Power distribution network protection and self-healing method, device, storage medium and product

Abstract

This application relates to the field of power system control technology, and discloses a distribution network protection and self-healing method, device, storage medium, and product. The method includes: acquiring the source-grid-load-storage status, which is determined by the operating status data of new energy equipment, energy storage system, and controllable load equipment; calculating and updating the overcurrent protection value in real time based on the source-grid-load-storage status; when the actual current exceeds the overcurrent protection value, sending alarm signals to the new energy edge intelligent agent (S-EA) and / or the energy storage edge intelligent agent (S-EA) and / or upstream and downstream switches based on the distribution network topology, and receiving feedback signals; if no feedback signal is received within a preset time, performing graded tripping actions according to the number of times the actual current exceeds the overcurrent protection value, thereby accurately distinguishing between real short-circuit faults and operational disturbances.

Description

Technical Field

[0001] This application relates to the field of power system control, and in particular to a method, device, storage medium and product for distribution network protection and self-healing. Background Technology

[0002] In the development of modern power systems, distribution networks are undergoing a structural transformation driven by the widespread integration of distributed power sources (such as photovoltaic and wind power), user-side energy storage systems, and large-scale controllable loads (such as electric vehicle charging facilities and flexible industrial loads). The penetration of these technologies has transformed the power flow characteristics of distribution networks from a traditional unidirectional stable mode to a complex bidirectional, highly volatile, and time-varying mode. The participation of some user-side resources in grid operation through virtual power plants (VPPs) further exacerbates the complexity and unpredictability of the distribution network's operating environment.

[0003] Under this new power system architecture, the closest existing technologies—traditional switch protection setting technology and adaptive feeder automation technology—are no longer adequate. Existing adaptive protection technologies typically only consider load current changes or single renewable energy output factors in their setting adjustment models, failing to construct a comprehensive dynamic model that integrates the real-time state of energy storage (SOC), predicted renewable energy output, and controllable load regulation capabilities. Therefore, in scenarios involving disturbances to normal operation, such as user-side energy storage charging and discharging switching or severe fluctuations in photovoltaic output due to cloud cover, existing protection systems still face a high risk of false tripping because they cannot accurately distinguish between such disturbances and actual short-circuit faults.

[0004] Another core deficiency in existing technologies lies in the lack of an effective distributed coordination mechanism. In areas where multiple distributed power sources jointly supply power, asynchronous switching actions during fault isolation can easily lead to unstable islanded operation of distributed power sources in some non-faulty areas, ultimately triggering inverter protection shutdowns and affecting the efficiency of renewable energy consumption. Furthermore, even after power restoration, existing technologies lack a mechanism for continuous assurance of power quality; after self-healing, if the line load suddenly increases without rapid energy replenishment measures, the voltage may drop significantly, failing to meet the stringent power quality requirements of sensitive loads such as data centers and precision equipment. Therefore, there is an urgent need for a new distributed coordination technology that can deeply integrate the protection and self-healing functions of distribution network switches with the dynamic status and control capabilities of source-grid-load-storage equipment to address the challenges of modern distribution network operation. Summary of the Invention

[0005] One objective of this application is to provide a distribution network protection and self-healing method, device, storage medium, and product, at least to solve the problem that the existing distribution network protection settings lack the ability to sense dynamic changes in the source, grid, load, and storage, leading to false tripping of protection under normal operating disturbances, as well as the problem that the lack of a coordination mechanism in the fault isolation and self-healing process makes it easy for secondary tripping or substandard power supply quality to occur.

[0006] To achieve the above objectives, some embodiments of this application provide the following aspects:

[0007] In a first aspect, this application provides a distribution network protection and self-healing method based on source-grid-load-storage coordination. The method is applied to a distribution network formed by distribution network switches, new energy equipment, energy storage systems, and controllable load equipment configured with edge agents (S-EAs). The method includes:

[0008] The source-grid-load-storage status is obtained, which is determined by the operating status data of the new energy equipment, the energy storage system, and the controllable load equipment.

[0009] Based on the source-grid-load-storage status, the overcurrent protection value is calculated and updated in real time;

[0010] When the actual current exceeds the overcurrent protection value, an alarm signal is sent to the new energy edge intelligent agent (S-EA), the energy storage edge intelligent agent (S-EA), and the upstream and downstream switches based on the topology of the power distribution network, and a feedback signal is received.

[0011] If the feedback signal is not received within the preset time, a tiered tripping action is performed based on the number of times the actual current exceeds the overcurrent protection value.

[0012] Secondly, some embodiments of this application also provide an electronic device, the electronic device comprising: one or more processors; and a memory storing computer program instructions, which, when executed, cause the processor to perform the steps of the method described above.

[0013] Thirdly, some embodiments of this application also provide a computer program product, including a computer program / instructions that, when executed by a processor, implement the steps of the method described above.

[0014] Compared with related technologies, the solution provided in this application introduces a distributed collaborative mechanism of source-grid-load-storage edge intelligent agent (S-EA), which deeply integrates the distribution network switch protection, fault diagnosis, and self-healing processes with the dynamic status of new energy, energy storage, and controllable load equipment. First, this application dynamically updates the overcurrent protection value based on real-time status (such as new energy output fluctuations and energy storage SOC). Compared with traditional fixed or simple adaptive settings, this fundamentally eliminates the problem of protection false tripping caused by normal operation disturbances such as distributed power source charging and discharging or sudden output changes, significantly improving power supply reliability and protection system stability.

[0015] Secondly, this application constructs a collaborative fault discrimination process. By receiving status feedback signals from surrounding equipment, it can accurately distinguish between actual short-circuit faults and operational disturbances, avoiding the blind spots of traditional protection settings under the complex power flow of modern distribution networks. Simultaneously, in the self-healing process, this application adds pre-voltage adjustment and load peak shaving steps before closing, and predicts the closing voltage based on the voltage drop calculation formula, successfully solving the problem of secondary tripping caused by source-load mismatch or voltage exceeding limits after closing.

[0016] Furthermore, this application utilizes energy storage to perform dynamic automatic energy replenishment or peak shaving after the self-healing switch, effectively ensuring the power quality of the restored power supply area, especially meeting the needs of sensitive loads with high power quality requirements. The ultimate effect is to significantly improve the protection reliability, fault identification accuracy, fault isolation speed, and power quality of the power supply area after self-healing in modern distribution networks under complex power flow environments. Attached Figure Description

[0017] One or more embodiments are illustrated by way of example with reference numerals in the accompanying drawings. These illustrations do not constitute a limitation on the embodiments. Elements with the same reference numerals in the drawings are denoted as similar elements. Unless otherwise stated, the figures in the drawings are not to be limited by scale.

[0018] Figure 1 A flowchart of a power distribution network protection and self-healing method provided as an exemplary embodiment of this disclosure;

[0019] Figure 2 A flowchart of another distribution network protection and self-healing method provided as an exemplary embodiment of this disclosure;

[0020] Figure 3 A flowchart of yet another distribution network protection and self-healing method provided as an exemplary embodiment of the present disclosure;

[0021] Figure 4 An architecture diagram of a power distribution network protection and self-healing method provided for an exemplary embodiment of this disclosure;

[0022] Figure 5An exemplary structural diagram of the electronic device provided for some embodiments of this application. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] Figure 1 This is a schematic diagram of a distribution network protection and self-healing method based on source-grid-load-storage coordination, provided as an exemplary embodiment of the present disclosure. The method is applied to a distribution network formed by distribution network switches, new energy equipment, energy storage systems and controllable load equipment configured with edge agents (S-EA).

[0025] Specifically, a deep interaction mechanism between switches and energy storage devices is constructed through "S-EA (Side-Based Intelligent Agent) + Distributed Collaborative Network". The core is to configure a source-grid-load-storage S-EA (Side-Based Intelligent Agent) for each distribution switch (segment, interconnection, branch), new energy equipment (photovoltaic inverter, wind power converter), energy storage system, and controllable load (charging pile, industrial controller). The S-EA is a hardware and software module with local computing, strategy generation, and collaborative communication capabilities, and is the core functional unit for realizing the distributed collaborative decision-making of this invention. Each intelligent agent forms a local communication network through HPLC (high-speed power line carrier, delay ≤50ms) or a 5G private network (delay ≤20ms) to achieve closed-loop control.

[0026] The method includes:

[0027] S101. Obtain the source-grid-load-storage status, which is determined by the operating status data of the new energy equipment, the energy storage system, and the controllable load equipment.

[0028] Specifically, in this step, the Distribution Network Switch (S-EA) acts as the core control node, interacting with surrounding topology-related devices in real time through a distributed collaborative network. The S-EA actively or passively acquires the source-grid-load-storage status, which is determined by the operating status data of the new energy equipment, the energy storage system, and the controllable load equipment.

[0029] S102. Calculate and update the overcurrent protection value in real time based on the source-grid-load-storage status.

[0030] Specifically, after acquiring the source-grid-load-storage status, the distribution network switch S-EA uses the operating status data of new energy equipment, energy storage systems, and controllable load equipment to calculate and dynamically update I_set(t) in real time. The overcurrent protection value integrates the impact of new energy output fluctuations and the remaining energy storage capacity on the line current. A dynamic calculation mechanism ensures that I_set(t) can be set with sufficient safety margin, thereby avoiding misjudging normal operating disturbances as short-circuit faults when new energy output fluctuates significantly or when energy storage charging / discharging states switch.

[0031] S103. When the actual current exceeds the overcurrent protection value, an alarm signal is sent to the new energy edge intelligent agent (S-EA), the energy storage edge intelligent agent (S-EA), and the upstream and downstream switches based on the topology of the power distribution network, and a feedback signal is received.

[0032] Specifically, when the distribution network switch S-EA detects that the actual current of the line exceeds the dynamic overcurrent protection value I_set(t) updated in real time in step S102, it immediately initiates the collaborative fault identification process. Based on its topology knowledge, the S-EA sends alarm signals to relevant neighboring devices, such as new energy S-EAs, energy storage S-EAs, and upstream / downstream distribution network switches. These alarm signals preferably include information such as a timestamp, overcurrent direction (forward / reverse), and current amplitude. Subsequently, the distribution network switch S-EA waits for and receives status feedback signals from these devices within a preset timeout period (e.g., ≤0.2 seconds). This feedback signal is the key basis for performing collaborative fault verification.

[0033] S104. If the feedback signal is not received within a preset time, a graded tripping action is performed based on the number of times the actual current exceeds the overcurrent protection value.

[0034] Specifically, this step describes the backup protection mechanism in case of distributed cooperative communication failure or feedback timeout. If the distribution network switch S-EA does not receive the necessary feedback signal within the preset timeout period (i.e., the information required for cooperative verification is missing), it abandons the cooperative discrimination path and instead executes the traditional counting-based protection logic. The distribution network switch S-EA counts the number of times the actual current exceeds the overcurrent protection value I_set(t); once this number reaches a preset threshold (e.g., 2 times), it is determined to be a real fault, and a tiered tripping action is initiated. This mechanism ensures that even in the event of communication anomalies or the failure of the faulty device to provide feedback, the protection system can still reliably and timely isolate the fault by relying on the counting logic.

[0035] In the above embodiments, by dynamically updating the overcurrent protection value based on real-time status (such as fluctuations in renewable energy output and energy storage SOC), compared to traditional fixed or simple adaptive settings, the problem of false tripping caused by normal operation disturbances such as charging and discharging of distributed power sources or sudden changes in output is fundamentally eliminated, significantly improving power supply reliability and the stability of the protection system. Furthermore, by receiving status feedback signals from peripheral devices, it can accurately distinguish between actual short-circuit faults and operational disturbances, avoiding the blind spots of traditional protection settings under the complex power flow of modern distribution networks.

[0036] Furthermore, in one embodiment, the operating status data of the new energy equipment, the energy storage system, and the controllable load equipment includes:

[0037] The real-time output and output forecast of the new energy equipment, the remaining capacity and voltage regulation capability of the energy storage system, and the current load and load reduction amount of the controllable load equipment.

[0038] Specifically, the determination of the source-grid-load-storage status is achieved through the real-time output and short-term output forecast of the new energy equipment, the SOC (remaining capacity) and voltage regulation capability of the energy storage system, and the current load and load reduction of the controllable load equipment. By comprehensively understanding this dynamic information, a multi-dimensional basis is provided for subsequent protection setting calculations, solving the problem of traditional setting methods lacking awareness of the status of distributed resources.

[0039] Specifically, the distribution network switch S-EA collects local electrical quantities in real time (three-phase voltage U, current I, power factor cosφ, frequency f, sampling frequency 2kHz), and receives status data from surrounding equipment through the local communication network:

[0040] New Energy S-EA: Real-time power output P_New Energy (t), power output forecast P_Forecast (t) for the next 30 seconds (based on the sunshine / wind speed prediction model, error ≤10%);

[0041] Energy storage S-EA: SOC (remaining capacity, accuracy ±2%), current charge / discharge power P_energy storage (t), voltage regulation capability ΔU_max (maximum adjustable voltage range, typically ±0.5kV);

[0042] Controllable load S-EA: Current load P_load (t), load that can be reduced ΔP_peak shaving (t) (e.g., charging piles can pause charging power, response time ≤ 1 second).

[0043] In one embodiment, after performing the tiered tripping action, the method further includes:

[0044] S201. Collect the available power capacity and load demand in the power outage area to determine whether the self-healing conditions are met.

[0045] Specifically, after the faulty switch completes its isolation action, the self-healing switch (S-EA) in the distribution network immediately takes over the self-healing task. The S-EA first collects information on all available power capacity (including solar, wind, and energy storage) within the power outage area, as well as the total load demand of the area, using network topology information. The S-EA then performs source-load matching, calculating power surplus or deficit to determine whether the resources in the area can meet the basic power supply requirements after self-healing, thus determining whether the self-healing conditions are met. If not, the self-healing process terminates or awaits external resource scheduling.

[0046] S202. If the self-healing condition is met, perform voltage drop calculations to predict the voltage change of the line after closing.

[0047] Specifically, after confirming that the self-healing conditions are met, to avoid voltage exceeding limits due to impact loads or power supply access at the moment of closing, the tie switch S-EA initiates a prediction step. The tie switch S-EA uses line parameters (resistance R, reactance X) and the calculated net active power deficit (P_net) and net reactive power deficit (Q_net) of the self-healing zone to calculate voltage drop and predict the voltage drop value ΔV_drop at critical nodes of the line after closing. This step aims to predict whether the voltage in the power-loss zone will exceed the acceptable range at the moment the tie switch closes, which is crucial for ensuring power supply quality.

[0048] S203. Adjust the closing voltage according to the prediction results.

[0049] Specifically, based on the predicted voltage drop value in the above steps, the tie switch S-EA determines whether the voltage after closing is within the acceptable range. If the predicted voltage exceeds the acceptable range (e.g., due to excessive voltage drop), the tie switch S-EA will immediately send a pre-adjustment command to the energy storage S-EA, requiring the energy storage system to adjust its charging and discharging state or generate reactive power before closing to boost the voltage. If energy storage alone cannot adjust the voltage to the acceptable range, the tie switch S-EA will also simultaneously call the controllable load S-EA to perform load shaving, thereby reducing load demand and adjusting the closing voltage to the acceptable range.

[0050] S204. After the closing voltage is adjusted, close the circuit in sequence.

[0051] Specifically, once the tie switch S-EA confirms that the predicted closing voltage is within the acceptable range after pre-voltage regulation and / or load peak shaving, it issues a command. The tie switch and sectionalizing switch will close sequentially according to the predetermined timing or topology, restoring power supply to the power-loss area. Furthermore, to further ensure power quality, after completing the closing action, the energy storage S-EA will continuously monitor the line voltage in real time and automatically perform actions such as discharging energy or charging peak shaving when the voltage deviates slightly, providing dynamic voltage and frequency support to ensure continuous and stable power supply after self-healing.

[0052] In the above embodiments, the self-healing process successfully resolved the secondary tripping problem caused by source-load mismatch or voltage exceeding limits after closing by adding pre-voltage adjustment and load peak shaving steps before closing and predicting the closing voltage based on the voltage drop calculation formula. Furthermore, continuous use of energy storage to perform dynamic automatic energy replenishment or peak shaving after self-healing effectively ensures the power quality of the restored power supply area, especially meeting the needs of sensitive loads with high power quality requirements. The final result is a significant improvement in the protection reliability, fault identification accuracy, fault isolation speed, and power quality of the power supply area after self-healing in modern distribution networks under complex power flow environments.

[0053] like Figure 2 In one embodiment, if the feedback signal is not received within a preset time, a tiered tripping action is performed based on the number of times the actual current exceeds the overcurrent protection value, specifically including:

[0054] The feedback signals include whether the new energy source is fluctuating and / or whether the energy storage is switching between charging and discharging and / or whether the downstream switch is detecting overcurrent.

[0055] If the feedback signal is not received within the preset time, and the number of times the actual current exceeds the overcurrent protection value reaches the first preset threshold, it is determined to be a real fault, and a graded tripping action is executed.

[0056] If the feedback signal is received and the number of times the actual current exceeds the overcurrent protection value reaches a second preset threshold, it is determined to be a disturbance overcurrent, and the overcurrent protection value is increased.

[0057] Specifically, firstly, when the collaborative judgment process is initiated, the feedback signals received by the distribution network switch S-EA are the key basis for determining the nature of the fault. These feedback signals include whether there are power output fluctuations in new energy sources and / or whether there is charging / discharging switching in the energy storage and / or whether the downstream switch detects overcurrent. These status signals are used for collaborative verification.

[0058] Secondly, for abnormal situations involving feedback timeouts, this embodiment sets up a first logical path: if the feedback signal is not received within a preset time, the distribution network switch S-EA accumulates and counts the number of times the actual current exceeds the dynamic overcurrent protection value. Once this number reaches a first preset threshold (e.g., 2 times), the system determines it as a real fault and executes a tiered tripping action. This logical path serves as a final backup mechanism to ensure power grid safety.

[0059] Finally, for special cases of operational disturbances, this embodiment sets up a second logical path: if a feedback signal is received and, after collaborative verification, is determined to be a disturbance overcurrent (for example, the feedback indicates the presence of new energy fluctuations), a second preset threshold will be activated to count false alarms. Specifically, when the number of times the actual current exceeds the overcurrent protection value (i.e., a disturbance has occurred) accumulates to the second preset threshold (e.g., 2 times), it is determined that the line frequently experiences transient or continuous disturbances, posing a high risk of false alarms. In this case, it is determined to be a disturbance overcurrent, and the switch does not perform a tripping action. Instead, it actively adapts to and avoids repeated false alarms of similar disturbances in a short period of time by increasing the overcurrent protection value (e.g., increasing the margin by 5%), thereby enhancing the robustness of the protection system.

[0060] Furthermore, in one embodiment, if the received feedback signal indicates that the actual current exceeds the overcurrent protection value a number of times, it is determined to be a disturbance overcurrent, the event is recorded, and the overcurrent protection value is raised for 30 seconds.

[0061] The fault identification and backup logic provided in this embodiment effectively solves the problems of communication anomalies and continuous operational disturbances in collaborative protection. By introducing a first preset threshold and a cumulative counting mechanism, a reliable timeout backup protection path is set up to ensure that even in extreme cases of communication interruption or no feedback from equipment, the real fault can still be isolated in a timely and reliable manner, greatly enhancing the protection system's resilience. At the same time, by introducing a second preset threshold and a disturbance counting mechanism, frequently occurring transient or continuous operational disturbance events in the line are identified, and an adaptive strategy to increase the overcurrent protection value is adopted to avoid repeated misjudgments in a short period of time. This avoids faultless tripping caused by misjudging repeated disturbances, thereby significantly improving the robustness of the protection and the reliability of power supply.

[0062] In one embodiment, the tiered tripping specifically refers to:

[0063] The switch with the largest detected overcurrent amplitude is identified as the near-fault switch, and the near-fault switch trips after a delay.

[0064] A switch whose overcurrent amplitude is less than the overcurrent amplitude threshold is identified as a remote fault switch. If the near fault switch does not trip, a supplementary trip is performed. The delay tripping time of the remote fault switch is greater than that of the near fault switch.

[0065] Specifically, firstly, based on the detected actual current amplitude, the switches participating in fault isolation are divided into two categories. The switch with the largest detected overcurrent amplitude is determined to be the near-fault switch. This switch is geographically closest to the fault point and is therefore given the highest priority for action. The near-fault switch performs a tripping action after a short delay, for example, a 0.1-second delay.

[0066] Secondly, switches whose detected overcurrent amplitude is less than the overcurrent amplitude threshold are identified as remote fault switches. These switches serve as backup protection, and their tripping delay is set to be greater than that of the near-fault switch to ensure that the near-fault switch has sufficient time to trip first and isolate the fault, for example, a 0.3-second delay. The remote fault switch only performs a supplementary tripping action if the near-fault switch fails to trip within its preset delay for some reason (such as mechanical failure or communication error), thus providing reliable backup protection and preventing the fault from escalating.

[0067] The tiered tripping mechanism provided in this embodiment effectively optimizes the speed and reliability of fault isolation. By clearly identifying switches as near-fault switches and far-fault switches based on overcurrent amplitude and assigning them differentiated delay tripping times (near-fault switches trip faster than far-fault switches), it ensures that the fault area can be isolated by the nearest switch at the fastest speed, minimizing fault isolation. Simultaneously, by setting the delayed tripping of the far-fault switch as a reliable backup protection, it ensures that if the near-fault switch fails to operate for any reason, the backup switch can trip promptly, significantly enhancing the reliability and resilience of the protection system and preventing the fault range from expanding.

[0068] In one embodiment, the overcurrent protection value is determined by weighting the line base load current, the real-time output and output forecast of new energy sources, the remaining capacity of the energy storage system, and a fixed safety margin.

[0069] Specifically, the dynamic overcurrent protection value I_set(t) is determined by a weighted sum of the following key terms: The first term is the line base load current I_base, representing the normal reference load current of the line. The second term is the absolute value of the difference between the real-time output of new energy and the predicted output value, |P_new energy(t). The first term, _Forecast(t)|, reflects the amplitude of instantaneous fluctuations in renewable energy output, used to set a margin sufficient to accommodate rapid fluctuations in photovoltaic or wind power. The second term, Energy Storage Remaining Capacity (SOC), is associated with the set upper limit of SOC (or the current SOC) and reflects the potential impact on line current when the energy storage system participates in charging, discharging, or voltage regulation. The third term, Fixed Safety Margin I_margin, is used to compensate for uncertainties such as model errors and communication delays. By weighted summing these four terms, the final protection setting I_set(t) is ensured to respond in real time to the dynamic changes in power flow in the distribution network.

[0070] Furthermore, in one embodiment, the overcurrent protection value I_set(t) is determined by the following formula:

[0071] I_set(t) = k1 × I_base + k2 × |P_new energy(t) - P_forecast(t)| / U_avg + k3 × (1 - SOC / 100) + I_margin

[0072] Where: I_base is the line base load current; P_new energy(t) is the real-time output of new energy; P_forecast(t) is the short-term output forecast value of new energy; U_avg is the line average voltage; SOC is the remaining energy storage capacity; I_margin is the fixed safety margin; k1, k2, k3 are the corresponding weighting coefficients.

[0073] Specifically, I_set(t): the dynamic overcurrent protection setting of the switch at time t, in A, with no default value, determined based on real-time calculation results;

[0074] In one embodiment, the overcurrent protection value I_set(t) is updated once every 50ms.

[0075] k1: Basic load weighting coefficient, used to adjust the degree of influence of basic load on the set value, has no unit, and the default value is 1.2;

[0076] I_base: Line base load current, taken as the average value over the past 5 minutes, in A. Common range is determined based on the actual line load (e.g., 8A).

[0077] k2: New energy fluctuation weighting coefficient, used to adjust the degree of influence of photovoltaic / wind power fluctuations on the set value. It has no unit and the default value is 0.8 (it can be appropriately increased in areas with high photovoltaic penetration).

[0078] P_New Energy(t): Real-time output of new energy (photovoltaic / wind power) at time t, in kW. Common range is determined based on actual equipment capacity (e.g., 78kW).

[0079] P_forecast(t): Short-term power output forecast of new energy sources at time t, calculated based on the sunshine / wind speed model (error ≤10%), in kW, with common ranges determined according to the forecast model output (e.g., 80kW).

[0080] U_avg: Line average voltage, used to convert power fluctuations into current fluctuations, in kV, with a common range of 10-10.5kV;

[0081] k3: Energy storage SOC weighting coefficient, used to adjust the degree of influence of remaining energy storage capacity on the set value, no unit, default value is 0.5;

[0082] SOC: Remaining energy storage capacity, accurate to ±2%, unit is %, common range is 0-100%;

[0083] I_margin: Fixed safety margin used to avoid erroneous operation due to measurement errors. The unit is A, and the default value is 0.2kA (i.e. 200A).

[0084] For example, when the energy storage SOC is less than 30% (k3 term increases), I_set(t) is raised (up to a maximum of 20%) to avoid false triggering of protection due to increased current caused by energy storage discharge; when the photovoltaic output fluctuation is greater than 20% (k2 term increases), I_set(t) is raised synchronously to resist power flow transfer disturbances.

[0085] This embodiment provides a dynamic overcurrent protection setting calculation scheme based on a precise mathematical model, which greatly enhances the adaptability and robustness of the protection system. By refining the calculation terms of the protection setting I_set(t) into four weighted sums: line base load, instantaneous fluctuations in renewable energy output, remaining energy storage capacity, and fixed safety margin, this scheme establishes protection settings directly related to the operating status of the power grid, source, load, and energy storage. In particular, by updating the setting every 50ms, it ensures that the setting can follow the rapid changes in power flow in the distribution network in real time and accurately. This dynamic setting mechanism can accurately set the necessary safety margin for normal operation disturbances such as fluctuations in renewable energy output or charging and discharging of energy storage, fundamentally solving the problem of faultless false tripping in modern distribution networks caused by traditional protection settings, and significantly improving the power supply reliability and renewable energy absorption capacity of the system. In addition, the physical meaning and adjustable range of the weighted coefficients k1, k2, and k3 are clearly defined, making this protection method highly configurable and practical for engineering applications.

[0086] Furthermore, in one embodiment, the voltage drop value ΔV_drop is calculated using the following formula:

[0087] ΔV_drop=(P_net×R+Q_net×X) / V_rated

[0088] Where: P_net is the net active power deficit in the self-healing region, Q_net is the net reactive power deficit in the self-healing region, R is the resistance of the de-energized line, X is the reactance of the de-energized line, and V_rated is the rated voltage of the line.

[0089] Specifically, ΔV_drop: the predicted line voltage drop value before the self-healing gate (the negative sign indicates voltage rise), in kV, with no default value, determined based on real-time calculation results;

[0090] P_net: Net active power deficit in the self-healing zone (negative value indicates power surplus), unit is kW, and the calculation method is P_net = P_current load - (P_available renewable energy + P_max energy storage discharge) (P_current load is the current total load of the self-healing zone, P_available renewable energy is the output power of renewable energy in the zone, P_max energy storage discharge is the maximum discharge power of energy storage).

[0091] Q_net: Net reactive power deficit in the self-healing zone, in kVar. It is calculated as Q_net = Q_current load - Q_available renewable energy (Q_current load is the current total reactive power load in the self-healing zone, Q_available renewable energy is usually 0, and photovoltaics are given priority in outputting active power by default).

[0092] R: Resistance of the power-destroyed line, in Ω, with a common range of about 0.2Ω for a 2km line (calculated at 0.1Ω / km).

[0093] X: Reactance of the de-energized line, in Ω, with a common range of about 0.3Ω for a 2km line (calculated at 0.15Ω / km).

[0094] V_rated: Rated line voltage, in kV. The standard rated voltage for distribution networks is 10kV.

[0095] This embodiment provides a calculation scheme based on the voltage drop value ΔV_drop, significantly enhancing the predictability and safety of the self-healing process. Through this formula, the system can accurately predict line voltage changes caused by source-load mismatch before closing, avoiding blind closing. The calculation method for the net active power deficit P_net in the formula, particularly by incorporating the available power of new energy sources and the maximum discharge power of energy storage, ensures that the calculation results accurately reflect the power support capacity of the self-healing zone. This precise prediction mechanism before closing provides a reliable quantitative basis for subsequent energy storage pre-voltage regulation and load peak shaving, thereby enabling precise adjustment of the closing voltage to within the acceptable range. This fundamentally eliminates the risk of secondary tripping due to voltage exceeding limits, greatly improving the success rate of the distribution network's self-healing process and power supply quality.

[0096] Furthermore, such as Figure 3 In one embodiment, the step of determining whether the self-healing conditions are met specifically includes:

[0097] Collect available power capacity and load demand in the power outage area;

[0098] If the available power capacity plus the preset margin is greater than or equal to the load demand, it is determined to be self-healing.

[0099] Specifically, after the fault switch trips and isolates, the tie switch (pre-set as the self-healing coordinator) immediately initiates the self-healing process, the total time of which does not exceed 1 second. The tie switch S-EA first collects all available power resources and load demands within the power outage area to calculate the supply-demand balance. Specifically, the collected "available power capacity" is calculated as P_new energy available + P_energy storage discharge_max (unit: kW), that is, the sum of the output power of new energy sources and the maximum discharge power of energy storage within the area; the collected "load demand" is calculated as P_current load. Peak shaving reserve (unit: kW) is the current total load minus the controllable load peak shaving reserve. Afterwards, the S-EA (Switching Switch-Agent) performs a source-load matching determination. The determination rule is: if P_available new energy + P_energy storage discharge_max ≥ 1.05 × P_current load, meaning that the available power supply capacity meets the current load demand while reserving a 5% margin (to cope with possible load fluctuations after closing), then it is determined to be self-healing, and subsequent self-healing steps continue. Otherwise, if the power supply is insufficient to meet the load demand with margin, the S-EA will coordinate with the VPP (Vehicle Power Supply) dispatch center to request supplementary power supply (e.g., calling energy storage resources from other areas) to ensure that the power supply capacity meets the standard within the specified response time (e.g., ≤3 seconds).

[0100] This embodiment provides a pre-judgment mechanism for self-healing initiation based on precise parameters and margin settings, effectively avoiding self-healing failures due to insufficient power capacity. By accurately quantifying and calculating the "available power capacity" and "load demand" within the power outage area before initiating the self-healing process, and introducing a certain redundancy power margin for source-load matching judgment, it ensures that self-healing is only initiated when power resources are sufficient and can cope with load transient fluctuations after shutdown, greatly improving the reliability and success rate of self-healing actions. Simultaneously, incorporating the VPP dispatch center into the collaborative process provides the ability to supplement external power for the self-healing process, further broadening the applicability and flexibility of distribution network self-healing.

[0101] like Figure 3 In one embodiment, the step of adjusting the closing voltage based on the prediction result specifically includes:

[0102] Based on the predicted results of the voltage drop calculation, the required voltage regulation amount is calculated.

[0103] If the required voltage regulation exceeds the maximum voltage regulation capacity of the energy storage system, the controllable load is controlled to perform load shaving to reduce the voltage drop until the required voltage regulation is within the range of the maximum voltage regulation capacity.

[0104] After obtaining the voltage regulation completion signal, the circuit breaker is closed in sequence.

[0105] Specifically, the interconnecting switch S-EA determines the target voltage value to ensure that the voltage is within the preset acceptable range after closing, based on the predicted voltage drop ΔV_drop and the line's rated voltage and voltage qualification standards. It then calculates the required voltage regulation amount ΔU_req. For example, if ΔV_drop is predicted to be 0.5kV, the interconnecting switch S-EA calculates ΔU_req to be 0.5kV and sends a voltage regulation command to the energy storage interconnecting switch S-EA, requesting the energy storage system to adjust the line voltage to 10kV + 0.5kV = 10.5kV. During this process, the interconnecting switch S-EA evaluates the maximum voltage regulation capability ΔU_max fed back by the energy storage edge agent (S-EA). If the required voltage regulation amount ΔU_req exceeds the maximum voltage regulation capacity ΔU_max of the energy storage, for example, if the energy storage voltage regulation capacity is insufficient (e.g., ΔU_max = 0.4kV < 0.5kV), the tie switch S-EA will immediately and synchronously call the controllable load S-EA to perform load peak shaving, for example, reducing the load by ΔP_peak shaving = 20kW, thereby reducing the net active power deficit P_net to reduce the voltage drop ΔV_drop, until the remaining voltage regulation demand is within the capacity range of ΔU_max. After the voltage regulation command is issued, the tie switch S-EA will wait for the energy storage S-EA to return a voltage regulation completion signal, which is preferably required to return within a strict response time (e.g., ≤ 0.5 seconds). Once the S-EA successfully obtains the voltage regulation completion signal, it confirms that the voltage is ready and enters the subsequent sequential closing stage.

[0106] This embodiment provides a collaborative voltage adjustment mechanism based on prediction results, effectively solving the problems of limited energy storage resources and voltage regulation capabilities during self-healing. By explicitly setting the predicted voltage drop to the acceptable range after closing as the target value for voltage adjustment, this scheme ensures the accuracy and effectiveness of voltage regulation. More importantly, this scheme innovatively designs a collaborative logic between energy storage pre-voltage regulation and controllable load peak shaving: when the required voltage regulation exceeds the maximum voltage regulation capacity of energy storage ΔU_max, the system can automatically and synchronously call upon load peak shaving resources, reducing P_net to assist in reducing ΔV_drop, thereby overcoming the limitation of the maximum energy storage capacity on the success of self-healing. In addition, closing the circuit is only required after receiving the "voltage regulation complete" signal within a preset response time (e.g., ≤0.5 seconds), further ensuring the timing accuracy and safety of the self-healing action, significantly improving the success rate of self-healing and the reliability of power restoration.

[0107] Furthermore, in one embodiment, the sequential closing step specifically includes:

[0108] The interconnecting switch S-EA closes first, and after the voltage stabilizes and remains stable for a preset time, it sends a closing permission signal to the sectionalizing switch.

[0109] The sectionalizing switches are closed sequentially from the power supply side to the load side. After each switch is closed, the voltage and frequency are checked after a preset time. If no abnormality is found, the next switch is closed.

[0110] After the circuit is closed, the energy storage S-EA monitors the voltage in real time. If the voltage is lower than the preset lower limit, it will automatically discharge to replenish energy. If the voltage is higher than the preset upper limit, it will automatically charge to reduce peak loads in order to ensure power quality.

[0111] Specifically, the closing operation logic (total time ≤ 1 second) includes:

[0112] The tie switch closes first, and after the voltage stabilizes (U∈10±2%kV, lasting 0.2 seconds), it sends a "closing permission signal" to the section switch.

[0113] The sectionalizing switches are closed in the order of "from the power supply side to the load side". After each switch is closed, wait 0.5 seconds to check the voltage / frequency (voltage ≤10.7kV and ≥9.3kV, frequency ≤50.2Hz and ≥49.8Hz). If there is no abnormality, close the next switch.

[0114] After the circuit is closed, the energy storage S-EA monitors the voltage in real time (sampling frequency 1kHz). If U < 9.7kV, it automatically discharges to replenish energy (discharge response time ≤ 100ms); if U > 10.3kV, it automatically charges to reduce peak load (charging response time ≤ 100ms) to ensure power supply quality.

[0115] This embodiment provides a precise and reliable closing logic and a continuous power quality assurance mechanism. First, by specifying that the tie switch closes first and waits for voltage stability for a preset time (e.g., 0.2 seconds) before sending a closing permission signal to the sectionalizing switches, it effectively avoids systemic impacts that could be caused by multiple switches closing simultaneously. Second, the sectionalizing switches close sequentially "from the power supply side to the load side," and after each switch closes, a preset time (e.g., 0.5 seconds) is waited for dual voltage and frequency detection, ensuring that each power restoration operation is performed under stable system conditions, greatly reducing the risk of failure during the self-healing process. Most importantly, this embodiment innovatively designs a real-time monitoring and rapid response mechanism for the energy storage system (S-EA) after closing (e.g., a 1kHz sampling frequency and a response time ≤100ms), ensuring that automatic discharge replenishment or charging peak shaving can be performed when there are minor voltage deviations, thereby continuously guaranteeing the power quality of the restored power supply area and meeting the needs of sensitive loads with stringent power quality requirements.

[0116] like Figure 4The system described in the above embodiment can be composed of the following modules: at the physical device layer, it includes intelligent switches (for state sensing and control execution), distributed photovoltaics, energy storage systems, and controllable loads. The intelligent switches, distributed photovoltaics, energy storage systems, and controllable loads are all equipped with edge agents (S-EAs). The intelligent switch unit integrates an S-EA module, a high-precision sensing module (sampling frequency 2kHz), and an HPLC / 5G communication module, possessing setpoint calculation, collaborative discrimination, and graded tripping functions, and is compatible with conventional 10kV distribution network switch models (such as the ZW20-12 intelligent vacuum circuit breaker). There is a source-load-storage intelligent interface between the modules: a standardized interface (supporting Modbus) compatible with photovoltaic inverters (such as Huawei SUN2000-100KTL), energy storage converters (such as Sungrow Power 50kW energy storage converter), and charging piles (such as TELD 60kW DC fast charging piles). -RTU or IEC61850 protocol) to realize status acquisition and command response; to coordinate through a distributed collaborative communication network, the distributed system communication network is based on HPLC (communication delay ≤50ms, coverage radius ≤1km) or 5G private network (delay ≤20ms, supports wide area coverage), supporting local communication of up to 100 devices; VPP collaborative scheduling module (optional): when the source and load do not match, it interacts with the regional VPP platform (such as the State Grid Jiangsu Electric Power VPP scheduling system) to call external power supply, and automatically enables load peak shaving (peak shaving response time ≤1 second) if it does not depend on it.

[0117] The following example, a 10kV source-grid-load-storage demonstration line in an industrial park, illustrates the implementation process of this application. The line parameters and initial state are as follows:

[0118] Topology: 1 110kV substation (power supply, U=10.5kV) → 3 sectionalizing switches (S1, S2, S3, all ZW20-12 type intelligent vacuum circuit breakers) → 1 tie switch (S4, connected to backup power supply, U=10.3kV).

[0119] Line parameters: S1-S2 section 1km (R=0.1Ω, X=0.15Ω), S2-S3 section 1km (R=0.1Ω, X=0.15Ω).

[0120] Source-load-storage: 100kW photovoltaic (P_forecast=80kW, inverter model Huawei SUN2000-100KTL) 2km downstream of S2, 50kWh energy storage (SOC=85%, ΔU_max=±0.5kV, inverter model Sungrow Power Supply 50kW) 1km downstream of S3, and two 60kW fast charging piles between S2 and S3 (P_load=100kW, ΔP_peak shaving=40kW, response time 0.8 seconds).

[0121] Communication method: HPLC (delay 45ms, coverage radius 0.8km).

[0122] Step 1: State Awareness and Setpoint Calculation (Normal Operation)

[0123] Data collected by S2 switch S-EA: I_base = 8A (average load current over the past 5 minutes), U_avg = 10.2kV, PV P_new energy = 78kW (close to the forecast 80kW), energy storage SOC = 85%. Substituting into the dynamic setpoint formula: Iset(S2) = 1.2 × 8 + 0.8 × / 10.2+0.5×( / 100)+0.2≈9.6+0.16+0.075+0.2=9.98A (approximately 10A, used as the real-time overcurrent protection setting for S2).

[0124] Step 2: Fault Triggering and Collaborative Judgment (Cable Fault in S2-S3 Section)

[0125] Event: At time t=0, a short circuit occurs in the cable segment S2-S3. S2 detects I_real=15A>10A and initiates collaborative discrimination.

[0126] ①t=0.05 seconds: S2 sends a "positive overcurrent alarm (15A)" to S1, S3, photovoltaic S-EA, and energy storage S-EA;

[0127] ②t=0.2 seconds: Feedback signal received: Photovoltaic S-EA "no output fluctuation", energy storage S-EA "no charge / discharge switching", S3 "detect overcurrent (12A)" → meets "≥2 non-fluctuation feedback", determined to be a real fault;

[0128] ③t=0.3 seconds: Perform tiered tripping: S2 (near fault end, I_real=15A) trips after a delay of 0.1 seconds (t=0.4 seconds to complete tripping); S3 (far fault end, I_real=12A) detects that S2 has tripped after a delay of 0.3 seconds, stops tripping, and only disconnects its own load side (charging pile).

[0129] Step 3: Self-healing preparation (communicator switch S4 is in control)

[0130] t=0.5 seconds: S4 initiates self-healing preparation:

[0131] ① Source-load matching: Available power capacity = available photovoltaic power (78kW) + energy storage discharge_max (30kW) = 108kW; Load demand = current load of charging pile (100kW) - peak shaving reserve (40kW) = 60kW; 108kW ≥ 1.05 × 60kW (63kW), which is judged as self-healing;

[0132] ② Voltage sag calculation: P_net = 60kW - 108kW = -48kW (the negative sign indicates power surplus), Q_net = 30kVar (reactive load of charging pile) - 0 (reactive output of photovoltaic power) = 30kVar; Substituting into the formula:

[0133] (The negative sign indicates a voltage rise of 0.06kV).

[0134] ③ Energy storage pre-voltage regulation: Because it is predicted that the voltage will rise by 0.06kV after closing (current backup power supply U=10.3kV, after closing U=10.3+0.06=10.36kV, which is within the national standard range), S4 sends a "maintain current voltage (10.3kV)" command to the energy storage, and the energy storage responds "voltage regulation completed" (t=0.8 seconds).

[0135] Step 4: Power-on execution and power quality assurance

[0136] t=0.9 seconds: S4 closes first, detects that the voltage stabilizes at 10.35kV (lasts for 0.2 seconds, t=1.1 seconds), and sends a "closing permission signal" to S3;

[0137] t=1.2 seconds: S3 is closed, and the detected voltage is 10.32kV and the frequency is 50.0Hz (no abnormality);

[0138] t=1.7 seconds: S3 sends a "closing permission signal" to S2, S2 closes the circuit, the voltage is detected as 10.28kV (no abnormality), and the self-healing is completed;

[0139] After closing the circuit breaker, the charging pile will return to full load (100kW) in t=2.0 seconds, and the voltage will drop to 10.1kV. The energy storage will automatically discharge to replenish the energy (discharge power 10kW). In t=2.1 seconds, the voltage will rise back to 10.2kV and stabilize within the national standard range.

[0140] Implementation and verification results:

[0141] Fault isolation time: 0.4 seconds; Total self-healing time: 1.7 seconds;

[0142] Voltage qualification rate after closing: 100% (voltage fluctuation range 10.1-10.36kV);

[0143] Power outage time in non-faulty areas: 0 seconds (only 1.7 seconds for S2-S3 section), a 66% reduction compared to traditional technology (5 seconds outage);

[0144] New energy consumption: During the fault isolation and self-healing process, the photovoltaic system did not shut down and continued to supply power to non-faulty areas, consuming approximately 0.3 kWh more photovoltaic power.

[0145] In the above embodiments, firstly, this application significantly reduces the protection maloperation rate; through source-load-storage collaborative discrimination and dynamic setting, the maloperation rate is reduced by more than 60% compared with traditional technology, solving the problem of faultless tripping caused by "energy storage discharge and photovoltaic fluctuation" in VPP scenarios. For example, in a pilot project in an industrial park (10kV line, 100kW photovoltaic + 50kWh energy storage + 2 60kW charging piles), the number of monthly maloperations was reduced from 5 times to less than 1 time, thereby reducing power outage losses.

[0146] Secondly, this application achieves dual assurance of second-level self-healing and power quality; the distributed collaborative logic ensures that the total time for fault isolation (≤0.5 seconds) + self-healing (≤2 seconds) is ≤2.5 seconds (far faster than the traditional centralized method of more than 10 seconds); during the self-healing process, energy storage pre-voltage regulation and dynamic energy replenishment ensure that the voltage qualification rate after closing reaches 99.8% (voltage stabilizes at 9.5-10.5kV), meeting the needs of sensitive loads such as precision equipment and data centers, and improving power supply reliability to 99.99%.

[0147] Furthermore, this application is deeply adapted to VPP and source-grid-load-storage scenarios; the coordinated operation of the switch and the source-grid-storage equipment within the VPP supports fault handling for multi-power supply joint power supply - for example, when a 10kV VPP line (2 100kW photovoltaic + 1 100kWh energy storage) fails, the switch synchronously isolates the fault and retains photovoltaic power supply in the non-faulty area, increasing the renewable energy consumption rate by 15% and reducing curtailment of solar power by approximately 5MWh / year.

[0148] Finally, this application features plug-and-play functionality and high scalability; when adding new photovoltaic, energy storage, or controllable loads, it only needs to be connected to the distributed collaborative network, and the switch S-EA automatically updates the state matrix and setpoint calculation logic without manual reconfiguration, improving operation and maintenance efficiency by 70%—adapting to the rapid promotion needs of "county-wide photovoltaics." For example, when adding 50 distributed photovoltaic units in a county, it only takes 1 day to complete the connection and commissioning, saving 5 days of operation and maintenance time compared to traditional solutions.

[0149] In addition, this application has significant economic advantages; it reduces power outage losses caused by malfunctions (the annual loss of a 10kV industrial line is reduced by more than 100,000 yuan); energy storage participates in voltage regulation to reduce the voltage regulation pressure on the main grid, reduces the number of main transformer tap changes (from an average of 20 times per month to 5 times), extends equipment life by 3-5 years, and reduces the main transformer operation and maintenance cost by about 20% per year.

[0150] Furthermore, some embodiments of this application also provide an electronic device. The electronic device can be various forms of digital computer, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, etc. The electronic device can also be various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices.

[0151] The electronic device includes: one or more processors; and a memory storing computer program instructions that, when executed, cause the processor to perform the steps of the methods provided in any one or more of the above embodiments. Figure 5 An exemplary structural diagram of the electronic device is disclosed. The electronic device includes one or more processors 1101, a memory 1102, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components are interconnected via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the electronic device, including instructions stored in or on memory to display graphical information of a GUI on an external input / output device (such as a display device coupled to the interface). In some other embodiments, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple electronic devices can be connected, each providing some of the necessary operations. The components, their connections and relationships, and their functions shown herein are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0152] The electronic device may further include an input device 1103 and an output device 1104. The processor 1101, memory 1102, input device 1103 and output device 1104 may be connected by a bus or other means, as shown in the figure, which is connected by a bus.

[0153] Input device 1103 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the electronic device, such as a touch screen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 1104 may include a display device, auxiliary lighting device (e.g., LED), and haptic feedback device (e.g., vibration motor). The display device may include, but is not limited to, a liquid crystal display, a light-emitting diode display, and a plasma display. In some embodiments, the display device may be a touch screen.

[0154] To provide interaction with the user, the electronic device can be a computer. The computer has: a display device (e.g., a cathode ray tube or LCD monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback); and input from the user can be received in any form (e.g., voice input or tactile input).

[0155] In this embodiment, a computer-readable medium stores a computer program / instructions that, when executed by a processor, implement the steps of the methods provided in any one or more of the above embodiments. This computer-readable medium may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into that device. The aforementioned computer-readable medium carries one or more computer-readable instructions.

[0156] The memory 1102 can serve as a non-transitory computer-readable storage medium, used to store non-transitory software programs, non-transitory computer-executable programs, and modules. The processor 1101 executes various functional applications and data processing of the server by running the non-transitory software programs, instructions, and modules stored in the memory 1102, thereby implementing the program instructions / modules corresponding to the methods provided in any one or more of the embodiments described above in this application.

[0157] The memory 1102 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the electronic device. Furthermore, the memory 1102 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, the memory 1102 may optionally include memory remotely located relative to the processor 1101, and these remote memories can be connected to the electronic device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0158] It should be noted that the computer-readable medium described in this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. Computer-readable media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, electrical connections having one or more wires, portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, optical fibers, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination thereof. In this application, a computer-readable medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0159] Computer-readable media include permanent and non-permanent, removable and non-removable media, which can store information by any method or technology. Information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase-change memory, static random access memory, dynamic random access memory, other types of random access memory, read-only memory, electrically erasable programmable read-only memory, flash memory or other memory technologies, read-only optical discs, digital versatile optical discs or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transfer medium that can be used to store information accessible by a computing device.

[0160] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including local area networks (LANs) or wide area networks (WANs), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0161] In the above embodiments, all or part of the implementation can be achieved through software, hardware, firmware, or any combination thereof. For example, it can be implemented using an application-specific integrated circuit (ASIC), a general-purpose computer, or any other similar hardware device. In some embodiments, the software program of this application can be executed by a processor to implement the above steps or functions. Similarly, the software program of this application (including related data structures) can be stored in a computer-readable recording medium, such as RAM memory, magnetic or optical drives, floppy disks, and similar devices. In addition, some steps or functions of this application can be implemented in hardware, for example, as circuitry that cooperates with a processor to perform the various steps or functions.

[0162] The computer program product provided in this application includes one or more computer programs / instructions. When executed by a processor, these computer programs / instructions generate, in whole or in part, the processes or functions described in this application. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state drive), etc.

[0163] The flowcharts or block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of devices, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-specific system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0164] The scope of this application is defined by the appended claims rather than the foregoing description, and is therefore intended to encompass all variations falling within the meaning and scope of equivalents of the claims. No reference numerals in the claims should be construed as limiting the scope of the claims. Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices recited in a device claim may also be implemented by a single unit or device in software or hardware. Terms such as "first," "second," etc., are used only for distinguishing descriptions and do not indicate any particular order, nor should they be construed as indicating or implying relative importance.

[0165] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily made by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims, and the above embodiments should be regarded as exemplary and non-limiting.

Claims

1. A distribution network protection and self-healing method based on source-grid-load-storage coordination, characterized in that, The method is applied to a power distribution network formed by distribution network switches, new energy equipment, energy storage systems, and controllable load equipment configured with edge agents (S-EAs), and the method includes: The source-grid-load-storage status is obtained, which is determined by the operating status data of the new energy equipment, the energy storage system, and the controllable load equipment. Based on the source-grid-load-storage status, the overcurrent protection value is calculated and updated in real time; When the actual current exceeds the overcurrent protection value, an alarm signal is sent to the new energy edge intelligent agent (S-EA) and / or the energy storage edge intelligent agent (S-EA) and / or the upstream and downstream switches based on the topology of the power distribution network, and a feedback signal is received. If the feedback signal is not received within the preset time, a graded tripping action is performed based on the number of times the actual current exceeds the overcurrent protection value; If the feedback signal is not received within a preset time, a tiered tripping action is performed based on the number of times the actual current exceeds the overcurrent protection value, specifically including: The feedback signals include whether the new energy source is fluctuating and / or whether the energy storage is switching between charging and discharging and / or whether the downstream switch is detecting overcurrent. If the feedback signal is not received within the preset time, and the number of times the actual current exceeds the overcurrent protection value reaches the first preset threshold, it is determined to be a real fault, and a graded tripping action is executed. If the feedback signal is received and the number of times the actual current exceeds the overcurrent protection value reaches a second preset threshold, it is determined to be a disturbance overcurrent, and the overcurrent protection value is increased.

2. The method according to claim 1, characterized in that, After performing the tiered tripping action, the method further includes: Collect available power capacity and load demand in the power outage area to determine whether the self-healing conditions are met; If the self-healing condition is met, voltage drop calculation is performed to predict the voltage change of the line after closing. Adjust the closing voltage based on the prediction results; After the closing voltage has been adjusted, close the circuit in sequence.

3. The method according to claim 1, characterized in that, The tiered tripping specifically refers to: The switch with the largest detected overcurrent amplitude is identified as the near-fault switch, and the near-fault switch trips after a delay. A switch whose overcurrent amplitude is less than the overcurrent amplitude threshold is identified as a remote fault switch. If the near fault switch does not trip, a supplementary trip is performed. The delay tripping time of the remote fault switch is greater than that of the near fault switch.

4. The method according to claim 1, characterized in that, The operating status data of the new energy equipment, the energy storage system, and the controllable load equipment include: The real-time output and output forecast of the new energy equipment, the remaining capacity and voltage regulation capability of the energy storage system, and the current load and load reduction amount of the controllable load equipment.

5. The method according to claim 1, characterized in that, The overcurrent protection value is determined by weighting the line base load current, the real-time output and output forecast of new energy sources, the remaining capacity of the energy storage system, and the fixed safety margin.

6. The method according to claim 2, characterized in that, The step of adjusting the closing voltage based on the prediction result specifically includes: Based on the predicted results of the voltage drop calculation, the required voltage regulation amount is calculated; If the required voltage regulation exceeds the maximum voltage regulation capacity of the energy storage system, the controllable load is controlled to perform load shaving to reduce the voltage drop until the required voltage regulation is within the range of the maximum voltage regulation capacity. After obtaining the voltage regulation completion signal, close the circuit in sequence.

7. An electronic device, characterized in that, The electronic device includes: One or more processors; and A memory storing computer program instructions, which, when executed, cause the processor to perform the steps of the method as described in any one of claims 1 to 6.

8. A computer-readable medium having a computer program / instructions stored thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 6.

9. A computer program product comprising a computer program / instructions, characterized in that, When the computer program / instructions are executed by the processor, they implement the steps of the method according to any one of claims 1 to 6.

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