A cluster control method of mobile emergency energy storage charging stations

Abstract

The application discloses a kind of cluster control methods of mobile emergency energy storage charging station, obtain the traffic information corresponding to the preset area and objective adjustment information, according to traffic information and initial configuration information, generate the initial charging site of mobile energy storage charging station in preset area;According to the site adjustment strategy of mobile energy storage charging station in preset area is generated according to objective adjustment information;According to site adjustment strategy, the initial charging site in preset area is adjusted in different time section, and the final charging site of mobile energy storage charging station is obtained.It can be based on the charging demand of new energy in population-intensive scene, comprehensive existing fixed energy storage charging station and mobile energy storage charging station, consider the demand of power distribution network reliability and economy, realize the configuration of mobile energy storage charging station in emergency power supply and daily operation two scenes, while being able to provide diversified application and service to local power energy load, improve the utilization of mobile energy storage charging station.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of cluster control of energy storage systems, in particular to a cluster control method of mobile emergency energy storage charging stations. BACKGROUND

[0002] With the development of new energy vehicles, the demand for power supply of new energy vehicles is increasing day by day. In the existing city, fixed energy storage charging stations such as charging stations and charging piles have been laid out synchronously. However, due to the distribution and planning of the original city structure, the number of charging piles and charging stations is limited, and the distribution interval is far apart, which cannot meet the charging demand of a large number of new energy vehicles. Based on this, the mobile energy storage charging station is a portable energy storage and charging equipment used to provide charging services for electric vehicles or other electronic devices. It has the characteristics of mobility and can adapt to some emergency charging demand.

[0003] However, the mobile energy storage charging station in the prior art is classified as emergency use, that is, it is selected when disasters or large accidents occur, or when it is applied in mountainous environments. This way will reduce the utilization rate of the mobile energy storage charging station. Especially in the city, the use frequency of new energy vehicles and new energy equipment is high, and the use of fixed energy storage charging stations is limited by location factors or time factors, which is relatively limited as a whole. SUMMARY

[0004] Therefore, the purpose of the present application is to provide a cluster control method of mobile emergency energy storage charging stations.

[0005] In order to achieve the above technical purpose, the technical solution adopted by the present application is:

[0006] A cluster control method of mobile emergency energy storage charging stations, comprising:

[0007] Obtaining charging demand information of a preset area, dividing the preset area into a plurality of preset subareas according to the charging demand information, and the charging demand information comprising charging quantity distribution data in a unit time period;

[0008] Obtaining charging supply information in the preset area, the charging supply information comprising fixed energy storage supply information and mobile energy storage supply information, the fixed energy storage supply information being charging information of a fixed energy storage charging station, and the mobile energy storage supply information being charging information of a mobile energy storage charging station;

[0009] Generating initial configuration information of the mobile energy storage charging station corresponding to each preset subarea according to the charging supply information and the charging demand information;

[0010] acquire traffic information corresponding to each preset area and objective adjustment information, the traffic information including path distribution information and traffic peak-valley state information of a traffic road in each preset area, and the objective adjustment information including weather data in each preset area;

[0011] generate an initial charging location of the mobile energy storage charging station in each preset area according to the traffic information and the initial configuration information;

[0012] generate a location adjustment strategy of the mobile energy storage charging station in each preset area according to the objective adjustment information;

[0013] adjust the initial charging location in each preset area according to the location adjustment strategy in different time segments to obtain a final charging location of each mobile energy storage charging station.

[0014] In some embodiments, generating the location adjustment strategy of the mobile energy storage charging station in each preset area according to the objective adjustment information comprises:

[0015] generating a final adjustment probability of the current preset area according to the objective adjustment information;

[0016] determining whether the adjustment probability is within a preset adjustment probability range, and if not, not adjusting the preset area;

[0017] if yes, acquiring an actual timestamp of the current preset area, obtaining a first traffic congestion state in the current preset area according to the actual timestamp and the traffic peak-valley state information, and obtaining first path distribution information and the initial charging location of the current preset area;

[0018] inputting the first traffic congestion state, the first path distribution information and the initial charging location into the trained neural network model, and the output result of the neural network model being the location adjustment strategy corresponding to the preset area.

[0019] In some embodiments, the objective adjustment information further includes historical charging peak-valley information in each preset area;

[0020] generating the final adjustment probability of the current preset area according to the objective adjustment information further comprises:

[0021] generating a first adjustment probability according to the weather data in the current preset area;

[0022] acquiring an actual timestamp of the current preset area, and obtaining real-time demand information in the preset area according to the actual timestamp and the historical charging peak-valley information;

[0023] acquiring real-time supply information of the mobile energy storage charging station in the current preset area, and generating a second adjustment probability according to the real-time supply information and the real-time demand information;

[0024] The first adjustment probability and the second adjustment probability are added to obtain a final adjustment probability.

[0025] In some embodiments, the objective adjustment information further comprises battery life information of each mobile energy storage charging station;

[0026] The real-time supply information of the mobile energy storage charging station in the current preset area is obtained, and the real-time supply information is further corrected according to the battery life information of the mobile energy storage charging station in the preset area.

[0027] The battery life information of the mobile energy storage charging station in the preset area is obtained, and a power supply correction parameter is generated according to the battery life information.

[0028] The real-time supply information in the current preset area is corrected according to the power supply correction parameter.

[0029] In some embodiments, the objective adjustment information further comprises traffic accident information of the current preset area;

[0030] The first traffic congestion state in the current preset area is obtained according to the actual time stamp and the traffic peak-valley state information, and the first traffic congestion state is further corrected according to the traffic accident information of the current preset area.

[0031] The traffic accident information of the current preset area is obtained according to the actual time stamp, and a traffic congestion correction parameter is generated according to the traffic accident information.

[0032] The first traffic congestion state is obtained according to the traffic peak-valley state information and the traffic congestion correction parameter.

[0033] In some embodiments, the objective adjustment information further comprises power state information of each mobile energy storage charging station;

[0034] The final adjustment probability of the current preset area is generated according to the objective adjustment information, and the final adjustment probability is further calculated according to the first adjustment probability, the second adjustment probability and the third adjustment probability.

[0035] The power state information of the mobile energy storage charging station in the preset area is obtained, and the mobile energy storage charging station in the charging state is selected according to the power state information.

[0036] The proportion of the mobile energy storage charging station in the charging state in the mobile energy storage charging station in the preset area is calculated, and the proportion is recorded as a third adjustment probability.

[0037] The first adjustment probability, the second adjustment probability and the third adjustment probability are added to obtain a final adjustment probability.

[0038] In some embodiments, the adjacent two preset areas are recorded as a first preset area and a second preset area, and the method further comprises:

[0039] The boundary area of the first preset area and the second preset area is obtained.

[0040] screening out the mobile energy storage charging station located in the border edge region in the first preset region and the second preset region according to the final charging location;

[0041] obtaining first charging supply information and first charging demand information of the first preset region in real time, and judging whether the first charging supply information and the first charging demand information are saturated;

[0042] If yes, a temporary scheduling strategy is generated according to the traffic information of the border edge region, and the final charging location of the mobile energy storage charging station in the border edge region is adjusted according to the temporary scheduling strategy, to obtain a temporary charging location.

[0043] In some embodiments, the final charging location of the mobile energy storage charging station in the border edge region is a to-be-scheduled charging location, and generating the temporary scheduling strategy according to the traffic information of the border edge region further includes:

[0044] obtaining an actual timestamp of the current border edge region, obtaining a second traffic congestion state in the current border edge region according to the actual timestamp and the traffic peak-valley state information, and obtaining second path distribution information and the to-be-scheduled charging location of the current border edge region;

[0045] inputting the second traffic congestion state, the second path distribution information and the to-be-scheduled charging location into the trained neural network model, and the output result of the neural network model is the temporary scheduling strategy corresponding to the border edge region.

[0046] In some embodiments, obtaining the border edge region of the first preset region and the second preset region includes:

[0047] obtaining a border line of the first preset region and the second preset region;

[0048] dividing the first preset region and the second preset region according to a preset width with the border line as a center reference line, to obtain the border edge region.

[0049] In some embodiments, the method further includes:

[0050] recording the number of times of generating the temporary scheduling strategy of the first preset region, and updating the initial configuration information of the mobile energy storage charging station corresponding to the first preset region when the number of times exceeds a preset scheduling threshold.

[0051] By using the above technical solution, the present application has the following beneficial effects compared with the prior art:

[0052] By acquiring preset area charging demand information, the preset area is divided into a plurality of preset sub-areas according to the charging demand information; charging supply information in the preset area is acquired; initial configuration information of the mobile energy storage charging station corresponding to each preset sub-area is generated according to the charging supply information and the charging demand information; traffic information and objective adjustment information corresponding to the preset area are acquired, and initial charging locations of the mobile energy storage charging station in each preset sub-area are generated according to the traffic information and the initial configuration information; a location adjustment strategy of the mobile energy storage charging station in each preset sub-area is generated according to the objective adjustment information; and the initial charging locations in each preset sub-area are adjusted according to the location adjustment strategy in different time segments to obtain the final charging location of each mobile energy storage charging station. Based on the charging demand of new energy in a population-dense scene, the existing fixed energy storage charging station and the mobile energy storage charging station are comprehensively utilized, the demand for power grid reliability and economy is considered, the configuration of the mobile energy storage charging station in emergency power supply and daily operation scenes is realized, diversified application and service of local power energy load are provided, and the utilization rate of the mobile energy storage charging station is improved. BRIEF DESCRIPTION OF DRAWINGS

[0053] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and all other drawings obtained by those skilled in the art without creative labor under the premise of the drawings.

[0054] Figure 1 is the first step diagram of the cluster control method described in the specific embodiment;

[0055] Figure 2 is the second step diagram of the cluster control method described in the specific embodiment;

[0056] Figure 3 is the third step diagram of the cluster control method described in the specific embodiment. DETAILED DESCRIPTION

[0057] The present application will be further described in detail below in combination with the drawings and embodiments. It is particularly pointed out that the following embodiments are only used to illustrate the present application, but do not limit the scope of the present application. Similarly, the following embodiments are only part of the embodiments of the present application, not all embodiments, and all other embodiments obtained by those skilled in the art without creative labor are within the scope of the present application.

[0058] For the convenience of understanding, the following terms appearing in the subsequent text are explained as follows:

[0059] Pre-set area: can be understood as the area where the mobile energy storage charging station needs to be laid out, such as in the unit of city, the pre-set area can be understood as the area divided by the whole city. The pre-set area is different according to the actual demand.

[0060] Pre-set sub-area: a plurality of pre-set sub-areas are obtained after the pre-set area is divided, such as when the city is taken as the pre-set area, the pre-set sub-areas can be divided into urban areas, counties and the like.

[0061] Please refer to Figure 1 The embodiment provides a cluster control method of mobile emergency energy storage charging station, comprising:

[0062] S101, acquiring pre-set area charging demand information, and cutting the pre-set area into a plurality of pre-set sub-areas according to the charging demand information, wherein the charging demand information comprises charging quantity distribution data in a unit time period;

[0063] S102, acquiring charging supply information in the pre-set area, wherein the charging supply information comprises fixed energy storage supply information and mobile energy storage supply information, the fixed energy storage supply information is charging information of a fixed energy storage charging station, and the mobile energy storage supply information is charging information of a mobile energy storage charging station;

[0064] S103, generating initial configuration information of the mobile energy storage charging station corresponding to each pre-set sub-area according to the charging supply information and the charging demand information;

[0065] S104, acquiring traffic information and objective adjustment information corresponding to the pre-set area, wherein the traffic information comprises path distribution information and traffic peak-valley state information of a traffic road in each pre-set sub-area, and the objective adjustment information comprises weather data in each pre-set sub-area;

[0066] S105, generating an initial charging location of the mobile energy storage charging station in each pre-set sub-area according to the traffic information and the initial configuration information;

[0067] S106, generating a location adjustment strategy of the mobile energy storage charging station in each pre-set sub-area according to the objective adjustment information;

[0068] S107, adjusting the initial charging location in each pre-set sub-area according to the location adjustment strategy in different time sections, to obtain a final charging location of each mobile energy storage charging station.

[0069] In step S101, the charging demand information of the preset area is acquired, and the charging demand information includes charging amount distribution data in a unit time period. Here, the charging demand information can be understood as follows: the charging amount statistical period of the preset area is the minimum unit time period, and the charging amount distribution data includes the charging amount and the charging distribution. The charging amount is the charging amount of all new energy devices in the preset area, and the charging distribution is the distribution of the new energy devices in the preset area when the new energy devices are in the charging state in the unit time period. For example, taking one day as the unit time period, the charging demand information can be understood as the charging amount of the new energy devices in the preset area and the distribution area of the charging points selected by the new energy devices in one day. The preset area is divided according to the charging demand information to obtain a plurality of preset sub-areas. The specific division steps can be: the preset area is divided according to the charging amount of the new energy devices and the concentrated distribution of the charging amount, and the sizes and shapes of the plurality of preset sub-areas obtained by the division can be different.

[0070] Further, in step S102, the charging supply information in the preset area is acquired. Here, the charging supply information includes fixed energy supply information and mobile energy supply information. It can be understood that the fixed energy supply information is the charging information of the fixed energy charging station, and specifically includes the charging amount that can be provided by the fixed energy charging station and the geographic position information of the fixed energy charging station. The mobile energy supply information is the charging information of the mobile energy charging station, and specifically includes the charging amount that can be provided by the mobile energy charging station. It can be understood that there are a plurality of mobile energy charging stations and a plurality of fixed energy charging stations in one preset area. Here, the charging supply information can be understood as including the charging information of the plurality of mobile energy charging stations and the charging information of the plurality of fixed energy charging stations.

[0071] In step S103, based on the charging demand information and the charging supply information in the preset area, the supply and demand distribution of the new energy devices to the energy in the preset area is obtained, and on this basis, the mobile energy charging station is configured in combination with the geographic position information of the fixed energy charging station. That is, the number of mobile energy charging stations in each preset sub-area is allocated, so that the supply and demand of the new energy devices to the energy in each preset sub-area is in a balanced state.

[0072] On this basis, steps S104 to S107 are related to the slice control of the mobile energy storage charging station in each preset area. Specifically, in step S104, the traffic information corresponding to the preset area and the objective adjustment information are obtained. Here, the traffic information can be understood as the path distribution information of the traffic road in the preset area and the traffic peak and valley state information. Specifically, the path distribution information of the traffic road can be divided into multiple preset areas synchronously, and the path distribution information corresponding to each preset area is obtained. The traffic peak and valley state information includes the peak period and the valley period of the traffic. For example, the morning peak and the evening peak are the peak periods in the traffic peak and valley state. Since the traffic has a clear flow direction, when the traffic peak and valley state information is divided according to the preset area, there is a certain time delay in the traffic peak and valley state information of different preset areas. For example, the peak period of the A preset area is one hour later than that of the B preset area, because the flow direction of the new energy vehicle is from the B preset area to the A preset area, and so on.

[0073] The traffic peak and valley state information is related to the path distribution information, that is, a certain road section has a peak period of traffic jam, and a certain road section is in a normal unblocked state. Therefore, the traffic information is one of the factors considered in the scheduling of the mobile energy storage charging station.

[0074] In step S105, the initial charging location of the mobile energy storage charging station in each preset area is generated according to the traffic information and the initial configuration information. Here, the initial charging location is the place where the mobile energy storage charging station can charge the new energy equipment when planning.

[0075] Further, the objective adjustment information can be understood as all factors that can affect the mobile energy storage charging station except the traffic information, such as weather factors. That is, when there is a disaster weather, the mobile energy storage charging station will change from the daily use state to the emergency state, which is also a reference benchmark for balancing the daily use state and the emergency state of the mobile energy storage charging station. Specifically, the objective adjustment information treasure of the embodiment shown herein is the weather data in each preset area. Here, the weather data can be obtained in real time from the existing meteorological system.

[0076] In step S106, the objective adjustment information is introduced to generate the location adjustment strategy of the mobile energy storage charging station in each preset area. This location adjustment strategy includes the scheduling of the mobile energy storage charging station in different time dimensions, and can also include the scheduling of the mobile energy storage charging station in the location, which can be set according to the actual demand.

[0077] In step S107, the initial charging locations in each preset area are adjusted according to the location adjustment strategy in different time segments to obtain the final charging locations of each mobile energy storage charging station. It should be noted that the final charging locations of each mobile energy storage charging station are also associated with the time dimension, and the final charging locations of each mobile energy storage charging station are different in different time segments. This way increases the mobility and schedulability of mobile energy storage charging stations in different time segments in a unit time period, further refining the adjustment capability of mobile energy storage charging stations in daily use and emergency use.

[0078] The embodiment is based on the charging demand of new energy in a densely populated scene, and comprehensively considers the existing fixed energy storage charging stations and mobile energy storage charging stations, taking into account the demand for power grid reliability and economy, to realize the configuration of mobile energy storage charging stations in emergency power supply and daily operation scenes, while providing diversified applications and services for local power load, and improving the utilization rate of mobile energy storage charging stations.

[0079] Please refer to Figure 2 In some embodiments, generating the location adjustment strategy of the mobile energy storage charging station in each preset area according to the objective adjustment information comprises:

[0080] S201, generating a final adjustment probability of the current preset area according to the objective adjustment information;

[0081] S202, determining whether the adjustment probability is within a preset adjustment probability range;

[0082] S203, if not, the preset area is not adjusted;

[0083] S204, if yes, obtaining an actual timestamp of the current preset area, obtaining a first traffic congestion state in the current preset area according to the actual timestamp and traffic peak-valley state information, and obtaining first path distribution information and an initial charging location of the current preset area;

[0084] S205, inputting the first traffic congestion state, the first path distribution information and the initial charging location into the trained neural network model, and the output result of the neural network model is the location adjustment strategy corresponding to the preset area.

[0085] The embodiment shows the specific steps of generating the location adjustment strategy according to the objective adjustment information. In step S201, the final adjustment probability is introduced, which can be understood as feedback of whether the current preset area needs to be adjusted. This way can reduce the probability of problems such as blind and universal mobilization of mobile energy storage charging stations, reduce the scheduling cost of mobile energy storage charging stations, and also make the whole cluster control logic more focused and save computing power.

[0086] Step S202 is a determination step to determine whether the final adjustment probability falls within a preset adjustment probability range. The preset adjustment probability range can be obtained through big data statistics or manually set. In step S203, if the final adjustment probability does not fall within the preset adjustment probability range, it indicates that no adjustment is required for the current preset area, and the final adjustment probability calculation for the next preset area can be performed. This process continues in this order until all preset areas have been traversed.

[0087] Steps S204 and S205 illustrate the specific steps for generating a location adjustment strategy when adjustments are required for a preset area. Specifically, in step S204, the actual timestamp of the preset area is obtained. Based on the actual timestamp, the current traffic peak and valley status data is obtained from the traffic peak and valley status information. For example, if the actual timestamp is 7:45, then the traffic peak and valley status information indicates that the current preset area is in peak traffic. Therefore, the congestion distribution data for the current traffic path is obtained and organized into a first traffic congestion state. Furthermore, the path distribution information for the current preset area and the initial charging locations of all mobile energy storage charging stations in the current preset area are simultaneously obtained. For ease of distinction, the path distribution information for the preset area in this step is recorded as the first path distribution information.

[0088] In step S205, the first traffic congestion status, the first path distribution information, and the initial charging location are input into the trained neural network model. The output of the neural network model is the location adjustment strategy corresponding to the preset area. Specifically, this embodiment provides a trained neural network model. The training process of the neural network model is as follows:

[0089] A basic neural network model is constructed. The input samples of the basic neural network model are the sample traffic congestion status, sample route distribution information, and sample initial charging locations of the sample area in the sample database. The output is the location adjustment strategy corresponding to the sample area. The sample traffic congestion status includes traffic congestion data of different congestion levels, and the sample route distribution information corresponds to the sample area. The basic neural network model is repeatedly trained by reading the sample traffic congestion status, sample route distribution information, and sample initial charging locations from the sample database to improve the accuracy of the basic neural network model, and ultimately obtain a trained neural network model.

[0090] The specific location adjustment strategy includes modification of the location of the mobile energy storage charging station in different time sections. For example, during peak hours, the initial charging location becomes a major congestion area. The initial charging location can be modified to a replacement charging location to reduce the impact of congestion caused by charging on the initial charging location during peak hours. For another example, the selection of the replacement charging location is obtained through a neural network model. During training of the neural network model, the building distribution information of the sample area can be added to enable the neural network model to simultaneously consider topographic factors and preferentially select open places to set the replacement charging location, and the like.

[0091] Further, in some embodiments, the objective adjustment information further includes historical charging peak-valley information in each preset area;

[0092] According to the objective adjustment information, the final adjustment probability of the current preset area is generated.

[0093] According to the weather data in the current preset area, a first adjustment probability is generated.

[0094] An actual timestamp of the current preset area is obtained. According to the actual timestamp and the historical charging peak-valley information, real-time demand information in the preset area is obtained.

[0095] Real-time supply information of the mobile energy storage charging station in the current preset area is obtained. According to the real-time supply information and the real-time demand information, a second adjustment probability is generated.

[0096] The first adjustment probability and the second adjustment probability are added to obtain the final adjustment probability.

[0097] The objective adjustment information shown in the embodiment further includes historical charging peak-valley information in each preset area. The historical charging peak-valley information is different from the traffic peak-valley state information. The traffic peak-valley state information reflects the congestion degree of the road, and the historical charging peak-valley information reflects the congestion degree of the new energy vehicle or device during charging.

[0098] Further, the embodiment obtains an actual timestamp of the preset area. According to the actual timestamp, the congestion degree of the new energy device charging corresponding to the current actual timestamp is obtained in the historical charging peak-valley information, that is, the real-time demand information in the preset area. At the same time, the real-time supply information of the mobile energy storage charging station in the current preset area is obtained. According to the real-time supply information and the real-time demand information, a second adjustment probability is generated.

[0099] Optionally, the second adjustment probability can be the ratio of the real-time demand information to the real-time supply information. When the demand information is greater than the real-time supply information, the second adjustment probability is greater than 1, indicating that the current demand is greater than the supply, and the scheduling of the mobile energy storage charging station is needed.

[0100] In this embodiment, the first adjustment probability is generated according to the weather data in the preset area, and the first adjustment probability can be understood as a probability value of disaster caused by the real-time weather data of the preset area. The higher the first adjustment probability is, the higher the probability of disaster caused by the current weather data is, and the scheduling of the mobile energy storage charging station is more for emergency demand. When the first adjustment probability is low, the probability of disaster caused by the current weather data is low, and the scheduling of the mobile energy storage charging station is more for daily demand.

[0101] Further, the first adjustment probability and the second adjustment probability are added to obtain a final adjustment probability. The addition operation can be a common superposition operation, and can also be a weighted operation. By setting a weight value, the final adjustment probability value calculated finally can more accurately reflect the influence of the current objective factor on the scheduling of the mobile energy storage charging station.

[0102] In some embodiments, the objective adjustment information further includes battery life information of each mobile energy storage charging station;

[0103] The real-time supply information of the mobile energy storage charging station in the current preset area further includes:

[0104] The battery life information of the mobile energy storage charging station in the preset area is obtained, and an electric quantity supply correction parameter is generated according to the battery life information;

[0105] The real-time supply information in the current preset area is corrected according to the electric quantity supply correction parameter.

[0106] In this embodiment, according to the battery life information and in combination with the charge-discharge power change curve of the battery, an electric quantity supply correction parameter corresponding to the battery life information can be obtained. The electric quantity supply correction parameter is also the effective charge-discharge power of the battery. The real-time supply information in the current preset area is corrected by using the electric quantity supply correction parameter.

[0107] This embodiment considers the battery life problem of the mobile energy storage charging station. With the increase of charge-discharge times, the charge-discharge power of the battery in the mobile energy storage charging station gradually decreases. When the preset area is divided, the charging supply information obtained is based on the charge-discharge power of the battery in the standard state. By introducing the battery life information, the charge-discharge power of the battery in the mobile energy storage charging station can be monitored in real time, so that the real-time supply information in each preset area is corrected, which helps the rationality of the location adjustment strategy in the subsequent steps.

[0108] In some embodiments, the objective adjustment information further includes traffic accident information of the current preset area;

[0109] The first traffic jam state in the current preset area according to the actual timestamp and the traffic peak-valley state information further comprises:

[0110] The traffic accident information of the current preset area is obtained according to the actual timestamp, and a traffic jam correction parameter is generated according to the traffic accident information;

[0111] The first traffic jam state is obtained according to the traffic peak-valley state information and the traffic jam correction parameter.

[0112] Traffic congestion can cause an increase in the probability of traffic accidents, and accidents can directly change the degree of traffic congestion. Based on this, in the present embodiment, the traffic accident information of the current preset area is obtained according to the actual timestamp, which can include the road path of the accident, the range of the roads affected by the accident, the time of the accident, and the estimated time for handling the accident.

[0113] The traffic jam correction parameter is generated according to the traffic accident information, which can be reflected by the extension of the congestion degree in the time section. Specifically, when the first traffic jam state is represented by the jammed path and the duration of the jam, the traffic jam correction parameter can be the extension rate of the duration corresponding to the current jammed path. For example, in the first traffic jam state under normal conditions, the duration of the D path in the 7-10 time section is 1.5 hours, and the traffic jam correction parameter corresponding to the D path is 1.25, so the duration of the D path in the corrected first traffic jam state is 1.5*1.25=1.875 hours.

[0114] The present embodiment introduces a traffic jam correction parameter to correct the first traffic jam state under the traffic accident state, so as to improve the rationality of the location adjustment strategy in the subsequent steps.

[0115] In some embodiments, the objective adjustment information further comprises the power state information of each mobile energy storage charging station;

[0116] Generating the final adjustment probability of the current preset area according to the objective adjustment information further comprises:

[0117] Obtaining the power state information of the mobile energy storage charging station in the preset area, and selecting the mobile energy storage charging station in the charging state according to the power state information;

[0118] Calculating the proportion of the mobile energy storage charging station in the charging state in the mobile energy storage charging station in the preset area, and recording it as the third adjustment probability;

[0119] The first adjustment probability, the second adjustment probability and the third adjustment probability are added to obtain the final adjustment probability.

[0120] In this embodiment, the objective adjustment information also relates to the power state information of each mobile energy storage charging station. Unlike the battery life information, the power state information reflects the real-time power of the current mobile energy storage charging station. In use, there is a problem that the battery power of a local mobile energy storage charging station is depleted after use and needs to be charged.

[0121] Further, the proportion of the mobile energy storage charging station in the charging state in the total amount of the mobile energy storage charging station in the current preset area is calculated, and the ratio is recorded as a third adjustment probability. When there are more mobile energy storage charging stations in the charging state, the third adjustment probability increases accordingly, and there is a state that the demand for charging power is greater than the supply in the current preset area, and the mobile energy storage charging station in the preset area needs to be dispatched.

[0122] The specific steps of adding the first adjustment probability, the second adjustment probability, and the third adjustment probability to obtain the final adjustment probability can be referred to the foregoing description, and will not be described in detail here.

[0123] Please refer to Figure 3 In some embodiments, the two adjacent preset areas are referred to as a first preset area and a second preset area, and the method further comprises:

[0124] S301, obtaining the boundary area of the first preset area and the second preset area;

[0125] S302, screening the mobile energy storage charging station in the boundary area in the first preset area and the second preset area according to the final charging location;

[0126] S303, obtaining the first charging supply information and the first charging demand information of the first preset area in real time, and determining whether the first charging supply information and the first charging demand information are saturated;

[0127] S304, if yes, generating a temporary dispatching strategy according to the traffic information of the boundary area, and adjusting the final charging location of the mobile energy storage charging station in the boundary area according to the temporary dispatching strategy to obtain a temporary charging location.

[0128] In this embodiment, the two adjacent preset areas are referred to as a first preset area and a second preset area. It should be noted that the first preset area is set as the one that needs to be supplemented with a mobile energy storage charging station, and the second preset area is set as the one that can provide a mobile energy storage charging station to the first preset area. The number of the second preset area can also be multiple, that is, any one of the preset areas adjacent to the first preset area can be referred to as the second preset area.

[0129] Under this premise, in step S301, the intersection edge region of the first preset area and the second preset area is obtained. In some embodiments, obtaining the intersection edge region of the first preset area and the second preset area comprises:

[0130] An intersection line of the first preset area and the second preset area is obtained.

[0131] The intersection line is taken as a center reference line, and the near-intersection line region of the first preset area and the near-intersection line region of the second preset area are divided according to a preset width, to obtain the intersection edge region.

[0132] In step S302, all mobile energy storage charging stations placed in the intersection edge region are screened according to the final charging location. Since the intersection edge region covers the regions of part of the first preset area and part of the second preset area, the aforementioned mobile energy storage charging stations can come from the first preset area and / or the second preset area.

[0133] In step S303, the first charging supply information and the first charging demand information of the current first preset area are obtained, and it is judged whether the first charging supply information and the first charging demand information are saturated. Here, it can be understood as whether the first charging supply information and the first charging demand information exceed the supply-demand balance relationship.

[0134] If yes, in step S304, a temporary scheduling strategy also needs to be generated according to the traffic information of the intersection edge region. The temporary scheduling strategy adjusts the final charging location of the mobile energy storage charging station in the intersection edge region to obtain a temporary charging location.

[0135] Further, in some embodiments, the final charging location of the mobile energy storage charging station in the intersection edge region is a to-be-scheduled charging location, and generating the temporary scheduling strategy according to the traffic information of the intersection edge region further comprises:

[0136] Obtaining an actual timestamp of the current intersection edge region, obtaining a second traffic congestion state in the current intersection edge region according to the actual timestamp and the traffic peak-valley state information, and obtaining second path distribution information and the to-be-scheduled charging location of the current intersection edge region.

[0137] The second traffic congestion state, the second path distribution information, and the to-be-scheduled charging location are input into the trained neural network model, and the output result of the neural network model is the temporary scheduling strategy corresponding to the intersection edge region.

[0138] The generation step of the temporary scheduling strategy is the same as the generation step of the location scheduling strategy, and the difference lies in that the reference parameters input into the neural network model are different. The reference parameters shown in the embodiment are the second traffic congestion state, the second path distribution information, and the to-be-scheduled charging location.

[0139] Through the above steps, part of the mobile energy storage charging station resources in the adjacent second preset area at the edge of the first preset area can be mobilized, that is, the embodiment can give the mobile energy storage charging station placed in the border edge area a certain flexible adjustment function. When the mobile energy storage charging station in the first preset area cannot ensure the charging demand of the new energy equipment, the local mobile energy storage charging station in the second preset area is called to the first preset area, and since it is the mobile energy storage charging station at the edge of the second preset area, the time cost of scheduling is small, and the charging demand of the second preset area will not be greatly affected. When there are multiple preset areas, temporary scheduling of the mobile energy storage charging station in a preset area can be realized according to the above steps to adapt to the sudden conditions that may occur in the urban application scene, such as traffic jams, traffic control, etc.

[0140] In some embodiments, the method further comprises:

[0141] The number of times of generating the temporary scheduling strategy of the first preset area is recorded, and when the number of times exceeds a preset scheduling threshold, the initial configuration information of the mobile energy storage charging station corresponding to the first preset area is updated.

[0142] In the embodiment, the number of times of generating the temporary scheduling strategy of the first preset area is monitored, and when the number of times of generating the temporary scheduling strategy exceeds a preset scheduling threshold, it indicates that there is a problem with the number of configurations of the mobile energy storage charging station in the current first preset area, and the initial configuration information of the mobile energy storage charging station in the current first preset area needs to be updated to increase the number of mobile energy storage charging stations in the first preset area. And based on the updated initial configuration information, the initial charging location in the first preset area is regenerated, and the location adjustment strategy corresponding to the initial charging location is generated. Further, after the above steps are executed, the number of times of generating the temporary scheduling strategy of the current first preset area is cleared. This way can monitor the temporary scheduling state of the first preset area, and also give the cluster control system of the entire mobile energy storage charging station a certain degree of self-adjustment and self-correction ability, and improve the rationality of the location planning of the mobile energy storage charging station.

[0143] By acquiring preset area charging demand information, the preset area is divided into a plurality of preset sub-areas according to the charging demand information; the charging supply information in the preset area is acquired; the initial configuration information of the mobile energy storage charging station corresponding to each preset sub-area is generated according to the charging supply information and the charging demand information; the traffic information and the objective adjustment information corresponding to the preset area are acquired, and the initial charging location of the mobile energy storage charging station in each preset sub-area is generated according to the traffic information and the initial configuration information; the location adjustment strategy of the mobile energy storage charging station in each preset sub-area is generated according to the objective adjustment information; the initial charging location in each preset sub-area is adjusted according to the location adjustment strategy in different time segments, and the final charging location of each mobile energy storage charging station is obtained. Based on the charging demand of new energy in a population-intensive scene, the existing fixed energy storage charging station and the mobile energy storage charging station are integrated, the demand for power grid reliability and economy is considered, the configuration of the mobile energy storage charging station in the emergency power supply and daily operation scenes is realized, the diversified application and service of the local power energy load can be provided, and the utilization rate of the mobile energy storage charging station is improved.

[0144] The above only describes some embodiments of the present application, and does not limit the protection scope of the present application, and any equivalent device or equivalent process transformation using the content of the specification and drawings, or direct or indirect application in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A cluster control method for a mobile emergency energy storage charging station, characterized in that: include: Obtaining charging demand information for a preset area, and dividing the preset area into a plurality of preset areas according to the charging demand information, wherein the charging demand information includes charging amount distribution data within a unit time period; Obtaining charging supply information within the preset area, the charging supply information including fixed energy storage supply information and mobile energy storage supply information, the fixed energy storage supply information being charging information of a fixed energy storage charging station, and the mobile energy storage supply information being charging information of a mobile energy storage charging station; Generating initial configuration information of the mobile energy storage charging station corresponding to each of the preset areas according to the charging supply information and the charging demand information; Obtaining traffic information and objective adjustment information corresponding to the preset areas, wherein the traffic information includes path distribution information and traffic peak and valley status information of traffic roads within each of the preset areas, and the objective adjustment information includes weather data within each of the preset areas; Generating an initial charging location of the mobile energy storage charging station in each of the preset areas according to the traffic information and the initial configuration information; Generating a location adjustment strategy for the mobile energy storage charging station within each of the preset areas according to the objective adjustment information; Adjusting the initial charging location within each of the preset areas in different time periods according to the location adjustment strategy to obtain a final charging location for each of the mobile energy storage charging stations; Generating a location adjustment strategy for the mobile energy storage charging station in each of the preset areas according to the objective adjustment information includes: Generating a final adjustment probability of the current preset area according to the objective adjustment information; Determining whether the adjustment probability is within a preset adjustment probability range, and if not, not adjusting the preset area; If so, obtaining an actual timestamp of the current preset area, obtaining a first traffic congestion state in the current preset area according to the actual timestamp and the traffic peak and valley state information, and obtaining first path distribution information and an initial charging location in the current preset area; Inputting the first traffic congestion status, the first route distribution information, and the initial charging location into a trained neural network model, wherein an output result of the neural network model is the location adjustment strategy corresponding to the preset area; The objective adjustment information also includes historical charging peak and valley information within each of the preset areas; Generating the final adjustment probability of the current preset area according to the objective adjustment information further includes: generating a first adjustment probability according to the current weather data in the preset area; Obtaining an actual timestamp of the current preset area, and obtaining real-time demand information within the preset area based on the actual timestamp and the historical charging peak and valley information; Acquire real-time supply information of the mobile energy storage charging station within the current preset area, and generate a second adjustment probability according to the real-time supply information and the real-time demand information; The first adjustment probability and the second adjustment probability are added together to obtain the final adjustment probability.

2. The cluster control method of the mobile emergency energy storage charging station according to claim 1, characterized in that: The objective adjustment information also includes battery life information of each mobile energy storage charging station; Obtaining the real-time supply information of the mobile energy storage charging station within the current preset area also includes: Obtaining battery life information of the mobile energy storage charging station within the preset area, and generating a power supply correction parameter based on the battery life information; The real-time supply information within the current preset area is corrected according to the power supply correction parameter.

3. The cluster control method of the mobile emergency energy storage charging station according to claim 2, characterized in that: The objective adjustment information also includes traffic accident information of the current preset area; Obtaining the first traffic congestion state in the current preset area according to the actual timestamp and the traffic peak and valley state information further includes: Acquire traffic accident information of the current preset area according to the actual timestamp, and generate a traffic congestion correction parameter according to the traffic accident information; The first traffic congestion state is obtained according to the traffic peak-valley state information and the traffic congestion correction parameter.

4. The cluster control method of the mobile emergency energy storage charging station according to claim 3 is characterized in that: The objective adjustment information also includes power status information of each mobile energy storage charging station; Generating the final adjustment probability of the current preset area according to the objective adjustment information further includes: Acquiring power status information of the mobile energy storage charging stations within the preset area, and screening out the mobile energy storage charging stations in a charging state according to the power status information; Calculating a proportion of the mobile energy storage charging stations in the charging state within the preset area, and recording the proportion as a third adjustment probability; The first adjustment probability, the second adjustment probability, and the third adjustment probability are added together to obtain the final adjustment probability.

5. The cluster control method of the mobile emergency energy storage charging station according to claim 4, characterized in that: The two adjacent preset areas are referred to as a first preset area and a second preset area, and the method further includes: Obtaining a boundary edge area between the first preset area and the second preset area; Filtering the mobile energy storage charging stations located in the first preset area and the second preset area according to the final charging location and located at the edge of the intersection; acquiring first charging supply information and first charging demand information of the first preset area in real time, and determining whether the first charging supply information and the first charging demand information are saturated; If so, a temporary scheduling strategy is generated according to the traffic information of the junction edge area, and the final charging location of the mobile energy storage charging station in the junction edge area is adjusted according to the temporary scheduling strategy to obtain a temporary charging location.

6. The cluster control method of the mobile emergency energy storage charging station according to claim 5, characterized in that: The final charging location of the mobile energy storage charging station in the boundary edge area is recorded as the charging location to be scheduled, and generating a temporary scheduling strategy based on the traffic information of the boundary edge area further includes: Obtaining an actual timestamp of the current junction edge area, obtaining a second traffic congestion state in the current junction edge area based on the actual timestamp and the traffic peak and valley state information, and obtaining second path distribution information and a charging location to be scheduled in the current junction edge area; The second traffic congestion status, the second path distribution information, and the charging location to be scheduled are input into a trained neural network model, and an output result of the neural network model is the temporary scheduling strategy corresponding to the boundary edge area.

7. The cluster control method of the mobile emergency energy storage charging station according to claim 6, characterized in that: Obtaining the boundary edge area between the first preset area and the second preset area includes: Obtaining a boundary line between the first preset area and the second preset area; The first preset area and the second preset area are divided according to a preset width with the boundary line as the center reference line to obtain the boundary edge area.

8. The cluster control method of the mobile emergency energy storage charging station according to claim 7, characterized in that: The method further comprises: The number of times the temporary scheduling strategy for the first preset area is generated is recorded, and when the number of times the temporary scheduling strategy is generated exceeds a preset scheduling threshold, the initial configuration information of the mobile energy storage charging station corresponding to the first preset area is updated.

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