A bus charging station regulation method based on charging pile state optimization

Abstract

The application provides a kind of based on charging pile state optimization public transport charging station regulation and control method, including collecting the load data of the feeder area where bus station is located and the charging data of individual electric bus, establishes electric bus regulation potential evaluation model;Establish cluster electric bus charging state regulation and control optimization model;According to actual data, obtain regulation and control electric bus charging regulation and control scheme.The application proposes electric bus regulation peak potential evaluation model based on the state of electric bus, on this basis, comprehensively consider the demand response income of aggregator and regulation and control response deviation two factors, establish electric vehicle bus charging state regulation and control double-layer optimization model;The application can maximize the peak shaving and valley filling effect of regulation and control under the premise of considering the maximization of demand response of aggregator and the minimization of response deviation, through the state regulation and control of electric bus, compared with the power continuity regulation of prior art, has more convenient implementation condition in actual engineering application.

Description

Technical Field

[0001] This invention relates to the field of bus charging control technology, and in particular to a bus charging station control method based on charging pile status optimization. Background Technology

[0002] With the large-scale integration of electric vehicles into the power grid and their disorderly charging and discharging, the power quality, reliability, and economic operation of the distribution network will be affected. In particular, it may exacerbate peak loads, putting additional pressure on the grid and potentially causing problems such as overloading or heavy loads on transmission lines or distribution transformers. Electric buses, as the earliest and primary target for the promotion of electric vehicles, account for a large proportion of charging in society, and their impact on charging load cannot be ignored. On the other hand, as a public transportation resource, buses, compared to private cars, have the characteristics of cluster management and unified charging, making them an ideal resource for grid regulation and utilization.

[0003] Currently, the general approach to charging regulation for electric vehicles is based on power regulation. This approach requires charging piles to have controllable power and communication capabilities in practical applications, which places high demands on equipment upgrades. In reality, most charging pile regulation is achieved by turning on and off to meet load conditions. Balancing the interests of all parties is a particularly important issue that needs to be addressed. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a bus charging station control method based on charging pile status optimization. This invention can minimize the feeder load rate and realize the transfer of peak load to off-peak periods.

[0005] The technical solution of this invention is: a method for regulating bus charging stations based on charging pile status optimization, comprising the following steps:

[0006] S1) Collect load data of the feeder area where the bus stop is located and charging data of individual electric buses;

[0007] S2) Establish an assessment model for the dispatch potential of electric buses;

[0008] S3) Establish an optimization model for the charging status control of clustered electric buses;

[0009] S4) Obtain and regulate the charging control scheme for electric buses based on actual data.

[0010] Preferably, in step S1), the charging data of the individual electric bus includes SOC status data, start charging time, and charging duration.

[0011] Preferably, in step S2), the establishment of the electric bus dispatch potential assessment model is as follows:

[0012] Typically, after a bus finishes its daily route, it will be charged at a bus charging station at night to prepare for the next day's route. Most buses are idle between the time they finish running at night and the time they start running the next day, and the idle time is longer than the time it takes to fully charge. Therefore, by taking advantage of the fact that the parking time is longer than the charging time, the charging time of buses can be reasonably arranged to achieve off-peak charging and reduce the peak nighttime charging.

[0013] When a bus pulls into a charging station to begin charging, the charging station needs to collect its entry time T. a Departure time T l Initial SOC value a and the target SOC value g Among them, the entry time T a Departure time T l In minutes, T a Represents the Tth day a minute;

[0014] Using 30-minute intervals as the optimization unit, a day is divided into 1 to 48 time periods; the starting time for charging stations to control the orderly charging of electric vehicles is:

[0015]

[0016] In the formula, Indicates rounding up;

[0017] The orderly charging of buses will conclude at the following time:

[0018] In the formula, Indicates rounding down;

[0019] Define the charging state of an electric vehicle: E ch,t

[0020]

[0021] When the SOC reaches the charging demand of the electric vehicle, the bus stops charging. Therefore, from the next time period until the electric vehicle leaves the charging station, the bus is in a parked state.

[0022] Let the number of buses in the charging station be . Nbus Then the charging load P of the charging station at time t s,t for:

[0023]

[0024] In the formula, The charging status of the i-th bus; P nLPower rating of bus charging stations;

[0025] In determining the peak shaving potential, it is necessary to ensure the charging needs of every bus, that is, the total charging amount during the night should meet the target SOC value:

[0026]

[0027] In the formula, SOC i,a Let SOC be the starting SOC of the i-th bus; i,g Let S be the target SOC of the i-th bus; T is the target SOC of the i-th bus, which is the number of nighttime periods; Δt is the control time interval; S i Let be the battery capacity of the i-th bus.

[0028] As a preferred embodiment, the establishment of the optimized model for regulating the charging status of the clustered electric buses is as follows:

[0029] S301) Obtain the total response task of the bus station through the upper-level model; establish the upper-level scheduling layer model, which is the bus charging station aggregator, and consider the objective of maximizing the demand response revenue as the optimization objective. The objective function of the i-th aggregator is:

[0030] f i =max△C ag,i =C fb -△C ser -C bo ;

[0031] Among them, △C ag,i For aggregator i, the revenue from demand response; C fb For the aggregator's compensation revenue; △C ser C is the service fee loss for the aggregator; b0 This refers to the compensation costs incurred by the aggregator for users.

[0032] The constraint is that the difference between the response task and the response quantity should be between -30% and 30%, as expressed below:

[0033]

[0034] Where T D For response time; where P AgDR,i (t) represents the response task issued by the distribution network in the t-th time period; This represents the actual total response volume of the aggregator in time period t.

[0035] S302) Establish a lower-level response layer model to measure the deviation between the regulated charging load curve and the target charging load curve, with the minimum deviation as the optimization objective:

[0036]

[0037] minD;

[0038] In the formula, N represents the number of time periods involved in demand response, and P... s,i and P goal,i These represent the charging station power and target load at the start of the i-th response period, respectively.

[0039] The target charging load curve for bus depots is calculated as follows:

[0040] P goal,t =P or,t -P need,t ;

[0041] In the formula, P or,t For the charging load of bus depots before peak shaving; P need,t For charging loads that need to be adjusted and reduced;

[0042] The model is solved based on actual data. The data items required for the optimization model are substituted into the model. Under the premise of meeting the constraints, the charging status of each charging pile in each time period is obtained. Then, the bus station aggregator issues time-period control instructions for each charging pile, thereby achieving the purpose of peak shaving and regulating regional load.

[0043] The beneficial effects of this invention are as follows:

[0044] 1. This invention proposes an assessment model for the peak-shaving potential of electric buses based on their state. On this basis, it comprehensively considers both the demand response revenue of aggregators and the deviation of regulation response to establish a two-layer optimization model for the regulation of the charging state of electric buses.

[0045] 2. Under the premise of maximizing the demand response of aggregators and minimizing the response deviation, the present invention can maximize the peak shaving and valley filling effect of regulation. Compared with the power continuous regulation of the prior art, the state regulation of electric buses has more convenient implementation conditions in practical engineering applications.

[0046] 3. This invention can minimize the feeder load rate while ensuring safe charging operation. Furthermore, by shifting some buses to the charging period from 5:30 to 7:00, it achieves the transfer of peak load to off-peak hours. This invention has a significant effect on peak shaving and regulation. Attached Figure Description

[0047] Figure 1 This is a flowchart of the method of the present invention;

[0048] Figure 2 This is a simulated total load curve of the feeder area in Embodiment 2 of the present invention;

[0049] Figure 3 This is a simulated feeder area load rate curve in Embodiment 2 of the present invention;

[0050] Figure 4 This is a diagram illustrating the determination of the charging load control target in Embodiment 2 of the present invention;

[0051] Figure 5 This is a diagram showing the comparison of regional charging load before and after regulation in Embodiment 2 of the present invention;

[0052] Figure 6 This is a statistical analysis of the number of buses charging before and after the adjustment in Embodiment 2 of the present invention. Detailed Implementation

[0053] The specific embodiments of the present invention will be further described below with reference to the accompanying drawings:

[0054] Example 1

[0055] like Figure 1 As shown in the figure, this embodiment of a bus charging station control method based on charging pile status optimization includes the following steps:

[0056] S1) Collect load data of the feeder area where the bus stop is located and charging data of individual electric buses, wherein the charging data of individual electric buses includes SOC status data, start charging time and charging duration.

[0057] S2) Establish an assessment model for the dispatch potential of electric buses, as detailed below:

[0058] Typically, after a bus finishes its daily route, it will be charged at a bus charging station at night to prepare for the next day's route. Most buses are idle between the time they finish running at night and the time they start running the next day, and the idle time is longer than the time it takes to fully charge. Therefore, by taking advantage of the fact that the parking time is longer than the charging time, the charging time of buses can be reasonably arranged to achieve off-peak charging and reduce the peak nighttime charging.

[0059] When a bus pulls into a charging station to begin charging, the charging station needs to collect its entry time T. a Departure time T l Initial SOC value a and the target SOC value g Among them, the entry time T a Departure time T l In minutes, T a Represents the Tth day a minute;

[0060] Using 30-minute intervals as the optimization unit, a day is divided into 1 to 48 time periods; the starting time for charging stations to control the orderly charging of electric vehicles is:

[0061]

[0062] In the formula, Indicates rounding up;

[0063] The orderly charging of buses will conclude at the following time:

[0064] In the formula, Indicates rounding down;

[0065] Define the charging state of an electric vehicle: E ch,t

[0066]

[0067] When the SOC reaches the charging demand of the electric vehicle, the bus stops charging. Therefore, from the next time period until the electric vehicle leaves the charging station, the bus is in a parked state.

[0068] Let N be the number of buses in the charging station. bus Then the charging load P of the charging station at time t s,t for:

[0069]

[0070] In the formula, The charging status of the i-th bus; P nL Power rating of bus charging stations;

[0071] In determining the peak shaving potential, it is necessary to ensure the charging needs of every bus, that is, the total charging amount during the night should meet the target SOC value:

[0072]

[0073] In the formula, SOC i,a Let SOC be the starting SOC of the i-th bus; i,g Let S be the target SOC of the i-th bus; T is the target SOC of the i-th bus, which is the number of nighttime periods; Δt is the control time interval; S i Let be the battery capacity of the i-th bus.

[0074] S3) Establish an optimization model for the charging status control of clustered electric buses, as detailed below:

[0075] S301) Obtain the total response task of the bus station through the upper-level model; establish the upper-level scheduling layer model, which is the bus charging station aggregator, and consider the objective of maximizing the demand response revenue as the optimization objective. The objective function of the i-th aggregator is:

[0076] f i =max△C ag,i =C fb -△C ser -C bo ;

[0077] Among them, △C ag,i For aggregator i, the revenue from demand response; C fb For the aggregator's compensation revenue; △C ser C is the service fee loss for the aggregator; b0 This refers to the compensation costs incurred by the aggregator for users.

[0078] The constraint is that the difference between the response task and the response quantity should be between -30% and 30%, as expressed below:

[0079]

[0080] Where T D For response time; where P AgDR,i (t) represents the response task issued by the distribution network in the t-th time period; This represents the actual total response volume of the aggregator in time period t.

[0081] S302) Establish a lower-level response layer model to measure the deviation between the regulated charging load curve and the target charging load curve, with the minimum deviation as the optimization objective:

[0082]

[0083] minD;

[0084] In the formula, N represents the number of time periods involved in demand response, and P... s,i and P goal,i These represent the charging station power and target load at the start of the i-th response period, respectively.

[0085] The target charging load curve for bus depots is calculated as follows:

[0086] P goal,t =P or,t -P need,t ;

[0087] In the formula, P or,t For the charging load of bus depots before peak shaving; P need,t For charging loads that need to be adjusted and reduced;

[0088] The model is solved based on actual data. The data items required for the optimization model are substituted into the model. Under the premise of meeting the constraints, the charging status of each charging pile in each time period is obtained. Then, the bus station aggregator issues time-period control instructions for each charging pile, thereby achieving the purpose of peak shaving and regulating regional load.

[0089] S4) Obtain and regulate the charging control scheme for electric buses based on actual data.

[0090] Example 2

[0091] This embodiment takes a feeder area with a large bus depot as an example. The total load of the feeder area is shown in the appendix. Figure 2 As shown in the appendix. Feeder load factor is as follows. Figure 3 As shown. The total nighttime duration is set to 8 hours, the bus battery capacity is 300kWh, the bus stop charging pile power is 60kWh, the number of buses is 120, the initial SOC state is a random number between [0.2, 0.5], and the target SOC state is 0.8.

[0092] After calculating and evaluating the peak-shaving potential of bus stops according to the method in Example 1, the target regulation amount can be obtained first based on the upper-level optimization model in step S3), such as... Figure 4 As shown. Further optimization of the lower-level optimization model yields the regulated regional charging load, as shown. Figure 5 As shown in the figure, the statistics of the number of buses charging during each control period are as follows: Figure 6 As shown.

[0093] The load rate curve of the feeder shows that it exceeds 80% between 0:30 and 1:30 AM, indicating a heavy load on the feeder. This load spike in the early morning is due to a large number of buses connecting to the bus charging station for fast charging, causing a rapid increase in load. Therefore, for situations where connecting to large bus charging stations leads to heavy or even overloaded feeders, it is necessary to implement orderly energy regulation within the bus charging stations to reduce peak loads and mitigate feeder operational risks.

[0094] Furthermore, simulation results show that the peak-shaving curve largely matches the target curve within the response period. The peak charging load decreased from 7MW to approximately 6.3MW, with a maximum peak reduction of 0.84MW. The feeder load rate decreased by a maximum of 10.2%, and the charging load was shifted to after 2:00 AM. The post-peak-shaving load rate was around 80%, ensuring safe operation. The number of buses charging between 0:30 and 1:30 AM after peak shaving was significantly reduced compared to before. The maximum number of buses charging before peak shaving was 120, which decreased to 107 after peak shaving. Before peak shaving, no buses were charging between 5:30 and 7:00 AM, but after peak shaving, buses began charging. This indicates that some buses shifted to the 5:30-7:00 AM charging period after peak shaving, achieving a shift from peak load to off-peak periods. Therefore, the method proposed in this invention has a significant effect on peak-shaving control.

[0095] The embodiments and descriptions above are merely illustrative of the principles and preferred embodiments of the present invention. Various changes and modifications may be made to the present invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed.

Claims

1. A method for regulating bus charging stations based on charging pile status optimization, characterized in that, Includes the following steps: S1) Collect load data of the feeder area where the bus stop is located and charging data of individual electric buses; S2) Establish an assessment model for the dispatch potential of electric buses, as detailed below: Typically, after a bus finishes its daily route, it will be charged at a bus charging station at night to prepare for the next day's route. Most buses are idle between the time they finish running at night and the time they start running the next day, and the idle time is longer than the time it takes to fully charge. Therefore, by taking advantage of the fact that the parking time is longer than the charging time, the charging time of buses can be reasonably arranged to achieve off-peak charging and reduce the peak nighttime charging. When a bus pulls into a charging station to charge, the charging station needs to collect its entry time. Departure time , initial value and goals value Among them, the entry time Departure time In minutes Indicates the first day minute; Using 30-minute intervals as the optimization unit, a day is divided into 1 to 48 time periods; the starting time for charging stations to control the orderly charging of electric vehicles is: ； In the formula, Indicates rounding up; The orderly charging of buses will conclude at: ； In the formula, Indicates rounding down; Define the charging state of electric vehicles for: ； when When the charging demand of electric vehicles is met, the buses stop charging. Therefore, from the next time period until the electric vehicles leave the charging station, the buses remain stationary. Let the number of buses in the charging station be . Then the charging load of the charging station at time t for: ； In the formula, The charging status of the i-th bus; Power rating of bus charging stations; In determining the peak shaving potential, it is necessary to ensure the charging needs of every bus, that is, the total charging amount during the night should meet the target SOC value: ； In the formula, The starting point of the i-th bus ; Let T be the target SOC of the i-th bus; T is the target SOC of the i-th bus and the number of nighttime periods. For the adjustment time interval; Let be the battery capacity of the i-th bus; S3) Establish an optimization model for the charging status control of clustered electric buses; details are as follows: S301) Obtain the total response task of the bus station through the upper-level model; establish the upper-level scheduling layer model, which is the bus charging station aggregator, and consider the objective of maximizing the demand response revenue as the optimization objective. The objective function of the i-th aggregator is: ； in, The revenue generated by aggregator i in response to demand; Compensation for aggregators; Losses due to service fees for aggregators; This refers to the compensation costs incurred by the aggregator for users. The constraint is that the difference between the response task and the response quantity should be between -30% and 30%, as expressed below: ； in For response time; among which, For the response tasks issued by the distribution network in time period t; This represents the actual total response volume of the aggregator in time period t. S4) Obtain and regulate the charging control scheme for electric buses based on actual data.

2. The method for regulating bus charging stations based on charging pile status optimization according to claim 1, characterized in that: In step S1), the charging data of the individual electric bus includes Status data, start charging time, and charging duration.

3. The method for regulating bus charging stations based on charging pile status optimization according to claim 1, characterized in that, Step S3) also includes the following steps: S302) Establish a lower-level response layer model to measure the deviation between the regulated charging load curve and the target charging load curve, with the minimum deviation as the optimization objective: ； ； In the formula, N represents the number of time periods involved in demand response. and These represent the charging station power and target load at the start of the i-th response period, respectively. The target charging load curve for bus depots is calculated as follows: ； In the formula, To reduce the charging load at bus depots before peak shaving; For charging loads that need to be adjusted and reduced; The model is solved based on actual data. The data items required for the optimization model are substituted into the model. Under the premise of meeting the constraints, the charging status of each charging pile in each time period is obtained. Then, the bus station aggregator issues time-period control instructions for each charging pile, thereby achieving the purpose of peak shaving and regulating regional load.

Images