A GCS-based switchboard decentralized control system

Abstract

The application discloses a power distribution cabinet distributed control system based on GCS, belongs to the technical field of power distribution systems, and can acquire safe and distributable power margin data based on actually measured power margin and a Monte Carlo method to predict power margin, so that power distribution of each loop is dynamically adjusted according to the differentiated charging demand of users, a control instruction is issued to a current limiter in series with a load side, the equivalent impedance of the current limiter is adjusted, and the current of the corresponding power supply loop is adjusted to realize power distribution. Compared with the prior art, the electric resource allocation of the application is more reasonable, the personalized charging demand of different users is taken into account, and the service flexibility of charging piles and user experience are improved.

Description

Technical Field

[0001] This invention relates to the field of power distribution system technology, and more specifically, to a distributed control system for power distribution cabinets based on GCS. Background Technology

[0002] With the increasing popularity of electric vehicles, charging stations have gradually become a widespread supporting infrastructure. Existing charging stations typically use GCS-type low-voltage withdrawable switchgear connected to the AC power grid. The GCS distribution cabinet has multiple drawers, each equipped with an independent power supply unit, which provides power to each charging interface of the charging station, thereby realizing the centralized distribution and management of multiple distributed charging loads.

[0003] In AC power supply systems, the grid side can be considered an ideal voltage source, with a basically constant output voltage and a current output capacity far exceeding that of conventional charging loads. This means the grid can provide any amount of current as needed without its own limitations. However, the GCS distribution cabinet, as an intermediate power distribution link, has internal busbars, incoming circuit breakers, fuses, and each outgoing circuit conductor with defined rated current carrying capacity and rated apparent power. When multiple charging interfaces operate simultaneously, causing the total current or total power flowing through the distribution cabinet to continuously exceed its rated capacity, it will lead to overheating of electrical equipment, accelerated insulation aging, and consequently triggering protective actions such as fuse blowing or circuit breaker tripping. In severe cases, this can result in a fire.

[0004] Therefore, in the existing GCS distribution cabinets that use the power grid to power charging piles, there is often a distribution system that centrally controls several distributed power supply units, and monitors and actively regulates the total current or total apparent power in real time to ensure that it operates within safe limits.

[0005] In existing distributed power control systems, fuses and circuit breakers are typically used to control the on / off state of each charging interface. The equivalent impedance of each power supply circuit is equal and fixed, and cannot be adjusted. User charging scenarios are complex; there may be users with urgent needs requiring rapid charging, or users with non-urgent needs who can accept delays or reduced power. Under this control method, once the charging interface is on, the actual charging power depends only on the grid voltage and the equivalent impedance of the load itself. The system cannot dynamically adjust the current distribution of each circuit according to the differentiated charging needs of users. This results in: users with urgent needs cannot obtain higher charging power to shorten charging time, while the charging process of non-urgent users may unnecessarily occupy system capacity, leading to an unreasonable allocation of power distribution resources. It is difficult to meet the personalized charging needs of different users, limiting the service flexibility and user experience of the charging system.

[0006] In view of this, the present invention is proposed to solve the above-mentioned technical problems. Summary of the Invention

[0007] The purpose of this invention is to provide a distributed control system for power distribution cabinets based on GCS to solve the aforementioned technical problems.

[0008] To achieve the above objectives, the present invention provides the following technical solution: A distributed control system for a power distribution cabinet based on GCS is provided. The power distribution cabinet based on GCS includes several power supply units. Each power supply unit is used to supply power to the corresponding charging interface load. A current limiter is connected in series in the power supply circuit of each power supply unit. The distributed control system is electrically connected to the current limiters of several power supply units and is configured such that the system adjusts the equivalent impedance of the current limiter by sending control commands to the current limiter, thereby adjusting the current of the corresponding power supply circuit to achieve power distribution. The control system obtains the latest usage time of each charging user at each power supply interface that is charging, and obtains the remaining charging time of all charging interfaces. The interface with the latest usage time among all interfaces is the first interface, and the latest usage time of the user charging the first interface is the first moment. The power supply unit is equipped with a data acquisition module. The control system obtains the daily charging frequency, start time, duration and average power data of the power supply circuit through the data acquisition module of the power supply unit. The Monte Carlo method is used to predict the probability distribution of the total power supply power of all power supply unit circuits at each moment from the present to the first moment. The predicted peak value of the total power supply power of the power supply unit at the corresponding moment or the upper limit of the charging power at the α1 confidence level is used as the first threshold. The control system acquires the total power data of all power supply units measured by the data acquisition module at the previous sampling time, and obtains the power margin of the GCS distribution cabinet as the first margin based on this data. The power margin of the GCS distribution cabinet is calculated as the second margin based on the first threshold of all power supply units at the next sampling time of the data acquisition module. The smaller of the first and second margins is taken as the allocable margin. The control system adjusts the impedance of the current limiter and distributes the allocable margin proportionally to each interface according to the remaining charging time of each interface.

[0009] Optionally, 90% ≤ α1 < 100%.

[0010] Optionally, the data acquisition module measures the number of daily charging cycles, the start time of each charging cycle, the duration of each charging cycle, and the average load power data of all power supply circuits and uploads them to the control system. The control system calculates the total circuit power data of all circuits at all sampling times in the corresponding charging event based on the average load power data measured by the corresponding data acquisition module at all sampling times, combined with the total unit voltage data of the power supply unit at the sampling time and the equivalent impedance data of the current limiter. Thus, the average circuit power data of all power supply circuits in each charging event is obtained.

[0011] Optionally, the average load power data of all power supply circuits for each charge, measured by the data acquisition module and uploaded to the control system, is the average apparent load power data.

[0012] Optionally, the data acquisition module is used to measure the number of daily charging cycles for all power supply circuits, the start time and duration of each charging event, and to measure the current data of all power supply circuits at all sampling moments during the charging process of each charging event. All data are uploaded to the control system. Based on the current data and the total voltage data of the unit to which it belongs, the control system calculates the total power data of all power supply circuits at all sampling moments during the charging process of each charging event, thereby obtaining the average power data of all power supply circuits in each charging event.

[0013] Optionally, the control system measures whether each charging interface is charging through the data acquisition module. After detecting that the interface load is unloaded before its agreed latest vehicle usage time, the control system generates an early vehicle retrieval signal to clarify that the interface is in a non-charging state. The control system corrects the power consumption status of each charging interface, obtains the latest vehicle usage time of each interface that is charging, obtains the remaining charging time of all charging interfaces, corrects and re-determines the first time.

[0014] This invention connects a current limiter in series on the load side, which can receive control commands to automatically adjust its own equivalent impedance, thereby adjusting the current of the corresponding power supply circuit to achieve power distribution; it uses the Monte Carlo method to predict the probability distribution of the total power supply of all power supply units' power supply circuits at all times in the future period from the current time to the latest pick-up time of all charging vehicles, and can predict the first threshold at any time in the future period based on this, thereby calculating the power margin of the GCS distribution cabinet at that time.

[0015] The data acquisition unit periodically and synchronously collects the voltage and current of the load according to the set time period, calculates the power at this sampling moment, and determines whether the interface is connected to the load and charging the load based on the presence or absence of power or voltage and current. The control system then determines the start time, duration and number of charging events per day for all interfaces.

[0016] The control system acquires the total power data of all power supply units measured by the data acquisition module at the previous sampling time, and calculates the power margin of the GCS distribution cabinet as the first margin. Based on the predicted second threshold of the next sampling time before the first sampling time by the data acquisition module of all power supply units, the control system calculates the power margin of the GCS distribution cabinet as the second margin. The smaller of the first and second margins is taken as the allocable margin. The control system adjusts the impedance of the current limiter to distribute the allocable margin proportionally to each interface according to the remaining charging time of each interface. For example, if the remaining charging times of the first, second, and third interfaces are 1:2:3 respectively, then the additional power allocated to the overall power supply units of the three interfaces by adjusting the impedance of the current limiter is also 1:2:3, and so on.

[0017] Therefore, compared with the prior art, the present invention can dynamically adjust the current distribution of each circuit according to the user's differentiated charging needs, so that the vehicles of non-emergency users can be charged slowly at a low price, and the charging process does not occupy too much system capacity; at the same time, it enables the vehicles of users who urgently need to use the car to obtain higher charging power, charge as much power as possible in a shorter time, and meet their needs to pick up the car in advance and extend the driving time as much as possible. It rationally allocates power resources, takes into account the personalized charging needs of different users, and improves the service flexibility and user experience of charging piles. Detailed Implementation

[0018] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0019] This invention provides a distributed control system for a power distribution cabinet based on GCS. The power distribution cabinet based on GCS includes several power supply units. Each power supply unit is used to supply power to the corresponding charging interface load. A current limiter is connected in series in the power supply circuit of each power supply unit. The distributed control system is electrically connected to the current limiters of several power supply units and is configured such that the system adjusts the equivalent impedance of the current limiter by sending control commands to the current limiter, thereby adjusting the current of the corresponding power supply circuit to achieve power distribution. The control system obtains the latest usage time of each charging user at each power supply interface that is charging, and obtains the remaining charging time of all charging interfaces. The interface with the latest usage time among all interfaces is the first interface, and the latest usage time of the user charging the first interface is the first moment. The power supply unit is equipped with a data acquisition module. The control system obtains the daily charging frequency, start time, duration and average power data of the power supply circuit through the data acquisition module of the power supply unit. The Monte Carlo method is used to predict the probability distribution of the total power supply power of all power supply unit circuits at each moment from the present to the first moment. The predicted peak value of the total power supply power of the power supply unit at the corresponding moment or the upper limit of the charging power at the α1 confidence level is used as the first threshold. The control system acquires the total power data of all power supply units measured by the data acquisition module at the previous sampling time, and obtains the power margin of the GCS distribution cabinet as the first margin. Based on the first threshold of all power supply units at the second threshold of the data acquisition module at the next sampling time, the control system calculates the power margin of the GCS distribution cabinet as the second margin. The smaller of the first and second margins is taken as the allocable margin. The control system adjusts the impedance of the current limiter and distributes the allocable margin proportionally to each interface according to the remaining charging time of each interface.

[0020] This invention incorporates a current limiter connected in series on the load side. This current limiter receives control commands to automatically adjust its equivalent impedance (resistance or impedance modulus), thereby regulating the current in the corresponding power supply circuit to achieve power distribution. Specifically, the current limiter can employ a purely resistive structure, controlling the equivalent resistance through the duty cycle of power electronic devices (such as IGBTs and MOSFETs) to achieve purely resistive regulation without inductive reactance. Alternatively, it can employ an impedance regulation structure containing inductive elements, adjusting the inductive reactance or total impedance modulus of the current limiter to regulate the inductive reactance or total impedance modulus connected in series with the power supply circuit, achieving continuous or graded control of the circuit current. By adjusting the equivalent impedance of the current limiter, this invention can dynamically allocate the actual charging power of each charging interface, thereby differentiating the charging current based on the user's urgency level within the total capacity constraint of the distribution cabinet, meeting complex charging needs.

[0021] Specifically, this invention uses the Monte Carlo method to predict the probability distribution of the total power supply of all power supply units' power supply circuits at all times within a future period from the current time to the latest pick-up time of all charging vehicles. Based on this, it can predict the first threshold at any time within this future period, thereby calculating the power margin of the GCS distribution cabinet at that time.

[0022] The data acquisition unit periodically and synchronously collects the voltage and current of the load according to the set time period, calculates the power at this sampling moment, and determines whether the interface is connected to the load and charging the load based on the presence or absence of power or voltage and current. The control system then determines the start time, duration and number of charging events per day for all interfaces.

[0023] The control system acquires the total power data of all power supply units measured by the data acquisition module at the previous sampling time, and calculates the power margin of the GCS distribution cabinet as the first margin. Based on the second threshold predicted by the data acquisition module of all power supply units at the next sampling time before the first time, the control system calculates the power margin of the GCS distribution cabinet as the second margin. The smaller of the first and second margins is taken as the allocable margin. The control system adjusts the impedance of the current limiter to distribute the allocable margin proportionally to each interface according to the remaining charging time of each interface. For example, if the remaining charging times of the first, second, and third interfaces are 1:2:3 respectively, then the additional power allocated to the overall power supply units of the three interfaces by adjusting the impedance of the current limiter is also 1:2:3, and so on.

[0024] Therefore, compared with the prior art, the present invention can dynamically adjust the current distribution of each circuit according to the user's differentiated charging needs, so that the vehicles of non-emergency users can be charged slowly at a low price, and the charging process does not occupy too much system capacity; at the same time, it enables the vehicles of users who urgently need to use the car to obtain higher charging power, charge as much power as possible in a shorter time, and meet their needs to pick up the car in advance and extend the driving time as much as possible. It rationally allocates power resources, takes into account the personalized charging needs of different users, and improves the service flexibility and user experience of charging piles.

[0025] This application will not elaborate on the Monte Carlo model used for prediction; it is sufficient that the prediction results can be obtained using the Monte Carlo method. Optional: The daily (0:00:00-23:59:59) charging event count for the charging interface is calculated as follows: Non-day-crossing charging events are recorded normally. For a single continuous charging event spanning multiple days, the first day is counted as one charging event, with the charging duration being from the start time of the first day to 23:59:59. If the charging duration on a non-first day exceeds the preset duration, that day is also counted as one charging event, with the charging duration being from 0:00:00 to the end time of charging. If the charging duration on a non-first day exceeds the preset duration and charging is not yet complete, the charging duration is 0:00:00-23:59:59. The preset duration is flexibly set according to actual conditions, preferably 0.5 hours. The control system calculates the probability distribution of the daily charging event count for all interfaces based on all historical data of the daily (0:00:00-23:59:59) charging event count for all previous operating periods of the charging interface. For example, the probability that the first interface has N charging events in all known historical data is P. N The charging events are sorted in ascending order of frequency, and their probabilities are incremented until they reach 100%.

[0026] The control system calculates the probability distribution of the charging start time intervals for all interfaces based on all historical data of the charging start times during all previous operating periods of the charging interfaces. For example, the probability that the first interface will start charging within the charging time interval of 0:00-2:00 in all known historical data is P. 0:00-2:00 The start time intervals are sorted sequentially by time, and the probabilities are accumulated until they reach 100%.

[0027] The control system calculates the probability distribution of charging duration intervals for all interfaces based on all historical data of single charging events during all previous operating periods of the charging interfaces. For example, let A be the charging duration of a single charging event for the first interface. The probability that A ≤ 0.5 hours is P. (A≤0.5) The probability that 0.5 < A ≤ 1 hour is P. (0.5＜A≤1) The probability that 1 < A ≤ 1.5 hours is P. (1＜A≤1.5) The probability that M hours < A is P. (M＜A) Arrange A in ascending order, and accumulate the probabilities until they reach 100%.

[0028] The aforementioned phrase, "the control system obtains the power supply circuit's..., each charging..., and average circuit power data through the power supply unit's data acquisition module," specifically refers to the control system obtaining the average total power of all power supply circuits in each charging event based on the measured and uploaded data. For GCS-based distribution cabinets, only the power supply circuits are involved. These circuits include series interfaces, current limiters, ammeters and voltmeters necessary for data acquisition, and switches or fuses necessary for the GCS distribution cabinet's switching function. Therefore, the circuit voltage of the power supply circuit is the unit voltage of the power supply unit, consistent with the grid supply voltage. The total power of the power supply circuit is the total power of its power supply unit, and the average total power of the power supply circuit during charging (circuit average power) is the average total power of the power supply unit during charging (circuit average power). The "total" in "total power" specifically refers to the total power of the entire circuit, including all series-connected components, not the partial power of its individual components.

[0029] The control system calculates the probability distribution of the average power range of the power supply circuit for each interface based on all historical data of the average power of all power supply circuits during each charging event across all previous operating periods of the charging interface. For example, if the first interface is in the first power supply circuit of the first power supply unit, and the average power of the first power supply circuit during a single charging event is B, the probability that B ≤ 3.5KW is P. (B≤3.5) The probability that 3.5 < B ≤ 7KW is P. (3.5＜B≤7) The probability that C KW < B is P. (C KW＜B) Arrange B in ascending order, and accumulate the probabilities until they reach 100%.

[0030] Step 1: Randomly generate a random number between 0 and 100, for example, 0.73. We find that the probability of 5 charging cycles is less than the probability of 7 charging cycles, meaning the random number falls within the cumulative range of 5 charging cycles. Therefore, we select 7 charging cycles.

[0031] Step 2: The three elements of a random charging event are (charging time, charging duration interval, and average circuit power). Specifically: a random number r between 0 and 100 is generated, for example, r = 0.72. The starting charging time interval that this random number falls into is selected. The probability of 15:00:00-15:30:00 in historical data is 11%, with a cumulative probability of 67%; the probability of 15:30:00-16:00:00 in historical data is 12%, with a cumulative probability of 79%. If r falls into the 15:00:00-15:30:00 interval, R = (r - probability of the selected interval in historical data) / (latest moment of the selected interval - earliest moment) = (0.72 - 0.67) / 0.5 hours = 0.1. The randomly selected charging start time is equal to the earliest moment of the selected interval delayed by q hours, where q = R * (latest moment of the selected interval - earliest moment) = 0.1 * 0.5 = 0.05 hours = 3 minutes. The randomly selected charging start time is 15:03:00.

[0032] A random number r between 0 and 100 is generated, for example, 0.58. The probability of 15-20KW in historical data is 40%, with a cumulative probability of 50%. The probability of 20-25KW in historical data is 30%, with a cumulative probability of 80%. 0.58 falls within the 15-20KW average circuit power range. R = (r - probability of the selected range in historical data) / (maximum average circuit power of the selected range - minimum average circuit power of the selected range) = (0.58 - 0.5) / (20 - 15) = 0.016. The randomly selected average circuit power = minimum average circuit power of the selected range + R * (maximum average circuit power of the selected range - minimum average circuit power of the selected range) = 15 + 0.016 * (20 - 15) = 15.08KW. A random number between 0 and 100 is generated, and the charging time within which this random number falls is determined, using the same extraction method as above.

[0033] Repeatedly extract the three elements of X charging events. That is, in step 2, a total of X charging events' three elements were randomly extracted, where X is the number of charging events extracted in step 1.

[0034] Step 3: Overlay the 7 events into a total power curve. First, create a timeline array. For each randomly generated charging event, define its duration within the charging period (from start time to start + duration). At each moment, the average power of the corresponding randomly selected circuit is entered. After the average power of all circuits at all charging moments of all events is entered, the average power of all circuits on the horizontal axis at the same moment is added together. The sum of the average power of all circuits at that moment is the first value. With the moment as the horizontal axis and the first value as the vertical axis, the total power curve of one simulation is obtained.

[0035] After several simulations, several total power curves are obtained. All ordinate values ​​at any given time are taken, and a range for the ordinate values ​​is set to obtain the probability distribution of the ordinate within that range. The quantile at any given time (the probability ≤ α1 of the specific quantile at that time) or the maximum value among all the first values ​​in all total power curves at any given time is taken as the predicted peak total power supply of the power unit at that time.

[0036] In one possible implementation, 90% ≤ α1 < 100%, and optionally, α1 is 92%, 95% or 97%.

[0037] In one possible implementation, the data acquisition module measures the number of daily charging cycles, the start time of each charging cycle, the duration of each charging cycle, and the average load power (either average apparent load power or average active load power, preferably average apparent load power) of all power supply circuits and uploads the data to the control system. The control system calculates the total circuit power data of all circuits at all sampling times in the corresponding charging event based on the average load power data measured by the corresponding data acquisition module at all sampling times, combined with the total unit voltage data of the power supply unit at the sampling time and the equivalent impedance data of the current limiter. Thus, the average circuit power data of all power supply circuits in each charging event is obtained.

[0038] In one possible implementation, the average load power data for each charge of all power supply circuits measured by the data acquisition module and uploaded to the control system is the average apparent load power data.

[0039] In one possible implementation, the data acquisition module is used to measure the number of daily charging cycles for all power supply circuits, the start time and duration of each charging event, and also to measure the current data of all power supply circuits at all sampling moments during the charging process of each charging event. All data are uploaded to the control system. The control system calculates the total power data of all power supply circuits at all sampling moments during the charging process of each charging event based on the current data and the total voltage data of the unit to which it belongs, thereby obtaining the average power data of all power supply circuits in each charging event.

[0040] In one possible implementation, the control system measures whether each charging port is charging through a data acquisition module. After detecting that the load on the port is unloaded before its agreed latest usage time, the control system generates an early vehicle retrieval signal to clarify that the port is in a non-charging state. The control system corrects the power consumption status of each charging port, obtains the latest usage time of each port that is charging, obtains the remaining charging time of all charging ports, corrects and redetermines the first time.

[0041] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A distributed control system for power distribution cabinets based on GCS, characterized in that, The GCS-based power distribution cabinet includes several power supply units. Each power supply unit is used to supply power to the corresponding charging interface load. A current limiter is connected in series in the power supply circuit of each power supply unit. The distributed control system is electrically connected to the current limiters of several power supply units and is configured such that the system adjusts the equivalent impedance of the current limiter by sending control commands to the current limiter, thereby adjusting the current of the corresponding power supply circuit to achieve power distribution. The control system obtains the latest usage time of each charging user at each power supply interface that is charging, and obtains the remaining charging time of all charging interfaces. The interface with the latest usage time among all interfaces is the first interface, and the latest usage time of the charging user at the first interface is the first moment. The power supply unit is equipped with a data acquisition module. The control system obtains the daily charging frequency, start time, duration and average power data of the power supply circuit through the data acquisition module of the power supply unit. The system uses the Monte Carlo method to predict the probability distribution of the total power supply power of all power supply unit circuits at each moment from the present to the first moment. The system predicts the peak value of the total power supply power of the power supply unit at the corresponding moment or the upper limit of the charging power at the α1 confidence level as the first threshold. The control system acquires the total power data of all power supply units measured by the data acquisition module at the previous sampling time, and obtains the power margin of the GCS distribution cabinet as the first margin based on this data. The control system calculates the power margin of the GCS distribution cabinet as the second margin based on the first threshold of all power supply units at the next sampling time of the data acquisition module. The smaller of the first and second margins is taken as the allocable margin. The control system adjusts the impedance of the current limiter and distributes the allocable margin proportionally to each interface according to the remaining charging time of each interface.

2. The distributed control system for distribution cabinets based on GCS according to claim 1, characterized in that, 90%≤α1＜100%。 3. The distributed control system for distribution cabinets based on GCS according to claim 1, characterized in that, The data acquisition module measures the daily charging frequency, start time, duration, and average load power data of all power supply circuits and uploads them to the control system. The control system calculates the total circuit power data of all circuits at all sampling times in the corresponding charging event based on the average load power data measured by the corresponding data acquisition module at all sampling times, combined with the total unit voltage data of the power supply unit at the sampling time and the equivalent impedance data of the current limiter. Thus, the control system obtains the average circuit power data of all power supply circuits in each charging event.

4. The distributed control system for distribution cabinets based on GCS according to claim 3, characterized in that, The average load power data for each charging cycle of all power supply circuits measured by the data acquisition module and uploaded to the control system is the average apparent load power data.

5. The distributed control system for distribution cabinets based on GCS according to claim 1, characterized in that, The data acquisition module is used to measure the number of daily charging cycles for all power supply circuits, the start time and duration of each charging event, and the current data of all power supply circuits at all sampling moments during the charging process of each charging event. All data are uploaded to the control system. The control system calculates the total power data of all power supply circuits at all sampling moments during the charging process of each charging event based on the current data and the total voltage data of the unit to which it belongs, thereby obtaining the average power data of all power supply circuits in each charging event.

6. The distributed control system for distribution cabinets based on GCS according to claim 1, characterized in that, The control system measures whether each charging interface is charging through the data acquisition module. After detecting that the interface load is unloaded before its agreed latest usage time, the control system generates an early vehicle retrieval signal to clarify that the interface is in a non-charging state. The control system corrects the power consumption status of each charging interface, obtains the latest usage time of each interface that is charging, obtains the remaining charging time of all charging interfaces, corrects and re-determines the first time.