CN113537589A - Ordered charging control method and device - Google Patents
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Abstract
The application discloses an ordered charging control method and device. Wherein, the method comprises the following steps: acquiring a resident real-time load, and acquiring a first charging power required by a charging plan at the next moment from the charging plan of the electric automobile; judging whether the sum of the resident real-time load and the first charging power is smaller than the platform area line-crossing load or not; if the judgment result is yes, executing a charging plan according to the first charging power pair at the next moment; and if the judgment result is negative, optimizing the charging plan of the electric automobile. The method and the device solve the technical problem that a local optimal solution is often obtained by solving the ordered charging optimization model through a traditional optimization method.
Description
Technical Field
The application relates to the field of ordered charging, in particular to an ordered charging control method and device.
Background
With the popularization and technical progress of electric automobiles, the sales volume of the electric automobiles is in a rapidly increasing situation. 8000 million electric cars were estimated in 2030 nationwide, of which 6400 ten thousand family passenger cars. The rapid growth of electric automobiles requires sufficient charging infrastructure construction, and large-scale electric automobiles charge in residential areas and inevitably cause huge impact on a power grid, so that the utilization efficiency of distribution transformers is influenced. The orderly charging realizes the peak-shifting power utilization of the charging load by adjusting the charging power, effectively improves the operation efficiency of the distribution network, and is an effective method for applying future large-scale electric automobile charging and improving the operation efficiency of the distribution network.
The ordered charging optimization is a nonlinear optimization problem, and can be solved through evolutionary algorithms such as genetic algorithm and PSO (particle swarm optimization), and can also be solved through traditional optimization methods such as nonlinear programming. The traditional optimization method has the characteristics of small calculated amount, high convergence speed and the like, but local optimal solution is often obtained.
In view of the above problems, no effective solution has been proposed.
Disclosure of Invention
The embodiment of the application provides an ordered charging control method and an ordered charging control device, which are used for solving the technical problem that a local optimal solution is often obtained by solving an ordered charging optimization model by using a traditional optimization method.
According to an aspect of an embodiment of the present application, there is provided an ordered charging control method including: acquiring a resident real-time load, and acquiring a first charging power required by a charging plan at the next moment from the charging plan of the electric automobile; judging whether the sum of the resident real-time load and the first charging power is smaller than the platform area line-crossing load or not; if the judgment result is yes, executing a charging plan according to the first charging power pair at the next moment; and if the judgment result is negative, optimizing the charging plan of the electric automobile.
Optionally, after the optimization of the charging plan of the electric vehicle, the method further includes: acquiring a second charging power required by the charging plan of the electric automobile at the next moment from the optimized charging plan of the electric automobile; judging whether the sum of the resident real-time load and the second charging power is smaller than the platform area line-crossing load or not; if the judgment result is yes, executing a charging plan according to the second charging power pair at the next moment; and if not, reducing the second charging power to enable the sum of the resident real-time load and the second charging power to be smaller than the platform area line crossing load.
Optionally, before determining whether the sum of the real-time load of the residents and the first charging power is less than the area offline load, the method further includes: judging whether a new charging requirement exists; if the judgment result is yes, the charging plan of the electric automobile is re-planned according to the new charging requirement.
Optionally, optimizing the charging plan of the electric vehicle includes: optimizing a charging plan of the electric automobile according to the following optimization model:
s.t.
max(pi+xi+Δxi，i＝1，…n)≤Ptransformer device
Li≤xi+Δxi，i＝1，…n≤Ui
Wherein V represents distribution network load fluctuation, CeRepresents the charging cost of the electric vehicle, CoRepresenting an orderly charging cost;
xirepresenting the average electric vehicle charging power of the ith time interval; piThe average load power of the power distribution network without the charging power of the electric automobile in the ith time interval is represented;
the average charging power of the total load of the power distribution network in the target time interval is represented; n is a time interval number, if the distribution network load fluctuation of the next 24h is considered, one hour is taken as a time period, and n is 24;
xijthe average charging power of the electric vehicle of j users in the ith time interval is represented;
representing the electricity price of the ith time interval; m isiIs the total number of charged cars in the ith time interval;
Δxijthe variation of the average charging power of the electric vehicle of j users in the ith time interval is represented;
indicating that the ith time interval adjusts the incentive price of j users.
Optionally, optimizing the charging plan of the electric vehicle further includes: obtaining a preliminary optimal solution of the optimization model by using a genetic algorithm; and processing the initial optimal solution by using a nonlinear programming method to obtain a target optimal solution of the optimization model.
Optionally, obtaining a preliminary optimal solution of the optimization model using a genetic algorithm includes: step S1, generating a charging plan population randomly; step S2, judging whether the iteration times of the genetic algorithm is less than or equal to the preset iteration times or the moderate function value is greater than or equal to the preset threshold value; step S3, if the judgment result is negative, obtaining a preliminary optimal solution of the optimization model; in step S4, if the determination result is yes, generating child chromosomes, performing variation on the generated child chromosomes to obtain a new generation charging plan population, and performing step S2.
Optionally, processing the preliminary optimal solution by using a nonlinear programming method to obtain a target optimal solution of the optimization model, including: and converting the optimization model into a single-target optimization problem solving model by using the normalization coefficient: minf (x)ij)＝λ1Ce+λ2Co+λ3V, wherein λ1+λ2+λ 31 is ═ 1; and calculating the target optimal solution of the optimization model according to the single-target optimization problem solution model and the constraint conditions of the optimization model.
According to another aspect of the embodiments of the present application, there is also provided an ordered charging control apparatus, including: the acquisition module is used for acquiring the real-time load of residents and acquiring first charging power required by a charging plan at the next moment from the charging plan of the electric automobile; the judging module is used for judging whether the sum of the real-time load of the residents and the first charging power is smaller than the line crossing load of the transformer area or not; the charging module is used for executing a charging plan according to the first charging power at the next moment under the condition that the judgment result is yes; and the optimization module is used for optimizing the charging plan of the electric automobile under the condition that the judgment result is negative.
According to still another aspect of the embodiments of the present application, there is provided a nonvolatile storage medium including a stored program, wherein a device in which the nonvolatile storage medium is located is controlled to execute the above ordered charging control method when the program runs.
According to still another aspect of the embodiments of the present application, there is also provided a processor configured to execute a program stored in a memory, where the program executes the above ordered charging control method.
In the embodiment of the application, the method comprises the steps of acquiring real-time load of residents, and acquiring first charging power required by a charging plan at the next moment from the charging plan of the electric automobile; judging whether the sum of the resident real-time load and the first charging power is smaller than the platform area line-crossing load or not; if the judgment result is yes, executing a charging plan according to the first charging power pair at the next moment; if the judgment result is negative, the charging plan of the electric automobile is optimized, the load data of the distribution room and the charging requirements of electric automobile users are collected in real time, the purposes of load peak clipping and valley filling of the distribution room and maximum saving of the charging cost of the users are achieved, and the ordered charging control strategy of the residential area is provided by taking the modes of load transfer, power reduction and the like. The method comprises the steps of firstly solving an initial solution by using a genetic algorithm, then determining a planned accurate solution by using a hybrid shaping plan by using a result of the genetic algorithm as a starting point, thereby realizing the technical effects of effectively reducing the load power of a cell and reducing the charging cost of a user, and further solving the technical problem that a local optimal solution is often obtained by solving an ordered charging optimization model by using a traditional optimization method.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
FIG. 1 is a flow chart of an ordered charge control method according to an embodiment of the application;
FIG. 2 is a flow chart of an ordered charging logic according to an embodiment of the present application;
FIG. 3 is a flow chart of an ordered charge control algorithm according to an embodiment of the present application;
FIG. 4 is a schematic representation of a genetic algorithm encoding according to an embodiment of the present application;
FIG. 5 is a schematic diagram of a random crossover of a genetic algorithm according to an embodiment of the present application;
FIG. 6 is a schematic diagram of random variation of a genetic algorithm according to an embodiment of the present application;
fig. 7 is a block diagram of an ordered charging control apparatus according to an embodiment of the present application.
Detailed Description
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only partial embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of this application and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the application described herein are capable of operation in sequences other than those illustrated or 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 accordance with an embodiment of the present application, there is provided an embodiment of an ordered charging control method, it should be noted that the steps illustrated in the flowchart of the figure may be implemented in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flowchart, in some cases, the steps illustrated or described may be implemented in a different order than presented herein.
Fig. 1 is a flowchart of an ordered charging control method according to an embodiment of the present application, as shown in fig. 1, the method includes the following steps:
step S102, acquiring a resident real-time load, and acquiring a first charging power required by a charging plan at the next moment from the charging plan of the electric automobile;
step S104, judging whether the sum of the real-time load of the residents and the first charging power is smaller than the line crossing load of the transformer area;
the transformer area refers to a power supply range or area of one transformer. The station area offline load refers to the maximum charging load which can be provided within the power supply range of one transformer.
Step S106, if the judgment result is yes, executing a charging plan according to the first charging power pair at the next moment;
and step S108, if the judgment result is negative, optimizing the charging plan of the electric automobile.
Through the steps, the ordered charging control strategy of the residential area is provided by acquiring the load data of the residential area and the charging requirements of electric vehicle users in real time, aiming at realizing load peak clipping and valley filling of the transformer area and maximally saving the charging cost of the users, and taking the modes of load transfer, power reduction and the like as means. The initial solution is firstly solved by using the genetic algorithm, and then the result of the genetic algorithm is taken as a starting point, and the accurate solution of the plan is determined by using the mixed shaping plan, so that the technical effects of effectively reducing the load power of the cell and reducing the charging cost of a user are realized.
According to an alternative embodiment of the present application, after the step S108 is executed to optimize the charging plan of the electric vehicle, the second charging power required by the charging plan at the next time is obtained from the optimized charging plan of the electric vehicle; judging whether the sum of the resident real-time load and the second charging power is smaller than the platform area line-crossing load or not; if the judgment result is yes, executing a charging plan according to the second charging power pair at the next moment; and if not, reducing the second charging power to enable the sum of the resident real-time load and the second charging power to be smaller than the platform area line crossing load.
According to another alternative embodiment of the present application, step S104 is executed to determine whether there is a new charging requirement before the sum of the real-time load of the residents and the first charging power is less than the platform area offline load; if the judgment result is yes, the charging plan of the electric automobile is re-planned according to the new charging requirement.
Fig. 2 is a logic flow chart of ordered charging according to an embodiment of the present application, where in ordered charging scheduling of an electric vehicle, it is first determined whether the capacity of a distribution network can meet the current charging and other power consumption requirements according to the charging requirements of users in a cell and other loads in the cell, and when the capacity of the distribution network is insufficient, an ordered charging optimization module is triggered to reduce a load peak, and a specific flow is as shown in fig. 2, where there are two cases for triggering the ordered charging optimization module: when a new EV is accessed, an optimization algorithm triggers and replans all charging plans of the accessed EV; and judging whether the total load is lower than the off-line load requirement or not if the charging plan at the next moment is met, and if not, triggering an algorithm to try to transfer the charging load to a load valley period. If the ordered charging algorithm optimization cannot meet the demand, the total load is ensured not to cross the line by forcibly reducing the charging load and reducing the demand of the charging amount of part of users.
In some optional embodiments of the present application, step S108 is implemented by: optimizing a charging plan of the electric automobile according to the following optimization model:
s.t.
max(pi+xi+Δxi，i＝1，…n)≤Ptransformer device
Li≤xi+Δxi，i＝1，…n≤Ui
Wherein V represents distribution network load fluctuation, CeRepresents the charging cost of the electric vehicle, CoRepresenting an orderly charging cost;
xirepresenting the average electric vehicle charging power of the ith time interval; piThe average load power of the power distribution network without the charging power of the electric automobile in the ith time interval is represented;
the average charging power of the total load of the power distribution network in the target time interval is represented; n is a time interval number, if the distribution network load fluctuation of the next 24h is considered, one hour is taken as a time period, and n is 24;
xijthe average charging power of the electric vehicle of j users in the ith time interval is represented;
representing the electricity price of the ith time interval; m isiIs the total number of charged cars in the ith time interval;
Δxijthe variation of the average charging power of the electric vehicle of j users in the ith time interval is represented;
indicating that the ith time interval adjusts the incentive price of j users.
The participants of the orderly charging of the electric automobile comprise a charging facility operator and an electric automobile user, and the orderly charging model has practical significance only by considering the benefits of both parties. The considered target of the operator side is to reduce the load fluctuation and the peak-valley difference and reduce the pile construction cost; the goal of electric vehicle users is to meet charging requirements while reducing charging costs. So orderly charging of the electric vehicle can be described as a multi-objective optimization problem.
In consideration of the limit of the power distribution capacity of a community, ordered charging is needed to be used for reducing the impact of electric vehicle charging on a distribution network and ensuring the safe operation of the distribution network, and distribution network load fluctuation is needed to be included as an optimization target in an ordered charging optimization model. The distribution network load fluctuation can be described as follows:
x in the formulaiRepresenting the average electric vehicle charging power of the ith time interval; piThe average load power of the power distribution network without the charging power of the electric automobile in the ith time interval is represented;
the average charging power of the total load of the power distribution network in the target time interval is represented; and n is a time interval number, and if the distribution network load fluctuation of the next 24h is considered, one hour is taken as a time period, and n is 24.
The time-of-use electricity price difference is fully utilized, the charging action of the electric automobile is enabled to occur in a low electricity price area as much as possible, the peak clipping and valley filling effects of a power grid can be achieved, and the economic benefit of a charging user is guaranteed. The electric vehicle charging cost can be described as follows:
x in the formulaijThe average charging power of the electric vehicle of j users in the ith time interval is represented;
representing the electricity price of the ith time interval; m isiIs the total number of charged cars in the ith time interval; and n is a time interval number, and if the distribution network load fluctuation of the next 24h is considered, one hour is taken as a time period, and n is 24.
In order to fully guide the application of ordered charging to a real cell, the incentive mechanism for the ordered charging participants must be considered, so that more electric vehicle users accept that their charging behavior is ordered. The incentive mechanism may be configured in different ways, here considering a simple design, to allocate a corresponding cost to the power transferred by the charging user. The cost of the ordered charge can be described as follows:
Δ x in the formulaijThe variation of the average charging power of the electric vehicle of j users in the ith time interval is represented;
representing the ith time interval to adjust the incentive price of the j user; m isiIs the total number of charged cars in the ith time interval; and n is a time interval number, and if the distribution network load fluctuation of the next 24h is considered, one hour is taken as a time period, and n is 24.
In summary, the optimization model for ordered charging is described as follows:
s.t.
max(pi+xi+Δxi，i＝1，…m)≤Ptransformer device ②
Li≤xi+Δxi，i＝1，…n≤Ui ③
The constraint condition is that the charging requirement of a user is kept unchanged in one day; constraint conditions guarantee that the charging load of the power distribution network is smaller than the limit of charging capacity; constraint condition and charging power limit of each charging interval.
In other alternative embodiments of the present application, the charging schedule of the electric vehicle is optimized by: obtaining a preliminary optimal solution of the optimization model by using a genetic algorithm; and processing the initial optimal solution by using a nonlinear programming method to obtain a target optimal solution of the optimization model.
In another alternative embodiment of the present application, the preliminary optimal solution of the optimization model is obtained by using a genetic algorithm, which comprises: step S1, generating a charging plan population randomly; step S2, judging whether the iteration times of the genetic algorithm is less than or equal to the preset iteration times or the moderate function value is greater than or equal to the preset threshold value; step S3, if the judgment result is negative, obtaining a preliminary optimal solution of the optimization model; in step S4, if the determination result is yes, generating child chromosomes, performing variation on the generated child chromosomes to obtain a new generation charging plan population, and performing step S2.
The solution of the multi-objective optimization problem can be converted into the solution of the single-objective optimization problem min f (x) by introducing a normalization coefficientij)＝λ1Ce+λ2Co+λ3V (2)
Wherein λ is1+λ2+λ3＝1。
Fig. 3 is a flowchart of an ordered charging control algorithm according to an embodiment of the present application, and as shown in fig. 3, an optimization problem is solved by a method combining a genetic algorithm and a nonlinear programming.
The steps for obtaining an ordered charging plan using a genetic algorithm are as follows:
1) a charging plan population was randomly generated, and the number of chromosomes in the population was set to 100.
2) And judging the fitness of the individual according to the target function (judging whether the cycle number i is less than or equal to M or whether the result improvement is greater than or equal to a set threshold epsilon).
3) The described crossover process is used to generate children.
4) And (5) carrying out mutation on the offspring chromosome.
5) And generating a new generation of population by crossing and mutation, and returning to the step 2) until an optimal solution is generated.
Through actual data verification, a genetic algorithm usually reaches near an optimal solution in 30 steps, and then the convergence speed becomes significantly slow, so that a definite solution cannot be obtained. This phenomenon can be avoided using conventional optimization methods.
Assuming that one hour is taken as a time interval, 24 intervals are corresponding to one day, and 24 decision variables uiRepresents the ordered charging direction: u. ofi1 means that the ordered charging schedule causes the section charge amount to increase; u. ofi0 means that the ordered charging schedule causes the section charge amount to decrease. 24 decision variables viRepresenting an intra-interval modulation value. The decision variables are encoded using a conventional bit-string encoding scheme, with the decision variables arranged in order. Decision variable uiThe representation being represented by one bit, decision variable viRepresented by an M-bit binary number, the decision variables of each trellis are represented by an M + 1-bit binary number. Fig. 4 depicts the encoding of 2 meshes.
The moderation function is defined according to the optimization problem as:
fit(uk，vk)＝wkf(xij) (3)
where
wherein the content of the first and second substances,
is the kth chromosome, wkPenalty factor function, w if a given chromosome satisfies the constraints (1) - (3)k1 is ═ 1; on the contrary, take wk＝10。
Fig. 5 is a schematic diagram of a random crossover of a genetic algorithm according to an embodiment of the present application, and fig. 6 is a schematic diagram of a random variation of a genetic algorithm according to an embodiment of the present application, in which a genetic process adopts a single-point hybridization method, two chromosomes are selected according to a certain hybridization rate, and then a single point is selected for hybridization. The mutation mode is to select a position on the chromosome for mutation according to a certain mutation rate.
The convergence conditions include: the maximum number of iterations is set to 40, and the change in the fitness function is smaller than the set threshold e, which is set to 1%.
According to an optional embodiment of the present application, the processing the preliminary optimal solution by using a nonlinear programming method to obtain a target optimal solution of the optimization model includes: and converting the optimization model into a single-target optimization problem solving model by using the normalization coefficient: m isinf(xij)＝λ1Ce+λ2Co+λ3V, wherein λ1+λ2+λ 31 is ═ 1; and calculating the target optimal solution of the optimization model according to the single-target optimization problem solution model and the constraint conditions of the optimization model.
By (u)1，v1，u2，v2，…u24，v24) Representing the optimal solution obtained by genetic algorithm, the initial values of the model variables in (1) can be calculated as follows:
by Δ xijFor decision variables, (2) expresses a typical nonlinear optimization problem, and (1) expresses decision variables DeltaxijThe limiting conditions can be solved by a nonlinear programming standard method to obtain the finally obtained delta xijI is 1, … n; j 1, … 24 is the optimal solution for ordered charging.
The method and the device adopt an ordered charging regulation and control strategy based on genetic and hybrid shaping planning algorithms, detect the load peak value when the charging behavior is optimally controlled, and selectively delay or cut off some charging requirements if the peak value exceeds a set threshold value.
The method and the device have the advantages that charging of electric automobiles in residential areas is used as a background, on the premise that the charging requirements of users are met and the fact that the loads of distribution networks in the residential areas do not cross lines is guaranteed, the load peak clipping and valley filling and the lowest charging cost of the users are used as targets, and an ordered charging control strategy mathematical model is established. The input data of the model is the charging requirement of each electric vehicle user and the real-time load data of the transformer area; and outputting a real-time charging power plan for each electric vehicle user.
The ordered charging control strategy algorithm is a combined algorithm based on a genetic algorithm and a hybrid shaping planning algorithm, can effectively reduce the load power of a cell, reduces the charging cost of a user, and verifies the effectiveness of the algorithm through actual cell operation data. Meanwhile, the algorithm fully considers the application scene of charging the large-scale electric vehicles in the future residential area, and has good applicability and popularization value.
In future, with the wide application of distributed energy equipment such as photovoltaic, energy storage, electric heating and the like, the types of electric equipment in residential areas are more abundant, and the greater challenge is also caused to the stable operation of a distribution network. And next, a multi-source cooperative control algorithm is constructed by combining the operation characteristics of different types of distributed energy equipment, terminal energy loads are aggregated, the power grid load scheduling is participated, friendly interaction with the power grid is formed, and the economical efficiency of the operation of the new energy equipment is further improved.
Fig. 7 is a block diagram of an ordered charging control apparatus according to an embodiment of the present application, and as shown in fig. 7, the apparatus includes:
the acquiring module 70 is used for acquiring the real-time load of the residents and acquiring first charging power required by a charging plan at the next moment from the charging plan of the electric automobile;
the judging module 72 is used for judging whether the sum of the real-time load of the residents and the first charging power is smaller than the line crossing load of the transformer area;
a charging module 74, configured to execute a charging schedule according to the first charging power pair at the next time if the determination result is yes;
and an optimizing module 76, configured to optimize the charging plan of the electric vehicle if the determination result is negative.
It should be noted that, reference may be made to the description related to the embodiment shown in fig. 1 for a preferred implementation of the embodiment shown in fig. 7, and details are not repeated here.
The embodiment of the application also provides a nonvolatile storage medium, which comprises a stored program, wherein the device where the nonvolatile storage medium is located is controlled to execute the above ordered charging control method when the program runs.
The nonvolatile storage medium stores a program for executing the following functions: acquiring a resident real-time load, and acquiring a first charging power required by a charging plan at the next moment from the charging plan of the electric automobile; judging whether the sum of the resident real-time load and the first charging power is smaller than the platform area line-crossing load or not; if the judgment result is yes, executing a charging plan according to the first charging power pair at the next moment; and if the judgment result is negative, optimizing the charging plan of the electric automobile.
The embodiment of the application also provides a processor, wherein the processor is used for running the program stored in the memory, and the above ordered charging control method is executed when the program runs.
The processor is used for running a program for executing the following functions: acquiring a resident real-time load, and acquiring a first charging power required by a charging plan at the next moment from the charging plan of the electric automobile; judging whether the sum of the resident real-time load and the first charging power is smaller than the platform area line-crossing load or not; if the judgment result is yes, executing a charging plan according to the first charging power pair at the next moment; and if the judgment result is negative, optimizing the charging plan of the electric automobile.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present application, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units may be a logical division, and in actual implementation, there may be another division, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
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 can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present application and it should be noted that those skilled in the art can make several improvements and modifications without departing from the principle of the present application, and these improvements and modifications should also be considered as the protection scope of the present application.
Claims (10)
1. An ordered charge control method, comprising:
acquiring a resident real-time load, and acquiring a first charging power required by a charging plan at the next moment from the charging plan of the electric automobile;
judging whether the sum of the resident real-time load and the first charging power is smaller than the platform area line crossing load or not;
if the judgment result is yes, executing the charging plan according to the first charging power pair at the next moment;
and if the judgment result is negative, optimizing the charging plan of the electric automobile.
2. The method of claim 1, wherein after optimizing the charging schedule of the electric vehicle, the method further comprises:
acquiring a second charging power required by a charging plan of the electric automobile at the next moment from the optimized charging plan of the electric automobile;
judging whether the sum of the resident real-time load and the second charging power is smaller than the platform area line crossing load or not;
if the judgment result is yes, executing the charging plan according to the second charging power pair at the next moment;
and if not, reducing the second charging power to enable the sum of the resident real-time load and the second charging power to be smaller than the platform area line crossing load.
3. The method according to claim 1, wherein before determining whether the sum of the resident real-time load and the first charging power is less than a district offline load, the method further comprises:
judging whether a new charging requirement exists;
if so, replanning the charging plan of the electric automobile according to the new charging requirement.
4. The method of claim 1, wherein optimizing the charging schedule for the electric vehicle comprises:
optimizing a charging plan of the electric vehicle according to the following optimization model:
s.t.
wherein V represents distribution network load fluctuation, CeRepresents the charging cost of the electric vehicle, CoRepresenting an orderly charging cost;
xirepresenting the average electric vehicle charging power of the ith time interval; piThe average load power of the power distribution network without the charging power of the electric automobile in the ith time interval is represented;
the average charging power of the total load of the power distribution network in the target time interval is represented; n is a time interval number, if the distribution network load fluctuation of the next 24h is considered, one hour is taken as a time period, and n is 24;
xijthe average charging power of the electric vehicle of j users in the ith time interval is represented;
representing the electricity price of the ith time interval; m isiIs the total number of charged cars in the ith time interval;
Δxijthe variation of the average charging power of the electric vehicle of j users in the ith time interval is represented;
indicating that the ith time interval adjusts the incentive price of j users.
5. The method of claim 4, wherein optimizing the charging schedule for the electric vehicle further comprises:
obtaining a preliminary optimal solution of the optimization model by using a genetic algorithm;
and processing the initial optimal solution by using a nonlinear programming method to obtain a target optimal solution of the optimization model.
6. The method of claim 5, wherein using a genetic algorithm to obtain a preliminary optimal solution for the optimization model comprises:
step S1, generating a charging plan population randomly;
step S2, judging whether the iteration times of the genetic algorithm is less than or equal to the preset iteration times or the moderate function value is greater than or equal to the preset threshold value;
step S3, if the judgment result is negative, obtaining a preliminary optimal solution of the optimization model;
and step S4, if the judgment result is yes, generating a child chromosome, carrying out variation on the generated child chromosome to obtain a new generation charging plan population, and executing step S2.
7. The method of claim 5, wherein processing the preliminary optimal solution using a non-linear programming method to obtain a target optimal solution of the optimization model comprises:
converting the optimization model into a single-target optimization problem solving model by using a normalization coefficient as follows:
minf(xij)＝λ1Ce+λ2Co+λ3v, wherein λ1+λ2+λ3＝1；
And calculating the target optimal solution of the optimization model according to the single-target optimization problem solution model and the constraint conditions of the optimization model.
8. An orderly charge control device, comprising:
the acquisition module is used for acquiring the real-time load of residents and acquiring first charging power required by a charging plan at the next moment from the charging plan of the electric automobile;
the judging module is used for judging whether the sum of the resident real-time load and the first charging power is smaller than the platform area line crossing load or not;
the charging module is used for executing the charging plan according to the first charging power pair at the next moment under the condition that the judgment result is yes;
and the optimization module is used for optimizing the charging plan of the electric automobile under the condition that the judgment result is negative.
9. A non-volatile storage medium, comprising a stored program, wherein a device on which the non-volatile storage medium is located is controlled to execute the ordered charging control method according to any one of claims 1 to 7 when the program runs.
10. A processor configured to run a program stored in a memory, wherein the program is configured to execute the ordered charge control method according to any one of claims 1 to 7 when running.
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