Charging pile scheduling method based on real-time peak and flat valley of power grid

Abstract

The invention relates to the technical field of power system electric energy distribution, and discloses a charging pile scheduling method based on real-time power grid peak and flat valley, which comprises the following steps: generating a first regulation factor which is reduced along with voltage drop by monitoring local power grid voltage in real time, and a second regulation factor which is increased along with reduction of the electricity price is generated by combining an electricity price level signal issued by a power grid, and the second regulation factor and the second regulation factor are multiplied to obtain a comprehensive regulation coefficient so as to dynamically control the output power of the charging pile. The charging pile responds to the electricity price signal and simultaneously self-adapts to the power grid bearing capacity, communication delay of centralized control is avoided, the defect that single electricity price scheduling cannot guarantee the safety of the power grid is overcome, and self-balance of electricity market rules and power grid physical constraints is achieved.

Description

Technical Field

[0001] The present invention relates to a charging pile scheduling method based on real-time power grid peaks and valleys, and belongs to the technical field of electric energy distribution in power systems. Background Art

[0002] The current mainstream solutions include static scheduling based on fixed time-of-use electricity prices and centralized control based on cloud computing. Static scheduling only adjusts the charging power according to the preset electricity price period, completely ignoring the real-time load status of the power grid. Although centralized control can achieve global optimization, its perception-decision-execution link has a minute-level delay. In typical scenarios such as the evening rush hour in large residential areas, these two solutions face fundamental limitations: when dozens of charging piles are connected at the same time, static scheduling cannot perceive the local voltage drop, resulting in the risk of overload of the distribution transformer, while centralized control cannot prevent the critical state before the voltage collapse due to communication delays.

[0003] On a deeper level, existing technologies have three systemic flaws: first, grid voltage fluctuations are treated as interference rather than control signals, causing the system to lose the opportunity to use physical laws to achieve autonomous regulation; second, scheduling decisions rely on external instructions and lack local autonomous response capabilities, making charging piles passive execution units; third, economic incentives are decoupled from the grid's carrying capacity, which can easily cause local overloads during electricity price troughs. Although the industry has tried to improve this by increasing communication bandwidth or strengthening cloud computing power, it has increased system complexity and deployment costs, and instead magnified the risk of resonance caused by group synchronization. In view of this, how to enable charging piles to autonomously adjust power according to the physical state of the grid and electricity price signals to achieve a dynamic balance between local real-time decision-making and grid security has become a key bottleneck restricting the development of smart charging technology. Summary of the Invention

[0004] The present invention provides a charging pile scheduling method based on real-time grid peaks and valleys, the main purpose of which is to solve the problem that charging piles cannot autonomously adapt to the real-time carrying capacity of the grid when responding to electricity price signals.

[0005] To achieve the above objectives, the present invention provides a charging pile scheduling method based on real-time grid peak and valley conditions, comprising the following steps: Step a: monitoring the local grid voltage at the charging pile access point and obtaining a real-time measurement value of the local grid voltage; Step b: generating a first adjustment factor based on the real-time measurement value of the local grid voltage and the established voltage suppression relationship function, wherein the first adjustment factor decreases as the real-time measurement value of the local grid voltage decreases; Step c, receiving an electricity price level signal issued by the grid side; Step d: generating a second adjustment factor based on the electricity price level signal and the established economic incentive relationship function, wherein the second adjustment factor increases as the electricity price indicated by the electricity price level signal decreases; Step e: multiplying the first adjustment factor by the second adjustment factor to obtain a comprehensive adjustment coefficient; Step f, based on the comprehensive adjustment coefficient, adjust the actual output power of the charging pile so that the actual output power is equal to the product of the maximum rated power of the charging pile and the comprehensive adjustment coefficient; wherein, when the drop percentage of the local grid voltage reaches the second voltage drop range, the first adjustment factor drops sharply from the preset first drop rate to the preset second drop rate, so that the output power of the charging pile is reduced to a preset suppression level.

[0006] Preferably, in step b, the first regulatory factor According to the percentage of local grid voltage drop To generate, the generation rule is: hour, ;when hour, from Linearly decrease to ;when hour, from Dropped sharply to ;when hour, .

[0007] Preferably, in step b, the voltage suppression relationship function on which the first adjustment factor is generated is stored in a lookup table in the controller of the charging pile.

[0008] Preferably, in step c, the electricity price level signal includes four electricity price levels, namely peak level, high level, flat level and valley level; in step d, the second adjustment factor is generated according to the electricity price level signal, and its generation rule is: when the electricity price level signal indicates the peak level, the second adjustment factor is 0.1; when the electricity price level signal indicates the high level, the second adjustment factor is 0.4; when the electricity price level signal indicates the flat level, the second adjustment factor is 0.8; when the electricity price level signal indicates the valley level, the second adjustment factor is 1.0.

[0009] Preferably, in step c, the electricity price level signal is received via a power line carrier communication method or a 4GCat-1 module wireless communication method.

[0010] Preferably, the following steps are also included: continuously monitoring the changing trend of the first adjustment factor within a preset time window; when the first adjustment factor undergoes more than three drastic jumps within the preset time window, it is determined that the oscillation judgment condition is met; before executing the adjustment action of increasing the output power of the charging pile based on the increase of the first adjustment factor, a randomized time delay is introduced, and the time delay range is 0.1 seconds to 1.5 seconds to eliminate the group synchronization effect.

[0011] Preferably, the length of the preset time window is 10 seconds.

[0012] Preferably, the method further includes the following steps: at a determined detection time, controlling the charging pile to inject a current pulse into the power grid; while injecting the current pulse, measuring the instantaneous change in the local power grid voltage; calculating the parameters characterizing the local line impedance of the charging pile access point based on the amplitude of the current pulse and the instantaneous change in voltage; and adjusting the voltage suppression relationship function on which the generation of the first adjustment factor is based on the parameters characterizing the local line impedance, so that the greater the local line impedance, the stronger the suppression effect of the first adjustment factor on the local power grid voltage change.

[0013] Preferably, the determined detection opportunity is the gap after the charging pile and the vehicle complete the handshake agreement and before the formal high-power charging, or the short gap after the charging task is completed; the amplitude of the current pulse is 5 amperes and the duration is 10 milliseconds.

[0014] Preferably, in step f, before adjusting the actual output power of the charging pile according to the comprehensive adjustment coefficient, the charging pile also performs the following arbitration steps: storing the historical output power value of the charging pile, and calculating the output power trend line based on the historical output power value; comparing the current power to be output with the output power trend line, and when the deviation between the current power to be output and the output power trend line is greater than the determined deviation threshold, determining that the power adjustment instruction is an abnormal instruction; when it is determined to be an abnormal instruction, rejecting the power adjustment instruction and maintaining the output power of the charging pile in the previous second.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. When charging piles convert the local grid voltage drop into a power suppression factor and multiply it by the economic incentive factor generated by the electricity price signal, the system naturally forms a dual negative feedback mechanism: the voltage drop triggers power convergence to alleviate grid pressure, and the low electricity price period releases charging demand. The product operation of the two makes the physical state of the grid a natural boundary condition for economic dispatch behavior, and realizes autonomous optimization of load distribution without central coordination. This not only avoids the communication delay of traditional centralized control, but also overcomes the defect that a single electricity price signal cannot respond to the real-time carrying capacity of the grid.

[0016] 2. Utilizing the inherent frequency cycle of the AC power grid as a global clock reference, the unique ID of a charging pile is mapped to a specific phase time window for communication scheduling. This coupled design, which transforms the physical properties of electricity into communication resources, enables hundreds of charging piles to spontaneously form an orderly broadcast sequence in a shared channel, eliminating group communication conflicts. The system only needs to add phase detection and time slice allocation logic at the firmware level to achieve a qualitative change from contention channels to conflict-free transmission, significantly improving the reliability of large-scale cluster control.

[0017] 3. Millisecond-level current pulses are injected during charging intervals, and voltage transients are captured simultaneously. Local line impedance parameters are inverted using Ohm's law. This mechanism reuses the power converter as a diagnostic sensor, allowing the charging pile to autonomously identify its own position characteristics in the power grid. The detection results dynamically modify the voltage suppression function, allowing charging piles at the end of weak networks to increase power constraints in advance. This closed-loop execution-perception-tuning system enables the system to distinguish between global overloads and local bottlenecks, maintaining overall charging efficiency while strengthening the protection of vulnerable nodes in a targeted manner.

[0018] 4. By continuously tracking the jump frequency of the voltage suppression factor to identify the budding state of oscillation, a randomized delay is injected into the power recovery command path. When three or more severe power fluctuations are detected, the system proactively breaks the synchronous response rhythm of the charging pile, making the load recovery process exhibit spatiotemporal discrete characteristics. This reflexive regulation mechanism, which uses its own decision output as control input, eliminates the group resonance problem that traditional solutions require complex phase compensation to suppress at almost zero computational cost, thereby enhancing the system's resilience under critical operating conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flowchart of the charging pile scheduling based on the peak and valley electricity prices and voltage state adjustment of the power grid according to the present invention; Figure 2 This is a flowchart of the local control scheduling of charging piles based on grid voltage and electricity price level adjustment of the present invention; Figure 3 This is a timing diagram of the power output adjustment process of the charging pile of the present invention.

[0020] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0021] In order to make the objectives, technical details and advantages of this technical solution more clearly presented, the specific implementation form of this technical solution will be elaborated in detail below. The description here is intended to explain this technical solution, but does not constitute any limitation on its scope of protection.

[0022] The present invention discloses a charging pile scheduling method based on real-time grid peaks and valleys, and a dual-factor dynamic power regulation mechanism integrated into the local controller of the charging pile. The mechanism consists of a voltage stress suppression module that responds to the physical state of the grid and an economic incentive response module that responds to the power market signal. The regulation factors output by the two jointly determine the final actual output power of the charging pile through multiplicative coupling operation, thereby achieving an autonomous and real-time balance between charging behavior and the grid carrying capacity and electricity price economy without the need for remote communication coordination; in typical application scenarios, such as concentrated charging loads in large residential areas in the evening, when a large number of charging piles are started at the same time, the pressure on the distribution transformer and the terminal line increases sharply. At this time, the scheduling strategy relying on static time-of-use electricity prices will aggravate the risk of voltage drop due to the inability to perceive the real-time carrying status of the local grid. To meet this challenge, the scheduling process of this solution starts with continuous monitoring of the local grid voltage at the charging pile access point, and continuously calculates the voltage drop percentage by obtaining the real-time measurement value of the local grid voltage. ; In view of this, the system built-in voltage suppression relationship function will be As input, a first adjustment factor is instantly generated which decreases with voltage drop. This function is materialized as a static lookup table stored in the charging pile controller to achieve a fast response in microseconds. Its built-in rules are precisely calibrated as follows: when the voltage drop is still within the safety margin, hour, Maintain , no power limit is imposed; when hour, from Smooth linear decrease to , forming a proportional power contraction commensurate with the grid pressure; and when the voltage drops into the critical warning zone In order to actively avoid the risk of voltage collapse, Then from It decreases nonlinearly to , perform strong load shedding; once , Then directly set it to , immediately suspending the charging task to ensure the safety of the local power grid. In this way, by converting the local voltage, the most direct physical indicator of power grid health, into an internal power constraint signal, the charging pile has the ability to autonomously adjust in an emergency to deal with local overloads.

[0023] Furthermore, in order to guide charging behavior to shift to periods when grid resources are abundant and electricity prices are low, the system is configured to receive electricity price level signals issued uniformly by the grid side through power line carrier communication or 4GCat-1 module wireless communication. This signal contains four discrete states: peak level, high level, flat level, and valley level. The economic incentive relationship function within the system generates a corresponding second adjustment factor based on the received signal level. The mapping rule of this function is determined as follows: peak level corresponds to , peak level corresponds to , corresponding to the same level , while the valley level corresponds to , this setting makes the second adjustment factor increase significantly as the electricity price decreases; eventually, the local controller of the charging pile will calculate the first adjustment factor in real time. The multiplication operation is performed with the second adjustment factor to obtain a comprehensive adjustment coefficient, and the actual output power of the charging pile is adjusted based on this coefficient to make it equal to the product of the maximum rated power of the charging pile and the comprehensive adjustment coefficient. This multiplicative coupling mechanism ensures that economic scheduling is always executed within the boundary conditions of power grid security. Even in the valley period with the lowest electricity price, once the local voltage drops severely due to excessive load, the first adjustment factor that drops sharply It will still dominate and lower the comprehensive regulation coefficient, thereby forcing the output power to be reduced to maintain the stability of the power grid, and realizing the autonomous dynamic balance between the power market rules and the physical constraints of the power grid; in order to further improve the adaptability and robustness of the system, this solution also integrates a set of line impedance perception and adjustment parameter self-tuning procedures based on active detection. The technical motivation is that the charging piles at different grid topological positions have different line impedances at their access points. The terminal position with larger impedance is more sensitive to load fluctuations. The use of a unified voltage suppression strategy is not the optimal solution. For this reason, the system is designed to execute a line impedance detection program in a short interval before the charging task is started or after the charging task is completed. At this detection time, the charging pile will actively inject a voltage with an amplitude of Ampere, duration The system measures the current pulse amplitude and the instantaneous change of the voltage at the injection point at a high speed. Based on the current pulse amplitude and the instantaneous change of the voltage, the system calculates a characteristic parameter that can characterize the local line impedance of the access point according to Ohm's law. This parameter is then used to automatically adjust the internal parameters of the voltage suppression relationship function. The adjustment logic is solidified as follows: the larger the measured local line impedance, the lower the voltage suppression relationship function. Follow The steeper the slope of the decline, the more sensitive the charging pile in a weak network environment can be in responding to voltage changes with power suppression, thereby achieving differentiated and precise on-site protection that matches its own electrical position.

[0024] At the same time, considering the group synchronization effect that may occur in the high-density charging pile cluster during the grid disturbance recovery phase, that is, a large number of charging piles increase power at the same time after the voltage recovers and cause secondary shocks and system oscillations, this solution introduces a decision behavior monitoring and random delay arbitration mechanism; this mechanism continuously monitors the first adjustment factor exist When the system detects a change trend within the sliding time window of 10 seconds and detects more than three violent jumps, the system determines that the oscillation determination condition is met. Under this condition, the controller will not execute any Before increasing the output power of the charging pile, a forced Seconds to This non-fixed small time difference actively injected into the power recovery path can effectively break the response synchronization of the charging pile group, making the overall load recovery process present a discrete smooth feature in time and space, thereby avoiding the potential system-level resonance risk at an extremely low computational cost and significantly enhancing the operational resilience of the entire charging system under critical working conditions; finally, in order to prevent abnormal power commands caused by communication errors or transient sensor failures from impacting the vehicle battery or power grid, the system also sets an arbitration defense line before executing the final power adjustment; the charging pile will continuously store its own historical output power rate value, and based on these historical data, such as the data points in the past 60 seconds, an output power trend line is calculated by the moving average method; after receiving a new power value to be executed calculated by the comprehensive adjustment coefficient, the system will first compare it with the current output power trend line. If the deviation between the power value to be executed and the trend line is greater than a predetermined deviation threshold, the power adjustment instruction is determined to be an abnormal instruction; once it is determined to be abnormal, the instruction will be rejected, and the charging pile will maintain the output power of the previous second unchanged, thereby effectively filtering out sudden and illogical power instructions and ensuring the smooth and safe charging process.

[0025] Example 1: This example aims to illustrate the specific operation examples and technical effects of the general technical solution described in a specific high-load, weak grid industrial application scenario. In a newly built high-density residential area, its supporting grid facilities are at the edge of the design capacity. Especially in the peak period from 19:00 to 21:00 in the evening, a large number of residents return home and connect their electric vehicles to charging piles. At this time, the regional grid operator, according to the predetermined plan, sends a signal to all charging piles in the area through power line carrier communication to enter the valley-level electricity price. An inherent technical contradiction then arises: the economic signal encourages all charging piles to charge at maximum power at the same time, but the physical carrying capacity of the local power grid cannot support this instantaneous, concentrated load surge. The distribution transformer faces overload and the voltage stability of the terminal line is directly threatened. Under this working condition, the charging pile group deployed with this technical solution begins to operate autonomously. First, all charging piles correctly receive and interpret the valley-level electricity price signal, and set their respective second adjustment factors to be based on the built-in economic incentive relationship function. However, just when the charging power was about to jump, the local grid voltage dropped significantly due to the simultaneous access of a large number of loads. The voltage drop percentage monitored by each charging pile was quickly surpass At this time, the voltage stress suppression module intervenes immediately and generates a voltage stress suppression module that is significantly smaller than the nonlinear sharp decline section in the voltage suppression relationship function. The first regulatory factor ,For example , which represents the physical security boundary of the power grid and the second moderating factor representing economic incentives The comprehensive adjustment coefficient obtained by multiplication limits the actual output power of the charging pile to the maximum rated power. Left and right, not , which achieves the goal of responding to the incentive of low electricity prices while enforcing compliance with the physical security constraints of the power grid, avoiding the inherent contradiction between economic dispatch and power grid security.

[0026] Furthermore, this scenario demonstrates the synergistic effect of multiple mechanisms within the system. For those charging piles that are calibrated as being at the end of the line and having a higher local line impedance during the initial installation of the line impedance detection program, their internal voltage suppression relationship function has been adaptively adjusted to be more sensitive, so that under the same global voltage drop, Next, they generate The value will be lower, e.g. , thus taking on more power reduction responsibilities than charging piles close to the transformer. This differentiated and precise suppression avoids extensive power suppression of the entire charging cluster and realizes optimized redistribution of loads in the grid topology. At the same time, as the power of some charging piles converges, the grid voltage attempts to rebound slightly, which may trigger a synchronous increase in the power of the charging piles and cause oscillations. However, since the previous drastic power changes have been captured by the decision-making behavior monitoring and random delay arbitration mechanism and determined to meet the oscillation judgment conditions, all charging piles introduce an asynchronous randomized time delay before executing the power increase command, which makes the load of the entire cluster The load recovery presents a smooth, discrete asynchronous state, effectively suppressing the generation of secondary shocks; ultimately, under this operating condition, the charging pile group continues to operate without any intervention from the upper-level dispatching platform, the regional power grid voltage is maintained within the preset stable operating range, no transformer overload tripping occurs, and the charging process of all users is not interrupted; the actual output power of each charging terminal is reflected as the result of the nonlinear coupling of the global economic signal and the local power grid physical state signal, and is corrected by the terminal's own electrical position parameters, so that the originally disordered and competing charging load collection forms an adaptive dynamic balance under the physical constraints of the power grid.

[0027] Example 2: This example aims to objectively verify the effectiveness of the core regulation mechanism of the scheduling method of the present invention under conditions where the physical constraints of the power grid conflict with the economic signals of the power market through hardware-in-the-loop simulation tests. To achieve this goal, a hardware-in-the-loop test platform was constructed, which includes a real-time digital simulator, a charging pile controller prototype, a programmable power supply and an electronic load. The platform reproduces a residential distribution network containing a distribution transformer and a terminal radial feeder line through a high-fidelity model, and can accurately simulate the dynamic drop of line voltage caused by load changes. In the test, the key simulation time step is set to 50 microseconds. The technical consideration for selecting this value is to balance the transient response accuracy of the simulation model and the computational load. The decision rule is that the step must be significantly smaller than the internal control period of the controlled object, namely the charging pile controller. Given that the voltage suppression response in this scheme needs to reach the millisecond level, in order to ensure that the simulator can present the dynamic process of voltage drop without distortion, 50 microseconds is selected as the time base.

[0028] After the test process is started, the system first operates stably under the baseline operating conditions. At this time, the grid load is medium and the electricity price level signal is at the flat level. At a certain moment T0, the test platform applies two conflicting excitations at the same time: the electricity price level signal is instantly switched from the flat level to the valley level through the signal generator. At the same time, a high-power inductive load is instantaneously put into the digital simulator to simulate a sudden drop in the local grid voltage. The key operating parameters of the system are continuously recorded. At the moment the excitation is applied, the second adjustment factor instantly jumps to 1.0 in response to the valley level signal, trying to drive the charging pile to the maximum rated power. However, the local grid voltage monitoring module captures the voltage drop percentage DeltaV, which quickly climbs from 1.5% to 6.2%. See Table 1. The DeltaV value triggers the voltage suppression relationship function, which makes the first adjustment factor It shrinks sharply from 1.0 to 0.3. Finally, the comprehensive adjustment coefficient is calculated as the product of the two, which is 0.3. The actual output power of the charging pile is suppressed to 30% of the maximum rated power.

[0029] Table 1: Record of key status parameter changes.

[0030]

[0031] The data records the final decision-making behavior of the system under the intention of economic incentive signals to drive power increase, which is represented by the physical security of the power grid. The mechanism of this phenomenon is that the multiplicative coupling relationship between the two adjustment factors functionally realizes a safety priority arbitration, that is, the total output power limit of the system is always subject to The results of this hardware-in-the-loop simulation test confirm that the charging pile scheduling method of the present invention can respond to the dynamic changes of the power grid autonomously and quickly by relying only on local measurement and calculation without the need for external communication and coordination, and can deterministically prioritize the stable operation of the power grid when there is a conflict between the two dimensions of economy and safety. Example 3: This example combines Figures 1 to 3 , a charging pile scheduling method based on real-time grid peak and valley conditions is described, such as Figure 1 As shown, first, through the local power grid step a, monitor the grid voltage at the access point and calculate the percentage of voltage drop Then, the first adjustment factor is generated through the voltage suppression relationship function step b. , this factor decreases as the voltage drops. Then, the grid side releases the electricity price level signal. The charging pile receives the electricity price level signal through step c and generates a second adjustment factor according to the economic incentive relationship function step d. The second adjustment factor increases as the electricity price decreases. Then, through the comprehensive adjustment coefficient calculation step e, the first adjustment factor is multiplied by the second adjustment factor to obtain the final comprehensive adjustment coefficient; at this time, the charging pile adjusts the output power according to the comprehensive adjustment coefficient so that the power is adapted to the carrying capacity of the grid and the electricity price signal to achieve real-time adjustment and optimization of the load. At the same time, the charging pile will continue to monitor the voltage change rate and determine whether oscillation occurs. If oscillation occurs, the random delay step is introduced to break the synchronous response to avoid the group synchronization effect.

[0032] like Figure 2 As shown, through the local power grid step, the charging pile monitors the local power grid voltage and obtains the real-time voltage measurement value step a. Next, the first adjustment factor is generated through the voltage suppression relationship function. , the factor decreases as the voltage decreases in step b. At the same time, the charging pile receives the peak / peak / flat / valley price signal of the electricity price level from the power grid, receives the electricity price level signal processing signal (PLC / 4GCat-1) in step c, and then generates a second adjustment factor according to the change of electricity price. The factor increases as the electricity price decreases in step d. After that, step e adjusts the first adjustment factor Multiplying it with the second adjustment factor, a comprehensive adjustment coefficient is obtained. Finally, the charging pile adjusts the actual output power according to the comprehensive adjustment coefficient to achieve the charging power adjustment and optimization step f.

[0033] like Figure 3 As shown, after the communication module of the charging pile receives a new power adjustment instruction, it sends it to the controller for processing. Then the controller starts to process the new power adjustment instruction and stores the historical data of the past 60 seconds. Next, the controller returns the historical data to the control module and starts to calculate the power trend line. At this time, by comparing the difference between the historical power and the trend line, the controller further determines whether it deviates from the expectation. If there is a deviation, the controller executes maintenance of the power of the previous second and makes corresponding adjustments. At the same time, if it is found that the power does not meet the requirements, the controller will execute power correction and start adjustment after the instruction is confirmed. Finally, by comparing the historical data with the current power correction instruction, the charging pile ensures the stability of the charging process and the rationality of the power output by maintaining the original power output.

[0034] Example 4: This example aims to provide a detailed supplement to the deterministic procedures for key adaptive parameters and logic thresholds involved in the aforementioned technical solutions, so as to illustrate how the charging pile can complete the autonomous calibration of the core control parameters according to the specific electrical environment in which it is located during the deployment phase. When the charging pile is first installed and the electrical connection is completed, its controller automatically executes a set of single in-situ self-calibration procedures during the initialization phase of the first power-on to cope with the differences in line impedance and local grid noise levels at different installation locations. After the procedure is started, the line impedance detection is first performed in the interval before the charging task begins, and the line impedance is injected into the charging pile. ampere, Millisecond current pulses are used to measure the instantaneous voltage change and calculate the parameters that characterize the local line impedance. Based on this measurement, the controller further calculates a dimensionless impedance sensitivity factor , which is calculated as ,in is a reference impedance value, corresponding to the ideal grid connection condition, and is a fixed linear proportional coefficient. Both are system constants determined during the design phase of the charging pile product. The factor is used to adjust the voltage drop threshold in the voltage suppression relationship function. The three-level threshold point originally set is 、 and Updated to 、 and , so when the charging pile is installed in a high impedance line, Larger, The corresponding increase leads to a decrease in the response thresholds of each level, making the charging pile more sensitive to the suppression of voltage drops. Subsequently, the self-calibration procedure quantitatively defines the oscillation judgment conditions. The procedure defines a violent jump as: within two consecutive control sampling cycles, the first adjustment factor The absolute value of the change is greater than its total range , that is, the condition is met ; In subsequent operation, the controller continues to identify effective sharp jumps based on this rule and The number of jumps is counted within a sliding time window of 10 seconds, providing an unambiguous trigger basis for the random delay arbitration mechanism.

[0035] The subsequent steps of the procedure are to provide a basis for setting the deviation threshold in the power regulation arbitration mechanism based on measured data. The controller executes a time 100 seconds of baseline charging, during which it charges at a moderate, constant overall regulation factor. Charging is performed and a sequence of actual output power readings is collected at high frequency. After the collection is completed, the controller calculates the standard deviation of the power sequence. , which reflects the normal power fluctuation amplitude caused by the background noise of the power grid at this specific installation location; finally, the deviation threshold used to determine whether the power regulation instruction is abnormal is set to six times the measured standard deviation, that is, This threshold, set based on statistical principles, is used to distinguish normal power fluctuations from real system instruction anomalies. By executing the above-mentioned one-time in-situ self-calibration procedure, the core control parameters of each charging pile have been completed from the universal factory preset to the personalized configuration that suits the local electrical environment. The sensitivity of its voltage response, the trigger conditions for oscillation suppression, and the judgment boundary of abnormal power are all configured with a traceable, deterministic basis based on physical measurements, thereby ensuring the behavioral consistency of the entire technical solution in different deployment environments.

[0036] Example 5: This example aims to specifically explain the data source and update mechanism of the core relationship function in the aforementioned scheme, so as to clarify its engineering implementation procedures in product design finalization and full life cycle management. In the initial research and development stage of the charging pile product, its core voltage suppression relationship function is determined through a series of controlled offline calibration experiments. This process uses a hardware-in-the-loop simulation platform to systematically scan different simulated line impedance and background load combinations to generate hundreds of stable and reproducible grid operating points; at each operating point, the output power of the charging pile is set to a serialized discrete value, and the percentage drop of the local grid voltage is simultaneously monitored. As well as power quality indicators such as voltage recovery time and oscillation amplitude, the power quality indicators can be plotted in a specific Under this condition, the maximum allowable charging power curve required to maintain grid stability, the key inflection points of this curve, such as achieve The power needs to be suppressed to ,Right now , and in Exceed Then the power needs to be suppressed to ,Right now , are determined after analysis of the experimental data. These data points are then solidified in the lookup table of the charging pile controller, forming the original data basis for the voltage suppression function.

[0037] To ensure that the charging pile can continuously adapt to the strategy changes of the power grid operator during long-term operation, its controller is designed to have the ability to securely and remotely update the economic incentive relationship function. The charging pile can establish a secure communication link with an authorized power grid operator server through its wireless communication module periodically or upon receiving specific instructions; the server can send a digitally signed data packet containing the mapping relationship between the new version of the electricity price level and the second adjustment factor. After receiving the data packet, the charging pile first uses the preset public key to verify the authenticity and integrity of its signature. Only after the verification is passed will the new mapping table be used to overwrite the existing economic incentive relationship function lookup table in the controller memory. This mechanism enables the economic dispatch behavior of the charging pile to be consistent with the latest electricity price policy of the power grid operator, thereby maintaining the long-term effectiveness and compliance of the plan under different power market rules.

[0038] Example 6: During the design and verification phase of the charging pile product, a signal-to-noise ratio optimization calibration process is performed to determine the parameters of the current pulse injected during line impedance detection. This process is carried out in an electromagnetic compatibility laboratory environment containing an adjustable noise generator. A series of current pulses of different amplitudes and durations are injected into the standard test load, and the instantaneous change of the local grid voltage is simultaneously collected. For each set of pulse parameters, namely the amplitude, the signal-to-noise ratio is calculated. With duration , calculate the signal-to-noise ratio of the measured voltage transient signal , and simultaneously calculate the total energy of the pulse injection ,The goal of this calibration procedure is to meet the preset signal-to-noise ratio threshold, i.e. , in order to achieve effective measurement conditions, to find the energy The minimum parameter combination, finally selected Ampere and Milliseconds are the operating point at which the measurement signal-to-noise ratio requirement can be achieved with minimal grid disturbance during the optimization process.

[0039] To enhance the reliability of the system in actual operation, the controller performs a power-on self-test procedure each time it is powered on, which includes a rationality check of the voltage sensor. This check is achieved by comparing the high-precision reading of the main AC voltage sensor with a low-precision AC voltage estimate calculated from the DC bus voltage of the internal auxiliary power supply. If the deviation between the two exceeds a preset safety range, the controller will automatically detect the voltage sensor. , the system determines that the main voltage sensor is faulty and prohibits the charging pile from entering the high-power operation mode, instead entering an alarm state that only maintains low-power communication; In addition, for the calculation of the output power trend line in the power regulation arbitration mechanism, its implementation method is determined to be a simple moving average algorithm, and the window length of the algorithm is set to the oscillation determination time window One quarter of a second, The window length is set to take into account both the smoothness of the trend line and the response speed to power changes.

[0040] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0041] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not limiting. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A charging pile scheduling method based on real-time power grid peak and valley conditions, characterized in that: The following steps are involved: Step a: monitoring the local grid voltage at the charging pile access point and obtaining a real-time measurement value of the local grid voltage; Step b: generating a first adjustment factor based on the real-time measurement value of the local grid voltage and the established voltage suppression relationship function, wherein the first adjustment factor decreases as the real-time measurement value of the local grid voltage decreases; Step c, receiving an electricity price level signal issued by the grid side; Step d: generating a second adjustment factor based on the electricity price level signal and the established economic incentive relationship function, wherein the second adjustment factor increases as the electricity price indicated by the electricity price level signal decreases; Step e: multiplying the first adjustment factor by the second adjustment factor to obtain a comprehensive adjustment coefficient; Step f, based on the comprehensive adjustment coefficient, adjust the actual output power of the charging pile so that the actual output power is equal to the product of the maximum rated power of the charging pile and the comprehensive adjustment coefficient; wherein, when the drop percentage of the local grid voltage reaches the second voltage drop range, the first adjustment factor drops sharply from the preset first drop rate to the preset second drop rate, so that the output power of the charging pile is reduced to a preset suppression level.

2. The method for scheduling charging piles based on real-time grid peaks and valleys according to claim 1, characterized in that: In step b, the first adjustment factor According to the percentage of local grid voltage drop To generate, the generation rule is: hour, ;when hour, from Linearly decrease to ;when hour, from Dropped sharply to ;when hour, .

3. The method for scheduling charging piles based on real-time grid peaks and valleys according to claim 1, characterized in that: In step b, the voltage suppression relationship function based on which the first adjustment factor is generated is stored in a lookup table in the controller of the charging pile.

4. The method for scheduling charging piles based on real-time grid peaks and valleys according to claim 1, characterized in that: In step c, the electricity price level signal includes four electricity price levels: peak level, high level, flat level, and valley level; in step d, the second adjustment factor is generated according to the electricity price level signal, and its generation rule is: when the electricity price level signal indicates the peak level, the second adjustment factor is 0.1; when the electricity price level signal indicates the high level, the second adjustment factor is 0.4; when the electricity price level signal indicates the flat level, the second adjustment factor is 0.8; when the electricity price level signal indicates the valley level, the second adjustment factor is 1.

0.

5. The method for scheduling charging piles based on real-time grid peaks and valleys according to claim 1, characterized in that: In step c, the electricity price level signal is received via a power line carrier communication method or a 4GCat-1 module wireless communication method.

6. The method for scheduling charging piles based on real-time grid peaks and valleys according to claim 1, characterized in that: The following steps are also included: Continuously monitor the changing trend of the first adjustment factor within the preset time window; when the first adjustment factor undergoes more than three drastic jumps within the preset time window, it is determined that the oscillation judgment condition is met; before executing the adjustment action of increasing the output power of the charging pile based on the increase of the first adjustment factor, a randomized time delay is introduced, and the time delay range is 0.1 seconds to 1.5 seconds to eliminate the group synchronization effect.

7. The method for scheduling charging piles based on real-time grid peaks and valleys according to claim 6, characterized in that: The default time window length is 10 seconds.

8. The method for scheduling charging piles based on real-time grid peaks and valleys according to claim 1, characterized in that: The following steps are also included: At the determined detection time, the charging pile is controlled to inject a current pulse into the power grid; while injecting the current pulse, the instantaneous change of the local power grid voltage is measured; Based on the amplitude of the current pulse and the instantaneous change in voltage, the parameters representing the local line impedance at the charging pile access point are calculated; And according to the parameter characterizing the local line impedance, the voltage suppression relationship function based on which the first adjustment factor is generated is adjusted, so that the greater the local line impedance, the stronger the suppression effect of the first adjustment factor on the local grid voltage change.

9. The method for scheduling charging piles based on real-time grid peaks and valleys according to claim 8, characterized in that: The determined detection time is the gap after the charging pile and the vehicle complete the handshake agreement and before the formal high-power charging, or the short gap after the charging task is completed; the amplitude of the current pulse is 5 amperes and the duration is 10 milliseconds.

10. The method for scheduling charging piles based on real-time grid peaks and valleys according to claim 1, characterized in that: In step f, before adjusting the actual output power of the charging pile according to the comprehensive adjustment coefficient, the charging pile also performs the following arbitration steps: storing the historical output power value of the charging pile, and calculating the output power trend line based on the historical output power value; comparing the current power to be output with the output power trend line, and when the deviation between the current power to be output and the output power trend line is greater than the determined deviation threshold, determining that the power adjustment instruction is an abnormal instruction; when it is determined to be an abnormal instruction, rejecting the power adjustment instruction and maintaining the output power of the charging pile in the previous second.

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