration and the charging power, the operation cost and the charging dissatisfaction degree of an initial charging strategy are respectively determined.

Optionally, the value of the adaptive inertia weight is determined by the current number of iterations in the iterative optimization process.

According to another aspect of the embodiment of the present invention, there is also provided an ordered charging device, including: the first determining module is used for determining theoretical charging duration and predicted charging duration of the target equipment based on the type of the target equipment; the acquisition module is used for acquiring the charging power of the battery of the target equipment along with the change of the charging duration and the charging state; the second determining module is used for determining an initial charging strategy based on the charging power and the predicted charging duration; and the optimization module is used for iteratively optimizing the initial charging strategy according to the self-adaptive inertia weight based on a preset constraint condition set by taking the minimum sum of the running cost and the charging dissatisfaction degree as a target in the preset iteration times, so as to obtain a target charging strategy.

According to another aspect of the embodiments of the present invention, there is also provided a computer readable storage medium, including a stored program, where the program when run controls a device in which the computer readable storage medium is located to perform any one of the above-described ordered charging methods.

According to another aspect of an embodiment of the present invention, there is also provided a computer apparatus including: a memory and a processor, the memory storing a computer program; and a processor for executing a computer program stored in the memory, the computer program when run causing the processor to perform any one of the ordered charging methods described above.

In the embodiment of the invention, a mode of comprehensively considering new energy consumption, user charging behavior, peak-valley electricity price cost, service life loss of a battery and charging efficiency characteristics is adopted when a charging strategy of an electric agricultural appliance is prepared, theoretical charging time length and predicted charging time length of target equipment are determined according to the type of the target equipment, wherein the theoretical charging time length is the shortest time length of charging which can be completed theoretically by the target equipment, the predicted charging time length is the time length of charging which is obtained by prediction and is allowed by the target equipment, the charging power adopted when the battery of the target equipment is charged is determined by the charging time length and the state of charge of the battery, then, an initial charging strategy is generated based on the determined charging power which dynamically changes and the predicted charging time length, wherein the initial charging strategy is a charging power composition scheme which comprises charging power values corresponding to each moment in the predicted charging time length, namely representing that the battery is charged according to the charging power which is larger at each moment, on the basis of the initial charging strategy, the embodiment of the invention can realize the optimal charging strategy according to the optimal charging effect by iteratively calculating, the optimal charging strategy is realized according to the optimal charging efficiency of the charging strategy and the optimal charging power consumption of the target agricultural appliance according to the optimal charging system on the basis of the optimal charging strategy on the premise of meeting a preset constraint condition set, and further solves the technical problems of disordered charging behavior, poor charging effect and increased service life loss of the battery caused by the charging behavior when the electric agricultural appliance is charged.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:

fig. 1 is a flowchart of an orderly charging method provided according to an embodiment of the present invention;

FIG. 2 is a flow chart of a mobile agricultural facility orderly charging method based on electricity usage behavior and battery characteristics provided in accordance with an alternative embodiment of the present invention;

FIG. 3 is a schematic diagram of historical data provided in accordance with an alternative embodiment of the present invention;

FIG. 4 is a schematic diagram of an optimal particle fit curve provided in accordance with an alternative embodiment of the present invention;

FIG. 5 is a power stack diagram of an optimal ordered charging scheme after algorithm convergence provided in accordance with an alternative embodiment of the present invention;

fig. 6 is a block diagram of an ordered charging device according to an embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled 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.

It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

In the prior art, a charging port and charging time cannot be reasonably allocated aiming at a charging scheme of an electric agricultural appliance, so that the agricultural appliance is difficult to charge, the electric quantity of a battery is insufficient, normal agricultural production is influenced, the satisfaction degree of farmers is influenced, peak-valley electricity price difference cannot be finely considered, the charging cost is further improved, the existing charging scheme can be slightly and effectively combined with new energy power generation levels such as photovoltaic and the like, and the energy utilization level conforming to basic agriculture is reduced, the new energy consumption rate is reduced, meanwhile, the state of charge cannot be effectively tracked, and the service life loss of the battery is also increased due to the charging behaviors of high frequency and low charging depth.

For the above problems, a high-efficiency and orderly charging strategy formulation scheme for electric agricultural appliances is not proposed in the prior art, only researches on orderly charging of electric vehicles are carried out, but the existing orderly charging scheme for electric vehicles still has the problem that consideration factors are not comprehensive enough, for example, the charging time is not taken as a condition for dynamic consideration; the typical photovoltaic power is obtained only by a statistical method, and the real-time performance of prediction is poor; the charged electricity price is not considered; battery charging power characteristics and life cost characteristics are not considered, and so on. The ordered charging of the electric automobile and the electric agricultural appliance has a certain gap, and the charging strategy formulation scheme of the electric automobile cannot be directly referred to, for example, the battery capacity and the charging and discharging power of different agricultural appliance equipment have large difference, and the charging frequency difference is obvious; or electric agricultural appliances, the charging time of which is difficult to predict compared to electric automobiles, etc. Therefore, the above-mentioned problems in the prior art have not been solved.

Aiming at the technical problems, the embodiment of the invention provides an orderly charging method embodiment,

it should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.

Fig. 1 is a flowchart of an orderly charging method according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:

step S102, determining theoretical charging duration and predicted charging duration of target equipment based on the type of the target equipment;

step S104, obtaining charging power of a battery of the target equipment along with the change of the charging duration and the state of charge;

step S106, determining an initial charging strategy based on the charging power and the predicted charging duration;

and S108, iteratively optimizing the initial charging strategy according to the self-adaptive inertia weight based on a preset constraint condition set by taking the minimum sum of the running cost and the charging dissatisfaction degree as a target in the preset iteration times, so as to obtain a target charging strategy.

Through the steps, adopting a mode of comprehensively considering new energy consumption, user charging behavior, peak-valley electricity price cost, service life loss of a battery and charging efficiency characteristics when a charging strategy of an electric agricultural appliance is prepared, determining theoretical charging time length and predicted charging time length of the target equipment according to the type of the target equipment, wherein the theoretical charging time length is the shortest time length of the target equipment which can finish charging theoretically, the predicted charging time length is the predicted time length of the target equipment which can be used for allowing charging, the charging power adopted when the battery of the target equipment is charged is determined by the charging time length and the state of charge of the battery, then generating an initial charging strategy based on the determined charging power of dynamic change and the predicted charging time length, wherein the initial charging strategy is a charging power composition scheme comprising charging power values corresponding to each moment in the predicted charging time length, namely representing that the battery is charged according to the charging power of each moment, on the basis of the initial charging strategy, on the premise of meeting a preset constraint condition set, the charging power is determined by the charging time length of the charging strategy which corresponds to the charging time length and the optimal, the optimal charging strategy is carried out according to the optimal charging power consumption of the target equipment according to the optimal charging time length, and the optimal charging efficiency is achieved according to the optimal charging strategy, and the charging efficiency is achieved according to the optimal charging strategy of the charging system, and the charging efficiency is achieved by the charging system according to the charging strategy of the agricultural charging system, and further solves the technical problems of disordered charging behavior, poor charging effect and increased service life loss of the battery caused by the charging behavior when the electric agricultural appliance is charged.

Wherein, the target equipment is an electric agricultural appliance.

As an alternative embodiment, determining the predicted charge duration of the target device based on the type of the target device includes: acquiring actual charging data in a third preset time range before the charging date based on the type of the target equipment, wherein the actual charging data comprises actual charging duration and actual charging frequency; determining actual charging probability distribution conditions corresponding to actual charging data; carrying out Gaussian distribution fitting on the actual charging probability distribution condition to obtain a fitting result; and generating a predicted charging duration based on the fitting result.

Since the farmer has his own charging behavior during the actual production process, for example, charging starts in the evening, charging is stopped the next morning, etc., i.e., the actual allowable charging period may be longer than the theoretical fastest charging period. Therefore, the present embodiment predicts the actually allowable charge time period of the target device. In this embodiment, first, actual charging data in a third predetermined time range before a charging day is obtained according to the type of the target device, the actual charging probability distribution situation of the target device of the type is determined based on the obtained actual charging probability distribution situation, gaussian distribution fitting is performed on the actual charging probability distribution situation, and finally, a predicted charging duration is randomly generated according to a fitting result. The actual charging time is from the connection of the charging pile to the disconnection of the charging pile.

When determining the actual charging probability distribution condition corresponding to the actual charging data, a monte carlo simulation algorithm can be adopted, wherein the monte carlo simulation algorithm is a statistical modeling method based on probability theory, a probability density model is selected through the statistical frequency of the historical data, and parameter fitting is performed to obtain a final probability density function, so that the charging duration of farmers can be simulated and generated.

The third predetermined time range may be set according to an application scenario or a prediction accuracy requirement, for example, may be approximately 30 days of the target device, and so on.

In addition, the formula of the fitting function of the Gaussian distribution adopted by the embodiment of the invention is as follows:

where x is the charge duration and μ and λ are the inverse gaussian distribution parameters.

Meanwhile, the embodiment of the invention adopts an incremental heuristic to calculate the values of the parameter mu and delta respectively, and sets the initial values of mu and delta to be 0.1, the numerical range [0.1,100], and the parameter increasing interval to be 0.1. The specific process is that delta is unchanged each time, and the parameter u is increased by 0.1; then calculating the function value F under the parameters mu and delta; then, after u is increased to 100, the parameter delta+0.01, and u is calculated from 0.01 in an increasing way; until the parameters mu and delta are both 100. The values of μ and δ, where the mean square error of the F sequence and the actual data distribution sequence is the smallest, are chosen as the final gaussian distribution parameter. The sum of the parameters mu is determined, and a final charging duration probability distribution function is obtained. The predicted charge duration of the target device may be randomly generated according to the F (x, μ, δ) profile.

As an alternative embodiment, obtaining the charging power of the battery of the target device according to the charging duration and the charging state comprises: acquiring the current state of charge of the battery; under the condition that the current charge state is smaller than the preset charge state, determining that the charging power is first power which changes along with the charging duration in a constant-current charging mode; and under the condition that the current charge state is greater than or equal to the preset charge state, determining that the charging power is the second power which changes along with the charging duration in the constant-voltage charging mode.

On one hand, because the battery capacity, rated charging power and charging efficiency of different types of electric agricultural appliances are greatly different, the charging duration of different devices are greatly different, and the battery charging power characteristics of the devices need to be combined when determining a charging strategy. On the other hand, the electric agricultural implement of the embodiment of the invention mainly uses lithium batteries, wherein the charging power of the batteries is not constant in practice and is mainly divided into constant current and constant voltage stages. Therefore, the embodiment of the invention divides the charging power adopted when the target equipment is charged into a constant-current charging mode and a constant-voltage charging mode according to the charge state of the battery. The charging effect which is more efficient and has smaller loss on the battery can be achieved by acquiring the current charge state of the battery of the target device in real time when the battery is charged and correspondingly adopting the constant-current charging mode or the charging power under the constant-voltage charging mode according to the current charge state of the battery. For example, in the initial stage of charging, the state of charge of the battery is low, the equivalent internal resistance of the battery is small and stable, and if a constant voltage is adopted, a large charging current is easy to generate, and a constant current charging mode is adopted at the moment; when the state of charge reaches the preset state of charge, the equivalent internal resistance of the battery increases rapidly, if the constant current mode is continuously adopted, the applied voltage requirement is increased, the requirement is difficult to meet, irreversible damage is generated to the battery, and the constant voltage mode is adopted at the moment.

Wherein, corresponding to different charging modes, the first power or the second power can be determined by the following method:

wherein P is _{REV_max} The rated maximum charging power of the battery is set; when the state of charge is in interval [0, SOC _th ]In the constant current mode, in the interval [ SOC ] _th ，1]When the device is in a constant pressure mode; epsilon (t) is a dynamic charging parameter in a constant voltage stage, and epsilon (t) =ln0.85 is taken in the embodiment of the invention; t (T) _th In the embodiment of the invention, the SOCth value is 0.8 for the time corresponding to the state of charge threshold SOCth.

As an alternative embodiment, the set of preset constraints comprises at least one of: charging power and state of charge constraints, charge duration constraints, battery charge and discharge life cost constraints.

In order to ensure that the target charging strategy is efficient and feasible, the preset constraint condition set is determined by comprehensively considering the aspects of new energy consumption, user charging behavior, peak-valley electricity price cost, service life loss of the battery, charging efficiency characteristics and the like.

The charge power and state of charge constraints are as follows:

the SOCi is the current state of charge of the battery, SOCend is the state of charge of the battery after charging, SOC0 is the state of charge of the battery after initial charging, PREV is the charging power, and PREV_MAX is the rated maximum charging power of the battery.

The charge duration constraint conditions are as follows:

0≤T _{L_i} ≤T _{R_i}

the constraint represents i the actual charging time T of the device _{L_i} Should be at a predicted charging time T based on monte carlo simulation _{R_i} And (5) completing the charging behavior.

The life cost constraint conditions of battery charge and discharge are as follows:

wherein L is _deep (t) is the depth of charge and discharge; n is n _loss Equivalent charge and discharge cycle times of the battery; n (N) _c The design cycle life for the standard test of the battery is given in units of times; n (N) _loss The total number of charge cycles experienced by the battery, i.e., the actual battery cycle life, in times at which the battery capacity decays by 20%.

As an optional embodiment, in a preset iteration number, with the goal of minimizing the sum of the running cost and the charging dissatisfaction, based on a preset constraint condition set, performing iterative optimization on an initial charging strategy according to an adaptive inertia weight, to obtain a target charging strategy, including: determining the operation cost and the charging dissatisfaction degree corresponding to the initial charging strategy; screening the initial charging strategy based on the operation cost and the charging dissatisfaction degree corresponding to the initial charging strategy to obtain a first candidate charging strategy; under the condition that a preset constraint condition set is met, according to a preset updating mode and the self-adaptive inertia weight, iteratively optimizing a first candidate charging strategy to obtain a second candidate charging strategy in a preset iteration number by taking the minimum sum of the running cost and the charging dissatisfaction degree as a target; determining an optimized evaluation factor of the second candidate charging strategy; and determining a target charging strategy based on the second candidate charging strategy and the optimized evaluation factor.

In this embodiment, each charging strategy is essentially a charging power composition scheme that includes charging power values corresponding to respective moments in time within a predicted charging duration, i.e., characterizes how much charging power should be used to charge the battery at the respective moments. Therefore, after determining the charging mode and the charging power based on the charge state of the battery and determining the predicted charging duration, a certain number of charging schemes, namely initial charging strategies, can be randomly generated, then the running cost and the charging dissatisfaction degree of the charging schemes are calculated, the optimal solution and the worst solution are determined, and the distances between the optimal solution and the worst solution, namely the positive ideal distance and the negative ideal distance, are respectively corresponding to the running cost and the charging dissatisfaction degree of each charging scheme, and then the schemes are screened according to the positive ideal distance and the negative ideal distance, so that the first candidate charging strategy is obtained.

After the first candidate charging strategy is obtained, a preset updating mode (for example, a population updating formula) and an adaptive inertia weight can be adopted to update a plurality of charging schemes serving as the first candidate charging strategy, at the moment, iteration is completed once, then the operation cost and the charging dissatisfaction degree of the first candidate charging strategy are calculated again, the optimal scheme calculated for the 2 nd time is obtained similarly to the method for determining the first candidate charging strategy, if the optimal scheme is better than the 1 st time, the optimal solution of the 1 st iteration is replaced, if the optimal scheme is not obtained, the original scheme is kept unchanged, the iteration steps are repeated until the preset iteration times are reached, calculation is stopped, and the final optimal scheme, namely the second candidate charging strategy and the operation cost and the charging dissatisfaction degree corresponding to the optimal scheme are output.

The preset updating mode may be as follows:

wherein X is _i For a charging power composition scheme, s is the iteration number of the algorithm; w is self-adaptationThe weight of inertia is needed; r1 and R2 are random coefficients, and the value range is [0,1 ]]；X _Pbest And X _Gbest The method comprises the steps of respectively obtaining an individual optimal solution and a global optimal solution; c1 and C2 are acceleration factors.

Then, according to the running cost and the charging dissatisfaction degree corresponding to the second candidate charging strategy, an optimization evaluation factor is determined, and since the objective function in the embodiment includes two sub-objectives, namely, the objective cost and the charging dissatisfaction degree, and the two sub-objectives may have respective optimal charging schemes, the embodiment needs to take the sum of the two sub-objectives as the final objective function, therefore, the embodiment introduces the optimization evaluation factor, the optimization evaluation factor is used for representing the degree that the two sub-objectives, namely, the objective cost and the charging dissatisfaction degree, are close to the optimal solution at the same time, and the smaller the value is, the closer the description is, namely, the better the charging scheme is.

The method for determining the optimization evaluation factor comprises the following steps:

wherein C is _j In order to optimize the evaluation factor,

the ideal distances are positive and negative respectively.

Wherein, the liquid crystal display device comprises a liquid crystal display device,

the calculation method of (2) is as follows:

wherein f' _{cost_j} (s) represents the normalized running cost of the charging scheme j at s iterations, f _{sati_j} (s) represents the normalized charge dissatisfaction of the charging scheme j at s iterations.

It should be noted that, in this embodiment, to improve the optimization efficiency of the initial charging strategy, a TOPSIS ordering method may be used to perform iterative optimization. TOPSIS is a typical multi-objective decision method, has the characteristics of simplicity, flexibility and high efficiency, can effectively eliminate dimensions, and intuitively selects an optimal charging scheme according to the relative distance from an ideal solution.

As an alternative embodiment, the determination of the running cost and the degree of dissatisfaction with charging includes: predicting a photovoltaic power generation power which varies with time in a charging day based on historical illumination data in a first predetermined time range before the charging day; predicting a time-varying base load during the charging day based on a historical load curve during a second predetermined time range prior to the charging day; determining a theoretical charging duration of the target device based on the type of the target device; determining the equivalent cycle life times of the battery in the current charge based on the charge and discharge depth of the battery; determining battery life loss costs based on the number of equivalent cycle life times; based on the photovoltaic power generation power, the base load, the theoretical charging duration, the predicted charging duration, the charging power and the battery life loss cost, the operation cost and the charging dissatisfaction degree of an initial charging strategy are respectively determined.

Aiming at the photovoltaic power generation power, the embodiment can adopt a BP neural network simulation method, train a neural network model based on historical illumination data in a first preset time range before charging day as sample data, and then predict the photovoltaic power generation power on the charging day by adopting the trained model. For example, 24-hour illumination data of the same day can be predicted from the last 30-day historical illumination data, and the data adoption period is 1 hour. The historical illumination data of nearly 30 days is taken as sample data, the data volume is 24 multiplied by 30, the neural network model is trained through the sample data each time, the input quantity and the output quantity are 24 multiplied by 1 historical illumination data, the actual illumination quantity of the following day is adopted to correct the output quantity, the neural network model is corrected through feedback, the simulation is stopped when the error is smaller than the target precision, a final prediction model is obtained, and the illumination prediction curve of the next day, namely the photovoltaic power generation power which changes with time in the charging day in the embodiment, is obtained through the prediction model.

Aiming at the base load, the power loads of different farmers may have certain user power consumption habits, and certain electric quantity rules are contained in a short period, so that the base load in the charging day is determined based on the corresponding coincidence average value of each moment in the history coincidence curve by adopting a method for replacing the approximate daily load average value in the embodiment. For example, a history load curve of the last 7 days may be selected, and for each hour, an average load value may be obtained to replace the load at the same time of the next day, where the calculation formula is as follows:

Wherein t is a certain time in 24 hours; m is the number of days of recent historical load, and the value is 7 in the embodiment; m is [1, M]Integer variable within the range; p (P) _load (t) is an uncontrollable typical load at time t; p (P) _{load_M} And (t, m) is the load power at the mth and the t times in the history.

For the battery life loss cost, the embodiment first determines the battery cycle charge life loss relationship between the battery charge and discharge depth and the discharge interval, and the formula is as follows:

wherein N is _loss The total number of charge cycles experienced by the battery, i.e., the actual battery cycle life, in times when the battery capacity decays by 20%; n (N) _c The design cycle life for the standard test of the battery is given in units of times; β1 and β2 are fitting parameters of test data; l (L) _deep 、

Depth of discharge and standard depth of discharge, respectively, where L _deep Is the difference between the charge state after charging and before charging, < >>

The value is 0.8; SOC (State of Charge) _ref Is lotus leafAnd the electric state standard value is 0.8.

Wherein depth of discharge L _deep The calculation formula is as follows:

L _deep ＝SOC _end -SOC ₀

the actual cycle times of the battery are converted into standard cycle times, so that the charge and discharge depth of each charge can be converted into cycle life times according to a battery cycle charge life loss relation formula, and the formula is as follows:

Wherein n is _loss Equivalent cycle times for this charge; SOC (State of Charge) _{dis_ref} 、SOC _ref Are parameters of less than 1. Thus, from depth of discharge L _deep The calculation formula shows that the higher the SOC at the start of discharge, the greater the depth of discharge, and the greater the life loss of the battery.

The life loss cost calculation formula of the battery is as follows:

wherein S is _bat Investment and construction costs for the battery; c (C) _bat For the equivalent loss cost of the device at the current charge.

In summary, the following manner may be employed in determining the running cost and the degree of charging dissatisfaction:

wherein F is _c (t) is a function value of real-time target optimization; f (f) _cost (t) is an operation cost function of farmers; f (f) _sati (t) is an agricultural productUser dissatisfaction function for the user; c (C) _electric (t) is a time-of-use electricity price; p (P) _load (t) is an unregulated base load; p (P) _PV (t) is photovoltaic real-time generation power; t (T) _{L_i} The actual charging time length of the ith electric agricultural implement; t (T) _{R_i} Representing a predicted charge duration of an ith electric agricultural implement; p (P) _{REV_i} (t) is the charging power of the ith electric agricultural implement at the time t; c (C) _{bat_i} (t) is the equivalent charge life loss cost of the ith electric agricultural implement at time t; SOC (State of Charge) _{end_i} (t) is the state of charge of the ith device at time t; the function std () and the function mean () represent functions for solving the mean square error and the average value, respectively; ti is the t time charging of the ith device

Wherein T is _{L_i} /T _R The smaller the value, the shorter the charging time, the lower the user dissatisfaction; 1-SOC _{end_i} The smaller the value of (t), the closer the battery state of charge is to 1 at the end of charging, the lower the user dissatisfaction; the smaller the values of the function std () and the function mean () are, the lower the per unit value of the user at the peak-valley difference is, the higher the power quality is, and the lower the user dissatisfaction is.

As an alternative embodiment, the value of the adaptive inertia weight is determined by the current number of iterations in the iterative optimization process. The embodiment proposes an adaptive inertial weight, the value of which is determined by the current iteration number, as follows:

wherein w is _max And w _m ⁱ _n The upper limit and the lower limit of the inertia weight are respectively the upper limit and the lower limit of the inertia weight, and the available values are 0.9 and 0.5; s is the current iteration number; gamma is an empirical adjustment factor in the range of [1,30]In this embodiment, the value is 3.

From the above, it can be seen that the adaptive inertial weight dynamically adjusts the magnitude according to the iteration times, keeps a larger value in the initial stage w of the optimization, ensures the global optimizing capability of the algorithm, namely, can rapidly determine the range of the optimal charging scheme, is smaller in the later stage w of the optimization, keeps the local optimizing of the algorithm, namely, can accurately determine the optimal charging scheme.

Based on the foregoing embodiment and the optional embodiments, an optional implementation manner is further provided in the present invention, and the following description is provided.

An alternative embodiment of the present invention proposes a method for orderly charging a mobile agricultural facility based on electricity consumption behavior and battery characteristics, and fig. 2 is a flowchart of a method for orderly charging a mobile agricultural facility based on electricity consumption behavior and battery characteristics according to an alternative embodiment of the present invention, as shown in fig. 2, the method includes the following steps: firstly, establishing a photovoltaic power generation power prediction method based on a neural network, a typical load generation method based on curve superposition, and a power-driven agricultural implement charging time probability model based on Monte Carlo simulation, and establishing a predictive photovoltaic, load and charging time generation method; then, establishing a mathematical model between the battery charging efficiency and the state of charge (SOC), and establishing a life cost model of the battery discharging depth and the discharging interval; furthermore, establishing a long-time-scale electric agricultural machine with an orderly charging power optimization method; and finally, solving the model by adopting a TOPSIS sequencing-based multi-target self-adaptive particle swarm algorithm to obtain an agricultural facility ordered charging optimization scheme containing agricultural photovoltaics. Alternative embodiments of the present invention are described in detail below.

(1) Agricultural photovoltaic prediction and uncontrollable typical load simulation

The method is mainly applied to a large-scale agricultural planting industry park containing photovoltaics in the optional implementation mode, and the optimization object is the charging time and power of each agricultural tool connected with the charging pile. Alternative embodiments of the present invention require consideration of photovoltaic power and uncontrolled load conditions during the charging cycle, and thus require photovoltaic power prediction methods and uncontrolled typical load simulation methods.

The photovoltaic prediction adopts a BP neural network simulation method, 24-hour illumination data of the same day is predicted according to the historical illumination data of the last 30 days, and the data adopts a period of 1h. The historical illumination data of nearly 30 days is taken as sample data, the data volume is 24 multiplied by 30, the neural network model is trained through the sample data each time, the input quantity and the output quantity are 24 multiplied by 1 historical illumination data, the actual illumination quantity of the following day is adopted to correct the output quantity, the neural network model is corrected through feedback, the simulation is stopped when the error is smaller than the target precision, the final prediction model is obtained, and the illumination prediction curve of the next day is obtained through the prediction model.

The power utilization load of the agricultural planting park has a certain user power utilization habit, and a certain electric quantity rule is contained in a short period, the optional implementation mode of the invention adopts a mode of replacing the approximate daily load average value, a historical load curve of the last 7 days is selected, the load at the same time in the next day is replaced by the average load value by taking each hour as an object, and the calculation formula is as follows:

Wherein t is a certain time in 24 hours; m is the number of days of recent historical load, and the value is 7 in the alternative embodiment of the invention; m is [1, M]Integer variable within the range; p (P) _load (t) is an uncontrollable typical load at time t; p (P) _{load_M} And (t, m) is the load power at the mth and the t times in the history.

(2) Electric agricultural implement charging behavior simulation and charging power model

1) Monte Carlo simulation-based charge duration simulation

In actual production, the farmer has his own charging behavior, for example, charging starts in the evening, charging is stopped in the morning the next day, and the actually allowable charging period may be longer than the theoretical fastest charging period TL.

Therefore, the alternative embodiment of the invention needs to carry out simulation evaluation on the charging time of farmers of each type of equipment. The Monte Carlo simulation algorithm is a statistical modeling method based on probability theory, a probability density model is selected according to the statistical frequency of historical data, and parameter fitting is carried out to obtain a final probability density function, so that the charging time of farmers can be simulated and generated. Taking a certain agricultural charging device i as an example, according to the actual charging duration (namely the duration from the time of connecting a charging pile to the time of disconnecting the charging pile) data of the device i for nearly 30 days, the optional embodiment of the invention calculates the duration and the frequency of each charging, and draws a probability partition map. According to the probability distribution diagram, the optional implementation mode of the invention adopts Gaussian distribution for fitting, and the formula of a fitting function is as follows:

In an alternative embodiment of the invention, the values of the parameters mu and delta are calculated by adopting an incremental heuristic, the initial values of mu and delta are set to be 0.1, the numerical range is 0.1,100, and the parameter increment interval is 0.1. The specific process is that delta is unchanged each time, and the parameter u is increased by 0.1; then calculating the function value F under the parameters mu and delta; then, after u is increased to 100, the parameter delta+0.01, and u is calculated from 0.01 in an increasing way; until the parameters mu and delta are both 100. The values of μ and δ, where the mean square error of the F sequence and the actual data distribution sequence is the smallest, are chosen as the final gaussian distribution parameter. The parameters mu and delta are determined so far, and the final charge duration probability distribution function F (x, mu, delta) is obtained. The duration TR of the charging of the device i can be randomly generated according to the F (x, μ, δ) profile.

2) Two-stage battery charging power calculation model

The electric agricultural appliance in the alternative embodiment of the invention has larger difference of battery capacity, rated charging power and charging efficiency, so that the charging time difference of different devices is larger, and the battery charging power characteristic of the devices is required to be combined. The calculation formula of the charging time length is as follows:

wherein TL is the charging duration; SOC (State of Charge) _end The charged state is the charged state; SOC (State of Charge) ₀ A state of charge for initial charging; q (Q) _C Is the capacity of the battery; η (eta) _REV (t) is the charging efficiency of the device at time t; p (P) _REV (t) is the charging power at time t;

the electric agricultural appliance of the alternative embodiment of the invention mainly takes a lithium battery as a main component, wherein the charging power of the battery is not constant in practice and mainly comprises two stages of constant current and constant voltage. When the charge state of the lithium battery is low in the initial stage of charging, the equivalent internal resistance of the battery is small and stable, and if a constant voltage is adopted, a large charging current is easy to generate, and a constant current charging mode is adopted at the moment; when the state of charge reaches the threshold SOC _th After that, the equivalent internal resistance of the battery is rapidly increased, if the constant-current mode is continuously adopted, the requirement on the applied voltage is increased, the requirement on the applied voltage is difficult to meet, irreversible damage is generated on the battery, and the constant-voltage mode is adopted at the moment. According to the alternative embodiment of the invention, a real-time charging power model is established according to a two-stage charging mode, and the formula is as follows:

wherein P is _{REV_max} The rated maximum charging power of the battery is set; when the state of charge is in interval [0, SOC _th ]In the constant current mode, in the interval [ SOC ] _th ，1]When the device is in a constant pressure mode; epsilon (t) is a dynamic charging parameter in a constant voltage stage, and epsilon (t) =ln0.85 is taken in an alternative embodiment of the invention; t (T) _th Time corresponding to state of charge threshold SOCth, wherein SOC _th The value is 0.8.

3) Battery charge depth and interval life cost model

The alternative embodiment of the invention establishes a battery cyclic charge life loss model considering the discharge depth and the discharge interval, and the formula is as follows:

wherein N is _loss The total number of charge cycles experienced by the battery, i.e., the actual battery cycle life, in times when the battery capacity decays by 20%; n (N) _c The invention can test the design cycle life of the battery in timesIn the alternative embodiment, 1000 times; β1 and β2 are fitting parameters of test data, and 1.98 and 2.79 are taken respectively in an alternative embodiment of the invention; l (L) _deep 、

The value is 0.8; SOC (State of Charge) _ref The value of the charge state standard value is 0.8.

Wherein depth of discharge L _deep The calculation formula is as follows:

L _deep ＝SOC _end -SOC ₀ (6)

the formula (7) converts the actual cycle number of the battery into the standard cycle number, so that the charge and discharge depth of each charge can be converted into the cycle life number according to the formula (5), and the formula is as follows:

wherein n is _loss Equivalent cycle times for this charge; SOC (State of Charge) _{dis_ref} 、SOC _ref Are parameters of less than 1. Therefore, as is clear from the formula (6), the higher the SOC at the start of discharge, the greater the depth of discharge, and the greater the life loss of the battery.

The life loss cost calculation formula of the battery is as follows:

(3) Charging power optimization model of long time scale

The method comprises the following steps that an optional embodiment of the invention establishes a long-scale real-time rolling charging power optimization method of an electric agricultural appliance, and a farmer charging duration assessment method based on Monte Carlo simulation is firstly used for obtaining a predicted charging duration for each device; and then taking a two-stage battery charging power characteristic model and battery life cost loss characteristics into consideration in the duration, and reasonably arranging the charging power of the device at each moment with the aim of lowest comprehensive operation cost and user dissatisfaction.

1) Objective function

According to the invention, the charging power of each device is arranged on a long time scale, and the objective function of the used optimization model is an ordered charging optimization model with user dissatisfaction and comprehensive cost, wherein the user dissatisfaction comprises three aspects of charging efficiency, the most full charge state at the end of charging and the best electric energy quality (namely, the minimum load peak-valley difference), and the formula is as follows:

F _c (t)＝min{f _cost (t)+f _sati (t)} (9)

wherein:

wherein F is _c (t) is a function value of real-time target optimization; f (f) _cost (t) is an operation cost function of farmers; f (f) _sati (t) is a user dissatisfaction function for farmers; c (C) _electric (t) is a time-of-use electricity price; p (P) _load (t) is an unregulated base load; p (P) _PV (t) is photovoltaic real-time generation power; t (T) _{L_i} The actual charging time length of the ith electric agricultural implement; t (T) _{R_i} Representing a predicted charge duration of an ith electric agricultural implement; p (P) _{REV_i} (t) is the charging power of the ith electric agricultural implement at the time t; c (C) _{bat_i} (t) is the equivalent charge life loss cost of the ith electric agricultural implement at time t; SOC (State of Charge) _{end_i} (t) is the ith device inState of charge at time t; the function std () and the function mean () represent functions for solving the mean square error and the average value, respectively; ti is the t time charging of the ith device

2) Constraint conditions

1. Agricultural implement charging power and capacity constraints

2. Duration of charge constraint

0≤T _{L_i} ≤T _{R_i} (13)

3. Battery charge-discharge life cost constraint

In the constraint conditions, the charge and discharge depth L is considered respectively _deep (t) equivalent charge-discharge cycle times of battery n _loss Constraint conditions.

(4) TOPSIS sorting-based multi-target particle swarm solving algorithm

The sub-targets of the multi-target optimization model established by the alternative embodiment of the invention are user dissatisfaction and comprehensive energy consumption cost respectively, which are values with different dimensions, and the sum cannot be directly carried out. TOPSIS is a typical multi-objective decision method, has the characteristics of simplicity, flexibility and high efficiency, can effectively eliminate dimensions, and intuitively selects an optimal charging scheme according to the relative distance from an ideal solution. Meanwhile, the optional implementation mode of the invention designs the self-adaptive inertia weight at first, and dynamically adjusts the local and global traversal capacities according to the iteration times.

1) Multi-target particle swarm solving model

The optimization variable of the alternative embodiment of the invention is the self-charging time length T of the agricultural implement connected with the charging pile _{R_i} The sequence formula of the optimization variable of the charging power at each moment in the range is as follows:

X _i ＝{P _{REV_1} (t ₁ ,t ₂ L,T _{R_1} ),P _{REV_2} (t ₁ ,t ₂ L,T _{R_2} ),L P _{REV_i} (t ₁ ,t ₂ L,T _{R_i} )} (15)

wherein P is _{REV_i} Charging power for the ith electric agricultural implement, T _{R_i} A predicted length of time for which to charge; charging power composition scheme X of electric farm machine group _i (namely, a charging power composition scheme consisting of electric agricultural tools) for one particle in the particle swarm algorithm, setting the population number in the particle swarm algorithm as M, the iteration number as s, and the optimization speed of the particle swarm algorithm as V _j ＝(v _j1 ,v _j2 ,...,v _jM ) The iterative calculation formula for example j in the particle swarm algorithm is as follows:

in the formula, s is the iteration number of the algorithm; w is the inertial weight of the particle swarm algorithm; r1 and R2 are random coefficients, and the value range is [0,1 ]]；X _Pbest And X _Gbest The method comprises the steps of respectively obtaining an individual optimal solution and a global optimal solution; c1 and C2 are acceleration factors and the value of the alternative embodiment of the invention is 1.49.

2) Adaptive inertial weight parameter optimization

The size of the inertia weight w in the optional implementation mode of the invention determines the optimization iteration speed of the particle swarm optimization, the optional implementation mode of the invention designs the self-adaptive inertia weight, dynamically adjusts the inertia weight w according to the iteration times, keeps a larger value in the initial phase w of just optimizing, ensures the global optimizing capability of the algorithm, is smaller in the later phase w of optimizing, and keeps the algorithm optimizing locally, and the formula is as follows:

wherein w is _max And w _min The upper limit and the lower limit of the inertia weight are respectively set to be 0.9 and 0.5; s is the current iteration number; gamma is an empirical adjustment factor in the range of [1,30 ]In an alternative embodiment of the present invention, the value is 3.

3) TOPSIS-based multi-objective optimal solution selection

Since each particle j is composed of a charging scheme, the objective function f for each particle j over s iterations can be based on this scheme _{cost_j} (s) and f _{sati_j} (s) further selecting the particle with optimal current iteration according to the objective function, wherein TOPSIS firstly needs to perform standardization processing on the objective function to eliminate the dimension difference, and the formula is as follows:

wherein f' _{cost_j} (s) represents the normalized objective function value of particle j at s iterations; max { f _{cost_j} (s)}、min{f _{cost_j} (s) } sub-targets f when each of the iterations is terminated s times _cost Maximum and minimum values that occur; equivalent pair of targets f _{sati_j} (s) normalized formula as follows:

and next, calculating a minimum particle distance vector group and a maximum particle distance vector group of the TOPSIS, wherein the calculation formula is as follows:

in the method, in the process of the invention,

the ideal distances are positive and negative respectively.

Next, according to the positive and negative ideal distances, the fitting degree of the particles is obtained, and the formula is as follows:

wherein C is _j The smaller the value, the better the particle is for the degree of adhesion of the particle j.

The following is an illustration of the practical application of an alternative embodiment of the invention.

An alternative embodiment of the invention adopts a certain agricultural industry park as an example verification object, the electric agricultural appliance is mainly a mobile agricultural facility, and the equipment type and main parameters are shown in table 1. The agricultural park has 4 of electric pile that fills, all adopts 7kW to fill slowly, should fill electric pile and contain the sensor, can perceive the real-time SOC state of battery when charging with the battery, possess time delay simultaneously, regularly and open the function of charging/stopping according to the instruction, fill electric pile load and by a 10kV/0.4 kV's box transformer and supply power, this transformer still supplies power for other basic uncontrollable electric loads of agricultural park simultaneously. The park is also internally provided with distributed photovoltaic, the installed capacity is 100kVA, power is transmitted to the power grid through the inverter in a concentrated mode, the operation mode is a spontaneous self-use mode, and the residual power is on-line mode.

TABLE 1

The multi-target particle swarm algorithm is adopted, the population size is 50, and the upper limit of iteration times is 200. The time-of-use electricity price cost is as follows: peak-valley period 22:00-next day 6:00, electricity price 0.261 yuan/kWh; flat period 6:00-8:00, 15:00-17:00, electricity price 0.51 yuan/kWh; peak period 8:00-15:00, 17:00-21:00, 0.759 yuan/kWh.

Fig. 3 is a schematic diagram of historical data provided according to an alternative embodiment of the present invention, in which a gaussian fitting parameter is used to simulate the charging duration of various agricultural tools, and an electric forklift is taken as an example, and images of probability density distribution of the charging duration and gaussian fitting distribution of the historical data are shown in fig. 3, where the gaussian fitting parameters μ and δ are 3.4 and 1.3, respectively. Similarly, the fitting parameters mu and delta of the movable electric irrigation machine are 6.4 and 0.2 respectively, the fitting parameters of the electric pick-up are 2.6 and 0.2, the fitting parameters of the small electric transport tool are 4.3 and 0.5, and the fitting parameters of the movable agricultural operation vehicle are 6.0 and 1.0.

Selecting 16:00 of a certain day as a study object, wherein the 4 charging piles are respectively connected with 1 electric pick-up card, and the SOC0 is 0.6;1 electric fork truck, the state of charge SOC0 is 0.8,1 electric irrigators, and the state of charge SOC0 is 0.3;1 mobile agricultural work vehicle, state of charge SOC0 is 0.4. And fitting the charging time according to the respective Gaussian distribution to obtain charging time of 3h, 8h, 6h and 5h respectively.

The charging power in the charging duration of each device is optimized, the objective function is the running cost and the satisfaction of farmers, the solution is carried out by using a multi-objective particle swarm algorithm based on TOPSIS sorting, and fig. 4 is a schematic diagram of an optimal particle fitness curve provided according to an alternative embodiment of the invention.

The multi-objective algorithm of the alternative embodiment of the invention realizes reliable iterative convergence at the 48 th time, the convergence result of the optimal fitting degree is 0.056, the comprehensive operation cost at the moment is 138.592 yuan, compared with the cost of disordered charging of 162.984 yuan, the cost is reduced by 14.964 percent, the user dissatisfaction degree is 3.407, and compared with the 3.451 of disordered charging, the cost is reduced by 1.275 percent.

Fig. 5 is a power stack diagram of an optimal ordered charging scheme obtained after algorithm convergence according to an alternative embodiment of the present invention, wherein the power stack diagram is located above the horizontal axis in fig. 5 and is the load of the electric facilities, and the photovoltaic power generation power is located below the horizontal axis. As can be seen from fig. 5, the photovoltaic power generation power is large at 16:00 and 17:00, 53.5kW and 29kW, respectively, at which time each electric agricultural implement is charged with the maximum charging power. In the period of 18:00-20:00 evening, the photovoltaic power is reduced, and at the moment, the SOC of each electric agricultural appliance reaches 80 percent, the charging power is automatically switched into a constant voltage charging state according to the alternative embodiment of the invention, and the maximum charging power is sequentially reduced; meanwhile, under the condition of meeting respective charging time length, charging power is reasonably distributed, and the peak-valley difference of charging load is ensured to be as low as possible and the state of charge (SOC) is ensured to be as high as possible; at the time of 21:00-23:00, the electric forklift is only in the charging time of the electric forklift, and according to the principle that a user is full as soon as possible, the electric forklift is in a full state, has no charging load and only has other basic electric loads.

In summary, alternative embodiments of the present invention have the following advantages:

1. the method for generating the distributed photovoltaic and typical load predictability is provided, and meanwhile, the charge duration prediction method based on Monte Carlo simulation is provided, so that an optimization scheme is prospective, and farmers can be effectively guided to develop effective charging; the charging power characteristic and the service life cost characteristic of the battery in the actual process are fully considered, and the charging power change condition of the battery in two stages of constant current and constant voltage is considered, so that the conventional constant power charging mode is improved;

2. meanwhile, the equivalent life cost is calculated according to the battery charging depth and the charge state interval more deeply, so that the ordered charging scheme is more suitable for the actual condition of the battery;

3. the charging power optimization model with long time scale is established, the charging power of different equipment at each moment can be adjusted according to different equipment, different initial charge states and predicted charging time length, and the optimization model has the characteristics of more flexibility and stronger applicability; meanwhile, the optimization model considers the comprehensive operation cost and the user satisfaction, covers the actual starting of electricity purchasing cost, battery life cost, load peak-valley difference condition, battery charging efficiency, battery full condition and the like, and has comprehensive consideration factors;

4. The particle swarm optimization method combining TOPSIS sorting and inertia weight is adopted for solving the optimization model, so that the optimizing capability of the solving algorithm can be adjusted in real time, meanwhile, dimension can be effectively eliminated in each iteration process, selection is more concise and efficient, and the calculation efficiency can be further improved by combining dynamic inertia weight.

According to an embodiment of the present invention, there is further provided an ordered charging device, and fig. 6 is a block diagram of an ordered charging device provided according to an embodiment of the present invention, as shown in fig. 6, where the ordered charging device includes: the first determining module 61, the obtaining module 62, the second determining module 63 and the optimizing module 64 are explained below.

A first determining module 61, configured to determine a theoretical charging duration and a predicted charging duration of the target device based on the type of the target device; an obtaining module 62, connected to the first determining module 61, configured to obtain a charging power of a battery of the target device according to a charging duration and a state of charge; a second determining module 63, connected to the acquiring module 62, for determining an initial charging strategy based on the charging power and the predicted charging duration; the optimizing module 64 is connected to the second determining module 63, and is configured to iteratively optimize the initial charging strategy according to the adaptive inertia weight based on the preset constraint condition set with the minimum sum of the running cost and the charging dissatisfaction degree as a target in the preset iteration number, so as to obtain a target charging strategy.

According to an embodiment of the present invention, there is further provided a computer readable storage medium, including a stored program, where the program, when run, controls a device in which the computer readable storage medium is located to perform the method of orderly charging any one of the above.

According to an embodiment of the present invention, there is also provided a computer apparatus including: a memory and a processor, the memory storing a computer program; and a processor for executing a computer program stored in the memory, the computer program when run causing the processor to perform any one of the ordered charging methods described above.

The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.

In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.

In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.

The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.

The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.

Claims

1. An ordered charging method, comprising:

determining a predicted charging duration of a target device based on a type of the target device;

acquiring charging power of a battery of the target equipment along with the change of the charging duration and the charging state;

determining an initial charging strategy based on the charging power and the predicted charging duration;

and in the preset iteration times, taking the minimum sum of the running cost and the charging dissatisfaction degree as a target, and carrying out iterative optimization on the initial charging strategy based on a preset constraint condition set according to the self-adaptive inertia weight to obtain a target charging strategy.

2. The method of claim 1, wherein the determining the predicted charge duration for the target device based on the type of target device comprises:

acquiring actual charging data in a third preset time range before the charging date based on the type of the target equipment, wherein the actual charging data comprises actual charging duration and actual charging frequency;

Determining actual charging probability distribution conditions corresponding to the actual charging data;

carrying out Gaussian distribution fitting on the actual charging probability distribution condition to obtain a fitting result;

and generating the predicted charging duration based on the fitting result.

3. The method of claim 1, wherein the obtaining the charging power of the battery of the target device as a function of the charging duration and the state of charge comprises:

acquiring the current state of charge of the battery;

under the condition that the current charge state is smaller than a preset charge state, determining that the charging power is first power which changes along with the charging duration in a constant-current charging mode;

and under the condition that the current charge state is larger than or equal to the preset charge state, determining that the charging power is the second power which changes along with the charging duration in the constant-voltage charging mode.

4. The method of claim 1, wherein the set of preset constraints comprises at least one of:

charging power and state of charge constraints, charge duration constraints, battery charge and discharge life cost constraints.

5. The method according to claim 1, wherein iteratively optimizing the initial charging strategy to obtain a target charging strategy based on a set of preset constraints and according to an adaptive inertia weight, with a goal of minimizing a sum of an operation cost and a charging dissatisfaction within a preset number of iterations, comprises:

Determining the operation cost and the charging dissatisfaction degree corresponding to the initial charging strategy;

screening the initial charging strategy based on the operation cost and the charging dissatisfaction degree corresponding to the initial charging strategy to obtain a first candidate charging strategy;

under the condition that the preset constraint condition set is met, carrying out iterative optimization on the first candidate charging strategy according to a preset updating mode and the self-adaptive inertia weight, and within the preset iteration times, taking the minimum sum of the running cost and the charging dissatisfaction degree as a target, so as to obtain a second candidate charging strategy;

determining an optimized evaluation factor of the second candidate charging strategy;

and determining the target charging strategy based on the second candidate charging strategy and the optimized evaluation factor.

6. The method of claim 1, wherein the manner in which the operating cost and the charging dissatisfaction are determined comprises:

predicting a photovoltaic power generation power that varies with time during a charging day based on historical illumination data within a first predetermined time range prior to the charging day;

predicting a time-varying base load over the charging day based on a historical load curve over a second predetermined time range prior to the charging day;

Determining a theoretical charging duration of the target device based on the type of the target device;

determining the equivalent cycle life times of the battery in the current charge based on the charge and discharge depth of the battery;

determining battery life loss costs based on the equivalent cycle life times;

based on the photovoltaic power generation power, the base load, the theoretical charging duration, the predicted charging duration, the charging power and the battery life loss cost, the operation cost and the charging dissatisfaction degree of the initial charging strategy are respectively determined.

7. The method according to any one of claims 1 to 6, wherein the value of the adaptive inertial weight is determined by the current number of iterations in an iterative optimization process.

8. An ordered charging device, comprising:

the first determining module is used for determining theoretical charging duration and predicted charging duration of the target equipment based on the type of the target equipment;

the acquisition module is used for acquiring the charging power of the battery of the target equipment along with the change of the charging duration and the charging state;

a second determining module configured to determine an initial charging policy based on the charging power and the predicted charging duration;

And the optimization module is used for iteratively optimizing the initial charging strategy according to the self-adaptive inertia weight based on a preset constraint condition set by taking the minimum sum of the running cost and the charging dissatisfaction degree as a target in the preset iteration times, so as to obtain a target charging strategy.

9. A computer-readable storage medium, characterized in that the computer-readable storage medium comprises a stored program, wherein the program, when run, controls a device in which the computer-readable storage medium is located to perform the ordered charging method of any one of claims 1 to 7.

10. A computer device, comprising: a memory and a processor, wherein the memory is configured to store,

the memory stores a computer program;

the processor for executing a computer program stored in the memory, which when run causes the processor to perform the ordered charging method of any one of claims 1 to 7.