Charging pile utilization rate imbalance compensation excitation algorithm

Abstract

The invention relates to the technical field of charging pile management, and discloses a charging pile utilization rate imbalance compensation excitation algorithm, which comprises the following steps of: 1, acquiring charging pile operation data of all stations and load data of all regional power grids, and classifying to form a data set; 2, analyzing the use conditions of different types of charging piles in real time, and generating a fast charging utilization rate and a slow charging utilization rate; 3, analyzing the load condition of each regional power grid, and generating a load index; 4, setting a monitoring period with a fixed time length, analyzing the loss degree of the charging pile at each station, generating a loss index, and having strong multi-dimensional analysis perception capability; and 5, setting a load threshold value, a fast charging threshold value, a slow charging threshold value and a loss threshold value with fixed numerical values, judging whether the regional power grid has a compensation excitation condition of unbalanced utilization rate of the charging pile or not, and outputting a corresponding excitation suggestion and a fault early warning signal, so that the intelligent management excitation effect is good.

Description

Technical Field

[0001] The present invention relates to the technical field of charging pile management, and in particular to an incentive algorithm for compensating for imbalanced utilization of charging piles. Background Art

[0002] With the rapid development of the new energy vehicle industry, charging piles, as a crucial supporting infrastructure for new energy vehicles, are expanding in scale. However, current charging pile utilization shows significant imbalances, with significant regional disparities. In urban core areas, due to dense populations and heavy traffic, demand for charging piles is high. This is particularly true in areas like commercial centers and those surrounding office buildings, where demand often outstrips supply during peak hours, forcing drivers to queue for charging. Conversely, in remote suburbs, older residential communities, and some highway service areas, charging piles remain idle for extended periods, resulting in extremely low utilization and significant resource waste. This phenomenon not only hinders the efficient allocation of resources but also hinders the further promotion of new energy vehicles, necessitating effective compensation and incentive measures to address this issue.

[0003] To alleviate this imbalance, compensation and incentives are crucial. This includes increasing financial subsidies for idle charging stations, introducing a sharing economy model, integrating idle charging stations across different regions and time periods, and encouraging users to utilize these resources. A unified intelligent platform should be established to enable real-time sharing and scheduling of charging station information, allowing drivers to easily query and reserve nearby available charging stations, regardless of operator, allowing them to use them as easily as shared bicycles.

[0004] Currently, traditional charging pile utilization imbalance compensation incentive algorithms lack dynamic perception capabilities and are unable to accurately determine whether the regional power grid meets the conditions for charging pile utilization imbalance compensation incentives based on the load conditions of the regional power grid at the current time point. This leads to unsatisfactory incentive effects. In addition, it is difficult to accurately estimate the failure risks of indoor and outdoor charging piles, resulting in a high equipment downtime rate. Summary of the Invention

[0005] In response to the shortcomings of the existing technology, the present invention provides a charging pile utilization imbalance compensation incentive algorithm, which has the advantages of strong multi-dimensional analysis perception capability and good intelligent management incentive effect. It solves the problem that the charging pile utilization imbalance compensation incentive algorithm lacks dynamic perception capability and has a high equipment downtime rate.

[0006] To achieve the above object, the present invention provides the following technical solution: a charging pile utilization imbalance compensation incentive algorithm, comprising the following steps:

[0007] Step 1: Connect to the big data platform through the network to obtain the charging pile operation data of all sites and the load data of all regional power grids, and classify them into operation data sets and regional data sets;

[0008] Step 2: Based on the operational data set, analyze the usage of different types of charging piles in real time and generate the corresponding fast charging utilization rate Kcl and slow charging utilization rate Mcl;

[0009] Step 3: Analyze the load of each regional power grid based on the regional data set and generate the corresponding load index Fhz;

[0010] Step 4: Set a fixed-length monitoring period Q, and then analyze the loss level of charging piles at each station based on the operational data set to generate the corresponding loss index Shz;

[0011] Step 5: Set fixed values ​​for the load threshold FHY, fast charging threshold KCY, slow charging threshold MCY, and loss threshold SHY. Then, combined with the fast charging utilization rate Kcl, slow charging utilization rate Mcl, load index Fhz, and loss index Shz, determine whether the regional power grid has the compensation incentive conditions for the imbalance in charging pile utilization, and output corresponding incentive suggestions and fault warning signals.

[0012] Preferably, in step one, the operation data set includes the total number of fast charging piles, the number of available fast charging piles, the total number of slow charging piles, the number of available slow charging piles, the charging environment, the number of times the fast charging piles are used, the number of times the slow charging piles are used, the number of fast charging pile failures and the number of slow charging pile failures at each site.

[0013] Preferably, in step 1, the regional data set includes the load power of each regional power grid, the number of installed charging piles and the number of permanent residents.

[0014] Preferably, in step 2, the calculation process of the fast charge utilization rate Kcl is as follows:

[0015] According to the operation data set, extract the charging pile operation data of the e-th station and mark the total number of fast charging piles at the e-th station as kz e , the number of fast charging piles available at the e-th station at the current time point is marked as ky e ;

[0016]

[0017] In the formula, Indicates the utilization rate of fast charging piles at the e-th station at the current time point Kcl e .

[0018] Preferably, in step 2, the calculation process of the slow charging utilization rate Mc1 is as follows:

[0019] According to the operational data set, the total number of slow charging piles at the e-th station is marked as mz e , mark the number of slow charging piles available at the e-th station at the current time as my e ;

[0020]

[0021] In the formula, Indicates the utilization rate of slow charging piles at the e-th station at the current time point Mcl e .

[0022] Preferably, in step 3, the load index Fhz calculation process is as follows:

[0023] According to the regional data set, extract the load data of the u-th regional power grid and mark the load power of the u-th regional power grid at the current time point as gl u , mark the number of charging piles installed in the u-th regional power grid as zs u , the number of permanent residents in the u-th regional power grid at the current time point is marked as rk u ;

[0024]

[0025] In the formula, represents the power load density of the uth region at the current time point, α1 represents the weight for the power load density, and α2 represents the weight for the number of permanent residents. Both α1 and α2 are constants, and α1+α2=1. Indicates that the load index Fhz of the u-th regional power grid is calculated according to the weights α1 and α2 u .

[0026] Preferably, in step 4, the loss index Shz calculation process is as follows:

[0027] According to the operation data set, the charging pile operation data of the e-th station in the monitoring period Q is counted, and the number of times the fast charging pile of the e-th station is used in the monitoring period Q is marked as ks e , mark the number of times the slow charging pile at the e-th station is used in the monitoring period Q as ms e , mark the number of fast charging pile failures at the e-th site within the monitoring period Q as kg e , mark the number of slow charging pile failures at the e-th site within the monitoring period Q as mg e ;

[0028] If the charging environment of the e-th station is indoors,

[0029]

[0030] In the formula, represents the average number of times each fast charging pile is used, and β1 represents the weight for the average number of times each fast charging pile is used. represents the failure rate of the fast charging pile, β2 represents the weight for the failure rate of the fast charging pile, represents the average number of times each slow charging pile is used, and β3 represents the weight for the average number of times each slow charging pile is used. represents the failure rate of the slow charging pile, β4 represents the weight for the failure rate of the slow charging pile, β1, β2, β3 and β4 are all constants, β1+β2+β3+β4=1, Indicates that the loss index Shz of the e-th site is calculated according to the weights β1, β2, β3 and β4 e ;

[0031] If the charging environment of the e-th station is outdoor,

[0032]

[0033] In the formula, ω1 represents the weight for the average number of times a fast charging pile is used, ω2 represents the weight for the failure rate of a fast charging pile, ω3 represents the weight for the average number of times a slow charging pile is used, and ω4 represents the weight for the failure rate of a slow charging pile. ω1, ω2, ω3, and ω4 are all constants, ω2>β2, ω4>β4, ω1+ω2+ω3+ω4=1, Indicates that the loss index Shz of the e-th site is calculated according to the weights of ω1, ω2, ω3 and ω4 e .

[0034] Preferably, in step five, when the load index Fhz is less than the load threshold FHY, it indicates that the load pressure of the regional power grid is small, and the compensation incentive conditions for the unbalanced utilization of charging piles are met; if the load index Fhz is greater than or equal to the load threshold FHY, it indicates that the load pressure of the regional power grid is large, and the compensation incentive conditions for the unbalanced utilization of charging piles are not met.

[0035] Preferably, in step five, when the regional power grid has incentive conditions for compensating for imbalanced utilization of charging piles, if the fast charging utilization rate Kcl of the sites within the regional power grid is ≤ the fast charging threshold KCY, fast charging preferential subsidies should be promptly issued to users; if the slow charging utilization rate Mcl of the sites within the regional power grid is ≤ the slow charging threshold MCY, slow charging preferential subsidies should be promptly issued to users; when both the fast charging utilization rate Kcl ≤ the fast charging threshold KCY and the slow charging utilization rate Mcl ≤ the slow charging threshold MCY are satisfied at the same time, fast charging preferential subsidies and slow charging preferential subsidies need to be issued simultaneously.

[0036] Preferably, in step five, when the loss index Shz of a single site is greater than or equal to the loss threshold SHY, it indicates that the charging pile at the single site is seriously damaged, a fault warning signal is output, and the issuance of preferential charging subsidies for the single site to users is suspended, reminding management personnel to carry out maintenance in a timely manner.

[0037] Compared with the prior art, the present invention provides an incentive algorithm for compensating for imbalanced charging pile utilization, which has the following beneficial effects:

[0038] 1. The present invention connects to a big data platform through a network to obtain the charging pile operation data of all sites and the load data of all regional power grids, and classifies them into operation data sets and regional data sets. Based on the operation data sets, the usage of different types of charging piles is analyzed in real time to generate the corresponding fast charging utilization rate Kcl and slow charging utilization rate Mcl, dynamically perceive user demand, analyze the load situation of each regional power grid based on the regional data sets, generate the corresponding load index Fhz, set a fixed-time monitoring period Q, and then analyze the loss degree of the charging pile at each site in combination with the operation data sets to generate the corresponding loss index Shz, accurately capture the equipment performance degradation trend, and have strong multi-dimensional analysis and perception capabilities.

[0039] 2. The present invention sets fixed values ​​of the load threshold FHY, fast charging threshold KCY, slow charging threshold MCY and loss threshold SHY, and then combines the fast charging utilization rate Kcl, slow charging utilization rate Mcl, load index Fhz and loss index Shz to determine whether the regional power grid has the compensation incentive conditions for the imbalance of charging pile utilization, and outputs corresponding incentive suggestions and fault warning signals. When the load index Fhz is less than the load threshold FHY, it means that the load pressure of the regional power grid is small and the compensation incentive conditions for the imbalance of charging pile utilization are met. The compensation mechanism should be activated in time to guide users to divert to idle pile types, balance resource utilization, and have a good intelligent management incentive effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a step diagram of the method of the present invention. DETAILED DESCRIPTION

[0041] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0042] Because traditional charging pile utilization imbalance compensation incentive algorithms lack dynamic perception capabilities, they cannot accurately determine whether the regional power grid meets the conditions for charging pile utilization imbalance compensation incentives based on the load conditions of the regional power grid at the current time point. This leads to unsatisfactory incentive effects. In addition, it is difficult to accurately estimate the failure risks of indoor and outdoor charging piles, resulting in a high equipment downtime rate. Therefore, a charging pile utilization imbalance compensation incentive algorithm is provided. Please refer to Figure 1, including the following steps:

[0043] Step 1: Connect to the big data platform through the network to obtain the charging pile operation data of all sites and the load data of all regional power grids, and classify them into operation data sets and regional data sets;

[0044] The operational data set includes the total number of fast charging piles, the number of available fast charging piles, the total number of slow charging piles, the number of available slow charging piles, the charging environment, the number of fast charging piles used, the number of slow charging piles used, the number of fast charging pile failures, and the number of slow charging pile failures at each station;

[0045] The regional dataset includes the load power of each regional power grid, the number of charging piles installed, and the number of permanent residents;

[0046] Step 2: Based on the operational data set, analyze the usage of different types of charging piles in real time, generate the corresponding fast charging utilization rate Kcl and slow charging utilization rate Mcl, and dynamically perceive user demand;

[0047] The calculation process of fast charging utilization rate Kcl is as follows:

[0048] According to the operation data set, extract the charging pile operation data of the e-th station and mark the total number of fast charging piles at the e-th station as kz e , the number of fast charging piles available at the e-th station at the current time point is marked as ky e ;

[0049]

[0050] In the formula, Indicates the utilization rate of fast charging piles at the e-th station at the current time point Kcl e ;

[0051] The calculation process of slow charging utilization rate Mcl is as follows:

[0052] According to the operational data set, the total number of slow charging piles at the e-th station is marked as mz e , mark the number of slow charging piles available at the e-th station at the current time as my e ;

[0053]

[0054] In the formula, Indicates the utilization rate of slow charging piles at the e-th station at the current time point Mcl e ;

[0055] Step 3: Analyze the load of each regional power grid based on the regional data set and generate the corresponding load index Fhz;

[0056] The calculation process of load index Fhz is as follows:

[0057] According to the regional data set, extract the load data of the u-th regional power grid and mark the load power of the u-th regional power grid at the current time point as gl u , mark the number of charging piles installed in the u-th regional power grid as zs u , the number of permanent residents in the u-th regional power grid at the current time point is marked as rk u ;

[0058]

[0059] In the formula, represents the power load density of the uth region at the current time point, α1 represents the weight for the power load density, and α2 represents the weight for the number of permanent residents. Both α1 and α2 are constants, and α1+α2=1. Indicates that the load index Fhz of the u-th regional power grid is calculated according to the weights α1 and α2 u ;

[0060] Step 4: Set a fixed-length monitoring period Q. Combined with the operational data set, analyze the loss level of charging piles at each site and generate a corresponding loss index Shz to accurately capture device performance degradation trends.

[0061] The calculation process of loss index Shz is as follows:

[0062] According to the operation data set, the charging pile operation data of the e-th station in the monitoring period Q is counted, and the number of times the fast charging pile of the e-th station is used in the monitoring period Q is marked as ks e , mark the number of times the slow charging pile at the e-th station is used in the monitoring period Q as ms e , mark the number of fast charging pile failures at the e-th site within the monitoring period Q as kg e , mark the number of slow charging pile failures at the e-th site within the monitoring period Q as mg e ;

[0063] If the charging environment of the e-th station is indoors,

[0064]

[0065] In the formula, represents the average number of times each fast charging pile is used, and β1 represents the weight for the average number of times each fast charging pile is used. represents the failure rate of the fast charging pile, β2 represents the weight for the failure rate of the fast charging pile, represents the average number of times each slow charging pile is used, and β3 represents the weight for the average number of times each slow charging pile is used. represents the failure rate of the slow charging pile, β4 represents the weight for the failure rate of the slow charging pile, β1, β2, β3 and β4 are all constants, β1+β2+β3+β4=1, Indicates that the loss index Shz of the e-th site is calculated according to the weights β1, β2, β3 and β4 e ;

[0066] If the charging environment of the e-th station is outdoor, the outdoor environment is more likely to cause charging pile loss.

[0067]

[0068] In the formula, ω1 represents the weight for the average number of times a fast charging pile is used, ω2 represents the weight for the failure rate of a fast charging pile, ω3 represents the weight for the average number of times a slow charging pile is used, and ω4 represents the weight for the failure rate of a slow charging pile. ω1, ω2, ω3, and ω4 are all constants, ω2>β2, ω4>β4, ω1+ω2+ω3+ω4=1, Indicates that the loss index Shz of the e-th site is calculated according to the weights of ω1, ω2, ω3 and ω4 e , strong multi-dimensional analysis and perception capabilities;

[0069] Step 5: Set fixed values ​​for the load threshold FHY, fast charge threshold KCY, slow charge threshold MCY, and loss threshold SHY. Combined with the fast charge utilization rate Kcl, slow charge utilization rate Mcl, load index Fhz, and loss index Shz, determine whether the regional power grid has the conditions for compensating for the imbalance in charging pile utilization, and output corresponding incentive recommendations and fault warning signals.

[0070] When the load index Fhz is less than the load threshold FHY, it indicates that the load pressure on the regional power grid is small, and the conditions for compensating for the imbalanced utilization of charging piles are met. The compensation mechanism should be activated in a timely manner to improve the utilization of charging piles. If the load index Fhz is greater than or equal to the load threshold FHY, it indicates that the load pressure on the regional power grid is large, and the conditions for compensating for the imbalanced utilization of charging piles are not met. It is necessary to avoid exacerbating the pressure on the power grid during high-load periods.

[0071] If the regional power grid has the incentive conditions for compensating for imbalanced charging pile utilization, if the fast charging utilization rate Kcl of the site within the regional power grid is ≤ the fast charging threshold KCY, fast charging preferential subsidies should be promptly issued to users. If the slow charging utilization rate Mcl of the site within the regional power grid is ≤ the slow charging threshold MCY, slow charging preferential subsidies should be promptly issued to users. When both the fast charging utilization rate Kcl ≤ the fast charging threshold KCY and the slow charging utilization rate Mcl ≤ the slow charging threshold MCY are met, fast charging preferential subsidies and slow charging preferential subsidies should be issued simultaneously to guide users to divert to idle charging pile types, balance resource utilization, and achieve good intelligent management incentive effects.

[0072] When the loss index Shz of a single site is greater than or equal to the loss threshold SHY, it indicates that the charging pile at the single site is severely damaged, a fault warning signal is output, and the preferential charging subsidies for the single site are suspended to users, reminding managers to carry out maintenance in a timely manner, reduce equipment downtime rate, and extend service life.

[0073] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. Charging pile utilization imbalance compensation incentive algorithm, characterized by: The following steps are involved: Step 1: Connect to the big data platform through the network to obtain the charging pile operation data of all sites and the load data of all regional power grids, and classify them into operation data sets and regional data sets; Step 2: Based on the operational data set, analyze the usage of different types of charging piles in real time and generate the corresponding fast charging utilization rate Kcl and slow charging utilization rate Mcl; Step 3: Analyze the load of each regional power grid based on the regional data set and generate the corresponding load index Fhz; Step 4: Set a fixed-length monitoring period Q, and then analyze the loss level of charging piles at each station based on the operational data set to generate the corresponding loss index Shz; Step 5: Set fixed values ​​for the load threshold FHY, fast charging threshold KCY, slow charging threshold MCY, and loss threshold SHY. Then, combined with the fast charging utilization rate Kcl, slow charging utilization rate Mcl, load index Fhz, and loss index Shz, determine whether the regional power grid has the compensation incentive conditions for the imbalance in charging pile utilization, and output corresponding incentive suggestions and fault warning signals.

2. The charging pile utilization imbalance compensation incentive algorithm according to claim 1 is characterized by: In step 1, the operation data set includes the total number of fast charging piles, the number of available fast charging piles, the total number of slow charging piles, the number of available slow charging piles, the charging environment, the number of times the fast charging piles are used, the number of times the slow charging piles are used, the number of fast charging pile failures, and the number of slow charging pile failures at each site.

3. The charging pile utilization imbalance compensation incentive algorithm according to claim 2 is characterized by: In step 1, the regional data set includes the load power, the number of installed charging piles, and the number of permanent residents of each regional power grid.

4. The charging pile utilization imbalance compensation incentive algorithm according to claim 3 is characterized by: In step 2, the calculation process of the fast charge utilization rate Kcl is as follows: According to the operation data set, extract the charging pile operation data of the e-th station and mark the total number of fast charging piles at the e-th station as kz e , the number of fast charging piles available at the e-th station at the current time point is marked as ky e ; In the formula, Indicates the utilization rate of fast charging piles at the e-th station at the current time point Kcl e .

5. The charging pile utilization imbalance compensation incentive algorithm according to claim 4 is characterized by: In step 2, the calculation process of the slow charge utilization rate Mc1 is as follows: According to the operational data set, the total number of slow charging piles at the e-th station is marked as mz e , mark the number of slow charging piles available at the e-th station at the current time as my e ; In the formula, Indicates the utilization rate of slow charging piles at the e-th station at the current time point Mcl e .

6. The charging pile utilization imbalance compensation incentive algorithm according to claim 5 is characterized by: In step 3, the load index Fhz calculation process is as follows: According to the regional data set, extract the load data of the u-th regional power grid and mark the load power of the u-th regional power grid at the current time point as gl u , mark the number of charging piles installed in the u-th regional power grid as zs u , the number of permanent residents in the u-th regional power grid at the current time point is marked as rk u ; In the formula, represents the power load density of the uth region at the current time point, α1 represents the weight for the power load density, and α2 represents the weight for the number of permanent residents. Both α1 and α2 are constants, and α1+α2=1. Indicates that the load index Fhz of the u-th regional power grid is calculated according to the weights α1 and α2 u .

7. The charging pile utilization imbalance compensation incentive algorithm according to claim 6 is characterized by: In step 4, the loss index Shz calculation process is as follows: According to the operation data set, the charging pile operation data of the e-th station in the monitoring period Q is counted, and the number of times the fast charging pile of the e-th station is used in the monitoring period Q is marked as ks e , mark the number of times the slow charging pile at the e-th station is used in the monitoring period Q as ms e , mark the number of fast charging pile failures at the e-th site within the monitoring period Q as kg e , mark the number of slow charging pile failures at the e-th site within the monitoring period Q as mg e ; If the charging environment of the e-th station is indoors, In the formula, represents the average number of times each fast charging pile is used, and β1 represents the weight for the average number of times each fast charging pile is used. represents the failure rate of the fast charging pile, β2 represents the weight for the failure rate of the fast charging pile, represents the average number of times each slow charging pile is used, and β3 represents the weight for the average number of times each slow charging pile is used. represents the failure rate of the slow charging pile, β4 represents the weight for the failure rate of the slow charging pile, β1, β2, β3 and β4 are all constants, β1+β2+β3+β4=1, Indicates that the loss index Shz of the e-th site is calculated according to the weights β1, β2, β3 and β4 e ; If the charging environment of the e-th station is outdoor, In the formula, ω1 represents the weight for the average number of times a fast charging pile is used, ω2 represents the weight for the failure rate of a fast charging pile, ω3 represents the weight for the average number of times a slow charging pile is used, and ω4 represents the weight for the failure rate of a slow charging pile. ω1, ω2, ω3, and ω4 are all constants, ω2>β2, ω4>β4, ω1+ω2+ω3+ω4=1, Indicates that the loss index Shz of the e-th site is calculated according to the weights of ω1, ω2, ω3 and ω4 e .

8. The charging pile utilization imbalance compensation incentive algorithm according to claim 7 is characterized by: In step five, when the load index Fhz is less than the load threshold FHY, it indicates that the load pressure of the regional power grid is small, and the compensation incentive conditions for the imbalance of charging pile utilization are met. If the load index Fhz is greater than or equal to the load threshold FHY, it indicates that the load pressure of the regional power grid is large, and the compensation incentive conditions for the imbalance of charging pile utilization are not met.

9. The charging pile utilization imbalance compensation incentive algorithm according to claim 8, characterized in that: In step five, when the regional power grid has the incentive conditions for compensating for imbalanced charging pile utilization, if the fast charging utilization rate Kcl of the sites within the regional power grid is ≤ the fast charging threshold KCY, fast charging preferential subsidies should be promptly issued to users; if the slow charging utilization rate Mcl of the sites within the regional power grid is ≤ the slow charging threshold MCY, slow charging preferential subsidies should be promptly issued to users; when both the fast charging utilization rate Kcl ≤ the fast charging threshold KCY and the slow charging utilization rate Mcl ≤ the slow charging threshold MCY are satisfied at the same time, fast charging preferential subsidies and slow charging preferential subsidies need to be issued simultaneously.

10. The charging pile utilization imbalance compensation incentive algorithm according to claim 9, characterized in that: In the step 5, when the loss index Shz of a single site is greater than or equal to the loss threshold SHY, it indicates that the charging pile at the single site is seriously damaged, a fault warning signal is output, and the charging preferential subsidy for the single site is suspended to the user, reminding the management staff to carry out maintenance in time.

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