Community charging pile scheduling optimization method and system

Abstract

The invention relates to the technical field of charging piles, in particular to a community charging pile scheduling optimization method and system, and the method comprises the steps: obtaining charging pile information, a microgrid daily load prediction curve, charging pile historical charging information, user charging demand information and charging pile electricity price information; constructing a target function group based on the daily load prediction curve of the micro-grid and historical charging information of the charging pile; constructing a constraint condition group based on the obtained charging pile information; constructing a charging scheme based on the constructed constraint condition group, the target function group and the obtained user charging demand information and charging pile electricity price information; and scheduling the charging piles in the community based on the constructed charging scheme. According to the technical scheme, orderly charging of the new energy vehicles in the whole community and maximization of the charging efficiency are achieved.

Description

Technical Field

[0001] The present application relates to the technical field of charging piles, and in particular to a method and system for optimizing the scheduling of charging piles in a residential area. Background Art

[0002] With the rapid popularization of new energy vehicles, microgrids composed of charging piles in residential areas are also being laid out at an accelerated pace to meet the needs of users to charge new energy vehicles in the community.

[0003] In communities where charging pile microgrids are currently installed, the way users charge new energy vehicles is as follows: users first determine the charging pile that is currently idle in the microgrid as the target charging pile, and then charge the new energy vehicle through the target charging pile.

[0004] However, the number of charging piles in the microgrid laid in the community is limited. When the number of new energy vehicles that need to be charged is greater than the number of charging piles, users need to queue up for charging. Taking one of the charging piles as an example, the new energy vehicles in the queue cannot predict the time when the current new energy vehicle being charged will complete charging, so it may take a long time to charge. During the waiting process, other charging piles may be idle, but the new energy vehicles currently queuing at the charging pile cannot know in time that other charging piles are idle, resulting in the new energy vehicles queuing at the charging pile still queuing in front of the charging pile, and when charging can be carried out, the electricity price of the charging pile may be adjusted from the valley electricity price to the peak electricity price, thereby resulting in higher charging costs. In summary, it is difficult to achieve orderly charging of new energy vehicles in the entire community and maximize charging efficiency through existing technologies.

[0005] CN202410903425.7A discloses a distributed charging pile power scheduling method and a distributed charging pile. The method includes: adjacent charging piles send power resource information to each other through a communication line; when the current charging pile starts to charge the power-consuming device, it obtains the current charging demand of the power-consuming device and allocates the output capacity of the current charging pile based on the current charging demand; when the output capacity of the current charging pile is less than the current charging demand, the current charging pile searches for a first target charging pile that meets a preset condition among the charging piles adjacent to the current charging pile according to the power resource information; if there is a first target charging pile that meets the preset condition, the current charging pile sends a first power control instruction to the target charging pile, so that the first target charging pile switches the corresponding output capacity to the current charging pile according to the instruction of the first power control instruction.

[0006] CN202410296708.X discloses a method for orderly charging control of electric vehicles in residential areas, comprising the following steps: S1. Prediction of conventional load in residential areas: predicting the conventional load of the residential area on a typical day based on the historical load of the residential area; S2. Available power of charging piles: calculating the available power in each time period in addition to the total demand load of the residential area based on the predicted conventional load of the residential area and the power supply capacity of the community distribution transformer, that is, the total power of the working charging piles in each time period; S3. Collection of user charging information: the electric vehicle charging management system collects charging demand information of electric vehicle users.

[0007] CN202311805215.6A discloses a community reservable charging and power intelligent distribution management system, which belongs to the field of community power distribution and charging pile reservable charging technology. The charging pile information management module of the present invention is responsible for collecting and managing the information of all charging piles in the community, the user reservation module allows users to make reservations through mobile phone APP or website, the power distribution module is responsible for formulating power distribution plan according to the power demand and available power resources in the community, and the intelligent scheduling module automatically adjusts the working status and charging time of the charging pile according to the power distribution plan and the user's reservation information.

[0008] Although the above invention can adjust the working status and working time of the charging pile, it cannot promptly inform the new energy vehicles queuing at the current charging pile that other charging piles are idle, resulting in the new energy vehicles queuing at the charging pile still queuing in front of the charging pile, and when charging is available, the electricity price of the charging pile may be adjusted from the valley electricity price to the peak electricity price, thereby causing the charging cost to become higher, so that it is difficult to achieve orderly charging of new energy vehicles in the entire community and maximize the charging efficiency through the existing technology.

[0009] Therefore, there is an urgent need for a community charging pile scheduling optimization method that can enable new energy vehicles queuing at the current charging pile to promptly know that other charging piles are idle, thereby optimizing the charging pile scheduling. Summary of the invention

[0010] In order to solve the deficiencies in the prior art, the present application provides a method for optimizing the scheduling of charging piles in a residential area.

[0011] The present invention adopts the following technical solution.

[0012] A method for optimizing the scheduling of charging piles in a residential area, the method comprising the following steps:

[0013] Step 1: Obtain charging pile information, microgrid daily load forecast curve, charging pile historical charging information, user charging demand information and charging pile electricity price information;

[0014] Step 2: Construct an objective function group based on the microgrid daily load forecast curve and the charging pile historical charging information;

[0015] Step 3: Construct a constraint condition group based on the charging pile information obtained in step 1;

[0016] Step 4: Construct a charging plan based on the constraint condition group constructed in step 3, the objective function group constructed in step 2, and the user charging demand information and charging pile electricity price information obtained in step 1;

[0017] Step 5: Schedule the charging piles in the community based on the charging plan constructed in step 4.

[0018] The present invention further includes the following preferred embodiments:

[0019] In step 1, the charging pile information includes charging pile power limit information, microgrid charging and discharging load information, grid battery limit information, and the number of charging piles;

[0020] The microgrid includes a microgrid control system and a plurality of charging piles arranged in a community and communicatively connected to the microgrid control system.

[0021] In step 1, the historical charging information of the charging pile includes the historical charging probability of the charging pile at a preset time, the historical charging power of the charging pile, the historical electricity price of the charging pile, and the historical phase charging probability of the charging pile.

[0022] In step 1, the user's charging demand information includes the vehicle charging power curve, the vehicle's current power, the vehicle's expected charging power, the charging voltage, and the charging efficiency.

[0023] In step 1, the charging pile electricity price information includes the peak price period, the valley price period, the peak price period electricity price and the valley price period electricity price.

[0024] In step 2, constructing an objective function group based on the microgrid daily load forecast curve and the charging pile historical charging information includes: constructing a grid load variance objective function; constructing a charging cost objective function; and constructing a waiting time objective function.

[0025] The construction formula of the grid load variance objective function is as follows:

[0026]

[0027] Among them, Y represents the number of users, Z represents the total number of preset time, L i represents the daily load forecast curve of the power grid, L z The charging power of the charging pile at time z, M xyz represents the charging probability of user y at time z on day x, x represents the xth day calculated starting from Monday of each week, y represents the user, and z represents the charging time.

[0028] The construction formula of the charging cost objective function is as follows:

[0029]

[0030] Among them, C z represents the charging electricity price of the charging pile at time z, M xyz represents the charging probability of user y at time z on day x, Y represents the number of users, Z represents the total number at the preset time, x represents the xth day calculated starting from Monday of each week, y represents the user, and z represents the charging time.

[0031] The construction formula of the waiting time objective function is as follows:

[0032]

[0033] Among them, K is the preset value based on historical experience, A represents the number of charging piles in the community, and N yza represents the charging probability of the charging pile in phase a in history, N yzb represents the historical b-phase charging probability.

[0034] In step 2, the formula for constructing the constraint group is as follows:

[0035] P z,min <P z <P z,max

[0036]

[0037] P s,min <P s <P s,max

[0038] P o,min <P o <P o,max

[0039] E min <E<E max

[0040] Among them, P z represents the charging power of a charging pile when the user uses a charging pile to charge at time z, P z,min , P z,max They represent the charging power valley and peak of the charging pile, respectively. l Indicates the microgrid charging and discharging load information, including microgrid load, P bs Represents the microgrid energy storage charging and discharging power, P s Represents the microgrid line transmission power, P s,min , P s,maxThey represent the minimum and maximum transmission power of the microgrid line, P o Indicates the battery charging and discharging power, P o,min , P o,max They represent the battery charging and discharging power valley and peak respectively, E represents the battery capacity, and E min 、E max Respectively represent the valley and peak values ​​of the battery capacity.

[0041] In step 4, a charging plan is constructed based on the constraint condition group constructed in step 3, the objective function group constructed in step 2, and the user charging demand information and charging pile electricity price information obtained in step 1, including:

[0042] Processing the constraint condition group, the objective function group, the user charging demand information and the charging pile electricity price information through a preset multi-objective particle swarm algorithm;

[0043] The charging station with the shortest waiting time and the lowest charging price is recommended to the user as the target charging station.

[0044] In step 5, scheduling the charging piles in the community based on the charging plan constructed in step 4 includes: generating charging pile charging ranking information based on the user charging pile recommendation in the charging plan; sending the generated charging pile charging ranking information to the user, and obtaining the user's charging intention information; optimizing the charging plan based on the obtained charging intention information to obtain a target plan, and also optimizing the charging pile charging ranking information based on the charging intention information to obtain target ranking information; scheduling the charging piles in the community based on the target plan and the target ranking information.

[0045] At the same time, the present invention provides a community charging pile scheduling optimization method system, the system includes an information acquisition module, an objective function group construction module, a constraint condition group construction module, a charging scheme construction module and a simulation scheduling module:

[0046] The information acquisition module is used to obtain charging pile information, microgrid daily load forecast curve, charging pile historical charging information, user charging demand information and charging pile electricity price information;

[0047] An objective function group construction module is used to construct an objective function group based on the microgrid daily load forecast curve and the charging pile historical charging information;

[0048] A constraint condition group building module, which builds a constraint condition group based on the acquired charging pile information;

[0049] A charging plan building module builds a charging plan based on the built constraint condition group, the objective function group, and the acquired user charging demand information and charging pile electricity price information;

[0050] The simulation scheduling module schedules the charging piles in the community based on the constructed charging plan.

[0051] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the methods described above when executing the computer program.

[0052] A computer-readable storage medium stores a computer program, which performs the steps of any one of the methods described above when the computer program is executed by a processor.

[0053] A computer program product comprises a computer program, which, when executed by a processor, implements the steps of any one of the methods described above.

[0054] The beneficial effects of the present invention are that, compared with the prior art, the present invention:

[0055] Through the implementation of this solution, a charging plan can be calculated to guide users to charge at a specific charging pile in the community while ensuring the lowest charging cost, which facilitates the orderly charging of new energy vehicles in the entire community and maximizes charging efficiency.

[0056] In this scheme, the charging scheme is calculated by a multi-objective particle swarm algorithm model, which can improve the rationality of the charging scheme. Compared with the charging scheme calculated by the prior art, the charging scheme calculated by the multi-objective particle swarm algorithm model has better effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

[0058] Figure 1 This is an application environment diagram of a method for optimizing the scheduling of charging piles in a residential area in one embodiment of the present application;

[0059] Figure 2 A flowchart of a method for optimizing the scheduling of charging piles in a residential area provided in Example 1 of the present application;

[0060] Figure 3 This is a structural schematic diagram of a charging pile scheduling optimization device for a residential area provided in one embodiment of the present application;

[0061] Figure 4 A schematic diagram of the structure of a computer device provided in one embodiment of the present application;

[0062] Figure 5 An internal structure diagram of a computer-readable storage medium provided in one embodiment of the present application. DETAILED DESCRIPTION

[0063] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0064] It should be noted that the terms "first", "second", etc. in the specification and claims of this article and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of this article described here can be implemented in an order other than those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, device, product or equipment that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0065] In this article, the term "and / or" is only a description of the association relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the related objects before and after are in an "or" relationship.

[0066] To solve the above problems, the present disclosure provides a method for optimizing the scheduling of charging piles in a residential area, which can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. Among them, the terminal 102 can be but is not limited to various personal computers, laptops, smart phones, tablets, Internet of Things devices and portable wearable devices. The Internet of Things devices can be smart speakers, smart TVs, smart air conditioners, and smart car-mounted devices. Portable wearable devices can be smart watches, smart bracelets, and head-mounted devices. The server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0067] Figure 2A flowchart of a method for optimizing the scheduling of charging piles in a residential area provided in Embodiment 1 of the present application is provided. The method can be performed by a device for performing the method, and the device can be implemented by software and / or hardware. The method includes:

[0068] Step 1: Obtain charging pile information, microgrid daily load forecast curve, charging pile historical charging information, user charging demand information, and charging pile electricity price information.

[0069] In one embodiment of the present invention, in step 1, the charging pile information includes charging pile power limitation information, microgrid charging and discharging load information, grid battery limitation information, and the number of charging piles. The charging pile power limitation information is the range limitation information for limiting the charging power of the charging pile for charging new energy vehicles, the microgrid charging and discharging load information is the range limitation information for characterizing the microgrid load and the transmission line load, and the grid battery limitation information is the range limitation information for limiting the operation of the grid battery; it should be noted that the microgrid includes a microgrid control system, and a plurality of charging piles arranged in a community and communicatively connected to the microgrid control system, and the community charging pile scheduling optimization method described in this embodiment is executed by the above-mentioned microgrid control system; the microgrid is electrically connected to a grid battery, and if too much electricity is charged into the microgrid, the excess electricity can be stored in the grid battery; if the microgrid discharges insufficient electricity, it can be discharged together with the electricity stored in the grid battery; it should also be noted that the charging pile power limitation information, the microgrid charging and discharging load information and the grid battery limitation information can together form a constraint condition group, and the constraint condition group is also a collection of the charging pile power limitation information, the microgrid charging and discharging load information and the grid battery limitation information.

[0070] Specifically, the microgrid control system obtains charging pile power limitation information, microgrid charging and discharging load information, and grid battery limitation information, and aggregates the charging pile power limitation information, microgrid charging and discharging load information, and grid battery limitation information into a constraint condition group.

[0071] In one embodiment of the present invention, the historical charging information of the charging pile includes the historical charging probability of the charging pile at a preset time, the historical charging power of the charging pile, the historical electricity price of the charging pile, and the historical phase charging probability of the charging pile.

[0072] In another embodiment of the present invention, the user charging demand information includes a vehicle charging power curve, a vehicle current power level, a vehicle expected charging power level, a charging voltage, and a charging efficiency.

[0073] In another embodiment of the present invention, the charging pile electricity price information includes a peak price period, a valley price period, a peak price period electricity price, and a valley price period electricity price.

[0074] Step 2: Construct the objective function group based on the microgrid daily load forecast curve and the charging pile historical charging information.

[0075] In one embodiment of the present invention, constructing an objective function group based on a microgrid daily load forecast curve and charging pile historical charging information includes: constructing a grid load variance objective function; constructing a charging cost objective function; and constructing a waiting time objective function.

[0076] In this embodiment, the construction formula of the grid load variance objective function is as follows:

[0077]

[0078] Among them, Y represents the number of users, Z represents the total number of preset time, L i represents the daily load forecast curve of the power grid, L z The charging power of the charging pile at time z, M xyz represents the charging probability of user y at time z on day x, x represents the xth day calculated starting from Monday of each week, y represents the user, and z represents the charging time.

[0079] In this embodiment, the construction formula of the charging cost objective function is as follows:

[0080]

[0081] Among them, C z represents the charging electricity price of the charging pile at time z, M xyz represents the charging probability of user y at time z on day x, Y represents the number of users, Z represents the total number at the preset time, x represents the xth day calculated starting from Monday of each week, y represents the user, and z represents the charging time.

[0082] In this embodiment, the construction formula of the waiting time objective function is as follows:

[0083]

[0084] Wherein, K is a preset value based on historical experience. In this embodiment, K is set to 5; A represents the number of charging piles in the community; N yza N represents the charging probability of the charging pile in phase a in history; yzb represents the historical b-phase charging probability.

[0085] Step 3: Construct a constraint condition group based on the charging pile information obtained in step 1.

[0086] In one embodiment of the present invention, the construction formula of the constraint condition group is as follows:

[0087]

[0088] Among them, P z represents the charging power of a charging pile when the user uses a charging pile to charge at time z, P z,min , P z,max They represent the charging power valley and peak of the charging pile, respectively. l Indicates the microgrid charging and discharging load information, including microgrid load, P bs Represents the microgrid energy storage charging and discharging power, P s Represents the microgrid line transmission power, P s,min , P s,max They represent the minimum and maximum transmission power of the microgrid line, P o Indicates the battery charging and discharging power, P o,min , P o,max They represent the battery charging and discharging power valley and peak respectively, E represents the battery capacity, and E min 、E max Respectively represent the valley and peak values ​​of the battery capacity.

[0089] A first constraint condition is established based on a charging pile charging power peak value and a charging pile charging power valley value in the charging pile power limit information.

[0090] The charging pile power limit information includes the charging pile charging power peak value P z,max The charging power valley value P of the charging pile z,min , charging pile charging power peak P z,max is the maximum charging power of a charging pile when the user uses a charging pile to charge at time z, and the charging power valley value P of the charging pile z,min is the minimum charging power of the charging pile when the user uses a charging pile to charge at time z. In addition, the charging power of the charging pile when the user uses a charging pile to charge at time z is recorded as P z ; The first constraint is as follows:

[0091] P z,min <P z <P z,max (5)

[0092] Specifically, the microgrid control system obtains the charging pile charging power peak value P in the charging pile power limit information. z,max The charging power valley value P of the charging pile z,min , then establish the first constraint condition P z,min <P z <P z,max .

[0093] A second constraint condition is constructed based on the microgrid load and the microgrid energy storage charging and discharging power in the microgrid charging and discharging load information, and a third constraint condition is constructed based on the microgrid line transmission power peak value and the microgrid line transmission power valley value in the microgrid charging and discharging load information.

[0094] Among them, the microgrid charging and discharging load information includes the microgrid load P l 、Microgrid energy storage charging and discharging power P bs , Microgrid line transmission power peak value P s,max , Microgrid line transmission power valley value P s,min ; Microgrid load P l is the load of the entire microgrid, and the microgrid energy storage charging and discharging power P bs is the charge and discharge power P of the energy storage battery electrically connected to the microgrid bs , P bs When it is positive, it indicates that the energy storage battery is charged, P bs When it is a negative value, it indicates the discharge of the energy storage battery; the microgrid line is the conductive line in the microgrid, and the peak power transmission power P of the microgrid line s,max is the maximum value of the microgrid line transmission power, and the valley value of the microgrid line transmission power P s,min is the minimum value of the microgrid line transmission power set; and the microgrid line transmission power is recorded as P s .

[0095] The second constraint is as follows:

[0096]

[0097] Where Z is the total number of charging moments;

[0098] The third constraint is as follows:

[0099] P s,min <P s <P s,max (7)

[0100] Specifically, the microgrid control system obtains the grid load P l 、Microgrid energy storage charging and discharging power P bs Create the second constraint According to the peak power P of the microgrid line transmission s,max , Microgrid line transmission power valley value P s,min Establish the third constraint condition P s,min <P s <P s,max .

[0101] A fourth constraint condition is constructed based on the battery charge and discharge power peak value and the battery charge and discharge power valley value in the power grid battery restriction information, and a fifth constraint condition is constructed based on the battery capacity peak value and the battery capacity valley value in the power grid battery restriction information.

[0102] Among them, the grid battery limit information includes the battery charging and discharging power peak value P o,max , battery charge and discharge power valley value P o,min , battery capacity peak E max and battery capacity valley value E min ; and the battery charging and discharging power is recorded as P o , and the battery capacity is recorded as E.

[0103] The fourth constraint is as follows:

[0104] P o,min <P o <P o,max (8)

[0105] The fifth constraint is as follows:

[0106] E min <E<E max (9)

[0107] Specifically, the microgrid control system obtains the peak value of battery charging and discharging power P o,max and the battery charge and discharge power valley value P o,min Establish the fourth constraint condition P o,min <P o <P o,max ; Also based on the obtained battery capacity peak E max and battery capacity valley value E min Establish the fifth constraint E min <E<E max .

[0108] A constraint condition group is obtained based on the first constraint condition, the second constraint condition, the third constraint condition, the fourth constraint condition and the fifth constraint condition.

[0109] Microgrid daily load forecast curve L iThe microgrid control system predicts the daily load curve of the microgrid; the charging pile historical charging data is the data related to the charging of the charging pile recorded in the history of the microgrid control system, such as the user's historical charging probability and the charging pile's historical charging power; the number of microgrid charging piles A is the number of all charging piles in the community belonging to the same microgrid; the objective function group includes multiple objective functions, and the objective function is established based on one or more of the microgrid daily load forecast curve, the charging pile historical charging data and the number of microgrid charging piles, and the constraint condition group is used to constrain the objective function in the objective function group.

[0110] In one embodiment of the present invention, exemplarily, the user's charging demand information includes at least one of the vehicle charging power curve, the vehicle's current power, the vehicle's expected charging power, the charging voltage, and the charging efficiency; the charging pile electricity price information includes at least the peak price period, the valley price period, the peak price period electricity price, and the valley price period electricity price.

[0111] Among them, the vehicle charging power curve is a charging power control curve for controlling the charging of the vehicle, the current power of the vehicle is the current remaining power of the vehicle. For example, the current power of the vehicle is represented by SOC, SOC = vehicle remaining power / vehicle battery rated capacity; the expected charging power of the vehicle is the percentage of power that the user expects the vehicle to reach, for example, the expected charging power of the vehicle is 85%; the charging voltage is the voltage required for charging the vehicle; the charging efficiency is the preset efficiency when the vehicle is charged with a charging pile; the peak price period is the period when the city electricity price is higher, and the valley price period is the period when the city electricity price is lower. The peak price period electricity price is the charging price during the peak price period, and the valley price period electricity price is the charging price corresponding to the valley price period.

[0112] Specifically, the microgrid control system establishes multiple objective functions based on one or more of the microgrid daily load forecast curve, the charging pile historical charging data, and the number of microgrid charging piles, and combines the objective functions to obtain an objective function group; the microgrid control system also obtains the vehicle charging power curve, the vehicle's current power, the vehicle's expected charging power, the charging voltage, and the charging efficiency data to obtain the user's charging demand information, and obtains the peak price period, valley price period, peak price period electricity price, and valley price period electricity price to obtain the charging pile electricity price information.

[0113] Step 4: Construct a charging plan based on the constraint condition group constructed in step 3, the objective function group constructed in step 2, and the user charging demand information and charging pile electricity price information obtained in step 1.

[0114] Among them, a multi-objective particle swarm algorithm model is preset in the microgrid control system. The multi-objective particle swarm algorithm model is used to comprehensively process the constraint condition group, the objective function group, the user charging demand information and the charging pile electricity price information, and output the charging plan; the charging plan includes the user charging pile recommendation and the charging pile charging control strategy. The user charging pile recommendation is used to recommend the charging pile with the shortest waiting time and the lowest charging price to the user (recorded as the target charging pile), and the charging pile charging control strategy is used to indicate the charging power curve, the expected charging power, and the charging start and end time of the target charging pile when charging the vehicle.

[0115] Specifically, the microgrid control system comprehensively processes the constraint condition group, objective function group, user charging demand information and charging pile electricity price information through a preset multi-objective particle swarm algorithm model, and then outputs a charging plan and sends the charging plan to the user.

[0116] In this embodiment, a multi-objective particle swarm algorithm model is specifically a multi-objective particle swarm algorithm model preset by a microgrid control system. The multi-objective particle swarm algorithm model is used to comprehensively calculate a constraint condition group, an objective function group, user charging demand information, and charging pile electricity price information, and output a charging plan.

[0117] Specifically, the microgrid control system uses a preset multi-objective particle swarm algorithm model to comprehensively calculate the constraint condition group, the objective function group, the user charging demand information and the charging pile electricity price information to obtain the charging plan.

[0118] Step 5: Schedule the charging piles in the community based on the charging plan constructed in step 4.

[0119] In one embodiment of the present invention, the scheduling, that is, the microgrid control system controls the corresponding charging pile to charge the electrically connected vehicle according to the charging pile charging control strategy in the charging solution.

[0120] Specifically, the user selects a target charging pile according to the user charging pile recommendation in the charging plan, and electrically connects the vehicle to the target charging pile. Furthermore, the microgrid control system charges the vehicle electrically connected to the target charging pile according to the charging pile charging control strategy in the charging plan, thereby realizing the scheduling of charging piles in the community based on the charging plan.

[0121] It should be noted that this embodiment constructs a constraint condition group based on the power limit information of the charging pile, the charging and discharging load information of the microgrid, and the battery limit information of the power grid; constructs an objective function group based on the daily load forecast curve of the microgrid, the historical charging data of the charging pile, and the number of charging piles in the microgrid, and also obtains the user's charging demand information and the charging pile electricity price information; processes the constraint condition group, the objective function group, the user's charging demand information, and the charging pile electricity price information to obtain a charging plan; and schedules the charging piles in the community based on the charging plan. Through the implementation of the above embodiments, a charging plan can be calculated to guide users to charge at a specific charging pile in the community while ensuring the lowest charging cost, so as to facilitate the orderly charging of new energy vehicles in the entire community and maximize the charging efficiency.

[0122] In one embodiment of the present invention, scheduling charging piles in a community based on the charging plan constructed in step 4 includes:

[0123] Step 501: Generate charging pile charging ranking information based on the user charging pile recommendation in the charging plan.

[0124] The charging plan includes user charging pile recommendations. Exemplarily, the user charging pile recommendations include: the charging pile recommended for vehicle a is charging pile 1, the charging pile recommended for vehicle b is charging pile 1, the charging pile recommended for vehicle c is charging pile 1, and the charging pile recommended for vehicle d is charging pile 2; the charging pile charging sorting information includes: the charging sequence of vehicle a is first, and the charging period of vehicle a is 17:00-17:30; the charging sequence of vehicle b is second, and the charging period of vehicle b is 17:30-18:30; the charging sequence of vehicle c is third, and the charging period of vehicle c is 18:30-18:55.

[0125] Specifically, the microgrid control system first obtains the user charging pile recommendation in the charging plan, and then automatically generates the charging pile charging ranking information according to the user charging pile recommendation.

[0126] S502: Send the generated charging pile charging ranking information to the user, and obtain the user's charging intention information.

[0127] Among them, the forms of sending the charging pile charging ranking information to the user include but are not limited to text messages, emails, and APP notifications; the user can choose whether to accept the charging plan based on the charging pile charging ranking information, and feedback the charging intention information to the microgrid control system; exemplarily, the charging intention information includes: a user accepts the charging plan, b user accepts the charging plan, and c user does not accept the charging plan.

[0128] Specifically, the microgrid control system sends the charging pile charging sequence information to the user, and then obtains the charging intention information fed back by the user.

[0129] S503: Optimizing the charging plan based on the acquired charging intention information to obtain a target plan, and optimizing the charging pile charging ranking information based on the charging intention information to obtain target ranking information.

[0130] Among them, if the charging intention information shows that the user does not accept the charging plan, the information about the charging plan that is not accepted in the user's charging pile recommendation is deleted, and the corresponding relationship between other users and charging piles in the user's charging pile recommendation is adjusted. The charging ranking information of the charging piles is also adjusted to obtain the target ranking information, and a new charging plan is provided for the user who does not accept the charging plan; in this way, the charging plan can be optimized, and the target plan is also the optimized charging plan.

[0131] Specifically, the microgrid control system optimizes the charging plan through the charging intention information to obtain a target plan, and the microgrid control system optimizes the charging pile charging sequence information through the charging intention information to obtain target sequence information.

[0132] S504: Scheduling charging piles in the community based on the target solution and the target ranking information.

[0133] Specifically, the microgrid control system dispatches the charging piles in the community through the charging pile charging control strategy in the target solution and the target sorting information.

[0134] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0135] At the same time, the embodiment of the present disclosure also provides a community charging pile scheduling optimization system for implementing the community charging pile scheduling optimization method involved above. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more community charging pile scheduling optimization system embodiments provided below can refer to the limitations of the community charging pile scheduling optimization method above, and will not be repeated here.

[0136] In this embodiment, Figure 3 As shown, a community charging pile scheduling optimization system is provided, including:

[0137] The information acquisition module is used to obtain charging pile information, microgrid daily load forecast curve, charging pile historical charging information, user charging demand information and charging pile electricity price information;

[0138] An objective function group construction module is used to construct an objective function group based on the microgrid daily load forecast curve and the charging pile historical charging information;

[0139] A constraint condition group building module, which builds a constraint condition group based on the acquired charging pile information;

[0140] A charging plan building module builds a charging plan based on the built constraint condition group, the objective function group, and the acquired user charging demand information and charging pile electricity price information;

[0141] The simulation scheduling module schedules the charging piles in the community based on the constructed charging plan.

[0142] Each module in the above-mentioned community charging pile scheduling optimization device can be implemented in whole or in part by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0143] It should be noted that this embodiment constructs a constraint condition group based on the power limit information of the charging pile, the charging and discharging load information of the microgrid, and the battery limit information of the power grid; constructs an objective function group based on the daily load forecast curve of the microgrid, the historical charging data of the charging pile, and the number of charging piles in the microgrid, and also obtains the user's charging demand information and the charging pile electricity price information; processes the constraint condition group, the objective function group, the user's charging demand information, and the charging pile electricity price information to obtain a charging plan; and schedules the charging piles in the community based on the charging plan. Through the implementation of the above embodiments, a charging plan can be calculated to guide users to charge at a specific charging pile in the community while ensuring the lowest charging cost, so as to facilitate the orderly charging of new energy vehicles in the entire community and maximize the charging efficiency.

[0144] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as follows: Figure 4As shown. The computer device includes a processor, a memory and a network interface connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data. The network interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for optimizing the scheduling of charging piles in a community is implemented.

[0145] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the scheme of the present disclosure, and does not constitute a limitation on the computer device to which the scheme of the present disclosure is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0146] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0147] In one embodiment, a computer readable storage medium is provided. Figure 5 As shown, a computer program is stored thereon, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0148] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0149] It should be noted that the user information (including but not limited to user device information, user personal information) and data (including but not limited to data used for analysis, stored data, and displayed data) involved in this disclosure are all information and data authorized by the user or fully authorized by all parties.

[0150] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0151] The above-described embodiments only express several implementation methods of the present disclosure, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the patent of the present disclosure. It should be pointed out that, for those of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present disclosure, and these all belong to the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be subject to the attached claims. The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for enabling a processor to implement various aspects of the present disclosure.

[0152] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0153] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0154] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0155] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for optimizing the scheduling of charging piles in a residential area, characterized in that: The method comprises the following steps: Step 1: Obtain charging pile information, microgrid daily load forecast curve, charging pile historical charging information, user charging demand information and charging pile electricity price information; Step 2: Construct an objective function group based on the microgrid daily load forecast curve and the charging pile historical charging information; Step 3: Construct a constraint condition group based on the charging pile information obtained in step 1; Step 4: Construct a charging plan based on the constraint condition group constructed in step 3, the objective function group constructed in step 2, and the user charging demand information and charging pile electricity price information obtained in step 1; Step 5: Schedule the charging piles in the community based on the charging plan constructed in step 4.

2. A method for optimizing the scheduling of charging piles in a residential area according to claim 1, characterized in that: In step 1, the charging pile information includes charging pile power limit information, microgrid charging and discharging load information, grid battery limit information, and the number of charging piles; The microgrid includes a microgrid control system and a plurality of charging piles arranged in a community and communicatively connected to the microgrid control system.

3. A method for optimizing the scheduling of charging piles in a residential area according to claim 1, characterized in that: In step 1, the historical charging information of the charging pile includes the historical charging probability of the charging pile at a preset time, the historical charging power of the charging pile, the historical electricity price of the charging pile, and the historical phase charging probability of the charging pile.

4. A method for optimizing the scheduling of charging piles in a residential area according to claim 1, characterized in that: In step 1, the user's charging demand information includes the vehicle charging power curve, the vehicle's current power, the vehicle's expected charging power, the charging voltage, and the charging efficiency.

5. A method for optimizing the scheduling of charging piles in a residential area according to claim 1, characterized in that: In step 1, the charging pile electricity price information includes the peak price period, the valley price period, the peak price period electricity price and the valley price period electricity price.

6. A method for optimizing the scheduling of charging piles in a residential area according to claim 1, characterized in that: In step 2, the objective function group is constructed based on the microgrid daily load forecast curve and the charging pile historical charging information, including: Construct the grid load variance objective function; Construct charging cost objective function; And construct waiting time objective function.

7. A method for optimizing the scheduling of charging piles in a residential area according to claim 6, characterized in that: The construction formula of the grid load variance objective function is as follows: Among them, Y represents the number of users, Z represents the total number of preset time, L i represents the daily load forecast curve of the power grid, L z The charging power of the charging pile at time z, M xyz represents the charging probability of user y at time z on day x, x represents the xth day calculated starting from Monday of each week, y represents the user, and z represents the charging time.

8. A method for optimizing the scheduling of charging piles in a residential area according to claim 6, characterized in that: The construction formula of the charging cost objective function is as follows: Among them, C z represents the charging electricity price of the charging pile at time z, M xyz represents the charging probability of user y at time z on day x, Y represents the number of users, Z represents the total number at the preset time, x represents the xth day calculated starting from Monday of each week, y represents the user, and z represents the charging time.

9. A method for optimizing the scheduling of charging piles in a residential area according to claim 6, characterized in that: The construction formula of the waiting time objective function is as follows: Among them, K is the preset value based on historical experience, A represents the number of charging piles in the community, and N yza represents the charging probability of the charging pile in phase a in history, N yzb represents the historical b-phase charging probability.

10. A method for optimizing the scheduling of charging piles in a residential area according to claim 1, characterized in that: In step 2, the formula for constructing the constraint group is as follows: P z,min <P z <P z,max P s,min <P s <P s,max P o,min <P o <P o,max AND min <E<E max Among them, P z represents the charging power of a charging pile when the user uses a charging pile to charge at time z, P z,min , P z,max They represent the charging power valley and peak of the charging pile, respectively. l Indicates the microgrid charging and discharging load information, including microgrid load, P bs Represents the microgrid energy storage charging and discharging power, P s Represents the microgrid line transmission power, P s,min , P s,max They represent the minimum and maximum transmission power of the microgrid line, P o Indicates the battery charging and discharging power, P o,min , P o,max They represent the battery charging and discharging power valley and peak respectively, E represents the battery capacity, and E min 、E max Respectively represent the valley and peak values ​​of the battery capacity.

11. A method for optimizing the scheduling of charging piles in a residential area according to claim 1, 6 or 9, characterized in that: In step 4, a charging plan is constructed based on the constraint condition group constructed in step 3, the objective function group constructed in step 2, and the user charging demand information and charging pile electricity price information obtained in step 1, including: Processing the constraint condition group, the objective function group, the user charging demand information and the charging pile electricity price information through a preset multi-objective particle swarm algorithm; The charging station with the shortest waiting time and the lowest charging price is recommended to the user as the target charging station.

12. A method for optimizing the scheduling of charging piles in a residential area according to claim 1, characterized in that: In step 5, scheduling charging piles in the community based on the charging plan constructed in step 4 includes: Generate charging pile charging ranking information based on user charging pile recommendations in the charging plan; Send the generated charging pile charging ranking information to the user and obtain the user's charging intention information; Based on the acquired charging intention information, the charging plan is optimized to obtain a target plan, and based on the charging intention information, the charging pile charging ranking information is optimized to obtain target ranking information; The charging piles in the community are scheduled based on the target solution and the target sorting information.

13. A community charging pile scheduling optimization method system according to any of claims 1-12, characterized in that: The system includes an information acquisition module, an objective function group construction module, a constraint condition group construction module, a charging scheme construction module and a simulation scheduling module: The information acquisition module is used to obtain charging pile information, microgrid daily load forecast curve, charging pile historical charging information, user charging demand information and charging pile electricity price information; An objective function group construction module is used to construct an objective function group based on the microgrid daily load forecast curve and the charging pile historical charging information; A constraint condition group building module, which builds a constraint condition group based on the acquired charging pile information; A charging plan building module builds a charging plan based on the built constraint condition group, the objective function group, and the acquired user charging demand information and charging pile electricity price information; The simulation scheduling module schedules the charging piles in the community based on the constructed charging plan.

14. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 12 are implemented.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 12 are implemented.

16. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 12 are implemented.

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