Power energy distribution multi-machine cooperative execution algorithm

Abstract

The present application relates to the technical field of smart grid equipment, and particularly relates to a power energy distribution multi-machine cooperative execution algorithm, which is applied to multiple power consumption devices sharing a single power input source and sharing a power transmission cable, and relies on power line carrier communication (HPLC) technology and a terminal decoder to realize localized monitoring and cooperative regulation and control, and comprises the following steps: step one: a terminal decoder is arranged in each power consumption device, and the decoder is adapted with the device through alternating current pile PWM reconstruction technology and direct current pile 485 communication technology. Through real-time load monitoring and accurate cooperative regulation and control, the power distribution margin does not need to be configured according to the peak load of the device, the invalid margin of power distribution can be reduced by 35% to 50%, the power resource and the cost of power distribution equipment can be greatly saved, and the present application is particularly suitable for scenarios in which the power supply capacity cannot be expanded in time when the backend device is newly added.

Description

Technical Field

[0001] This invention relates to the field of smart grid equipment technology, and in particular to a multi-machine collaborative execution algorithm for power energy distribution. Background Technology

[0002] In power energy distribution scenarios, it is extremely common for multiple electrical devices to share a single power transmission cable and a single power input source. Electric vehicle charging station clusters (including AC charging stations, DC charging stations, commercially operated charging stations, and privately installed charging stations) are a typical application scenario. In such scenarios, the probability of multiple devices operating at full load simultaneously is theoretically low. However, sufficient upstream power distribution margin must be reserved to cope with the extreme situation of all devices operating simultaneously. This results in a significant amount of ineffective waste of power distribution resources. Furthermore, when new downstream devices are added, it is often impossible to expand the power supply capacity at any time, further exacerbating the contradiction between the shortage of power distribution resources and the waste of ineffective margin.

[0003] According to industry research data, the probability of multiple charging pile clusters operating at full capacity simultaneously is only 3.2% (data source: China Electricity Council's "2025 New Energy Vehicle Charging Infrastructure Operation Report"). Among these, the probability of commercially operated charging pile clusters operating at full capacity simultaneously is 4.7%, while the probability of private charging pile clusters operating at full capacity simultaneously is only 1.8%, and the average daily duration of full-load coordinated operation does not exceed 20 minutes. Despite this, existing power distribution designs still need to configure power distribution margins based on the peak demand of all equipment operating at full capacity simultaneously, resulting in an ineffective power distribution margin ratio as high as 45% to 60%, causing a serious waste of power resources and power distribution equipment costs.

[0004] To address the aforementioned issues, the industry urgently needs a collaborative control solution that can monitor the usage of multiple electrical devices in real time and precisely reduce the usage of these devices when the total power supply load is insufficient. This would reduce ineffective power distribution margins, adapt to scenarios where the power supply capacity cannot be expanded at any time when new backend devices are added, and achieve efficient utilization of power resources.

[0005] Currently, traditional power energy distribution and equipment collaborative control schemes mainly adopt a centralized management and control model using cloud platforms. This involves unified monitoring of all electrical equipment via the cloud platform, collecting power consumption data from each device, performing cloud-based calculations, and then issuing control commands to devices that can be derated, thereby achieving power reduction and load balancing. While this scheme can achieve load regulation to a certain extent, it suffers from irreconcilable technical contradictions in practical applications, failing to meet the safety and efficiency requirements of high-power electrical equipment (such as electric vehicle charging stations). The specific contradictions are as follows: All electrical equipment must be connected to the same cloud platform for management and control. However, in the case of electric vehicle charging piles, the equipment types are complex (DC piles, AC piles) and the operating entities are diverse (commercially operated piles, privately installed piles). It is difficult for equipment from different manufacturers and with different operating models to be connected to the same cloud platform, making unified operation and maintenance extremely difficult and resulting in the inability to fully cover collaborative control.

[0006] Cloud data communication has a significant time delay (usually 10s to 60s), while the load of high-power electrical equipment such as car charging piles changes rapidly. When the total power supply load suddenly approaches or exceeds the power distribution limit, the delayed control commands cannot be responded to in time, which can easily lead to electrical safety accidents such as line overload and tripping. In severe cases, it may damage the equipment or even cause a fire.

[0007] Furthermore, existing collaborative control solutions largely rely on preset device numbers and communication parameters, making them unsuitable for scenarios with dynamically changing device numbers and mixed device coexistence. Their control strategies are also limited, failing to achieve precise, tiered control based on load warning levels, and struggling to cope with sudden increases in line load, further restricting their application in complex scenarios such as electric vehicle charging stations. Therefore, developing a multi-machine collaborative execution algorithm capable of localized rapid response, adaptability to mixed device scenarios, and precise, tiered control is crucial for addressing these industry pain points. Summary of the Invention

[0008] The purpose of this invention is to provide a multi-machine collaborative execution algorithm for power energy distribution. This algorithm has no cloud dependency, responds quickly, provides hierarchical control, is highly resistant to shocks, supports plug-and-play devices, and has wide adaptability.

[0009] To achieve the above objectives, the present invention provides the following technical solution: a multi-machine collaborative execution algorithm for power energy distribution, applied to multiple electrical devices sharing a single power input source and a single power transmission cable, relying on power line communication (HPLC) technology and a terminal decoder to achieve localized monitoring and collaborative control, including the following steps: Step 1: Deploy a terminal decoder inside each electrical device, and use AC pile PWM reconstruction technology and DC pile 485 communication technology to adapt the decoder to the device. Deploy a central detection device and initialize the early warning parameters and control parameters. Step 2: The central monitoring equipment collects the total power load of the line in real time, calculates the full-load risk factor, and broadcasts it to all terminal decoders via HPLC; Step 3: When the full load risk factor is ≥95%, the terminal decoder starts edge computing and triggers the collaborative control logic; Step 4: By combining dynamic random execution with predefined priorities, the terminal decoder generates execution times in the random pool and adjusts them sequentially, and completes secondary adjustment by combining dynamic feedback; Step 5: Implement three-level reduction control measures of 5%, 15%, and 50% based on the full-load risk factor; Step 6: When the full-load risk factor remains below 95%, control is terminated, and the equipment restores power according to a random mechanism.

[0010] In the power energy distribution multi-machine collaborative execution algorithm of the present invention, the terminal decoder integrates an HPLC decoding module, an edge computing module and a power regulation module. The edge computing module has a built-in load detection algorithm for recording the maximum power demand of the equipment.

[0011] In the multi-machine collaborative execution algorithm for power energy distribution of the present invention, the electrical equipment is a car charging pile, including one or more of AC charging piles, DC charging piles, commercially operated charging piles, and privately installed charging piles.

[0012] In the power energy distribution multi-machine collaborative execution algorithm of the present invention, the sampling frequency of the central detection device is not less than 5Hz, the control response time of HPLC one-way communication is ≤500ms, and the entire process does not rely on cloud platform network communication.

[0013] In the multi-machine collaborative execution algorithm for power energy distribution of the present invention, the dynamic random execution mechanism has a first adjustment random time range of 0-4 seconds and a second deep adjustment random time range of 0-6 seconds.

[0014] In the power energy distribution multi-machine collaborative execution algorithm of the present invention, the predefined priority is set according to the equipment type, power demand, and operating status. High-power output equipment has high priority, and deeply adjusted equipment has low priority. It supports manual setting, remote modification through the cloud platform, or time-based total power setting.

[0015] In the multi-machine collaborative execution algorithm for power energy distribution of the present invention, the full load risk factor is calculated as: real-time total load / maximum output power of the line × 100%.

[0016] In the multi-machine collaborative execution algorithm for power energy distribution of the present invention, the early warning thresholds for the hierarchical control are optimally defined as follows: primary warning: 95% ≤ full load risk coefficient < 100%, implement 5% power derating; intermediate warning: 100% ≤ full load risk coefficient < 110%, implement 15% power derating; advanced warning: full load risk coefficient ≥ 110%, implement 50% power derating.

[0017] In the power energy distribution multi-machine collaborative execution algorithm of the present invention, the power recovery phase adopts a 0~20 second random execution mechanism to avoid a sudden increase in line load caused by the synchronous recovery of multiple devices.

[0018] In the present invention, a multi-machine collaborative execution algorithm for power energy distribution is optimally designed to support the access of mixed devices and the dynamic addition and deletion of devices, without being restricted by device manufacturers or operating modes, and without requiring the pre-installation of a fixed number of terminal decoders.

[0019] The technical effects and advantages of this invention are as follows: 1. Completely solves the problems of slow response and poor equipment compatibility of traditional cloud platform control solutions. Through localized HPLC communication and edge computing, it achieves a control response time of ≤500ms, is compatible with all types of electrical equipment with different operating modes (especially car charging piles), and adapts to mixed equipment and dynamically added equipment scenarios.

[0020] 2. Through real-time load monitoring and precise coordinated control, there is no need to configure power distribution margin according to the peak load of the equipment, which can reduce the ineffective power distribution margin by 35% to 50%, greatly saving power resources and power distribution equipment costs. It is especially suitable for scenarios where the power supply capacity cannot be expanded at any time when new back-end equipment is added.

[0021] 3. The three-level hierarchical control strategy can quickly respond to sudden increases in line load and avoid safety accidents such as line overload and tripping; the dynamic random execution mechanism avoids sudden drops and increases in load, further improving the stability and safety of power system operation.

[0022] 4. No large-scale modification of existing equipment is required. The function can be realized simply by deploying terminal decoders. The equipment is easy to adapt and maintain. It supports plug and play and can be widely used in scenarios where multiple machines share the power input source, such as car charging pile clusters and industrial power equipment clusters.

[0023] 5. No need to build and maintain a cloud platform, reducing cloud communication and server operation and maintenance costs; the terminal decoder autonomously completes edge computing and collaborative control without manual intervention, reducing manual operation and maintenance costs, and avoiding control failures caused by cloud platform failures. Attached Figure Description

[0024] Figure 1 This is a flowchart of the present invention. Detailed Implementation

[0025] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0026] Example 1: Refer to Figure 1This invention provides a multi-machine collaborative execution algorithm for power energy distribution. This embodiment takes a cluster containing 10 mixed-type car charging piles as an example to illustrate the specific implementation process of the algorithm under normal load fluctuation scenarios.

[0027] Step 1: Equipment Deployment and Parameter Initialization. Ten car charging piles are selected, consisting of: 4 AC commercial operation piles, 3 DC commercial operation piles, and 3 private AC installation piles. Each charging pile is equipped with a terminal decoder integrating an HPLC decoding module, an edge computing module, and a power control module. For AC charging piles, PWM regeneration technology is used to acquire power signals and transmit control commands. Specifically, the decoder reads the PWM duty cycle in the AC pile's control guidance circuit, parses the current maximum output power, and sends a derating command to the charging pile by adjusting the PWM signal duty cycle. For DC charging piles, a 485 communication interface is used to connect to the charging pile controller to obtain the maximum output power of the DC pile and issue control commands. After the decoder and equipment are adapted, the maximum power requirements of each device are recorded: 7kW for each of the 4 AC commercial piles, 60kW for each of the 3 DC commercial piles, and 3.5kW for each of the 3 private AC piles.

[0028] Deploy a central monitoring device on the power transmission line and initialize the following parameters: Maximum output power threshold of the line: 100kW; Primary warning threshold: 95% (i.e., full load risk factor ≥ 95% and < 100%). Intermediate and advanced warning threshold: 110% (i.e., ≥110%). First adjustment of the random time pool: 0~4 seconds; Second adjustment of the random time pool: 0~6 seconds; Recovery phase random time pool: 0~20 seconds.

[0029] Meanwhile, device priorities are predefined via the terminal decoder: 3 DC commercial charging piles (high-power devices) are set to high priority, 4 AC commercial charging piles (medium-power devices) are set to medium priority, and 3 private AC charging piles (highly likely to have been deeply adjusted) are set to low priority. Priorities can be manually or automatically adjusted during subsequent operation based on actual charging demand.

[0030] Step 2: Real-time load monitoring. The central monitoring equipment collects the total power load data of the power transmission line in real time at a sampling frequency of 5Hz (i.e., sampling once every 0.2 seconds) and calculates the full-load hazard factor according to the following formula: Full-load hazard factor = (Real-time total load / Maximum output power of the line) × 100%. The central monitoring equipment broadcasts the calculated full-load hazard factor to all terminal decoders on the line in real time through HPLC one-way communication technology. This broadcasting process does not require a cloud platform or external network, and is entirely based on power cable transmission, with a single broadcast delay of ≤10ms.

[0031] Step 3: Early Warning Judgment and Control Trigger. Assume the current scenario is: 6 out of 10 charging piles are AC charging (2 commercial piles, 2 private piles), while 1 DC commercial pile is charging at 60kW. The remaining devices are in standby. The real-time total load of the line is calculated as follows: 2 AC commercial piles: 2 × 7kW = 14kW; 2 private AC piles: 2 × 3.5kW = 7kW; 1 DC commercial pile: 60kW; Total: 14 + 7 + 60 = 81kW. The full load risk factor = 81 / 100 × 100% = 81% < 95%. All terminal decoders are in standby mode and no control operation is performed.

[0032] Subsequently, the remaining two commercial AC charging piles started charging, each adding 7kW of load, bringing the total line load to 95kW. The full load risk factor was 95 / 100 × 100% = 95%, reaching the initial warning threshold. The central monitoring equipment broadcast the full load risk factor as 95%. After receiving this signal, all terminal decoders determined through their own edge computing modules that the warning threshold had been reached, triggering the collaborative control logic.

[0033] Step 4: The execution steps of the coordinated control logic are as follows: (1) Predefined priority takes effect: High-priority DC commercial piles (60kW equipment) are marked as the first batch of control targets.

[0034] (2) Dynamic random execution: All terminal decoders that trigger regulation (a total of 10 devices) generate their own random execution times in a random pool of 0 to 4 seconds. Assume that the random time generated by the high-priority DC pile is 0.5 seconds, and the random times generated by other devices are 1.2 seconds, 2.5 seconds, 3.0 seconds, etc. According to the random time order, the high-priority DC pile executes the regulation operation first.

[0035] (3) Primary early warning control (95% ≤ full load risk factor < 100%): According to the graded control strategy, a 5% reduction control is implemented. The high-priority DC pile reduces its current output power by 5% from 60kW, i.e., to 57kW. After the control, the total load of the line is reduced from 95kW to 92kW, and the full load risk factor = 92 / 100 × 100% = 92%.

[0036] (4) Dynamic Feedback and Control Termination: The central monitoring equipment detects in real time that the full-load risk factor has dropped to 92% (below 95%) and broadcasts the status change via HPLC. Upon receiving this signal, other terminal decoders that have not yet performed control cancel their random counting and stop the control operation. At this time, the total load of the line stabilizes at 92kW, and all charging piles operate normally.

[0037] Step 5: Handling sudden load increases. After 30 seconds of stable operation at 92kW, a new DC commercial charging pile is started, requiring 60kW of power. However, the current remaining capacity of the line is only 100-92=8kW, which is insufficient to meet the startup requirements of this device. The central monitoring equipment detects that the total load of the line jumps from 92kW to 152kW instantaneously (but in reality, due to line protection, the load will quickly trigger regulation). The full load danger factor = 152 / 100 × 100% = 152%, exceeding the intermediate warning threshold (100%) and the advanced warning threshold (110%), directly entering the advanced warning state.

[0038] (1) Advanced Early Warning Control: According to the graded strategy, a 50% step reduction control is implemented. All terminal decoders re-trigger coordinated control, and a new random execution time is generated in the 0-4 second random pool (the devices that have already undergone control are also regenerated). All devices that are charging (including the original 6 AC charging piles, 1 DC charging pile that has been reduced in rate, and 1 newly started DC charging pile) will simultaneously reduce their output power by 50%. Specific reduction results: Original high-priority DC charging pile: 57kW → 28.5kW; Newly started DC charging pile: 60kW → 30kW; 2 commercial AC charging piles: 7kW each → 3.5kW each; 2 private AC charging piles: 3.5kW each → 1.75kW each; The remaining standby devices remain at 0kW. The total load on the line drops to 28.5 + 30 + 3.5×2 + 1.75×2 = 28.5+30+7+3.5 = 69kW within 3 seconds. The full load hazard factor is 69 / 100×100% = 69%, which is far below the safety threshold.

[0039] (2) After the load stabilizes, the central detection equipment continues to monitor. When the full load risk factor is below 95% for 10 consecutive seconds (69% in this example), the termination signal is controlled by broadcasting HPLC. All terminal decoders stop further derating operations.

[0040] Step 6: Termination of Control and Power Restoration Once the line load stabilizes, the power is gradually restored to normal levels to meet the charging needs of each device. The restoration process also employs a dynamic, random execution mechanism to avoid a sudden surge in load caused by the simultaneous restoration of all devices.

[0041] (1) The central monitoring equipment detected that the current total load of the line is 69kW and the remaining capacity is 31kW. Each terminal decoder determines whether the remaining capacity of the line is sufficient to meet its recovery needs based on the power requirements of its own equipment (60kW for DC piles and 7kW or 3.5kW for AC piles).

[0042] (2) Assume the current remaining capacity is 31kW, and priority will be given to meeting the partial recovery needs of high-priority equipment (DC commercial piles). Each terminal decoder generates a recovery execution time in a random time pool of 0~20 seconds, and gradually increases the power in a random order, with each increase step not exceeding 10% of the original demand.

[0043] (3) Assuming the random recovery time of the high-priority DC pile is 3 seconds, its power is increased from 28.5kW to 40kW (an increase of 11.5kW), the total load of the line increases to 80.5kW, and the remaining capacity is 19.5kW. Other equipment is restored randomly in sequence, and the full load risk factor is always monitored to not exceed 95% during the process.

[0044] (4) After multiple rounds of random negotiation, the power distribution of all equipment finally reached a dynamic balance: the original high-priority DC piles were restored to 52kW, the newly started DC piles were restored to 48kW, the commercial AC piles were maintained at 6.5kW each, and the private AC piles were maintained at 3kW each. The total line load was approximately 52+48+6.5×2+3×2 = 52+48+13+6 = 119kW. However, in actual operation, due to continuous adjustment by the algorithm, the full load risk factor will be maintained between 90% and 95%. The above values ​​are only for illustrative purposes. The actual system will be controlled within 100kW.

[0045] It should be noted that in actual operation, when the remaining capacity is insufficient, the terminal decoder will actively suppress the recovery requests of some devices until a device completes charging and releases power, thereby achieving multi-device autonomous collaboration.

[0046] Example 2: This example demonstrates the emergency handling capability of the algorithm of the present invention under extreme load impact, and uses the same equipment deployment and parameter settings as in Example 1.

[0047] Scenario Description: Assume the power line is currently operating normally with a total load of 60kW (full load risk factor 60%). At this time, three DC commercial charging piles simultaneously start charging, each requiring 60kW of power, resulting in an instantaneous total load demand of 60 + 180 = 240kW, far exceeding the line's maximum output power of 100kW. The central monitoring equipment detects the sudden load surge at a sampling frequency of 5Hz. The total load recorded at the first sampling point (within 0.2 seconds) may display as 100kW due to the line protection mechanism not tripping in time (in reality, it is overloaded). The full load risk factor is calculated as 100 / 100 × 100% = 100%, but the actual inrush current is much greater than this. To simulate a real-world situation, assume the central monitoring equipment directly captures an instantaneous load of 125kW (full load risk factor 125%), reaching the advanced warning threshold (≥110%).

[0048] The specific implementation steps are as follows: (1) Warning Trigger: The central detection equipment broadcasts a full-load risk factor of 125% via HPLC. After receiving the broadcast (delay ≤ 10ms), all terminal decoders are immediately identified as high-level warnings by the edge computing module.

[0049] (2) Emergency Control Execution: Following the tiered control strategy, a 50% reduction in power is implemented. The terminal decoders of all online charging piles (including existing equipment and 3 newly started DC charging piles) generate random execution times in a 0-4 second random pool. Due to the extremely rapid response, all devices complete power adjustments almost simultaneously (within 2 seconds). The existing equipment (assuming 2 AC commercial charging piles, each 7kW, and 1 DC charging pile, 30kW) will be derated by 50% respectively: the AC charging pile will be reduced to 3.5kW and the DC charging pile will be reduced to 15kW.

[0050] The three newly started DC charging piles each reduced their power from 60kW to 30kW. The total load decreased to: 3.5×2 + 15 + 30×3 = 7 + 15 + 90 = 112kW, with a full load risk factor of 112%, still higher than 100%. At this point, the intermediate warning logic was triggered.

[0051] (3) Secondary Control: The central monitoring equipment detects that the full-load risk factor is still ≥100%, and continues broadcasting. The equipment that has already undergone control regenerates a random time of 0-6 seconds and performs a second deep control (still at a 50% step size of the high-level warning; according to the algorithm, the step size of the secondary control depends on the current warning level. At this time, the full-load risk factor is 112%, still a high-level warning, and continues to execute a 50% step size, but to avoid excessive derating, the algorithm is designed to reduce the derating by 50% each time based on the current power). In actual execution, after the second derating: AC charging piles: 3.5kW → 1.75kW, DC charging piles: 7.5kW → 3.75kW, existing DC charging piles: 15kW → 7.5kW. New DC charging pile: 30kW → 15kW each, total load: 1.75×2 + 7.5 + 15×3 = 3.5 + 7.5 + 45 = 56kW, full load risk factor 56%.

[0052] (4) Safe recovery: The line load dropped from 125kW to 56kW within 5 seconds, completely avoiding the tripping accident. After the system stabilized for 10 seconds, the central detection equipment broadcast the control termination signal, and each terminal decoder started the random recovery process (0~20 seconds random pool), gradually increasing the power to the range allowed by user demand, while ensuring that the full load risk factor is always below 95%.

[0053] Example 3: This example illustrates the adaptability of the algorithm of the present invention to plug-and-play and dynamically added devices.

[0054] Scenario: During the normal operation of the 10 charging piles in Example 1, it is necessary to add one private AC charging pile (maximum power 3.5kW) to the same power transmission line. The implementation steps are as follows: (1) Physical access: Install the new charging pile and connect it to the power cable. Pre-deploy the terminal decoder inside the charging pile (or install it on site).

[0055] (2) Automatic identification: The central detection equipment periodically sends a device discovery broadcast via the HPLC communication protocol. After a newly connected terminal decoder is powered on, it automatically responds to the broadcast and sends device registration information (including device type, maximum power requirement, communication address, etc.) to the central detection equipment. The entire process requires no manual intervention.

[0056] (3) Parameter synchronization: The central detection equipment sends the current line's warning threshold, random pool parameters, and graded control step size to the new decoder via HPLC unicast or broadcast. The new decoder completes parameter initialization in ≤5 seconds.

[0057] (4) Incorporation into coordinated control: The new equipment officially becomes a member of the multi-machine coordinated scheduling. When the full load risk factor of the line reaches the warning threshold, the terminal decoder of the new equipment also participates in random execution and hierarchical control, and its priority is automatically set according to the preset rules (private AC piles are low priority by default).

[0058] (5) Load impact assessment: Since the maximum power of the new equipment is only 3.5kW and the algorithm will be dynamically adjusted, the power distribution of the original equipment will be automatically fine-tuned (for example, through secondary regulation or random negotiation during the recovery phase) to ensure that the total load of the line does not exceed the safety threshold, and no modification or shutdown of the original system is required.

[0059] As can be seen from the above embodiments, the multi-machine collaborative execution algorithm for power energy distribution proposed in this invention can achieve localized rapid response (≤500ms), hierarchical precise control, and dynamic random negotiation in mixed equipment scenarios without relying on a cloud platform. It also supports plug-and-play equipment and effectively solves the core problems in the prior art, such as waste of power distribution ineffective margin, lagging collaborative control, and poor equipment compatibility. It has extremely high practical value and promotion prospects.

[0060] Example 4: To verify the effectiveness, practicality, and safety of the algorithm of this invention, a test platform for a cluster of electric vehicle charging stations was built. Ten hybrid devices (4 commercial AC charging stations, 3 commercial DC charging stations, and 3 private AC charging stations) were selected, sharing a power transmission line with a maximum output power of 100kW. The algorithm of this invention was compared with a traditional cloud platform control scheme. Test items included control response time, proportion of ineffective power distribution margin, compatibility of hybrid devices, load fluctuation amplitude, and safety early warning processing capability. Specific test data are as follows: Regulation response time 30s~60s ≤500ms 10S Percentage of ineffective power distribution margin 48% 5% Uncontrollable Hybrid device compatibility Only supports devices on the same platform (60% compatibility). Supports all types / operating modes of devices (100% compatibility) Unable to achieve Load fluctuation range (during regulation) ±15% ±5% No relevant standards Advanced alert (120% load) processing effect Line tripping (100% failure rate in handling) Reduce the load to below 100% within 5 seconds (100% success rate). It needs to drop below 100% within 10 seconds. Dynamic device compatibility The cloud platform needs to be reconfigured (adaptation time ≥ 24 hours). Plug and play (adaptation time ≤ 5s) Approval from relevant departments is required to increase Experimental results show that the performance indicators of the algorithm of this invention are superior to those of traditional cloud platform control schemes and meet industry standard requirements. It can achieve rapid collaborative control in mixed equipment scenarios, effectively reduce power distribution ineffective margin, avoid large fluctuations in line load, and ensure power safety.

[0061] It should be noted that by abandoning reliance on cloud platform network communication, HPLC power line carrier communication technology and localized edge computing are adopted to achieve rapid transmission of load monitoring and control commands (response time ≤200ms), completely solving the problem of lagging control in traditional solutions and ensuring power safety. At the same time, HPLC technology enables communication compatibility of equipment from different manufacturers, of different types, and with different operating modes, solving the problem of unified management in mixed equipment scenarios.

[0062] Innovative Control Logic: The innovative control logic combines "dynamic random execution + predefined priority" to avoid large fluctuations in line load (including sudden drops in load and sudden increases during recovery) caused by synchronous adjustments of all devices. By automatically selecting different random pool designs and multiple control mechanisms, it achieves autonomous collaborative scheduling of multiple devices without relying on central command allocation. It can achieve multi-machine collaborative effects through the calculation logic of a single machine, adapting to scenarios with dynamically changing numbers of devices.

[0063] The design employs a three-tiered control mechanism, implementing different levels of derating control based on varying full-load risk coefficients of the lines. This ensures both normal equipment operation (minor early warning and fine-tuning) and rapid response to sudden load increases (advanced early warning and emergency control), achieving a balance between precise control and safety assurance, and effectively mitigating the impact of sudden equipment activation on the lines.

[0064] No large-scale modifications to existing equipment are required; the functionality can be achieved simply by deploying a terminal decoder inside the device. It is compatible with high-power electrical equipment scenarios such as car charging piles and can effectively reduce ineffective power distribution margin (tested to reduce ineffective power distribution margin by 35% to 50%). It solves the pain point of not being able to expand power supply capacity at any time when adding backend equipment and has strong practicality and promotional value.

[0065] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A multi-machine collaborative execution algorithm for power energy distribution, characterized in that, This technology is applied to multiple electrical devices sharing a single power input source and a single power transmission cable. It utilizes power line communication (HPLC) technology and a terminal decoder to achieve localized monitoring and coordinated control, including the following steps: Step 1: Deploy a terminal decoder inside each electrical device, and use AC pile PWM reconstruction technology and DC pile 485 communication technology to adapt the decoder to the device. Deploy a central detection device and initialize the early warning parameters and control parameters. Step 2: The central detection equipment collects the total power load of the line in real time, calculates the full-load risk factor, and broadcasts it to all terminal decoders via HPLC; Step 3: When the full load risk factor is ≥95%, the terminal decoder starts edge computing and triggers the collaborative control logic; Step 4: By combining dynamic random execution with predefined priorities, the terminal decoder generates execution times in the random pool and adjusts them sequentially, and completes secondary adjustment by combining dynamic feedback; Step 5: Implement three-level reduction control measures of 5%, 15%, and 50% based on the full-load risk factor; Step 6: When the full-load risk factor remains below 95%, control is terminated, and the equipment restores power according to a random mechanism.

2. The multi-machine collaborative execution algorithm for power energy distribution according to claim 1, characterized in that, The terminal decoder integrates an HPLC decoding module, an edge computing module, and a power control module. The edge computing module has a built-in load detection algorithm to record the maximum power requirement of the equipment.

3. The multi-machine collaborative execution algorithm for power energy distribution according to claim 1, characterized in that, The electrical equipment is a car charging pile, including one or more of the following: AC charging pile, DC charging pile, commercially operated charging pile, and privately installed charging pile.

4. The multi-machine collaborative execution algorithm for power energy distribution according to claim 1, characterized in that, The sampling frequency of the central detection equipment is no less than 5Hz, the control response time of HPLC one-way communication is ≤500ms, and the entire process does not rely on cloud platform network communication.

5. The multi-machine collaborative execution algorithm for power energy distribution according to claim 1, characterized in that, In the dynamic random execution mechanism, the first adjustment random time range is 0~4 seconds, and the second deep adjustment random time range is 0~6 seconds.

6. The multi-machine collaborative execution algorithm for power energy distribution according to claim 1, characterized in that, The predefined priorities are set according to the device type, power requirements, and operating status. High-power output devices have high priority, while deeply adjusted devices have low priority. Manual setting, remote modification via cloud platform, or time-based total power setting are supported.

7. The multi-machine collaborative execution algorithm for power energy distribution according to claim 1, characterized in that, The full load risk factor = real-time total load / maximum output power of the line × 100%.

8. The multi-machine collaborative execution algorithm for power energy distribution according to claim 1, characterized in that, The warning thresholds for the graded control are as follows: Primary warning: 95% ≤ full load risk factor < 100%, implement 5% power derating; Intermediate warning: 100% ≤ full load risk factor < 110%, implement 15% power derating; Advanced warning: full load risk factor ≥ 110%, implement 50% power derating.

9. The multi-machine collaborative execution algorithm for power energy distribution according to claim 1, characterized in that, The power recovery phase employs a random execution mechanism of 0-20 seconds to avoid a sudden increase in line load caused by simultaneous recovery of multiple devices.

10. The multi-machine collaborative execution algorithm for power energy distribution according to claim 1, characterized in that, The algorithm supports the access of mixed devices and the dynamic addition and deletion of devices, and is not limited by device manufacturers or operating models, and does not require the pre-installation of a fixed number of terminal decoders.

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