Ice disaster power distribution network reliability improvement method based on micro-meteorological perception multi-resource cooperation

Abstract

This invention provides a method for improving the reliability of distribution networks during ice storms based on micro-meteorological sensing and multi-resource collaboration, relating to the field of distribution network disaster prevention technology. The method includes: simulating a distribution network fault sequence during the ice storm evolution process using Markov chains based on real-time ice storm data acquired during the ice storm and a pre-defined distribution network recovery plan; determining the expected value of critical load losses based on the distribution network fault sequence; generating a new distribution network recovery plan with the objective function of minimizing power supply loss if the expected value is greater than a preset risk threshold; estimating the transient impact intensity based on the new distribution network recovery plan; selecting target equipment whose regulation performance meets preset regulation performance standards when the transient impact intensity exceeds a preset safety limit, and using the target equipment to reduce the transient impact intensity; and executing the new distribution network recovery plan when the reduced transient impact intensity is less than the preset safety limit. This method reduces expected power outage energy losses and improves the power supply reliability of the distribution network during ice storms.

Description

Technical Field

[0001] This invention relates to the field of disaster prevention technology for power distribution networks, and in particular to a method for improving the reliability of power distribution networks during ice storms based on micro-meteorological sensing and multi-resource collaboration. Background Technology

[0002] Severe ice storms such as snow and freezing rain can cause widespread damage to power distribution networks, leading to large-scale power outages and disruptions to critical loads. Currently, various strategies exist to enhance the post-disaster recovery capabilities of power distribution networks, such as emergency recovery through distribution network reconfiguration and distributed power source dispatching.

[0003] However, existing technologies still have the following shortcomings: First, they lack adaptive perception of the dynamic evolution of ice storms, and pre-planned recovery plans may fail or bring secondary risks under actual ice conditions; second, they lack transient safety verification of fault recovery operations, and loop-closing operations may trigger secondary fault hazards such as inrush surges. Therefore, how to improve the reliability of the distribution network under extreme weather conditions such as ice storms and shorten the outage time of critical loads has become an urgent problem to be solved. Summary of the Invention

[0004] This invention provides a method for improving the reliability of distribution networks during ice storms based on micro-meteorological sensing and multi-resource collaboration, in order to solve the technical problems in existing technologies where distribution network recovery schemes cannot adapt to the dynamic evolution of ice storms and where loop-closing operations lack transient safety verification.

[0005] On the one hand, this invention provides a method for improving the reliability of ice-affected power distribution networks based on micro-meteorological sensing and multi-resource collaboration, including: Based on real-time ice disaster data obtained during the ice disaster and a pre-established distribution network recovery plan, the distribution network fault sequence during the ice disaster evolution process is simulated using Markov chains. The expected value of critical load loss is determined based on the fault sequence of the distribution network; If the expected value is greater than the preset risk threshold, a new power distribution network restoration scheme is generated with the goal of minimizing power supply loss. Based on the new power distribution network restoration plan, the transient impact intensity is estimated; When the transient impact intensity exceeds a preset safety limit, a target device whose adjustment performance meets the preset adjustment performance standard is selected, and the target device is used to reduce the transient impact intensity. When the reduced transient impact intensity is less than or equal to the preset safety limit, the new power distribution network restoration plan is executed.

[0006] Optionally, the power distribution network restoration plan is formulated in the following manner: Obtain historical icing thickness and historical wind speed for each area of ​​the power distribution network; Based on the historical ice thickness and historical wind speed, a failure probability model for overhead lines, a power output attenuation model for photovoltaic power generation equipment, and a power output degradation model for distributed power sources are established. Based on the failure probability model, the power output attenuation model, and the power output degradation model, a power distribution network restoration scheme is obtained.

[0007] Optionally, the failure probability model includes: The weight of the ice layer is determined based on the equivalent density of the ice layer, the equivalent ice-receiving area of ​​the conductor in contact with the ice, and the ice thickness. ; in, The weight of the ice covering the surface of the conductor; This represents the equivalent density of the ice layer. The equivalent ice-covered area of ​​the conductor in contact with the ice. The thickness of the ice covering the conductor; Based on air density, drag coefficient, windward projected area and wind speed, determine the stress on the tower under wind load; ; in, For the tower to bear the force; air density; This refers to the drag coefficient; The projected area facing the wind; Wind speed; Based on the weight of the ice accumulation and the stress on the tower, a failure probability model is determined; ; in, This is the failure sensitivity coefficient, used to characterize the degree of influence of increased load on the failure probability; This is a failure probability model.

[0008] Optionally, the output attenuation model includes: Based on the rated output power of the photovoltaic array, the thickness of ice covering the photovoltaic panel surface and the temperature deviation, the actual output power of the photovoltaic array under ice disaster conditions is determined as a power attenuation model. ; in, This represents the actual output power of the photovoltaic array under ice storm conditions. This refers to the rated output power of the photovoltaic array. The attenuation coefficient of photovoltaic power output due to icing; The thickness of the ice coating on the surface of the photovoltaic panel; This is the coefficient representing the influence of temperature deviation on photovoltaic power output. Temperature deviation refers to the deviation of the ambient temperature from the reference operating temperature.

[0009] Optionally, the output degradation model includes: Based on the rated available output of distributed power sources and the time-varying degradation coefficient, an output degradation model is obtained; ; in, Let t be the actual available output of the distributed power source at time t; This refers to the initial or rated available output of a distributed power source. It represents the time-varying degradation coefficient under the influence of disasters.

[0010] Optionally, determining the expected value of critical load loss based on the distribution network fault sequence includes: Construct the Markov state transition probability matrix; ; in, ; Here is the Markov state transition probability matrix; For the system to be in state Transition to state The one-step transition probability; For probability operators; For a moment The system status; For a moment The system status; Based on the state transition probability matrix, determine the probability of each fault state; Determine the expected value of critical load loss based on the probability of failure conditions; ; This represents the expected value of critical load losses. For the system to be in the first The probability of a fault state; For the first The load loss corresponding to each fault state.

[0011] Optionally, the objective function includes: ; in, For the set of scheduling time periods; For the set of load nodes; For the collection of emergency resources; For a set of switches; For nodes Load importance weight; For time period node The power of the unsupplied load; The scheduling time step; For resources The cost coefficient for invocation or execution; For resources During the period Input decision variables; For switch Operating cost coefficient; For time period switch Action variables.

[0012] Optionally, a new distribution network restoration scheme is generated with the objective function of minimizing power supply loss, satisfying at least one of the following constraints: Constraints include: power balance and load loss; priority restoration of critical loads; network operation constraints; distributed power generation and photovoltaic output constraints; mobile energy storage constraints; photovoltaic energy storage and charging station constraints; drone communication coverage constraints; and emergency repair team operation constraints.

[0013] Optionally, estimating the transient impact intensity based on the new power distribution network restoration plan includes: The regulation power of the photovoltaic-energy storage-charging integrated station is obtained, and the loop closing operation information is extracted from the new distribution network restoration scheme; wherein, the loop closing operation information includes the equivalent voltage amplitude on the island side, the equivalent voltage amplitude on the main grid side, and the equivalent impedance of the loop closing circuit; Divide the difference between the equivalent voltage amplitude on the island side and the equivalent voltage amplitude on the main grid side by the equivalent impedance of the closed loop, and then subtract the regulation power of the photovoltaic-energy storage-charging integrated station to obtain the closed loop inrush current; ; in, This is an estimated value for the closed-loop impact current; This represents the equivalent voltage amplitude on the islanded side before loop closure; The equivalent voltage amplitude on the main grid side; The equivalent impedance of the closed loop; The regulating power of the photovoltaic-energy storage-charging integrated station.

[0014] Optionally, the target device is a photovoltaic-energy storage-charging integrated station, and the method of using the target device to reduce the transient impact intensity includes: ; in, This refers to the local node voltage after loop-closed regulation; This represents the equivalent voltage amplitude on the islanded side before loop closure; This refers to the voltage regulation sensitivity or damping coefficient.

[0015] This invention provides a method for improving the reliability of distribution networks during ice storms based on micro-meteorological sensing and multi-resource collaboration. It determines the expected value of critical load losses based on the distribution network fault sequence. If the expected value is greater than a preset risk threshold, a new distribution network recovery plan is generated with minimizing power supply loss as the objective function. Based on the new recovery plan, the transient impact intensity is estimated. When the transient impact intensity exceeds a preset safety limit, a target device whose regulation performance meets a preset regulation performance standard is selected, and the target device is used to reduce the transient impact intensity. When the reduced transient impact intensity is less than or equal to the preset safety limit, the new distribution network recovery plan is executed. This invention can adjust the recovery plan in real time according to the dynamic evolution of the ice storm, significantly improving the recovery speed and rate of critical loads; by using target devices to reduce transient impact intensity, it effectively avoids secondary power outages during the recovery process, enhances the safety of recovery operations, reduces expected power outage energy losses, and improves the power supply reliability of the distribution network under ice storm conditions. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0017] Figure 1 This is a flowchart illustrating the method for improving the reliability of ice disaster distribution networks based on micro-meteorological sensing and multi-resource collaboration provided in an embodiment of the present invention. Figure 2 This is a schematic diagram comparing the critical load recovery rate under different strategies provided in the embodiments of the present invention; Figure 3 This is a schematic diagram comparing the expected unsupplied power energy under different strategies provided in the embodiments of the present invention; Figure 4 This is a schematic diagram of the event and action timeline of the strategy provided in this embodiment of the invention in a simulation scenario. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0019] Figure 1This is a flowchart illustrating the method for improving the reliability of ice disaster distribution networks based on micro-meteorological sensing and multi-resource collaboration, provided in an embodiment of the present invention.

[0020] See Figure 1 Methods for improving the reliability of power distribution networks during ice storms based on micro-meteorological sensing and multi-resource collaboration may include: Step 101: Based on the real-time ice disaster data obtained during the ice disaster and the pre-established distribution network recovery plan, simulate the distribution network fault sequence during the ice disaster evolution process using Markov chains.

[0021] Step 102: Determine the expected value of critical load loss based on the fault sequence of the distribution network.

[0022] Step 103: If the expected value is greater than the preset risk threshold, a new power distribution network restoration scheme is generated with the goal of minimizing power supply loss.

[0023] It is understandable that if the expected value is less than or equal to the preset risk threshold, the current power distribution network restoration plan can continue to be implemented.

[0024] Step 104: Estimate the transient impact intensity based on the new power distribution network restoration plan.

[0025] Step 105: When the transient impact intensity is greater than the preset safety limit, select a target device whose adjustment performance meets the preset adjustment performance standard, and use the target device to reduce the transient impact intensity.

[0026] Step 106: When the reduced transient impact intensity is less than or equal to the preset safety limit, execute the new power distribution network restoration plan.

[0027] In this embodiment, ice disaster data may include ice thickness and wind speed on equipment. The expected value of critical load loss is determined based on the distribution network fault sequence. If the expected value is greater than a preset risk threshold, a new distribution network recovery plan is generated with minimizing power supply loss as the objective function. Based on the new distribution network recovery plan, the transient impact intensity is estimated. When the transient impact intensity exceeds a preset safety limit, a target device whose regulation performance meets a preset regulation performance standard is selected, and the transient impact intensity is reduced using the target device. When the reduced transient impact intensity is less than or equal to the preset safety limit, the new distribution network recovery plan is executed. This invention can adjust the recovery plan in real time according to the dynamic evolution of the ice disaster, significantly improving the recovery speed and recovery rate of critical loads; by using target devices to reduce transient impact intensity, it effectively avoids secondary power outages during the recovery process, enhances the safety of the recovery operation, reduces expected power outage energy loss, and improves the power supply reliability of the distribution network under ice disasters.

[0028] In one embodiment of this specification, the power distribution network restoration scheme can be formulated in the following manner: Step 1: Obtain historical icing thickness and historical wind speed for each area of ​​the power distribution network; Step 2: Based on historical icing thickness and historical wind speed, establish a failure probability model for overhead lines, a power output attenuation model for photovoltaic power generation equipment, and a power output degradation model for distributed power sources. Step 3: Based on the failure probability model, power attenuation model, and power degradation model, obtain the power distribution network restoration scheme.

[0029] In this embodiment, by using the failure probability model, power attenuation model, and power degradation model, the failure risk of overhead lines under icing and wind loads, the power attenuation degree of photovoltaic panels after icing, and the power degradation level of distributed power sources under ice disaster conditions can be assessed in advance. This allows the pre-formulated recovery plan to fully consider the actual impact of ice disasters on equipment reliability and power availability, and avoid post-disaster recovery delays or failures.

[0030] In one embodiment of this specification, the failure probability model may include: Based on the equivalent density of the ice layer, the equivalent ice-receiving area of ​​the conductor in contact with the ice, and the ice thickness, the ice weight is determined as shown in formula (1). (1); in, The weight of ice covering the surface of the conductor can be expressed in kg or N (or N if measured by a gravimetric analyzer). This is the equivalent density of the ice layer, in kg / m³. The equivalent ice-covered area of ​​the conductor in contact with the ice, in m²; The ice thickness on the conductor is expressed in meters (m). Based on air density, wind resistance coefficient, windward projected area and wind speed, the force on the tower under wind load is determined as shown in formula (2); (2); in, The force on the tower is expressed in N; Air density, unit: kg / m³; The drag coefficient is dimensionless. The projected area facing the wind is in m². Wind speed, in m / s; Based on the weight of the ice and the stress on the tower, the failure probability model is determined as shown in formula (3); (3); in, This is the failure sensitivity coefficient, used to characterize the degree of influence of increased load on the failure probability; This is a failure probability model.

[0031] In this embodiment, the weight of ice accumulation and wind load can comprehensively reflect the mechanical impact of ice disasters on overhead lines, avoiding risk assessment bias caused by considering only a single load factor, thereby more accurately identifying high-risk lines.

[0032] In one embodiment of this specification, the output attenuation model may include: Based on the rated output power of the photovoltaic array, the thickness of the ice covering the photovoltaic panel surface and the temperature deviation, the actual output power of the photovoltaic array under the ice disaster conditions is determined as the output power attenuation model, as shown in formula (4). (4); in, The actual output power of the photovoltaic array under ice storm conditions, in kW or MW; Rated output power of the photovoltaic array, in kW or MW; This is the attenuation coefficient of photovoltaic power output due to icing, which can be expressed as 1 / m. The thickness of the ice coating on the photovoltaic panel surface, in meters (m). This is the influence coefficient of temperature deviation on photovoltaic power output, and its unit can be written as 1 / ℃; Temperature deviation refers to the deviation of the ambient temperature from the reference operating temperature, expressed in °C.

[0033] In this embodiment, the actual output power of the photovoltaic array under ice disaster conditions is determined based on the rated output power of the photovoltaic array, the thickness of the ice covering the photovoltaic panel surface, and the temperature deviation. This fully considers the impact of ice covering on the performance of the photovoltaic array, thereby accurately evaluating the output of the photovoltaic array.

[0034] In one embodiment of this specification, the output degradation model may include: Based on the rated available output of the distributed power source and the time-varying degradation coefficient, the output degradation model is obtained, as shown in formula (5); (5); in, The actual available output of the distributed power source at time t, in kW or MW; Initial or rated available output of distributed power sources, in kW or MW; This is the time-varying degradation coefficient under the influence of disasters, and its value is generally in the range of [0,1].

[0035] In this embodiment, the time-varying degradation coefficient can dynamically display the attenuation trend of distributed power output during the development of ice storms, avoiding the mismatch between the recovery plan and the actual power supply capacity caused by using a fixed output value, and improving the feasibility of the recovery plan.

[0036] In one embodiment of this specification, determining the expected value of critical load loss based on a distribution network fault sequence includes: Construct the Markov state transition probability matrix as shown in formula (6); (6); in, ; Here is the Markov state transition probability matrix; For the system to be in state Transition to state The one-step transition probability; For probability operators; For a moment The system status; For a moment The system status; Based on the state transition probability matrix, determine the probability of each fault state; Based on the probability of the fault state, the expected value of the critical load loss is determined, as shown in formula (7). (7); This represents the expected value of critical load losses. For the system to be in the first The probability of a fault state; For the first The load loss corresponding to each fault state is expressed in kW, MW, or MWh.

[0037] In this embodiment, by using Markov dynamic simulation to determine the probability transition of distribution network fault states during the evolution of an ice storm, the risk of critical load loss under different times and fault scenarios can be identified, thus preventing the original plan from failing due to dynamic changes in the disaster situation. Critical loads can be set according to the actual conditions of the region; for example, hospitals and communication hubs can be designated as critical loads.

[0038] In one embodiment of this specification, the objective function may include the following formula (8): (8); in, For the set of scheduling time periods; For the set of load nodes; For the collection of emergency resources; For a set of switches; For nodes Load importance weight, dimensionless; For time period node The power of the unsupplied load, in kW or MW; The scheduling time step is expressed in hours (h). For resources The cost coefficient for invocation or execution; For resources During the period Input decision variables; For switch Operating cost coefficient; For time period switch Action variables.

[0039] In this embodiment, by comprehensively considering the load loss due to power outages and the cost of resource scheduling, a balance is achieved between the restoration effect and the economic cost, ensuring that the generated new distribution network restoration scheme meets the power supply restoration requirements while minimizing scheduling costs. The emergency resource set can be understood as various emergency equipment that can be scheduled during the ice storm recovery process, including grid switches, distributed power sources, mobile energy storage systems, drones, etc. The switch set may include sectionalizing switches, tie switches, circuit breakers, etc.

[0040] In one embodiment of this specification, a new distribution network restoration scheme is generated with the objective function of minimizing power supply loss, satisfying at least one of the following constraints: Power balance and load loss constraints, as shown in formulas (9) to (12); (9); in, Mainnet during time period To the node The active power provided; the main grid can refer to the transmission network that provides upstream power to the distribution network. Active power output for distributed power sources; The active power provided to the system by mobile energy storage; Active power regulation provided to PSCIS; Absorbs power to charge energy storage; For nodes The set of connected adjacent nodes; , It has contributed significantly to the tidal flow of the branch line; For nodes During the period The active power load demand; For nodes During the period The power of the unsupplied load.

[0041] (10); (11); (12); in, This represents the actual power restored to node i during time period t. Mainnet during time period To the node The reactive power provided; For reactive power output of distributed power sources; The reactive power regulation power provided for PSCIS; , The reactive power of the branch line; For nodes During the period The reactive load demand.

[0042] Critical load priority recovery constraints, such as Equation (13) and Equation (14). (13); in, A set of critical load nodes; This is a set of non-critical load nodes; Non-critical nodes The actual power supply restored during time period t; Non-critical nodes Load demand during time period t; This is an index for non-critical nodes.

[0043] (14); in, For critical load nodes The minimum power supply guarantee ratio, with a value range of [0,1].

[0044] Network operation constraints, such as Equations (15) to (20); (15); in, branch road During the period The state variable is 1 when closed and 0 when open. branch road Maximum allowed transmission capacity; (16); (17); in, Branch road set; For time period The total number of nodes in the network is now restored. For time period Number of independent power supply areas or islands. Let be the minimum voltage allowed at node i. This represents the maximum voltage allowed at node i. Let be the actual voltage of node i at time t.

[0045] (18); (19); (20); in, For switch During the period State variables; The switch status during time period t-1; For switch action variables; For time period The maximum number of switch operations allowed to be performed within the specified range.

[0046] Distributed power generation and photovoltaic output constraints, as shown in formulas (21) to (23). (twenty one); (twenty two); in, For time period node The actual output of the distributed power source; This represents the maximum available output under the impact of the ice storm. For nodes Rated power of distributed power sources; This is the ice disaster degradation coefficient, typically taking a value in [0,1]. (twenty three); in, For nodes Time period Photovoltaic power output; For nodes Photovoltaic rated power; For nodes The icing degradation coefficient of photovoltaic modules; For nodes Time period The thickness of the ice cover; This is the combined correction factor for solar irradiance and weather conditions, with a value range of [0,1].

[0047] Mobile energy storage constraints, as shown in formulas (24) to (31); (twenty four); (25); (26); in, The charging power of mobile energy storage m during time period t; Let m be the discharge power of the mobile energy storage device during time period t; This is a charging state variable, which takes the value of 1 when charging. This is a discharge state variable, which is set to 1 during discharge. The maximum charging power of the mobile energy storage m; The maximum discharge power of the mobile energy storage unit m.

[0048] (27); (28); in, For mobile energy storage During the period The state of charge; For mobile energy storage During the period The state of charge; For charging efficiency; For discharge efficiency; This is the rated energy storage capacity. For mobile energy storage The minimum energy storage. For mobile energy storage Maximum energy storage.

[0049] (29); (30); (31); in, For time period Mobile energy storage Is it located at node? The value is 1 when it is located, and 0 otherwise. For the node Move to node Travel time; To execute or not arrive The transition decision variable is 1 when transitioning, and 0 otherwise. For time period Mobile energy storage Is it located at node? .

[0050] The constraints of photovoltaic energy storage and charging integrated station (PSCIS constraints) are as shown in formulas (32) to (34). (32); (33); (34); in, This represents the maximum charging power of the PSCIS. This represents the maximum discharge power of the PSCIS. The active power regulation of the PSCIS is t. The minimum reactive power of the PSCIS at time t; The maximum reactive power of the PSCIS at time t; The reactive power of the PSCIS is at time t. This represents the maximum apparent power capacity of the PSCIS.

[0051] Unmanned aerial vehicle (UAV) communication coverage constraints, such as Equation (35) and Equation (36); (35); (36); in, For drones; For drones During the period Is it a node? Provides communication coverage; 1 indicates yes, 0 indicates no. For nodes During the period Minimum communication guarantee requirements.

[0052] Operational constraints for emergency repair teams, as shown in formulas (37) to (41). (37); (38); (39); Among them, F represents the assembly of the emergency repair team; For the repair team During the period Is the fault point... Operation; Fault point The time when the repair is completed; Fault point During the period Indicator variables for completed repair; Fault point Total repair man-hours required.

[0053] (40); (41); in, branch road During the period Has it been repaired and is it usable? This refers to the branch's operational state variable.

[0054] Evaluation indicators, such as formulas (42) and (43). (42); (43); in, The critical load recovery rate for time period t; The energy source for expected power outages is expressed in kWh or MWh.

[0055] In this embodiment, power balance and load loss constraints prevent system instability caused by power shortages or excesses; critical load priority restoration constraints ensure that limited power resources are prioritized for critical load users such as hospitals and communication hubs; network operation constraints limit branch capacity, node voltage, network radial structure, and the number of switching operations to ensure the safe operation of the distribution network after restoration; distributed power generation and photovoltaic output constraints prevent the scheme from becoming infeasible due to overestimating power output; mobile energy storage constraints limit charging and discharging power, state of charge, and spatiotemporal transfer to ensure the scheduling scheme is feasible in practice; photovoltaic-energy storage-charging integrated station power constraints prevent equipment overload damage; drone communication coverage constraints ensure uninterrupted communication during post-disaster recovery; and emergency repair team operation constraints ensure that physical faults can be repaired in a timely manner. These constraints collectively guarantee that the generated new distribution network restoration scheme is feasible, safe, and reliable.

[0056] In one embodiment of this specification, estimating the transient impact intensity based on the new power distribution network restoration plan includes: The regulation power of the photovoltaic-energy storage-charging integrated station is obtained, and the loop closing operation information is extracted from the new distribution network restoration scheme. The loop closing operation information includes the location of the loop closing point, the equivalent voltage amplitude on the islanded side, the equivalent voltage amplitude on the main grid side, and the equivalent impedance of the loop closing circuit. Divide the difference between the equivalent voltage amplitude on the island side and the equivalent voltage amplitude on the main grid side by the equivalent impedance of the closed loop, and then subtract the regulation power of the photovoltaic-storage-charging integrated station to obtain the closed loop impact current, as shown in formula (44). (44); in, This is an estimated value for the closed-loop inrush current (surge current), in amperes (A). This represents the equivalent voltage amplitude on the islanded side before loop closure, in V or kV. The equivalent voltage amplitude on the main grid side, in V or kV; The equivalent impedance of the closed loop is expressed in Ω. The regulating power of the photovoltaic-energy storage-charging integrated station is expressed in kW or MW.

[0057] In this embodiment, the difference between the equivalent voltage amplitude on the island side and the equivalent voltage amplitude on the main grid side is divided by the equivalent impedance of the loop-closing circuit, and then the regulation power of the photovoltaic-storage-charging integrated station is subtracted to obtain the loop-closing inrush current. This allows for the prediction of whether the transient inrush intensity is within a safe range before the loop-closing operation, thus avoiding relay protection malfunction or equipment damage due to excessive inrush current.

[0058] If a direct loop closure is found to potentially trigger an excessive inrush current exceeding the safety threshold, the rapid adjustment capability of the PSCIS (Power Supply Control System) is utilized to mitigate the impact: on one hand, the relevant PSCIS injects (or absorbs) power at the moment of loop closure, reducing the voltage amplitude and phase difference between the two sides, thereby suppressing the inrush current amplitude; on the other hand, if necessary, measures such as phased and gradual load connection are taken to control the inrush current within a safe range. Simultaneously, voltage dips and frequency shifts during the loop closure process are checked to ensure that sensitive components do not malfunction. After a transient safety check, the corresponding switching operation is performed to restore power. After completing the current stage of reconstruction and scheduling measures, the dispatch center continues to monitor the ice conditions and network status in subsequent periods: if the ice storm continues to develop and new fault threats emerge, the risk assessment and scheme adjustment are repeated cyclically until the ice storm ends and all loads are fully restored to power, achieving a closed-loop adaptive recovery throughout the entire process.

[0059] In one embodiment of this specification, the target device is a photovoltaic-energy storage-charging integrated station. The target device is used to reduce the intensity of transient impacts, including the following formula (45): (45); in, This refers to the local node voltage after loop regulation, in V or kV. This represents the equivalent voltage amplitude on the islanded side before loop closure; This is the voltage regulation sensitivity or damping coefficient, which is dimensionless.

[0060] In this embodiment, the voltage difference between the islanded side and the main grid side during the loop closing operation is effectively reduced, thereby suppressing the amplitude of the loop closing inrush current, avoiding excessive inrush current impact caused by excessive voltage difference, ensuring that the loop closing operation is performed within the transient safety constraints, and preventing secondary faults during the recovery process.

[0061] In some embodiments, when the reduced transient impact intensity is less than or equal to a preset safety limit, a new distribution network restoration plan is executed. The new distribution network restoration plan may include: coordinating and controlling multiple emergency resources, implementing distribution network restoration in stages; specifically, through distribution network reconfiguration, using remote-controlled switches to isolate faulty areas and reconstruct the network topology, transferring healthy feeders to handle lost loads; adjusting the output of distributed power sources to prioritize power supply to local critical loads; rapidly deploying mobile energy storage systems near important loads to provide temporary power and support local voltage; using drones carrying emergency communication base stations to provide communication coverage to disaster areas, or carrying infrared imaging inspection equipment to accelerate fault location; fully utilizing the rapid charging and discharging capabilities of photovoltaic-energy storage-charging integrated stations to support isolated loads and participate in power regulation; and dispatching repair teams to the site promptly to replace hardware faults such as fallen poles and broken wires based on traffic conditions and fault priority.

[0062] The above resources, connected to the network and dispatch center, form a collaborative recovery system integrating source, network, load, storage, and communication. This strategy employs a mixed-integer programming model to uniformly optimize the spatial location and temporal progression of multiple resource types, dynamically deciding on MESS access points, switching operation sequences, and maintenance routes across multiple time periods. Through rolling scheduling at multiple time scales, the load recovery plan is continuously optimized and adjusted during the evolution of the disaster.

[0063] To verify the effectiveness of the strategy proposed in this invention, an improved model of the IEEE 33-node distribution system was used for simulation studies, and an ice disaster scenario was constructed by combining historical icing data from the northern plains. A specific winter freezing disaster was selected as a typical case: assuming that initially some overhead lines collapsed due to icing, causing the distribution network structure to be disconnected, and approximately 40% of critical loads lost power. The effects of the following four recovery schemes were compared: (1) No response measures: No special dispatching is adopted, and the power is restored by relying solely on fault self-isolation and manual emergency repair. After the fault occurs, the power-loss loads need to wait for the physical repair of the line before power can be restored, resulting in long power outage times and continuous interruption of critical loads. This scenario serves as a baseline and represents the traditional passive response mode.

[0064] (2) Traditional emergency plan: A simple distribution network reconfiguration and local power generation plan is prepared in advance. After the fault occurs, the dispatcher performs manual switching operations according to the plan to transfer some of the lost loads to healthy feeders; at the same time, existing emergency power sources such as diesel generators are started to supply power to the islanded loads. This plan can reduce the scope of the power outage to a certain extent, but it does not consider dynamic changes in the disaster situation, has limited resources, and has not verified transient safety.

[0065] (3) Network Coordination Scheme [2]: A multi-resource scheduling strategy coordinating power distribution, communication and transportation networks is adopted. After a fault occurs, mobile energy storage vehicles are immediately deployed to the power outage area to supply power to critical loads; drones are used to restore on-site communication and ensure smooth dispatch instructions; at the same time, the route of the maintenance team is optimized to accelerate physical repairs. This scheme can make full use of mobile resources to achieve faster load recovery and smaller load loss, but it does not adopt risk adaptive adjustment and does not consider the role of PSCIS and transient security issues.

[0066] (4) The strategy of this invention: a multi-stage strategy that integrates micro-meteorological sensing, risk assessment, multi-resource coordination and PSCIS regulation. Before the ice storm, mobile energy storage is pre-deployed to key nodes, and UAVs are on standby to cover communication blind spots; at the moment of the fault, the faulty section is automatically isolated and most of the load power is quickly restored through the backup contact line, while PSCIS participates in suppressing the inrush current to ensure smooth loop closing operation; then the risk of disaster evolution is continuously assessed, and resources are dynamically scheduled to deal with new faults until all loads are restored.

[0067] Figure 2 This diagram illustrates a comparison of critical load recovery rates under different strategies provided in this embodiment of the invention. It can be seen that in the no-response scenario, relying solely on emergency repairs within 12 hours of the fault, the load is only fully restored in the 12th hour, with the power recovery rate remaining around 60% for an extended period. The traditional solution increases the recovery rate to 80% in the first hour through network transfer, but the remaining load still needs to wait until the line is repaired in the 10th hour to fully recover. The multi-resource collaborative solution, due to the rapid deployment of mobile energy storage and backup power, restores approximately 95% of the load in the second hour and completes full restoration in the sixth hour. However, because it does not consider loop-closing transient limitations, this solution may experience brief secondary power outages during the restoration process (e.g., a secondary load loss occurs in the 11th hour in the case simulation), resulting in faster overall recovery but posing safety risks. In contrast, this strategy increases the critical load recovery rate to 90% within 30 minutes of the fault through pre-emptive scheduling, almost fully restoring the load within one hour and maintaining stable power supply. Thanks to risk prediction and proactive resource deployment, this strategy significantly shortened the downtime of critical loads; no secondary power outages occurred as in the collaborative solution, demonstrating higher recovery efficiency and security.

[0068] Figure 3 This is a schematic diagram comparing the expected energy loss due to unsupplied power under different strategies provided in this embodiment of the invention. The horizontal axis represents the recovery scheme type, and the vertical axis represents the total EENS (assuming the total demand of the critical load is 10MW, then the unit of EENS is MWh). The smaller the value, the better the power supply guarantee. This strategy significantly reduces the energy loss due to unsupplied power compared to other schemes. From Figure 3Quantitatively, it can be seen that the unsupplied power volume varies significantly among the different scenarios: the ENS for the no-measure scenario is approximately 48 MWh, the traditional scenario is reduced to 22 MWh, the coordinated scenario is further reduced to 8 MWh, while this strategy is only about 3 MWh. This means that this strategy reduces the expected load loss caused by ice storms to about 6% of the no-measure scenario. Compared to the coordinated scenario, the ENS of this strategy is only about 37% (i.e., a reduction of nearly 2 / 3), fully demonstrating the superiority of the proposed multi-stage integrated strategy in reducing power outage losses.

[0069] To visually demonstrate the specific implementation of this strategy in the post-disaster recovery process, Figure 4 This is a schematic diagram of the event and action timeline of the strategy provided in this embodiment of the invention in a simulated scenario. The ice storm lasted approximately 6 hours from time 0; Line 5 failed in the first hour, and repairs were completed in the seventh hour; Line 7 also failed in the third hour and was repaired in the ninth hour. "UAV communication support" provided emergency communication continuously for 0–3 hours after the disaster, and "mobile energy storage deployment" supplied power to the isolated loads for 1–4 hours. During the 2.5–3 hours, PSCIS power regulation was performed, which, together with the tie-line loop-closing operation completed in the third hour, successfully suppressed the inrush current and avoided secondary power outages. As the main loads recovered in the fourth hour and the ice conditions eased, mobile energy storage and drones were gradually withdrawn. The multi-resource, multi-stage synergistic process of this strategy, as shown in the figure, ensured orderly and efficient recovery throughout the entire ice storm evolution.

[0070] Table 1 shows the comparison of the characteristics and performance of different recovery strategies. It can be seen that this strategy integrates the advantages of other schemes, ensuring both the fastest recovery speed and the minimum load loss, while also filling the gaps in the safety aspects of existing technologies through risk adaptation and transient verification. In summary, this invention provides a practical technical solution that can effectively improve the reliability of the power distribution network in the northern plains region against ice storms and reduce power outage losses caused by the disaster.

[0071] Table 1. Comparison of characteristics and performance of different recovery strategies In summary, the present invention has the following outstanding advantages: The recovery rate of critical loads has been significantly improved. Through proactive microgrid support and mobile energy storage connections, power supply to critical loads such as hospitals and communication hubs is prioritized to the greatest extent possible. Compared to uncontrolled or traditional solutions, the recovery rate of critical loads in the initial hours after a disaster is significantly increased. As shown in the simulation case, the proposed method reduces the maximum load loss rate to only about 6%, significantly better than the 40% loss under no-measures scenario. The multi-source collaborative strategy ensures extremely short power outage times for critical users, enhancing the ability to safeguard people's livelihoods during disasters.

[0072] Reduce Expected Energy Outages (EENS). This strategy effectively reduces the total amount of unsupplied energy due to faster recovery and less load interruption. Compared to no strategy and general coordination solutions, EENS can be reduced to approximately one-tenth of their respective orders of magnitude. For example, in case studies, the EENS in the no-measure scenario was as high as 48 MWh, while after introducing PSCIS and risk-adaptive scheduling, it was reduced to less than 5 MWh (see [link to case study]). Figure 2 and Figure 3 (As shown in the comparison). The significant reduction in energy outage losses demonstrates the remarkable economic benefits of comprehensive measures in ensuring power supply after disasters.

[0073] This strategy exhibits strong adaptability to disaster uncertainties. Utilizing ultra-short-term micro-weather forecasts and Markov risk assessment, it can adjust recovery plans in real time based on the spatiotemporal evolution of disaster conditions such as icing thickness and wind force. Compared to methods that only consider a single static scenario, this strategy overcomes the limitation of fixed plans failing to adapt to weather changes. Regardless of whether the duration of the ice disaster extends or its impact area expands, the system can dynamically assess and adjust scheduling decisions, demonstrating high flexibility and robustness. This risk-aware and adaptive capability ensures near-optimal recovery results under various uncertain conditions.

[0074] Enhancing the safety of the recovery process. By performing transient safety checks before reconnecting the loop and actively suppressing inrush current using PSCIS, this strategy effectively avoids the risk of secondary faults during the recovery process. Compared to solutions that do not consider transient factors, the inrush current during loop connection is significantly reduced, and switching operations are smoother and safer. This not only protects equipment from damage caused by closing overcurrent but also avoids delays in recovery operations due to safety concerns. By improving the transient feasibility of the solution, this strategy ensures the safety and controllability of the entire post-disaster recovery process, further improving the reliability of the distribution network.

[0075] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units.

[0076] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as OM / AM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0077] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for improving the reliability of distribution networks during ice storms based on micro-meteorological sensing and multi-resource collaboration, characterized in that, include: Based on real-time ice disaster data obtained during the ice disaster and a pre-established distribution network recovery plan, the distribution network fault sequence during the ice disaster evolution process is simulated using Markov chains. The expected value of critical load loss is determined based on the fault sequence of the distribution network; If the expected value is greater than the preset risk threshold, a new power distribution network restoration scheme is generated with the goal of minimizing power supply loss. Based on the new power distribution network restoration plan, the transient impact intensity is estimated; When the transient impact intensity exceeds a preset safety limit, a target device whose adjustment performance meets the preset adjustment performance standard is selected, and the target device is used to reduce the transient impact intensity. When the reduced transient impact intensity is less than or equal to the preset safety limit, the new power distribution network restoration plan is executed.

2. The method for improving the reliability of ice-affected power distribution networks based on micro-meteorological sensing and multi-resource collaboration as described in claim 1, characterized in that, The power distribution network restoration plan was formulated in the following manner: Obtain historical icing thickness and historical wind speed for each area of ​​the power distribution network; Based on the historical ice thickness and historical wind speed, a failure probability model for overhead lines, a power output attenuation model for photovoltaic power generation equipment, and a power output degradation model for distributed power sources are established. Based on the failure probability model, the power output attenuation model, and the power output degradation model, a power distribution network restoration scheme is obtained.

3. The method for improving the reliability of ice-affected power distribution networks based on micro-meteorological sensing and multi-resource collaboration as described in claim 2, characterized in that, The failure probability model includes: The weight of the ice layer is determined based on the equivalent density of the ice layer, the equivalent ice-receiving area of ​​the conductor in contact with the ice, and the ice thickness. ； in, The weight of the ice covering the surface of the conductor; This represents the equivalent density of the ice layer. The equivalent ice-covered area of ​​the conductor in contact with the ice. The thickness of the ice covering the conductor; Based on air density, drag coefficient, windward projected area and wind speed, determine the stress on the tower under wind load; ； in, For the tower to bear the force; air density; This refers to the drag coefficient; The projected area facing the wind; Wind speed; Based on the weight of the ice accumulation and the stress on the tower, a failure probability model is determined; ； in, This is the failure sensitivity coefficient, used to characterize the degree of influence of increased load on the failure probability; This is a failure probability model.

4. The method for improving the reliability of ice-affected power distribution networks based on micro-meteorological sensing and multi-resource collaboration according to claim 2, characterized in that, The output attenuation model includes: Based on the rated output power of the photovoltaic array, the thickness of ice covering the photovoltaic panel surface and the temperature deviation, the actual output power of the photovoltaic array under ice disaster conditions is determined as a power attenuation model. ； in, This represents the actual output power of the photovoltaic array under ice storm conditions. This refers to the rated output power of the photovoltaic array. The attenuation coefficient of photovoltaic power output due to icing; The thickness of the ice coating on the surface of the photovoltaic panel; This is the coefficient representing the influence of temperature deviation on photovoltaic power output. Temperature deviation refers to the deviation of the ambient temperature from the reference operating temperature.

5. The method for improving the reliability of ice-affected power distribution networks based on micro-meteorological sensing and multi-resource collaboration according to claim 2, characterized in that, The output degradation model includes: Based on the rated available output of distributed power sources and the time-varying degradation coefficient, an output degradation model is obtained; ； in, Let t be the actual available output of the distributed power source at time t; This refers to the initial or rated available output of a distributed power source. It represents the time-varying degradation coefficient under the influence of disasters.

6. The method for improving the reliability of ice-affected power distribution networks based on micro-meteorological sensing and multi-resource collaboration according to claim 1, characterized in that, The determination of the expected value of critical load loss based on the distribution network fault sequence includes: Construct the Markov state transition probability matrix; ； in, ; Here is the Markov state transition probability matrix; For the system to be in state Transition to state The one-step transition probability; For probability operators; For a moment The system status; For a moment The system status; Based on the state transition probability matrix, determine the probability of each fault state; Determine the expected value of critical load loss based on the probability of failure conditions; ； This represents the expected value of critical load losses. For the system to be in the first The probability of a fault state; For the first The load loss corresponding to each fault state.

7. The method for improving the reliability of ice-affected power distribution networks based on micro-meteorological sensing and multi-resource collaboration as described in claim 1, characterized in that, The objective function includes: ； in, For the set of scheduling time periods; For the set of load nodes; For the collection of emergency resources; For a set of switches; For nodes Load importance weight; For time period node The power of the unsupplied load; The scheduling time step; For resources The cost coefficient for invocation or execution; For resources During the period Input decision variables; For switch Operating cost coefficient; For time period switch Action variables.

8. The method for improving the reliability of ice-affected power distribution networks based on micro-meteorological sensing and multi-resource collaboration according to claim 1, characterized in that, Generate a new distribution network restoration scheme with the objective function of minimizing power supply loss, satisfying at least one of the following constraints: Constraints include: power balance and load loss; priority restoration of critical loads; network operation constraints; distributed power generation and photovoltaic output constraints; mobile energy storage constraints; photovoltaic energy storage and charging station constraints; drone communication coverage constraints; and emergency repair team operation constraints.

9. The method for improving the reliability of ice-affected power distribution networks based on micro-meteorological sensing and multi-resource collaboration according to claim 1, characterized in that, The step of estimating the transient impact intensity based on the new power distribution network restoration plan includes: The regulation power of the photovoltaic-energy storage-charging integrated station is obtained, and the loop closing operation information is extracted from the new distribution network restoration scheme; wherein, the loop closing operation information includes the equivalent voltage amplitude on the island side, the equivalent voltage amplitude on the main grid side, and the equivalent impedance of the loop closing circuit; Divide the difference between the equivalent voltage amplitude on the island side and the equivalent voltage amplitude on the main grid side by the equivalent impedance of the closed loop, and then subtract the regulation power of the photovoltaic-energy storage-charging integrated station to obtain the closed loop inrush current; ； in, This is an estimated value for the closed-loop impact current; This represents the equivalent voltage amplitude on the islanded side before loop closure; The equivalent voltage amplitude on the main grid side; The equivalent impedance of the closed loop; The regulating power of the photovoltaic-energy storage-charging integrated station.

10. The method for improving the reliability of ice-affected power distribution networks based on micro-meteorological sensing and multi-resource collaboration according to claim 9, characterized in that, The target device is a photovoltaic-energy storage-charging integrated station. The method of using the target device to reduce the intensity of the transient impact includes: ； in, This refers to the local node voltage after loop-closed regulation; This represents the equivalent voltage amplitude on the islanded side before loop closure; This refers to the voltage regulation sensitivity or damping coefficient.

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