A method for collaborative equipment upgrade based on an energy management system

Abstract

This application relates to a method for collaborative equipment upgrades based on an energy management system (EMS). The method includes: acquiring key state information of the power grid in real time through the EMS; dynamically generating equipment upgrade strategies using an equipment collaborative upgrade model within the EMS; performing digital twin simulations using the equipment collaborative upgrade model to evaluate the feasibility of the equipment upgrade strategies; and optimizing the equipment upgrade strategies based on the evaluation results. This application integrates the equipment collaborative upgrade model into the EMS, transforming the EMS from a passive "energy monitor" into an active "nerve center of the energy system." This allows for deep perception of the real-time state of the power grid, intelligent scheduling of computing and communication resources, and the generation and optimization of various equipment upgrade strategies. This enables safe, smooth, and efficient collaborative upgrades of massive amounts of power grid equipment, solving the problem of improving the reliability of upgrades for massive amounts of power grid equipment.

Description

Technical Field

[0001] This application relates to the field of Internet of Things (IoT) technology, and in particular to a method for collaborative upgrading of devices based on an energy management system. Background Technology

[0002] With the rapid development of the Energy Internet and distributed energy, the number of downstream devices managed by a single EMS (Energy Management System) is growing exponentially, including smart meters, photovoltaic inverters, power converters (PCS), battery controllers (BMS), and charging piles. These devices are numerous, diverse in brand, and use various communication protocols. Traditional device upgrade methods (firmware / software over-the-air) face numerous bottlenecks.

[0003] First, traditional "one-to-one" or "broadcast" upgrade strategies struggle to handle massive, heterogeneous device clusters. Network congestion, device offline status, and power outages lead to low upgrade success rates, high rollback rates, and lengthy cycles. Second, upgrade operations may interrupt normal device operation, and brief downtime of critical equipment can cause power imbalances, protection malfunctions, or even partial power outages, making it difficult to find an upgrade window. Third, traditional upgrades are static and predefined, making it difficult to dynamically adjust based on real-time grid conditions, device health, and service priorities.

[0004] Currently, no effective solution has been proposed for the problem of improving the reliability of upgrading massive amounts of power grid equipment in related technologies. Summary of the Invention

[0005] This application provides a method for collaborative equipment upgrade based on an energy management system, which at least addresses the problem of how to improve the reliability of upgrading massive amounts of power grid equipment in related technologies.

[0006] In a first aspect, embodiments of this application provide a method for collaborative equipment upgrade based on an energy management system, the method comprising:

[0007] The power grid's energy management system acquires key status information of the power grid in real time, including power flow distribution, load forecasting, renewable energy generation status, and current operating mode.

[0008] Based on the key status information, an equipment upgrade strategy is dynamically generated through an equipment collaborative upgrade model, wherein the equipment collaborative upgrade model is built within the energy management system;

[0009] The feasibility of the equipment upgrade strategy is then evaluated through digital twin simulation using the equipment collaborative upgrade model, and the equipment upgrade strategy is optimized based on the evaluation results.

[0010] In some embodiments, based on the key status information, dynamically generating a device upgrade strategy through a device collaborative upgrade model includes:

[0011] Based on the key status information, a device upgrade strategy is dynamically generated through a device collaborative upgrade model. The device upgrade strategy includes a time-series off-peak upgrade strategy, a function cluster upgrade strategy, and an adaptive rolling upgrade strategy.

[0012] During the execution of any device upgrade strategy, for the device to be upgraded, the current power of the device to be upgraded is obtained to calculate the compensation target power; a power compensation device is found from the cluster of devices to be upgraded, and the output power of the power compensation device is smoothly adjusted; when the output power is stably close to the compensation target power, the offline upgrade of the device to be upgraded is performed; after the upgrade is completed, the power is switched back.

[0013] In some embodiments, based on the key status information, dynamically generating a time-series staggered upgrade strategy through a device collaborative upgrade model includes:

[0014] Based on the key status information, the device collaborative upgrade model is used to schedule upgrades in a staggered manner according to the functional type and time sensitivity of the devices to be upgraded, so as to generate a time-series staggered upgrade strategy.

[0015] In some embodiments, based on the key status information, a time-series staggered upgrade strategy is generated by using a device collaborative upgrade model to schedule upgrades according to the functional type and time sensitivity of the devices to be upgraded.

[0016] Based on the key status information, the equipment collaborative upgrade model arranges the equipment of the same functional type to be upgraded into the same upgrade batch, and arranges the corresponding upgrade sequence based on the time sensitivity of different functional types of equipment, so as to generate a time-staggered upgrade strategy.

[0017] In some embodiments, based on the key status information, dynamically generating a functional cluster upgrade strategy through a device collaborative upgrade model includes:

[0018] Based on the key status information, the devices to be upgraded are grouped and arranged according to the cluster type and geographical region through the device collaborative upgrade model to generate a functional cluster upgrade strategy.

[0019] In some embodiments, based on the key status information, a functional cluster-based upgrade strategy is generated by grouping the devices to be upgraded according to their cluster type and geographical region using a device collaborative upgrade model.

[0020] Based on the key status information, the device collaborative upgrade model arranges devices of the same cluster type and in the same geographical area to be upgraded into the same upgrade batch and the same upgrade sequence to generate a functional cluster upgrade strategy.

[0021] In some embodiments, based on the key status information, dynamically generating an adaptive rolling upgrade strategy through a device collaborative upgrade model includes:

[0022] Based on the key status information, the upgrade batches and timing of the devices to be upgraded are adaptively adjusted according to the grid load status through the equipment collaborative upgrade model, so as to generate an adaptive rolling upgrade strategy.

[0023] In some embodiments, digital twin simulations are performed using the device collaborative upgrade model to assess the feasibility of the device upgrade strategy, and the device upgrade strategy is optimized based on the assessment results, including:

[0024] The equipment upgrade strategy is simulated digitally using the equipment collaborative upgrade model to obtain the system risk index, total upgrade time, and resource cost of the equipment upgrade strategy.

[0025] Based on the system risk index, total upgrade time, and resource cost, the objective function is:

[0026]

[0027] The device upgrade strategy is optimized, where Risk_System() represents the system risk index, Time_Total() represents the total upgrade time, Cost_Resource() represents the resource cost, α, β and γ represent the corresponding weighting weights, and the objective function Minimize F() is used to reduce the system risk index, total upgrade time and resource cost of the device upgrade strategy to the minimum.

[0028] In some embodiments, the device upgrade strategy is digitally twinned using the device collaborative upgrade model to derive a system risk index for the device upgrade strategy, including:

[0029] The equipment upgrade strategy is digitally simulated using the aforementioned equipment collaborative upgrade model, utilizing the system risk index calculation function:

[0030]

[0031] The system risk index Risk_System of the equipment upgrade strategy is calculated, where R_power is the power deficit risk, representing the degree of power imbalance caused by equipment offline during the upgrade simulation; R_protection is the protection failure risk, representing whether there is a protection blind spot during the upgrade simulation; R_quality is the power quality risk, representing whether voltage fluctuations and harmonic pollution may exceed limits during the upgrade simulation; R_security is the security risk, representing the probability of security risk events occurring during the upgrade simulation; and w1, w2, w3, and w4 represent the corresponding weighting weights.

[0032] In some embodiments, after dynamically generating multiple device upgrade strategies based on the key status information through a device collaborative upgrade model, the method includes:

[0033] The hash value of the upgrade firmware package of the device upgrade strategy is stored in the blockchain of the energy management system. After the device to be upgraded receives the upgrade firmware package, it calculates the hash value of the received package and compares it with the hash value stored on the chain. If the comparison matches, the upgrade is performed. After the device to be upgraded completes the upgrade, the status information of the upgrade success or failure is broadcast to the blockchain as a transaction record.

[0034] Compared to related technologies, this application provides a method for collaborative equipment upgrades based on an energy management system. This method uses the power grid's energy management system to acquire key state information of the power grid in real time, including power flow distribution, load forecasting, renewable energy generation status, and current operating mode. Based on this key state information, a collaborative equipment upgrade model is dynamically generated within the energy management system to create equipment upgrade strategies. The model is then used for digital twin simulation to assess the feasibility of the upgrade strategies, and the strategies are optimized based on the assessment results. This integrates the collaborative equipment upgrade model into the energy management system, transforming it from a passive "energy monitor" into an active "nerve center" of the energy system. This allows for deep perception of the real-time state of the power grid, intelligent scheduling of computing and communication resources, and the generation and optimization of various equipment upgrade strategies. This enables safe, smooth, and efficient collaborative upgrades of massive amounts of power grid equipment, solving the problem of improving the reliability of upgrades for such equipment. Attached Figure Description

[0035] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0036] Figure 1This is a flowchart illustrating the steps of a device collaborative upgrade method based on an energy management system according to an embodiment of this application.

[0037] Figure 2 This is a schematic diagram of the internal structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0038] To make the objectives, technical solutions, and advantages of this application clearer, the application is described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application. All other embodiments obtained by those skilled in the art based on the embodiments provided in this application without inventive effort are within the scope of protection of this application.

[0039] Obviously, the accompanying drawings described below are merely some examples or embodiments of this application. Those skilled in the art can apply this application to other similar scenarios based on these drawings without any inventive effort. Furthermore, it is understood that although the efforts made in this development process may be complex and lengthy, for those skilled in the art related to the content disclosed in this application, any changes to design, manufacturing, or production based on the technical content disclosed in this application are merely conventional technical means and should not be construed as insufficient disclosure of the content of this application.

[0040] In this application, the reference to "embodiment" means that a specific feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment that is mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described in this application may be combined with other embodiments without conflict.

[0041] Unless otherwise defined, the technical or scientific terms used in this application shall have the ordinary meaning understood by one of ordinary skill in the art to which this application pertains. The terms “a,” “an,” “an,” “the,” and similar words used in this application do not indicate quantity limitation and may indicate singular or plural. The terms “comprising,” “including,” “having,” and any variations thereof used in this application are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or modules (units) is not limited to the listed steps or units, but may also include steps or units not listed, or may include other steps or units inherent to these processes, methods, products, or devices. The terms “connected,” “linked,” “coupled,” and similar words used in this application are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. “Multiple” used in this application refers to two or more. “And / or” describes the relationship between related objects, indicating that three relationships may exist; for example, “A and / or B” can represent: A alone, A and B simultaneously, and B alone. The character " / " generally indicates that the preceding and following objects are in an "or" relationship. The terms "first," "second," and "third" used in this application are merely to distinguish similar objects and do not represent a specific ordering of the objects.

[0042] This application provides a method for collaborative equipment upgrade based on an energy management system. Figure 1 This is a flowchart illustrating the steps of a device collaborative upgrade method based on an energy management system according to an embodiment of this application, as follows: Figure 1 As shown, the method includes the following steps:

[0043] Step S102: Obtain key status information of the power grid in real time through the power grid's energy management system. The key status information includes power flow distribution, load forecast, renewable energy generation status, and current operating mode.

[0044] It should be noted that the power grid EMS (Energy Management System) acquires real-time data on key statuses such as power flow distribution, load forecasting, renewable energy (photovoltaic, hydropower) generation, and current operating modes. Simultaneously, it clearly understands the business functions and roles of all equipment to be upgraded within the energy network (e.g., an inverter is supporting a critical load, or an energy storage system is in frequency regulation mode).

[0045] Step S104: Based on key status information, dynamically generate equipment upgrade strategies through the equipment collaborative upgrade model. The equipment collaborative upgrade model is built within the energy management system. The equipment upgrade strategies include time-series off-peak upgrade strategies, functional cluster upgrade strategies, and adaptive rolling upgrade strategies.

[0046] It should be noted that this embodiment of the application constructs a device collaborative upgrade model based on a power grid-device collaborative digital twin within the EMS. Before executing the upgrade task, the model does not simply issue upgrade packages, but first generates strategies, then performs "simulation and deduction" to evaluate the best upgrade scheme, and finally executes the upgrade action.

[0047] Step S104 specifically includes the following steps:

[0048] Step S1041: Based on key status information, dynamically generate a time-series staggered upgrade strategy through the device collaborative upgrade model;

[0049] Step S1041 specifically involves, based on key status information, using a device collaborative upgrade model to stagger upgrade times according to the functional type and time sensitivity of the devices to be upgraded, thereby generating a time-series staggered upgrade strategy. Specifically, the device collaborative upgrade model arranges devices with the same functional type to be upgraded in the same upgrade batch, and arranges the corresponding upgrade sequence based on the time sensitivity of devices with different functional types, thus generating a time-series staggered upgrade strategy.

[0050] For example, for 120 grid devices (40 photovoltaic inverters, 30 energy storage PCS units, and 50 smart meters), the upgrade window is today from 14:00 to 20:00 (6 hours of available time). Based on key status information, the device collaborative upgrade model dynamically generates various device upgrade strategies. A specific example of a time-series off-peak upgrade strategy is as follows:

[0051] Strategy 1: Off-peak timing upgrade strategy

[0052] 1. Phased Plan

[0053] Batch division principle: Arrange batches according to equipment function type and time sensitivity to avoid peak periods.

[0054] Total number of batches: 6.

[0055] Batch size distribution: [10, 15, 25, 30, 25, 15] units.

[0056] 2. Sequence and Timing

[0057] Batch 1 (14:00-14:30): 10 smart meters (time-sensitive - low-risk devices).

[0058] Batch 2 (14:45-15:30): 15 photovoltaic inverters (time-sensitive - utilizing midday high sunlight);

[0059] Batch 3 (15:45-16:45): 25 energy storage PCS units (time-sensitive - critical equipment that needs to be completed before load increases);

[0060] Batch 4 (17:00-18:00): 30 smart meters (time-sensitive - avoiding peak electricity consumption times);

[0061] Batch 5 (18:15-19:00): 25 photovoltaic inverters (time-sensitive - evening load stability period);

[0062] Batch 6 (19:15-19:45): 15 energy storage PCS units (time-sensitive - final work).

[0063] 3. Key Indicators for Digital Twin Inference

[0064] Total upgrade time: 5 hours and 45 minutes;

[0065] Peak power risk: 0.12 (low risk);

[0066] Bandwidth utilization: 45-65% (stable);

[0067] System stability: Voltage fluctuation <2%, no protection blind zone.

[0068] Step S1042: Based on key status information, dynamically generate a functional cluster upgrade strategy through the device collaborative upgrade model;

[0069] Step S1042 specifically involves grouping devices to be upgraded according to their cluster type and geographical region using a collaborative upgrade model based on key status information, thereby generating a functional cluster-based upgrade strategy. Specifically, the collaborative upgrade model assigns devices of the same cluster type and geographical region to the same upgrade batch and time sequence to generate a functional cluster-based upgrade strategy.

[0070] For example, for 120 grid devices (40 photovoltaic inverters, 30 energy storage PCS units, and 50 smart meters), the upgrade window is 14:00-20:00 today (6 hours of available time). Based on key status information, the device collaborative upgrade model dynamically generates multiple device upgrade strategies. A specific example of a functional cluster upgrade strategy is as follows:

[0071] Strategy Two: Functional Cluster Upgrade Strategy

[0072] 1. Phased Plan

[0073] Batch division principle: Grouped by equipment functional clusters and geographical regions.

[0074] Total number of batches: 4.

[0075] Batch size distribution: [35, 30, 35, 20] units.

[0076] 2. Sequence and Timing

[0077] Batch 1 (14:00-15:00): North District Photovoltaic Cluster (35 inverters);

[0078] Batch 2 (15:15-16:15): South Zone Energy Storage Cluster (30 PCS+BMS);

[0079] Batch 3 (16:30-17:30): Smart metering cluster (35 electricity meters);

[0080] Batch 4 (17:45-18:30): Mixed equipment cluster (20 key equipment units).

[0081] 3. Internal Cluster Coordination Mechanism

[0082] Photovoltaic cluster upgrade: Inverters on the same feeder are started and stopped in batches to ensure smooth power output;

[0083] Energy storage cluster upgrade: Adopt the "N-1" principle to ensure that there is always backup capacity;

[0084] Metering cluster upgrade: Divided by transformer area to avoid overall data loss.

[0085] 4. Key Indicators for Digital Twin Inference

[0086] Total upgrade time: 4 hours and 30 minutes;

[0087] Peak power risk: 0.25 (medium risk);

[0088] Cluster efficiency: Upgrade efficiency of devices within the same cluster is improved by 40%;

[0089] Communication load: regionally centralized, with optimized routing.

[0090] Step S1043: Based on key status information, dynamically generate an adaptive rolling upgrade strategy through the device collaborative upgrade model;

[0091] Specifically, step S1043 involves using a device collaborative upgrade model to adaptively adjust the upgrade batches and timing of the devices to be upgraded according to the grid load status, based on key status information, to generate an adaptive rolling upgrade strategy.

[0092] For example, for 120 grid devices (40 photovoltaic inverters, 30 energy storage PCS units, and 50 smart meters), the upgrade time window is 14:00-20:00 today (6 hours of available time). Based on key status information, the device collaborative upgrade model dynamically generates multiple device upgrade strategies. A specific example of the adaptive rolling upgrade strategy is as follows:

[0093] Strategy 3: Adaptive Rolling Upgrade Strategy

[0094] 1. Phased Plan

[0095] Batch partitioning principle: Dynamic batch adjustment based on real-time system status;

[0096] Initial batch number: 8 batches;

[0097] Dynamic adjustment range: 5-10 batches;

[0098] Batch size: Dynamic, 10-20 units / batch.

[0099] 2. Sequence and Timing

[0100] Basic timeframe:

[0101] Phase 1 (14:00-16:00): Rapid upgrade period (3 batches, 15 units / batch);

[0102] Phase 2 (16:00-18:00): Cautious upgrade period (3 batches, 12 units / batch);

[0103] Phase 3 (18:00-19:30): Conservative upgrade period (2 batches, 8 units / batch).

[0104] 3. Dynamic adjustment mechanism

[0105] Real-time monitoring indicators: system load rate, backup capacity, and communication quality.

[0106] Adjust trigger conditions:

[0107] - Load rate <70%: Increase batch size to 20 units and shorten intervals;

[0108] - Load rate 70-85%: Maintain standard batch of 15 units;

[0109] - Load rate > 85%: Reduce batch size to 10 units and extend interval to 45 minutes.

[0110] 4. Key Indicators for Digital Twin Inference

[0111] Estimated total time: 5 hours 30 minutes (variable);

[0112] Risk Adaptive: The risk index is maintained within a controllable range of 0.15-0.35;

[0113] Resource utilization rate: dynamically adjusted to 60-90% based on load.

[0114] In step S106, a digital twin simulation is then performed using the equipment collaborative upgrade model to assess the feasibility of the equipment upgrade strategy, and the equipment upgrade strategy is optimized based on the assessment results.

[0115] Step S106 specifically involves using a digital twin model to simulate the equipment upgrade strategy using a collaborative equipment upgrade model, thereby deriving the system risk index, total upgrade time, and resource cost of the equipment upgrade strategy; based on the system risk index, total upgrade time, and resource cost, the objective function is then applied:

[0116]

[0117] The device upgrade strategy is optimized, where Risk_System() represents the system risk index (the most critical indicator), Time_Total() represents the total upgrade time, Cost_Resource() represents the resource cost (such as network traffic and server load), α, β and γ represent the corresponding weighting weights, and the objective function Minimize F() is used to reduce the system risk index, total upgrade time and resource cost of the device upgrade strategy to the minimum.

[0118] It should be noted that the digital twin simulation of the equipment collaborative upgrade model is not a simple simulation, but a process of solving a multi-objective constrained optimization problem. The digital twin simulation seeks to find the optimal solution among multiple competing objectives, minimizing the system risk index, total upgrade time, and resource costs of the equipment upgrade strategy. Furthermore, the weights α, β, and γ are not fixed but dynamically adjusted based on the real-time state of the system. For example, when the grid stability is fragile, weight α approaches 1, and other objectives are compromised.

[0119] Step S106 preferably uses the system risk index Risk_System as the most critical core indicator. A digital twin simulation of the equipment upgrade strategy is performed using the equipment collaborative upgrade model, utilizing the calculation function of the system risk index:

[0120]

[0121] The system risk index Risk_System for the equipment upgrade strategy is calculated, where R_power represents the power deficit risk, characterizing the degree of power imbalance caused by equipment offline during the upgrade simulation (which can be calculated in conjunction with load forecasting and generation forecasting models); R_protection represents the protection failure risk, characterizing whether there are protection blind spots during the upgrade simulation (e.g., whether there are protection blind spots in the system when upgrading smart circuit breakers); R_quality represents the power quality risk, characterizing whether voltage fluctuations and harmonic pollution may exceed limits during the upgrade simulation; R_security represents the security risk, characterizing the probability of security risk events occurring during the upgrade simulation (e.g., the probability of upgrade package verification failure, malicious interruption of the upgrade process, etc.); w1, w2, w3, and w4 represent the corresponding weighting weights.

[0122] It should be noted that the weighted weights w1, w2, w3, and w4 represent the relative importance and priority of each sub-risk (power deficit risk, protection failure risk, power quality risk, and safety risk) when calculating the overall system risk index (Risk_System). Their values ​​are not fixed but dynamically adjusted, which is the core manifestation of the "intelligence" and "adaptiveness" of the entire strategy engine. Specifically:

[0123] w1 — Power deficit risk weight corresponding to R_power: Measures the system's tolerance for power imbalance caused by equipment upgrades. The higher the weight, the more the engine will tend to avoid any upgrade strategies that could lead to a power deficit.

[0124] When the system is in a tight balance operation (insufficient reserve capacity), the load is at its peak, the output forecast of renewable energy (photovoltaic, wind power) is unstable or extremely low, or the power grid dispatch center issues a stable operation requirement, w1 needs to be increased.

[0125] w2 — Protection failure risk weight corresponding to R_protection: Measures the system's sensitivity to temporary failures of protection functions. The higher the weight, the more the engine will prioritize ensuring the integrity of the protection system.

[0126] In situations where the system is operating under weak conditions (a line or transformer is heavily loaded, relying on precise protection actions), severe weather (such as thunderstorms or strong winds, which increase the probability of power grid failures and enhance reliance on the protection system), or when the protection system needs to be on standby at all times (such as during important power grid operations), w2 needs to be increased.

[0127] w3 — Power quality risk weight corresponding to R_quality: Measures the system's tolerance for power quality degradation such as voltage fluctuations and harmonics. The higher the weight, the less likely the engine will upgrade power quality sensitive devices (such as reactive power compensation devices and power supply units for precision loads) simultaneously.

[0128] In situations where there are industrial loads in the power grid that are highly sensitive to power quality (such as semiconductor manufacturing and data centers), where the voltage of a certain node is found to be at the critical value of the qualified range, or where the harmonic content of the system is close to the national standard limit, it is necessary to increase w3.

[0129] w4 — Security risk weight corresponding to R_security: Measures the security and reliability requirements of this upgrade task itself. The higher the weight, the more conservative and secure the engine will adopt an upgrade verification strategy (such as requiring blockchain verification, increasing the number of retries, and reducing the batch size).

[0130] In cases where the source of the upgrade package is very important or has not been verified before (such as a major security vulnerability patch), the current network environment has poor security (such as suspected network attacks), or the target device is in an extremely critical position (such as potentially bricking or causing extremely serious consequences), it is necessary to increase w4.

[0131] The dynamic adjustment of the w-series weights is itself an optimization problem. For dynamic adjustment based on the built-in rule base, the pseudocode example is as follows:

[0132] If the current time ∈ [18:00, 21:00] (evening peak load), then w1 = 0.6, w2 = 0.2, w3 = 0.1, w4 = 0.1.

[0133] If the weather station issues a yellow alert for thunderstorms, then w2 = 0.5, w1 = 0.3, w3 = 0.1, w4 = 0.1.

[0134] If the upgrade package security level is "CRITICAL", then w4 = 0.7, ...

[0135] Therefore, w1, w2, w3, and w4 are dynamic weighting coefficients that quantify the real-time operating status of the power grid and inject it into the risk assessment algorithm of the upgrade strategy. By adjusting them, the engine can flexibly adapt to ever-changing power grid scenarios, always prioritizing the avoidance of the most critical risks, thereby achieving truly intelligent, safe, and adaptive collaborative upgrades.

[0136] To illustrate, consider the following scenario: It's 11 PM, the load is low, and the photovoltaic output is zero, but the weather forecast indicates strong winds in an hour. The status awareness is as follows: R_power is low (because the load is low and reserves are sufficient), R_protection is high (because strong winds may cause short circuits, requiring highly reliable system protection), R_quality is low (non-sensitive period), and R_security is moderate.

[0137] The model uses the "severe weather warning" rule in the rule base to automatically adjust the weights: w2 (protection risk weight) is significantly increased, for example, from 0.2 to 0.5; w1 (power risk weight) is decreased; w3 and w4 remain moderately or slightly adjusted.

[0138] Decision results: When generating and optimizing equipment upgrade strategies, due to the high w2, the model will try its best to avoid upgrading equipment such as relay protection devices and line intelligent terminals; instead, it may only upgrade equipment that does not affect the protection system at all (such as some meters); or simply postpone the entire upgrade plan, because the simulation found that any equipment upgrade may introduce unacceptable risks in the next hour (when the protection system needs it most).

[0139] In addition, the model must comply with some hard constraints when generating and optimizing equipment upgrade strategies, such as: the total available capacity of the cluster during equipment upgrade >= the scheduling demand capacity * safety factor (e.g., 110%), the total communication traffic of the equipment being upgraded in the same batch <= the available bandwidth of the current network link, and key core equipment (such as substation gateways) must be scheduled for upgrade windows separately, etc.

[0140] After obtaining the optimal device upgrade strategy in step S106, regardless of whether the strategy is a time-sequential peak-shifting upgrade strategy, a function cluster upgrade strategy, or an adaptive rolling upgrade strategy, the method provided in this application embodiment further includes step S107: during the execution of any device upgrade strategy, for the device to be upgraded, the current power of the device to be upgraded is obtained to calculate the compensation target power; a power compensation device is found from the cluster of devices to be upgraded, and the output power of the power compensation device is smoothly adjusted; when the output power is stable and close to the compensation target power, the offline upgrade of the device to be upgraded is performed; and the power is switched back after the upgrade is completed.

[0141] It should be noted that in step S107, a precise control algorithm of "first supplement, then upgrade, relay alternation" is proposed for grid equipment such as energy storage clusters (PCS+BMS) and photovoltaic clusters. This algorithm can be nested into different upgrade strategies to achieve smooth power transition and improve the stability of collaborative upgrades. Essentially, it is an active seamless switching technology based on power feedforward, which has high application value in scenarios such as microgrids, data center power supply, and new energy power plant management. The core idea of ​​this algorithm is "power substitution" and "timing coordination." By pre-activating a "backup" device to take over its power load before the upgraded device exits, and then smoothly handing it over after the upgrade, it ensures that the total output power of the system does not change abruptly during the entire upgrade process, achieving "zero power impact" or "smooth transition." Specifically, the algorithm formula parameters involved are as follows:

[0142] A: The device to be upgraded (target device);

[0143] Pa(t): The actual output power of device A at time t (this is a quantity that changes with time).

[0144] B: Backup equipment (compensation equipment) that is dispatched to compensate for power.

[0145] Pb(t): Output power of device B at time t;

[0146] Pb original: The original output power of device B before the compensation task begins (typically 0 or a low light load value).

[0147] k: Power safety factor (k>1), used to cope with power fluctuations that may occur during the upgrade (such as photovoltaic cloud shading, small sudden increase in load, etc.).

[0148] Pcomp: The target power value that needs to be compensated;

[0149] t: Control cycle;

[0150] Tramp: Power ramp time (i.e., the time required for device B to smoothly increase power Tramp-up and the time required to smoothly decrease power Tramp-down, and the time required for device A to smoothly increase power Tramp-up-A).

[0151] Specifically, the algorithm steps involved are as follows:

[0152] Step 1: Power Calculation. At the initial moment t0 when the upgrade process is triggered, the model (digital twin) obtains the current output power value Pa(t0) = RealTimeMeasurement(A) of device A through real-time monitoring, and then calculates the compensation target power Pcomp = Pa(t0)*k. The digital twin uses real-time data to mirror the physical entity to obtain an accurate Pa(t0). Introducing a safety factor k (e.g., k = 1.1 or 1.2) is a crucial step, ensuring that the compensation device B is capable of handling short-term power spikes, providing additional stability margin for the system, and preventing system voltage or frequency drops due to insufficient compensation power.

[0153] Step 2: Compensation Scheduling. The model finds a well-functioning device B in the cluster capable of providing power Pcomp; then it issues a command to B, requesting its output power to smoothly ramp up from a lower base power Pb original to Pcomp. The formula for the power ramp-up command for device B is:

[0154] Pb ref(t) = Pb original+min(t-t0 / Tramp-up,1)*(Pcomp–Pb original)

[0155] Where t0 <= t <= t0 + Tramp-up, the calculated Pb ref(t) is the power reference value sent to device B. The min() function ensures a linear ramp-up process, reaching the target compensated power from the original power within the Tramp-up time. This smooth change avoids shocks to device B itself. Furthermore, in a real system, the power change rate dP / dt is also limited to protect hardware such as the PCS and battery.

[0156] Step 3: Perform the upgrade. Continuously monitor the output power of device B. When Pb(t) stabilizes near Pcomp (within a preset error band) and remains stable for a short period (Tstable), it is confirmed that the system power has been fully compensated. At this point, an offline upgrade command is sent to device A. This "waiting for stabilization" step is crucial, as it ensures that the compensated power is fully in place before device A goes offline. After device A goes offline, its power Pa drops to 0. However, since B has already provided power of Pcomp = Pa(t0)*k, the total system power not only does not decrease but also has a slight surplus, ensuring absolute stability.

[0157] Step 4: Power Reversion. After equipment A completes its upgrade and comes back online, the control system instructs equipment A to resume its original power Pa(t0), while simultaneously controlling equipment B to smoothly revert its power back to its original level Pb. The power ramp-up command formula for equipment A is:

[0158] Pa ref(t) = min(t-t2 / Tramp-up-A,1)* Pa(t0)

[0159] Where t2 is the time when device A goes online.

[0160] The power ramp-down command formula for device B is as follows:

[0161] Pb ref(t) = Pcomp-min(t-t2 / Tramp-down,1)*(Pcomp–Pb original)

[0162] Where t2 <= t <= t2 + Tramp-down. The back-cut process also needs to be smooth. Ideally, the power ramp-up of A and the power ramp-down of B should be synchronized, so that the power ramp-up of A + the power ramp-down of B (negative value) ≈ 0, thereby keeping the total power constant. Finally, the system state is restored to: A outputs Pa(t0), and B outputs Pb original.

[0163] Step 5: Iterative Upgrade. The above four steps are encapsulated into a standard "upgrade unit operation." When multiple devices in the cluster need to be upgraded, this operation is applied sequentially to the next device to be upgraded, forming a "relay." When upgrading device C, device D can be scheduled as its compensation device, and so on. Through "compensation before interruption" and ramp control, power interruptions and surges are completely eliminated. Uninterrupted "hot upgrades" are achieved, greatly improving system availability. The introduction of a safety factor k enhances the system's robustness. This is applicable to various types of power electronic equipment clusters, such as energy storage PCS and photovoltaic inverters.

[0164] In some embodiments, after step S104 of the above embodiments, which dynamically generates multiple device upgrade strategies based on key status information through a device collaborative upgrade model, the method provided in this application includes:

[0165] The hash value of the upgrade firmware package of the device upgrade strategy is stored in the blockchain of the energy management system. After the device to be upgraded receives the upgrade firmware package, it calculates the hash value of the received package and compares it with the hash value stored on the chain. If the comparison matches, the upgrade is performed. After the device to be upgraded completes the upgrade, the status information of the upgrade success or failure is broadcast to the blockchain as a transaction record.

[0166] It should be noted that the IoT devices connected to the power grid are mostly heterogeneous devices. To address the security and trust issues associated with these heterogeneous devices, lightweight blockchain technology is introduced. The specific process is as follows:

[0167] Hash on-chain: The hash value of the upgrade firmware package is stored in the EMS's local lightweight blockchain after the upgrade task is generated.

[0168] Trusted verification: After receiving the upgrade package, the downstream device not only verifies it through traditional methods, but also calculates the hash value of the received package and compares it with the hash stored on the chain to ensure that the upgrade package is complete and has not been tampered with.

[0169] Status Evidence: The status information of each device upgrade success or failure is broadcast to the node (which can be a regional gateway acting as a light node) as a transaction record, forming an immutable upgrade audit log.

[0170] This mechanism ensures the traceability, non-repudiation, and tamper-proof nature of the upgrade process, greatly enhancing security.

[0171] It should be further noted that the steps shown in the above process or in the flowchart of the accompanying figures can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0172] This embodiment provides an electronic device including a memory and a processor. The memory stores a computer program, and the processor is configured to run the computer program to perform the steps in any of the above method embodiments.

[0173] Optionally, the electronic device may further include a transmission device and an input / output device, wherein the transmission device is connected to the processor and the input / output device is connected to the processor.

[0174] Optionally, the electronic device may further include a processor, memory, network interface, display screen, and input device connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a device collaborative upgrade method based on an energy management system. The display screen may be a liquid crystal display (LCD) or an e-ink display. The input device may be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the device's casing, or an external keyboard, touchpad, or mouse.

[0175] It should be noted that the specific examples in this embodiment can refer to the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0176] Furthermore, in conjunction with the device collaborative upgrade method based on the energy management system in the above embodiments, this application embodiment can provide a storage medium for implementation. This storage medium stores a computer program; when executed by a processor, the computer program implements any of the device collaborative upgrade methods based on the energy management system in the above embodiments.

[0177] In one embodiment, Figure 2 This is a schematic diagram of the internal structure of an electronic device according to an embodiment of this application, such as... Figure 2 As shown, an electronic device is provided, which can be a server, and its internal structure diagram can be as follows. Figure 2 As shown, the electronic device includes a processor, a network interface, internal memory, and non-volatile memory connected via an internal bus. The non-volatile memory stores an operating system, computer programs, and a database. The processor provides computing and control capabilities, the network interface communicates with external terminals via a network, the internal memory provides an environment for the operating system and computer programs to run, the computer programs are executed by the processor to implement a device collaborative upgrade method based on an energy management system, and the database stores data.

[0178] Those skilled in the art will understand that Figure 2 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. A specific electronic device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0179] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. This computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0180] Those skilled in the art should understand that the technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments have been described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0181] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the invention patent. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for collaborative equipment upgrade based on an energy management system, characterized in that, The method includes: The power grid's energy management system acquires key status information of the power grid in real time, including power flow distribution, load forecasting, renewable energy generation status, and current operating mode. Based on the key status information, equipment upgrade strategies are dynamically generated through the equipment collaborative upgrade model. The equipment collaborative upgrade model is built within the energy management system, and the equipment upgrade strategies include a time-series off-peak upgrade strategy, a functional cluster upgrade strategy, and an adaptive rolling upgrade strategy. During the execution of any device upgrade strategy, for the device to be upgraded, the current power of the device to be upgraded is obtained to calculate the compensation target power; a power compensation device is found from the cluster of devices to be upgraded, and the output power of the power compensation device is smoothly adjusted; when the output power is stably close to the compensation target power, the offline upgrade of the device to be upgraded is performed; after the upgrade is completed, the power is switched back. The feasibility of the equipment upgrade strategy is then evaluated through digital twin simulation using the equipment collaborative upgrade model, and the equipment upgrade strategy is optimized based on the evaluation results.

2. The method according to claim 1, characterized in that, Based on the aforementioned key status information, the time-series staggered upgrade strategy is dynamically generated through the device collaborative upgrade model, including: Based on the key status information, the device collaborative upgrade model is used to schedule upgrades in a staggered manner according to the functional type and time sensitivity of the devices to be upgraded, so as to generate a time-series staggered upgrade strategy.

3. The method according to claim 2, characterized in that, Based on the aforementioned key status information, a time-series staggered upgrade strategy is generated by using a device collaborative upgrade model to schedule upgrades according to the functional type and time sensitivity of the devices to be upgraded. This strategy includes: Based on the key status information, the equipment collaborative upgrade model arranges the equipment of the same functional type to be upgraded into the same upgrade batch, and arranges the corresponding upgrade sequence based on the time sensitivity of different functional types of equipment, so as to generate a time-staggered upgrade strategy.

4. The method according to claim 1, characterized in that, Based on the aforementioned key status information, the functional cluster upgrade strategy is dynamically generated through the device collaborative upgrade model, including: Based on the key status information, the devices to be upgraded are grouped and arranged according to the cluster type and geographical region through the device collaborative upgrade model to generate a functional cluster upgrade strategy.

5. The method according to claim 4, characterized in that, Based on the aforementioned key status information, the devices are grouped and arranged according to their cluster type and geographical region using a collaborative upgrade model to generate a functional cluster upgrade strategy, including: Based on the key status information, the device collaborative upgrade model arranges devices of the same cluster type and in the same geographical area to be upgraded into the same upgrade batch and the same upgrade sequence to generate a functional cluster upgrade strategy.

6. The method according to claim 1, characterized in that, Based on the aforementioned key status information, the adaptive rolling upgrade strategy is dynamically generated through the device collaborative upgrade model, including: Based on the key status information, the upgrade batches and timing of the devices to be upgraded are adaptively adjusted according to the grid load status through the equipment collaborative upgrade model, so as to generate an adaptive rolling upgrade strategy.

7. The method according to claim 1, characterized in that, The feasibility of the equipment upgrade strategy is evaluated through digital twin simulation using the aforementioned equipment collaborative upgrade model, and the equipment upgrade strategy is optimized based on the evaluation results, including: The equipment upgrade strategy is simulated digitally using the equipment collaborative upgrade model to obtain the system risk index, total upgrade time, and resource cost of the equipment upgrade strategy. Based on the system risk index, total upgrade time, and resource cost, the objective function is: The device upgrade strategy is optimized, where Risk_System() represents the system risk index, Time_Total() represents the total upgrade time, Cost_Resource() represents the resource cost, α, β and γ represent the corresponding weighting weights, and the objective function Minimize F() is used to reduce the system risk index, total upgrade time and resource cost of the device upgrade strategy to the minimum.

8. The method according to claim 7, characterized in that, By performing a digital twin simulation of the equipment upgrade strategy using the aforementioned equipment collaborative upgrade model, the system risk index of the equipment upgrade strategy is derived, including: The equipment upgrade strategy is digitally simulated using the aforementioned equipment collaborative upgrade model, utilizing the system risk index calculation function: The system risk index Risk_System of the equipment upgrade strategy is calculated, where R_power is the power deficit risk, representing the degree of power imbalance caused by equipment offline during the upgrade simulation; R_protection is the protection failure risk, representing whether there is a protection blind spot during the upgrade simulation; R_quality is the power quality risk, representing whether voltage fluctuations and harmonic pollution may exceed limits during the upgrade simulation; R_security is the security risk, representing the probability of security risk events occurring during the upgrade simulation; and w1, w2, w3, and w4 represent the corresponding weighting weights.

9. The method according to claim 6, characterized in that, After dynamically generating multiple device upgrade strategies based on the key status information using a device collaborative upgrade model, the method includes: The hash value of the upgrade firmware package of the device upgrade strategy is stored in the blockchain of the energy management system. After the device to be upgraded receives the upgrade firmware package, it calculates the hash value of the received package and compares it with the hash value stored on the chain. If the comparison matches, the upgrade is performed. After the device to be upgraded completes the upgrade, the status information of the upgrade success or failure is broadcast to the blockchain as a transaction record.

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