Coordination and distribution method and system for mobile power stations

Abstract

The invention discloses a mobile power station coordination and distribution method and system. The method comprises the following steps: acquiring energy demand information of each region, traffic information among regions, equipment information of each mobile power station and environment information of each region in a target range; obtaining the demand priority of each region based on the energy demand information of each region and the environment information of each region; scheduling is initialized, corresponding mobile power stations are matched for all the areas according to the priority from high to low, and an initial matching table is output; inputting the initial matching table into an improved ant colony algorithm, and performing iterative optimization to obtain a distribution scheme; and each mobile power station and each region receive and execute the distribution scheme. Through the above mode, the method can efficiently and rapidly provide an accurate and reliable scheduling distribution scheme, meets the power demands of each region as much as possible, effectively saves the time, improves the processing efficiency, and meets the demands of special power scenes.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electric power, in particular to a coordination and distribution method and system of mobile power stations. BACKGROUND

[0002] With the continuous progress of technology, the power grid coverage in China is becoming more and more extensive, the power market is becoming more and more deep, and the development of electric equipment and devices is also becoming more and more extensive. According to the annual power facility industry special report in China, the proportion of terminal energy consumption of electric energy in the energy consumption of electric energy in China has increased significantly, which puts forward new requirements for the power supply side.

[0003] Under this background, for some special situations, the power supply problem of the area which the power grid cannot completely cover, such as islands, field operations, outdoor scientific investigations, post-disaster reconstruction, etc., new demands are put forward. Usually, the deployment of mobile power stations is used to coordinate, but for complex power supply demands, it is necessary to quickly come up with a suitable power supply coordination scheme and make immediate adjustments to the scheme, and at present there is still a lack of such a scheme. SUMMARY

[0004] Therefore, the present application mainly provides a coordination and distribution method and system of mobile power stations to solve the problem of dispatching mobile power stations in emergency, repair, outdoor and other special situations.

[0005] To solve the above technical problems, one technical solution adopted by the present application is to provide a coordination and distribution method of mobile power stations, comprising the following steps: S10: obtaining regional energy demand information in a target range, inter-regional traffic information, mobile power station equipment information, and regional environment information; S20: obtaining the demand priority of each region based on the regional energy demand information and the regional environment information; S30: dispatching initialization, matching the corresponding mobile power station for the regions in order of priority from high to low, and outputting an initial matching table; S40: setting improved ant colony algorithm parameters based on the inter-regional traffic information, the mobile power station equipment information and the regional environment information, inputting the initial matching table into the improved ant colony algorithm, and iteratively optimizing to obtain a distribution scheme; S50: each mobile power station and each region receiving and executing the distribution scheme.

[0006] In one possible implementation, after the step of obtaining regional energy demand information in a target range, inter-regional traffic information, mobile power station equipment information, and regional environment information, the method comprises: S11: data cleaning, data completion and confidence calculation are performed on the regional energy demand information, the inter-regional traffic information, the mobile power station device information and the regional environment information.

[0007] In a possible implementation, the step of obtaining the demand priority of each region based on the regional energy demand information and the regional environment information comprises: S21: a scoring model is constructed, the regional energy demand information and the regional environment information are used to assign values to the regions, and the demand priority is obtained according to the assigned values.

[0008] In a possible implementation, the step of scheduling initialization, matching the mobile power stations to the regions according to the priority from high to low, and outputting an initial matching table comprises: S31: the demand of each region is analyzed one by one according to the priority from high to low, the matching result of the mobile power station to the region is obtained according to the constraint of the inter-regional traffic information, the mobile power station device information and the regional environment information, and the initial matching table is output according to the matching result, wherein the initial matching table comprises the regions that have been successfully matched and the regions that have not been successfully matched.

[0009] In a possible implementation, the step of setting the improved ant colony algorithm parameters based on the inter-regional traffic information, the mobile power station device information and the regional environment information, inputting the initial matching table into the improved ant colony algorithm, and iteratively optimizing to obtain a distribution scheme comprises: S41: the parameters of the ant colony algorithm are initialized, the ant colony size is set according to the regions that have not been successfully matched, the pheromone parameter is set according to the adaptability weight of the power station to the region, the heuristic function is set according to the real-time adaptability and data confidence of the power station to the region demand, the iteration number, the pheromone evaporation coefficient and the pheromone update mechanism are set, and the distribution scheme is output after the preset number of iterations.

[0010] In a possible implementation, after the step of setting the iteration number and the pheromone evaporation coefficient and outputting the distribution scheme after iteration, the step comprises: S42: the total adaptability of the distribution scheme in each iteration is evaluated, and the optimal scheme is selected.

[0011] In a possible implementation, the step of obtaining the regional energy demand information, the inter-regional traffic information, the mobile power station device information and the regional environment information in the target range further comprises: S12: If the information of a region is interrupted, the last valid data is matched, and only steps S20 and S30 are executed, and the matching result in the initial matching table is output as the final scheme.

[0012] In a possible implementation, the step of acquiring the energy demand information of each region, the traffic information between regions, the information of each mobile power station, and the environmental information of each region further includes: S13: Continuously record each item of data and store it as a regional offline data pool, and if information receiving of multiple regions is interrupted, the last time data is acquired from the regional offline data pool for processing, and a global allocation scheme is output.

[0013] In a possible implementation, the step of continuously recording each item of data and storing it as a regional offline data pool, and if information receiving of multiple regions is interrupted, the last time data is acquired from the regional offline data pool for processing, and a global allocation scheme is output further includes: S14: If data receiving is restored after the region is offline, the original data is overwritten by new data according to a time stamp, and a new allocation scheme is acquired again; if the new allocation scheme conflicts with the original allocation scheme, a warning is issued to apply for intervention.

[0014] To solve the above technical problems, another technical solution adopted by the present application is to provide a mobile power station coordination and allocation system suitable for a mobile power station coordination and allocation method, which includes: An acquisition module is configured to acquire the energy demand information of each region, the traffic information between regions, the information of each mobile power station, and the environmental information of each region. An analysis module is configured to acquire the demand priority of each region based on the energy demand information of each region and the environmental information of each region. A first algorithm module is configured to initialize scheduling, match a corresponding mobile power station to each region according to the priority from high to low, and output an initial matching table. A second algorithm module is configured to input the initial matching table into an improved ant colony algorithm, and iteratively optimize to acquire an allocation scheme. An execution module is configured to receive and execute the allocation scheme by each mobile power station and each region.

[0015] The present application has the following beneficial effects: Different from the prior art, the present application discloses a mobile power station coordination and allocation method, which acquires data of each region, combines the advantages of two algorithms, and can efficiently and quickly issue an accurate and reliable scheduling allocation scheme for the mobile power station scheduling scene in emergency, repair, outdoor, and other special situations, so as to meet the power demand of each region as much as possible, effectively save time, and meet the demand of special power consumption scenes. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein: Figure 1 This is a schematic flowchart of a method for coordinating and allocating mobile power stations according to an embodiment of this application; Figure 2 This is a modular structure diagram of a coordination and distribution system for a mobile power station according to an embodiment of this application.

[0017] Explanation of key component symbols: 10-A coordination and distribution system for mobile power stations; 11-Acquisition module; 12-Analysis module; 13-First algorithm module; 14-Second algorithm module; 15-Execution module. Detailed Implementation

[0018] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0019] The terms "first," "second," and "third" used in the embodiments of this application are for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first," "second," or "third" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0020] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. As will be apparent to those of ordinary skill in the art, embodiments described herein can be combinable with other embodiments.

[0021] Referring to Figure 1 The embodiments of the present application include a method for coordinating and distributing mobile power stations, comprising the following steps: S10: Collecting regional energy demand information, inter-regional traffic information, mobile power station equipment information, and regional environmental information within a target range; S20: Obtaining demand priorities of each region based on the regional energy demand information and the regional environmental information; S30: Initializing dispatching, matching corresponding mobile power stations for each region according to the priorities from high to low, and outputting an initial matching table; S40: Setting parameters of an improved ant colony algorithm based on the inter-regional traffic information, the mobile power station equipment information, and the regional environmental information, inputting the initial matching table into the improved ant colony algorithm, and iteratively optimizing to obtain a distribution scheme; S50: Each mobile power station and each region receiving and executing the distribution scheme.

[0022] The embodiments are aimed at power supply coordination in areas such as islands, field operations, outdoor scientific investigations, post-disaster reconstruction, and other areas where conventional power grids cannot cover. In order to accurately match power supply resources and power demand scenarios, coordination is performed according to various data.

[0023] Specifically, in step S10, the target range refers to the entire region involved in this dispatching, and each region refers to each place, facility, or a certain range of area that needs power support. The sum of each region constitutes the target range. The information of each region is collected and summarized, including regional energy demand information, inter-regional traffic information, mobile power station equipment information, and regional environmental information. The collection method can be automatic data collection through a set collection device, or manual data collection and active uploading. The embodiments do not limit the data collection method.

[0024] In the embodiment, the regional energy demand information can include urgency, power demand, and power interruption duration, wherein the urgency refers to the urgency of power supply demand, for example, the urgency of a hospital or a rescue site is higher than that of a temporary residence site; the power demand refers to the power of required power, for example, the specifications of industrial power and residential power are different, and the daily average power supply amount can be calculated, which can be selected according to the specific scene; and the power interruption duration refers to the time when power supply is lost, and the longer the power interruption duration, the higher the urgency of power demand.

[0025] The traffic information between regions includes road distance, road condition, and available traffic tools between regions, the road distance refers to the distance of the road available for the traffic tool between regions, which is different from the straight-line distance; the road condition refers to the specific road condition of the road connecting two places, for example, the road can be disconnected or blocked and cannot be normally used in the case of disaster; and the available traffic tool refers to the type of traffic tool corresponding to the road, which involves the matching problem of the mobility of the mobile power station, for example, if the mobile power station is a container type power supply cabin, it needs to be considered whether it can be transported by the traffic tool.

[0026] The mobile power station equipment information includes output power, residual energy amount, mobility, and deployment mode of each mobile power station, wherein the mobility refers to whether the mobile power station has the mobility, and the deployment mode refers to the installation mode of the mobile power station, such as hoisting, trailer, etc.

[0027] The regional environment information includes environmental risk, geographical condition, and climate, the environmental risk refers to the risk that can cause harm such as flood, landslide, and typhoon, the geographical condition includes the altitude and slope of the specific site, and the climate refers to the temperature, precipitation, and wind power resources, which are particularly related to the mobile power station including photovoltaic and wind power generation.

[0028] In step S20, according to the urgency of the demand of each region and whether the constraint of the environment information of each region is met, the score is calculated according to the principle of giving priority to guaranteeing life safety and emergency situation based on the regional energy demand information and the regional environment information, and the priority of each region is arranged.

[0029] In step S30, based on the logic of the greedy algorithm, the matching is sequentially performed from high to low in the order of the demand priority, that is, the region with urgent demand is preferentially met, and the matching result is output as an initial matching table. The initial matching table includes the successfully matched region and the power station, the region that fails to meet the demand, and the remaining power station, and is used as the input of the improved ant colony algorithm for subsequent improvement.

[0030] In step S40, the initial matching table is processed by using the improved ant colony algorithm, a plurality of ants are used for searching, each ant corresponds to the matching of a region and a power station, a group of ants correspond to the entire distribution scheme, and the optimal distribution scheme is obtained after iteration for several times, and is output as the final result.

[0031] In step S50, each mobile power station starts to perform according to the allocation scheme after receiving the allocation scheme by each region.

[0032] The present application combines the greedy algorithm with the improved ant colony algorithm, which can take into account the advantages of the greedy algorithm in rapid initialization and rapid analysis, and the advantages of the ant colony algorithm in avoiding local optimal solution, and combines the advantages of the two to better adapt to the application scenarios of the present application, so as to quickly analyze and propose an accurate and optimal allocation scheme.

[0033] In an embodiment, after the step of obtaining the energy demand information of each region, the traffic information between each region, the mobile power station equipment information, and the environmental information of each region, the method further comprises: S11: performing data cleaning, data completion, and confidence calculation on the energy demand information of each region, the traffic information between each region, the mobile power station equipment information, and the environmental information of each region.

[0034] In step S11, the collected data is preprocessed, which includes data cleaning, data completion, and confidence calculation. Specifically, data cleaning includes unifying the collected data, such as unifying daily power consumption and daily power consumption time into real-time power demand, and digitizing urgency, such as marking life support as 3, marking livelihood use as 2, and marking the rest as 1 (the larger the number, the higher the urgency). Data completion is to detect the data, if missing values or obvious error data are found, the error data is removed, and the missing values are completed. For short period (missing data within 5 minutes) data missing, linear interpolation can be used for interpolation; for long period (missing data between 5 to 15 minutes) data missing, historical data of the region can be selected for interpolation; for super long period (more than 15 minutes) data missing, preset value can be used for estimation processing, the preset value can be set according to the nature of the region, the number of people involved, related electrical equipment and experience. Confidence C is the comprehensive timeliness and data integrity, which can be obtained according to the following formula: C=α·A+β·B, Wherein A refers to the timeliness score, B refers to the data integrity score, a refers to the timeliness coefficient, and b refers to the data integrity coefficient. In this embodiment, the timeliness score A is assigned according to the acquisition time of the data, such as 1.0 for data acquired within 30 minutes, 0.8 for data acquired within 30 to 120 minutes, 0.5 for data acquired within 2 to 6 hours, 0.2 for data acquired within 6 to 12 hours, and 0 for data acquired more than 12 hours, and a warning is issued, suggesting that other ways of acquiring data be taken, that is, the higher the timeliness of the data, the higher the score; the data integrity B is copied according to the missing condition of the data, such as 1.0 for data without missing, 0.8 for data missing minor fields, 0.3 for data missing important fields or missing a large number of minor fields, and 0 for data missing a large number of important fields, and a warning is issued, wherein the important fields refer to important data such as urgency green and power demand, and the minor fields refer to fields with weak influence such as climate and environment. Further, the importance of each item of data can be assigned to further improve the accuracy of the confidence of the acquired data.

[0035] In an embodiment, based on the regional energy demand information and the regional environmental information, the step of obtaining the demand priority of each region comprises: S21: constructing a scoring model, assigning values to each region according to the regional energy demand information and the regional environmental information, and obtaining the demand priority according to the assigned values.

[0036] Specifically, a scoring model is constructed to quantify and arrange the demand priority of each region. The demand priority score S can be obtained by the following formula: , Wherein X is the index weight, and Y is the index score. The index is an evaluation index of each region extracted from the regional energy demand information and the regional environmental information, such as urgency, power demand, environmental risk, and power supply interruption time, each index is assigned a corresponding weight and the total weight should be 1, then each index is scored, the scoring rule can score each sub-item under each index, and finally the priority score is obtained by multiplying and adding the index weight and the corresponding index score. In this embodiment, the scoring rule is shown in Table 1: Table 1 Scoring rule

[0037] According to the scoring rule, the demand priority of each region is obtained, sorted and used in the subsequent calculation steps.

[0038] In an embodiment, for dispatch initialization, the step of matching the mobile power station corresponding to each region according to the priority from high to low and outputting the initial matching table comprises: S31: Analyze the demand of each region one by one according to the priority from high to low, and obtain the matching result of the mobile power station to each region according to the constraint of the traffic information between regions, the mobile power station equipment information, and the environmental information of each region, and output an initial matching table according to the matching result, the initial matching table including the regions that have been successfully matched and the regions that have not been successfully matched.

[0039] Specifically, after obtaining the demand priority of each region, the demand priority is sorted and matched in order from high to low, while considering the constraint relationship between the data. A greedy algorithm is used, that is, to ensure larger demand first, to construct a better solution as an initial scheme, and to optimize it later.

[0040] In the embodiment, the score F and the sorting of the demand priority of the region are shown in Table 2: Table 2: Demand priority score of region

[0041] That is, after sorting according to the priority score, the power supply demand of the temporary hospital, the resettlement point, and the material storage point is satisfied in turn to form an initial matching table. While matching, the constraints formed according to the relationship between the data are also screened, for example, in the deployment mode, it is difficult to implement in heavy rain; whether the specifications of the mobile power station match the power consumption facilities of the region; whether the mobility of the mobile power station can meet the current road conditions, etc. After considering the constraints of each data, an initial matching table is generated, as shown in Table 3: Table 3: Initial matching table

[0042] In an embodiment, the parameters of the improved ant colony algorithm are set based on the traffic information between regions, the mobile power station equipment information, and the environmental information of each region, and the initial matching table is input into the improved ant colony algorithm for iterative optimization to obtain the allocation scheme, including: S41: Initialize the parameters of the ant colony algorithm, set the ant colony size according to the regions that have not been successfully matched, set the pheromone parameter according to the adaptability weight of the power station to the region, set the heuristic function according to the real-time adaptability of the power station to the region demand and the data confidence, set the iteration number, the pheromone evaporation coefficient, and the pheromone update mechanism, and output the allocation scheme after iterating for a preset number of times.

[0043] Specifically, the improved ant colony algorithm is used for optimization of the distribution scheme. The ant colony size can be set according to the number of regions that have not been matched successfully. For example, if there are 5 regions that have not been matched, the initial size of the ant colony is 5, and each ant corresponds to a region. The pheromone τ(i, j) represents the fitness weight of the mobile power station i assigned to the region j, and the initial value T0=0.5. The heuristic function η(i, j) represents the real-time fitness of the mobile power station i to the region j, which means that the current matching degree of the mobile power station to a certain region is determined according to the current data. The number of iterations should consider both the optimization effect and the calculation speed. In this embodiment, the number of iterations is selected to be 500 times. The pheromone evaporation coefficient ρ=0.1 is used to avoid falling into a local optimal solution.

[0044] Further, the heuristic function η can be designed as: , where P is the power fitness, L is the distance fitness, E is the environmental fitness, and C is the confidence. The power fitness P is: P=min(p_max(j) / p(i), 1.5), that is, the smaller one is selected and does not exceed 1.5, and the former refers to the ratio of the maximum power required by the region j to the power that the mobile power station i can provide.

[0045] The distance fitness L is: L=1-t(i,j) / t_max, where t(i, j) refers to the time required for the mobile power station i to move to the region j, and t_max refers to the longest moving time in all combinations of mobile power stations moving to the region within the target range. Therefore, the higher the distance fitness L score obtained by subtracting the ratio from 1, the less time is used, which has the advantage of time dimension.

[0046] The environmental fitness E is valued in combination with the deployment method and environmental risk. For example, the trailer deployment method can be implemented in a rainstorm environment, and the score is 1. The hoisting type is difficult to implement in a rainstorm environment, and the score is 0.5.

[0047] The confidence is as shown above, and the acquisition method is described in step S11.

[0048] In the previous step S31, the environmental fitness and other factors are used as constraints for the preliminary decision of non-matching. However, this non-matching is only a preliminary judgment, and subsequent power supply to each region is still needed. Therefore, this step fully considers the above factors to propose a feasible distribution scheme to ensure overall power supply in the region.

[0049] The ant transfer probability O(i, j), that is, the probability of the ant selecting the power station j to be assigned to the region i, is: , Wherein m is pheromone importance factor, the greater the value of the factor, the more dependent on historical experience in iteration, n is heuristic function importance factor, the greater the value of the factor, the more dependent on real-time fitness in iteration. In the embodiment, m takes the value of 1.2, and n takes the value of 0.8.

[0050] The pheromone updating mechanism can refer to the following formula: , Wherein τ(i,j) refers to pheromone, the subscript k +1 is the pheromone obtained by the next time pheromone according to the pheromone of the previous time k , the triangular mark Δ refers to the pheromone change amount between k and k +1, the smaller the change amount, the closer to the optimal solution, and ρ refers to the pheromone evaporation coefficient, ρ=1 in the embodiment.

[0051] In running, the un-matched area in the initial allocation is imported with the mobile power station, and the iteration is performed according to the preset number of times, the mobile power station is selected according to each ant corresponding to an area, the matching scheme is completed for the un-matched area, and the complete allocation scheme is generated in combination with the initial matching table in the previous step.

[0052] In an embodiment, the number of iterations and the pheromone evaporation coefficient are set, and after the step of outputting the allocation scheme after iteration, the step includes: S42: The total fitness of the allocation scheme in each iteration is evaluated, and the optimal solution is selected.

[0053] Specifically, the heuristic function η(i,j) covering each item of fitness and the demand priority score S are combined to obtain the comprehensive score F, according to: , The score of each scheme is obtained, and the scheme with the highest score is output as the optimal solution.

[0054] In an embodiment, the step of obtaining the energy demand information of each region in the target range, the traffic information between each region, the mobile power station equipment information, and the environmental information of each region further includes: S12: If the information of a certain region is interrupted, the matching is performed according to the last valid data, and only steps S20 and S30 are executed, and the matching result in the output initial matching table is taken as the final scheme.

[0055] Due to the particularity of the scenario to which the present application is directed, signal disconnection is prone to occur due to power supply interruption. If such a situation occurs, operation is performed according to the last valid data received, and when the disconnection time is short, the data received in the last round still has a certain reliability. At this time, light-weight operation is performed, i.e., only steps S20 and S30 are executed, so that a simple scheme can be quickly obtained and executed in advance to restore power supply and communication as soon as possible. Subsequently, the scheme can be coordinated and modified on the basis of the scheme to further optimize and improve it.

[0056] Specifically, when matching, the power of the mobile power station can be simply estimated according to 1.2 times the demand, and the demand priority is satisfied in turn to realize light-weight operation.

[0057] In an embodiment, the step of obtaining the energy demand information of each region in the target range, the traffic information between each region, the mobile power station device information of each region, and the environmental information of each region further comprises: S13: Continuously record each item of data and store it as a regional offline data pool. If information reception is interrupted in multiple regions, the last data is obtained from the regional offline data pool for processing, and a global allocation scheme is output.

[0058] When information interruption occurs in multiple regions, the demand situation of each region can be determined from historical data, and the last data is processed. Some data properties in the offline data pool, such as urgency and environmental demand, do not change and remain for a short period of time. In addition, the occurrence of information interruption in multiple regions also indicates that the demand will expand. The demand power can be estimated according to 1.5 times the previous data to provide as much power as possible to restore power as soon as possible.

[0059] In an embodiment, the step of continuously recording each item of data and storing it as a regional offline data pool, and if information reception is interrupted in multiple regions, obtaining the last data from the regional offline data pool for processing, and outputting a global allocation scheme further comprises: S14: If data reception is restored after regional offline, the new data is overwritten on the original data according to the time stamp, and a new allocation scheme is obtained. If the new allocation scheme conflicts with the original allocation scheme, a warning is issued to apply intervention.

[0060] When power supply is gradually restored, data uploaded by each region will be received again. At this time, the original data is overwritten with new data according to the time stamp, and scheme matching is performed again. If the original scheme has been started and some mobile power stations have begun to move, and the newly obtained scheme conflicts with it, a warning should be issued to remind the dispatcher to suggest manual intervention, and consider whether to stop the original scheme and whether to call back the mobile power stations that have already started on the road, so as to avoid waste and mismatch of resources.

[0061] The above is the explanation of the method for coordinating and allocating mobile power stations in the embodiments of the present application. The system for coordinating and allocating mobile power stations 10 in the embodiments of the present application is described below. Please refer to Figure 2 The embodiments of the present application also include a system for coordinating and allocating mobile power stations 10, which is suitable for the method for coordinating and allocating mobile power stations and includes: The acquisition module 11 is configured to acquire the energy demand information of each region, the traffic information between regions, the equipment information of each mobile power station, and the environmental information of each region. The analysis module 12 is configured to acquire the demand priority of each region based on the energy demand information of each region and the environmental information of each region. The first algorithm module 13 is configured to initialize scheduling and match the corresponding mobile power station for each region according to the priority from high to low, and output an initial matching table. The second algorithm module 14 is configured to input the initial matching table into an improved ant colony algorithm and iteratively optimize to obtain an allocation scheme. The execution module 15 is configured to receive and execute the allocation scheme by each mobile power station and each region.

[0062] Since the system embodiments correspond to the method embodiments, the system for coordinating and allocating mobile power stations provided by the present application is described in the method embodiments, which will not be described here again, and has the same technical effects as the method for coordinating and allocating mobile power stations.

[0063] The above is only the embodiment of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation using the content of the specification and drawings, or direct or indirect application in other related technical fields, is also included in the patent protection scope of the present application.

Claims

1. A method for coordinating and allocating mobile power stations, characterized in that, Includes the following steps: S10: Obtain energy demand information for each region within the target area, traffic information between regions, information on mobile power station equipment, and environmental information for each region; S20: Based on the energy demand information and environmental information of each region, obtain the demand priority of each region; S30: Initialize the scheduling, match the corresponding mobile power station for each region according to the priority from high to low, and output the initial matching table; S40: Based on the traffic information between the regions, the mobile power station equipment information, and the environmental information of the regions, set the parameters of the improved ant colony algorithm, and input the initial matching table into the improved ant colony algorithm to iteratively optimize and obtain the allocation scheme; S50: Each mobile power station and each region receives and executes the allocation scheme.

2. The method for coordinating and allocating mobile power stations according to claim 1, characterized in that, After the steps of obtaining energy demand information for each region within the target area, traffic information between regions, information on mobile power station equipment, and environmental information for each region, the following steps are included: S11: Perform data cleaning, data completion, and confidence calculation on the energy demand information of each region, the traffic information between each region, the mobile power station equipment information, and the environmental information of each region.

3. The method for coordinating and allocating mobile power stations according to claim 1, characterized in that, The step of obtaining the demand priority of each region based on the energy demand information and environmental information of each region includes: S21: Construct a scoring model, assign values ​​to each region based on the energy demand information and environmental information of each region, and obtain the demand priority based on the assigned values.

4. The method for coordinating and allocating mobile power stations according to claim 1, characterized in that, The step of initializing the scheduling, matching corresponding mobile power stations to each region according to priority from high to low, and outputting an initial matching table includes: S31: Analyze the demand of each region in descending order of priority, and obtain the matching result of the mobile power station for each region based on the constraints of traffic information between the regions, equipment information of each mobile power station, and environmental information of each region. Output the initial matching table based on the matching result. The initial matching table includes regions that have been successfully matched and regions that have not been successfully matched.

5. The method for coordinating and allocating mobile power stations according to claim 4, characterized in that, The steps of setting improved ant colony algorithm parameters based on traffic information between regions, mobile power station equipment information, and environmental information of each region, and inputting the initial matching table into the improved ant colony algorithm for iterative optimization to obtain an allocation scheme include: S41: Initialize the parameters of the ant colony algorithm, set the ant colony size according to the unmatched areas, set the pheromone parameters according to the fit weight of the power station to the region, set the heuristic function according to the real-time fit and data confidence of the power station to the region's needs, set the number of iterations, pheromone evaporation coefficient, and pheromone update mechanism, and output the allocation scheme after iterating a preset number of times.

6. The method for coordinating and allocating mobile power stations according to claim 5, characterized in that, After the step of outputting the allocation scheme after a preset number of iterations, the following steps are included: S42: Perform an overall fitness evaluation on the allocation scheme in each iteration and select the optimal scheme.

7. The method for coordinating and allocating mobile power stations according to claim 1, characterized in that, The steps of obtaining energy demand information for each region within the target area, traffic information between regions, information on mobile power station equipment, and environmental information for each region also include: S12: If information acquisition in a certain area is interrupted, then the last valid data is used for matching, and only steps S20 and S30 are executed. The matching results in the output initial matching table are used as the final solution.

8. The method for coordinating and allocating mobile power stations according to claim 1, characterized in that, The steps of obtaining energy demand information for each region within the target area, traffic information between regions, information on mobile power station equipment, and environmental information for each region also include: S13: Continuously record various data and store them in the regional offline data pool. If information reception is interrupted in multiple regions, retrieve the most recent data from the regional offline data pool, process it, and output a global allocation scheme.

9. The method for coordinating and allocating mobile power stations according to claim 8, characterized in that, The step of continuously recording various data and storing them in a regional offline data pool, and retrieving the most recent data from the regional offline data pool for processing and outputting a global allocation scheme when information reception is interrupted in multiple regions, further includes: S14: If data reception is restored after the area goes offline, the new data will overwrite the original data according to the timestamp, and a new allocation scheme will be obtained again; if the new allocation scheme conflicts with the original allocation scheme, a warning will be issued to request intervention.

10. A coordination and allocation system for mobile power stations, applicable to the coordination and allocation method for mobile power stations as described in any one of claims 1 to 9, characterized in that, include: The data acquisition module is used to obtain energy demand information, traffic information between regions, information on mobile power station equipment, and environmental information of each region within the target area. The analysis module is used to obtain the demand priority of each region based on the energy demand information and environmental information of each region. The first algorithm module is used to initialize the scheduling and match the corresponding mobile power stations for each region according to priority from high to low, and output the initial matching table; The second algorithm module is used to input the initial matching table into the improved ant colony algorithm and iteratively optimize it to obtain the allocation scheme. The execution module is used by each mobile power station and each region to receive and execute the allocation scheme.

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