Power distribution system planning and operation joint optimization method under multi-scene natural disasters

Abstract

The invention relates to a power distribution system planning and operation joint optimization method under multi-scene natural disasters. The method comprises the following steps: constructing a planning and operation joint optimization model of a corresponding disaster scene with the goal of minimizing the annual total cost of a power distribution system; wherein the annual total cost comprises load reduction cost, line reinforcement cost and distributed power supply and energy storage equipment installation cost; the model integrates a group of constraint conditions for describing behaviors of the power distribution system in a disaster scene; based on historical data and meteorological prediction information, generating a plurality of disaster scenes with different disaster severity degrees, and taking disaster key parameters of each disaster scene as input parameters of the planning and operation joint optimization model; and carrying out convex processing on nonlinear constraints in the planning and operation joint optimization model, and solving by utilizing an optimization solver to obtain an optimal planning scheme and an optimal operation scheme of the power distribution system corresponding to the disaster scene. The toughness of the power distribution system is improved when the power distribution system faces natural disasters.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of power system planning and operation and smart grid technology, and particularly relates to a power distribution system planning and operation joint optimization method under multiple scenarios of natural disasters. BACKGROUND

[0002] With the intensification of global climate change, the frequency and intensity of natural disasters such as typhoons, floods, and earthquakes are increasing, bringing unprecedented challenges to the system. The traditional power distribution system is not resilient enough when facing these high-impact low-probability events, resulting in large-scale power outages and long service interruptions, which seriously affects social economy and residents' life. In the current new power distribution system, with the wide application of distributed generation, energy storage and microgrid technologies, how to effectively integrate these resources and improve the carrying capacity and resilience of the power distribution system under natural disasters has become a key problem to be solved.

[0003] The new power distribution system not only needs to meet the daily power supply demand, but also needs to have the ability to quickly respond and self-recover under extreme weather and disaster conditions. However, with the large-scale grid connection of renewable energy, the power balance and power quality problems of the new power distribution system have become increasingly prominent. The current new power distribution system still faces many challenges in carrying capacity: first, the optimal configuration of distributed energy and energy storage, how to reasonably allocate these resources under different disaster scenarios to maximize system resilience; second, the formation and operation control of microgrids, how to quickly build effective microgrids to ensure key load power supply when disasters occur; third, line reinforcement and reconstruction, how to select the optimal reinforcement strategy under budget constraints to reduce the risk of line interruption. In view of this, a new power distribution system planning and operation joint optimization method is proposed to improve the resilience and new energy carrying capacity under multiple scenarios of natural disasters, which is of great significance to improve the disaster resistance of the power distribution system and ensure the safety of power supply.

[0004] Although progress has been made in the current research on the resilience improvement of new power distribution systems, there are still obvious deficiencies in the aspects of systematization, synergy and economy. First, the planning and operation are disconnected, and there is a lack of synergy optimization framework. Most researches are limited to independent optimization of a single technical dimension, and an integrated model of "infrastructure-operation control" has not been established. Second, the lack of dynamic synergy mechanism leads to insufficient flexibility in disaster response. Existing models mostly use static or predefined strategies, and fail to establish a real-time synergy mechanism for dynamic reconstruction and resource scheduling in disasters. Most researches only calculate the load reduction amount through preset parameters, without considering the linkage between topology adaptive adjustment and energy storage strategy in disaster evolution, and lack of modeling of active response of operation resources. Finally, the economic constraint modeling is weak, and the balance of cost and benefit is ignored. The trade-off analysis under budget constraints is generally insufficient, and existing models often separate normal operation cost and flexible investment, without integrating all types of costs into multi-period budget analysis, and lacking of quantification of marginal benefits of different reinforcement intensity and operation strategy combinations, which makes it difficult to implement the scheme. SUMMARY

[0005] In view of the above analysis, the embodiments of the present application aim to provide a power distribution system planning and operation joint optimization method under multiple scenario natural disasters, to solve the problems of insufficient resilience of power distribution system, limited new energy carrying capacity and difficult synergy optimization of comprehensive cost due to the disconnection of planning and operation, rigid dynamic response and insufficient economic consideration in the existing power distribution system when responding to multiple scenario natural disasters.

[0006] The present application provides a power distribution system planning and operation joint optimization method under multiple scenario natural disasters, comprising the following steps: A planning and operation joint optimization model corresponding to disaster scenarios is constructed, with the objective of minimizing the annual total cost of the power distribution system. The annual total cost includes load reduction cost, line reinforcement cost and distributed power and energy storage device installation cost. The model integrates a set of constraint conditions for describing the behavior of the power distribution system under disaster scenarios; Based on historical data and meteorological prediction information, a plurality of disaster scenarios with different disaster severity are generated, and the disaster key parameters of each disaster scenario are used as input parameters of the planning and operation joint optimization model. The disaster key parameters include disaster scenario occurrence probability weight ; The nonlinear constraints in the planning and operation joint optimization model are convexified, and the model is solved by an optimization solver to obtain the optimal planning scheme and the optimal operation scheme of the power distribution system corresponding to the disaster scenario.

[0007] Compared with the prior art, the present application can at least achieve one of the following beneficial effects: 1. Innovative application of convex equations and mixed integer quadratic constraint programming model; The convex equation is introduced in the power distribution system planning and operation optimization, and the mixed integer quadratic constraint programming model is applied to solve the problem of resilience and new energy carrying capacity improvement under multiple scenario natural disasters, which not only simplifies the complex power system equation and improves the solving efficiency of the model, but also enables the model to consider both planning and operation factors. Through the model development in the GAMS environment and the verification of the IEEE 33 node test system, the model has significant advantages in reducing load reduction and investment cost, and provides theoretical support for the resilience improvement of the power distribution system; 2. Dynamic microgrid formation and line fault probability modeling under multiple scenario natural disasters; In view of the uncertainty and severity of natural disasters, a multi-scenario disaster modeling method is proposed, which dynamically forms a microgrid to cope with the damaged power grid by considering different wind speed and disaster intensity scenarios. At the same time, a line fault probability and reinforcement process model based on convex equation is developed, which breaks through the limitation of single disaster intensity assumption, and through the comprehensive modeling of dynamic microgrid formation and line fault probability, the power distribution system can respond and recover more flexibly and effectively in the face of complex and variable natural disasters, significantly improving the resilience of the system; 3. Planning-operation joint optimization to realize annual minimum cost; A planning-operation joint optimization framework is constructed, which integrates the pre-planning resource configuration and post-operation resilience improvement technology to minimize the total cost of load reduction, line reinforcement and distributed generation installation, which not only reduces the economic loss of the power distribution system in natural disasters, but also improves the overall resilience and sustainability of the system through comprehensive optimization, and through case analysis and comparison, the significant effect of the mixed technology in reducing load reduction and investment cost is verified, which provides theoretical support and practical guidance for the planning and operation of actual power distribution systems.

[0008] In the present application, the above technical solutions can be combined with each other to realize more preferred combination schemes. Other features and advantages of the present application will be described in the subsequent specification, and some advantages will become apparent from the specification, or will be understood by implementing the present application. The purpose and other advantages of the present application can be achieved and obtained from the contents specifically pointed out in the specification and the drawings. BRIEF DESCRIPTION OF DRAWINGS

[0009] The accompanying drawings are included to provide a further understanding of the application and are incorporated herein and constitute a part of the detailed description. The drawings illustrate embodiments of the application and, together with the description, serve to explain the principles of the application. In the drawings: Figure 1 A flow chart of a power distribution system planning and operation joint optimization method under multiple scenario natural disasters in an embodiment of the present application is shown in the figure; Figure 2A flow chart of the modeling idea of the power distribution system planning and operation joint optimization in the embodiment of the present application; Figure 3 A flow chart of the optimization solution in the embodiment of the present application; Figure 4 A schematic diagram of the influence of the IEEE33 node system under the typhoon disaster in the embodiment of the present application; Figure 5 A schematic diagram of the power distribution system encountering the typhoon disaster (Scenario 1 and 2-Case 1); Figure 6 A schematic diagram of the power distribution system encountering the typhoon disaster (Scenario 1 and 2-Case 2); Figure 7 A schematic diagram of the power distribution system encountering the typhoon (Scenario 1-Case 3); Figure 8 A schematic diagram of the power distribution system encountering the typhoon (Scenario 2-Case 3); Figure 9 The active power injected from the upper power grid under each scenario (Case 3); Figure 10 The reactive power injected from the upper power grid under each scenario (Case 3); Figure 11 The active power generated by the DG located at bus 5, 16 and 33 (Case 3); Figure 12 The reactive power generated by the DG located at bus 5, 16 and 33 (Case 3); Figure 13 The active power generated by the wind power under each scenario (Case 3); Figure 14 The active power generated by the photovoltaic unit under each scenario (Case 3); Figure 15 The active power consumed / generated by the energy storage located at bus 4-28 (Case 3); Figure 16 The total active and reactive load reduction power (Case 3); Figure 17 The voltage distribution of the power distribution at the peak period (6:00 pm) (Case 3). DETAILED DESCRIPTION

[0010] The preferred embodiments of the present application will be described in detail below with reference to the accompanying drawings, wherein the accompanying drawings form a part of the present application and are used to explain the principles of the present application together with the embodiments of the present application, but are not used to limit the scope of the present application.

[0011] One specific embodiment of the present application discloses a multi-scenario natural disaster under power distribution system planning and operation joint optimization method, as shown in the following steps: Figure 1 ​ Step S1: Construct a joint optimization model for planning and operation in the corresponding disaster scenario with the objective of minimizing the annualized total cost of the power distribution system; wherein the annualized total cost includes load reduction cost, line reinforcement cost, and installation cost of distributed power sources and energy storage equipment; the model integrates a set of constraints to describe the behavior of the power distribution system under disaster scenarios; Step S2: Based on historical data and meteorological forecast information, generate multiple disaster scenarios with different degrees of severity. Use the key disaster parameters of each scenario as input parameters for the joint planning and operation optimization model; wherein, the key disaster parameters include the probability weights of the disaster scenario occurrence. ; Step S3: Perform convexity processing on the nonlinear constraints in the joint optimization model of planning and operation, and use the optimization solver to solve the model to obtain the optimal planning scheme and optimal operation scheme of the power distribution system corresponding to the disaster scenario.

[0012] This invention aims to address the challenges of enhancing the resilience and optimizing the cost of power distribution systems in the face of natural disasters. By combining infrastructure reinforcement strategies and operational flexibility technologies, it improves the recovery and continuous power supply capabilities of power distribution systems. Furthermore, through the construction of a joint optimization framework and a convex MIQCP (Mixed Integer Quadratically Constrained Programming) model, it achieves enhanced resilience and cost optimization of power distribution systems in the face of natural disasters. The invention involves collaborative planning and operation, and the overall framework is as follows: Figure 2 As shown.

[0013] (1) The “planning-operation” approach complements each other. Planning resources become an important means to support the operation of the new power distribution system, while the refined scheduling and operation strategy can effectively ensure the reliability of the planning scheme and realize the overall improvement of the resilience of the power distribution system. By incorporating planning decisions such as line reinforcement, installation of distributed power sources and energy storage, and operation decisions such as microgrid formation, switch status adjustment, power output scheduling and load reduction into a unified mixed integer quadratic constraint programming model, the needs of long-term planning and short-term operation are comprehensively considered to ensure that the power distribution system can have a solid physical foundation and respond flexibly when facing natural disasters, so as to achieve rapid power restoration. (2) Convexity processing and solving efficiency. Through convexity processing, the solving efficiency and stability are improved. First, by using the linearized alternating current flow model, the originally complex nonlinear alternating current flow equation is simplified to a linear equation, thereby greatly reducing the solving difficulty. Second, for the circular feasible region in the distributed power output constraint, a polygon approximation processing method is adopted to approximate the circular constraint to a polygon constraint, further simplifying the model structure. Convexity processing not only simplifies the solving process, but also improves the accuracy and reliability of the solving result, so that the model can obtain a high-quality solution in a shorter time; (3) Multi-scenario and uncertainty modeling. A multi-scenario model is established to address the uncertainty of natural disasters. Based on historical data and weather forecast information, multiple representative disaster scenarios are generated, and the impact of natural disasters on the distribution system is simulated in detail in each scenario. By considering the randomness and uncertainty of disasters, as well as the evolution process and physical damage of disasters, strong support is provided for the flexible response and rapid recovery of the distribution system. Through multi-scenario simulation analysis, the performance of different optimization strategies in different disaster scenarios is evaluated, and the optimal optimization scheme is selected.

[0014] Step S1, comprising steps S11-S12.

[0015] A joint optimization mathematical model is constructed to realize planning-operation collaborative optimization, wherein the planning decisions include line reinforcement and installation of distributed power sources such as photovoltaic, wind turbine and energy storage; and the operation decisions cover microgrid formation, switch state, output scheduling, energy storage charging and discharging and load reduction. Through planning-operation joint optimization, the resilience of the distribution system in the face of natural disasters is effectively improved.

[0016] Step S11, constructing an objective function.

[0017] With the increasing number of emergency situations caused by natural disasters to the distribution system, the problems of load reduction and curtailment are highlighted. An objective function is constructed to minimize the annual total cost, and all costs are converted into comparable annual costs, thereby realizing the overall optimization goal of the distribution system.

[0018] The objective function of the planning and operation joint optimization model is as follows: ; Wherein, is the annual total cost; is a set of disaster scenarios; is the disaster occurrence probability weight; is the operation cost annualization weight coefficient, is the load loss value, is the bus priority factor of the load; For the corresponding disaster scenario Down Moment bus The amount of active load that is reduced; For line reinforcement annual weight coefficient, For line reinforcement cost coefficient, For line Binary decision variable whether to reinforce; For line Length; For new energy equipment cost weight coefficient, Respectively, the unit capacity installation cost coefficient of distributed power generation device, wind turbine, photovoltaic unit and energy storage; 、 、 、 Respectively, binary decision variables whether to install synchronous distributed generator, wind turbine, photovoltaic unit and energy storage at bus Load reduction cost, Line reinforcement cost; Distributed power supply and energy storage equipment installation cost.

[0019] Unit , yuan / megawatt hour, indicating the average loss caused to users for providing 1 megawatt hour of electricity less; Unit, MW megawatt; Unit , yuan / km, Binary decision variable, 0 for not reinforced, 1 for reinforced; Unit km; 、 、 、 Unit kW or kWh; 、 、 、 Binary decision variable, 0 for not installed, 1 for installed.

[0020] Planning decision variable; Running decision variable.

[0021] The distributed power supply includes at least one of synchronous distributed generator, wind turbine and photovoltaic unit.

[0022] Step S12, constructing constraint conditions for describing the behavior of the power distribution system under the disaster scenario.

[0023] ​The constraint conditions of the planning and operation joint optimization model include: AC power flow constraints, microgrid dynamic formation constraints, radial operation constraints, load shedding constraints, distributed power output constraints, energy storage constraints, and line fault and reinforcement model constraints; wherein the AC power flow constraints and the distributed power output constraints are nonlinear constraints; The AC power flow constraints are used to construct a power balance equation and a voltage-current relationship based on DistFlow to describe the steady-state operation characteristics of the distribution system. The microgrid dynamic formation constraints are used to dynamically divide the distribution network into multiple electrically isolated microgrids in the disaster scenario, with the bus uniquely belonging to one microgrid, the microgrid being internally connected and the microgrids being isolated from each other. The radial operation constraints are used to ensure that the distribution network maintains a radial structure without loops and is connected in both normal operation and microgrid operation modes. The load shedding constraints are used to limit the active and reactive load shedding of each bus in the disaster scenario when power generation is insufficient, and are associated with the priority factor of the load. The distributed power output constraints are used to limit the active and reactive power output range of each type of distributed power source. The energy storage constraints are used to limit the charge and discharge power, capacity, and state transition logic of the energy storage. The line fault probability and reinforcement model constraints are used to calculate the line fault probability based on wind speed scenario parameters, and determine the on-off state of the line in the disaster scenario based on whether the line fault probability exceeds a predetermined threshold.

[0024] The nonlinear constraints in the planning and operation joint optimization model are convexified, including: The AC power flow constraint equation is replaced by a linearized AC power flow constraint based on DistFlow. The circular feasible region constraint of the active and reactive power coupling of the distributed power output constraint is replaced by a polygonal approximation constraint.

[0025] (1) AC power flow constraints: a convex distribution system analysis framework is constructed based on the linearized model of DistFlow, which improves the calculation efficiency by linearization to address the low calculation efficiency caused by the nonlinear characteristics of traditional AC power flow.

[0026] (a) Power balance constraints: dynamic balance constraint relationships between the upper grid, distributed energy (including diesel main generators and subordinate units such as wind power, photovoltaic, energy storage, etc.), and load demand are constructed as follows: ; wherein, is the scenario The active power (MW) injected into the bus from the superior grid at time The reactive power (Mvar) injected into the bus from the superior grid at time The active power (MW) injected into the bus from the superior grid at time The active power (MW) injected into the bus from the superior grid at time The reactive power (Mvar) injected into the bus from the superior grid at time The active power (MW) injected into the bus from the superior grid at time The reactive power (Mvar) injected into the bus from the superior grid at time The active power (MW) injected into the bus from the superior grid at time The reactive power (Mvar) injected into the bus from the superior grid at time The active power (MW) injected into the bus from the superior grid at time The reactive power (Mvar) injected into the bus from the superior grid at time The active power (MW) injected into the bus from the superior grid at time The reactive power (Mvar) injected into the bus from the superior grid at time The active power (MW) injected into the bus from the superior grid at time The reactive power (Mvar) injected into the bus from the superior grid at time The active power (MW) injected into the bus from the superior grid at time The reactive power (Mvar) injected into the bus from the superior grid at time The active power (MW) injected into the bus from the superior grid at time The reactive power (Mvar) injected into the bus from the superior grid at time The active power (MW) injected into the bus from the superior grid at time The reactive power (Mvar) injected into the bus from the superior grid at time The active power (MW) injected into the bus from the superior grid at time The reactive power (Mvar) injected into the bus from the superior grid at time The active power (MW) injected into the bus from the superior grid at time The reactive power (Mvar) injected into the bus from the superior grid at time The active power (MW) injected into the bus from the superior grid at time The reactive power (Mvar) injected into the bus from the superior grid at time The active power (MW) injected into the bus from the superior grid at time The reactive power (Mvar) injected into the bus from the superior grid at time The active power (MW) injected into the bus from the superior grid at time The reactive power (Mvar) injected into the bus from the superior grid at time The active power (MW) injected into the bus from the superior grid at time The reactive power (Mvar) injected into the bus from the superior grid at time The active power (MW) injected into the bus from the superior grid at time The reactive power (Mvar) injected into the bus from the superior grid at time The active power (MW) injected into the bus from the superior grid at time The reactive power (Mvar) injected into the bus from the superior grid at time The active power (MW) injected into the bus from the superior grid at time The reactive power (Mvar) injected into the bus from the superior grid at time The active power (MW) injected into the bus from the superior grid at time The reactive power (Mvar) injected into the bus from the superior grid at time The active power (MW) injected into the bus from the superior grid at time The reactive power (Mvar) injected into the bus from the superior grid at time The active power (MW) injected into the bus from the superior grid at time The reactive power (Mvar) injected into the bus from the superior grid at time The active power (MW) injected into the bus from the superior grid at time The reactive power (Mvar) injected into the bus from the superior grid at time The reactive power (Mvar) injected into the bus from the superior grid at time The reactive power (Mvar) injected into the bus from the superior grid at time The reactive power (Mvar) injected into the bus from the superior grid at time The reactive power (Mvar) injected into the bus from the superior grid at time

[0027] (b) Voltage-current constraints: By defining the voltage difference between adjacent buses and the coupling relationship between power, voltage, and current, voltage-current constraints are established as follows: ; in, , Scenes Down Time bus and The square of the voltage (kV²); , The lines are respectively The resistance and reactance.

[0028] (c) Operational constraints, including: 1) Bidirectional power characteristics to accommodate the bidirectional flow of energy in modern power distribution systems: ; in, For the scene Down Time Node and Active power on the line; For the scene Down Time Node and Reactive power on the line.

[0029] 2) Line connection status to ensure correct representation of network topology: ; in, This is a binary variable, representing the disaster scenario. Next node and The connection status between the lines is indicated by a value of 1, which represents a connected state, and 0, which represents a disconnected state. Similarly.

[0030] 3) Power constraint for line interruption, handling special cases when the line is interrupted: ; in, , and , These are the upper and lower limits of the active and reactive power of the line, respectively.

[0031] 4) Voltage limit constraints to ensure that node voltages operate within a safe range.

[0032] ; in, Divided into lower and upper limits of voltage amplitude; For the scene Down time The voltage value of the node. It is the square of the bus voltage.

[0033] 5) Line thermal capacity constraint to prevent line overload. ; in, For the line The thermal limit; For the line In disaster scenarios Down The square of the current at any given moment.

[0034] 6) Upper-level power injection constraints to ensure the rationality and stability of external power input. ; in, Scenes Down External power supply at all times Active / reactive power injected into the node; These represent the upper limits of active and reactive power injected from the external power source, respectively.

[0035] (2) Microgrid formation constraints are the core of the resilience of power distribution system operation.

[0036] Microgrid formation, as a key means to enhance the resilience of new power distribution systems, uses convexity processing to ensure that the microgrid formation process strictly adheres to bus islanding constraints, internal microgrid connectivity constraints, and inter-microgrid isolation constraints, including: (a) Bus islanding constraint During the formation of a microgrid, each bus can only belong to one microgrid (island) at any given time, as shown below: ; in, It is the collection of microgrids in the system; Indicates in the scene Lower busbar Does it exist in microgrids? In this context, it means that all buses in the power distribution system can be uniquely assigned to a specific microgrid at any time, thus avoiding the isolated existence of buses; A binary variable, representing the scene Next Line connection status between nodes in a microgrid and ‌, where 1 means connected and 0 means disconnected, Similarly.

[0037] (b) Microgrid internal connectivity constraints. As an independent power supply unit, the microgrid must maintain internal connectivity, and by predefining the number of microgrids, the system is divided into any number of independent microgrids, ensuring that there is an effective connection path between the buses in each microgrid, as follows: ; where, is the set of all buses in the microgrid ; and are binary variables indicating whether a distributed power source and energy storage is installed on bus in scenario ‌. (c) Microgrid isolation constraints. To achieve island operation, through linearization and constraint processing, it is ensured that there is no physical connection between any two buses belonging to different microgrids, thereby maintaining the isolation state between microgrids, as follows: ; where, and represent the positive and negative parts of the isolation constraint between nodes in the th microgrid, respectively, used to measure the strength of the isolation constraint between two microgrids; is a binary variable indicating whether bus belongs to microgrid in scenario (1: yes, 0: no), Similarly.

[0038] (3) Radial operation constraints. The radial operation constraint is the core constraint of the safety of the distribution system, to ensure the acyclicity and connectivity of the network topology.

[0039] ‌(a) Acyclicity constraint‌ The number of connected lines in the network must be equal to the number of buses minus one, to ensure that the distribution network does not form a loop, avoiding the risk of current circulation and short circuit, applied in normal and emergency operation modes (especially when microgrids are formed) to ensure that the acyclicity requirement is met.

[0040] (b) Connectivity constraint: All buses in the system are connected to the upper grid to ensure that any bus in the system can be connected to the upper grid through a path. In the process of microgrid formation, the connection between buses in each island must also be guaranteed, as the distribution system may be divided into multiple islands. Based on the constraints of acyclicity and connectivity, the radial characteristics of the distribution network in the process of microgrid formation are jointly constrained.

[0041] (4) Load shedding constraint, when the power generation is insufficient to meet the power load in the process of distribution system operation, in order to ensure the stable operation of the distribution system and the power supply of the key load, the load shedding strategy needs to be implemented, as follows: (a) Active power and reactive power load shedding ; wherein, is the bus index, which represents the bus in the distribution system, used to identify the geographical location or electrical connection point; is the scenario index, which represents the uncertainty scenario, used to deal with the stochastic optimization problem; is the time period index; is the active power amount that needs to be shed at bus at time in scenario ; is the reactive power amount that needs to be shed at bus at time in scenario , which is associated with through equation (13) to ensure that the active and reactive power shedding ratios are consistent and maintain system voltage stability; is the power factor angle tangent value, which represents the tangent value of the power factor angle of bus load, which is a known parameter and depends on the load characteristics. (b) Load shedding amount constraint

[0042] ; wherein, is the upper limit of the load shedding active power, which ensures that the shedding amount does not exceed the total available active load of the bus and the time period, preventing insufficient load shedding and achieving supply and demand balance, and equation (32) constrains the load shedding amount to be non-negative.

[0043] (5) Distributed power output constraint, in the joint optimization of distribution system planning and operation, the active power and reactive power generated by the distributed power are reasonably limited to ensure the stable operation of the system. The active power and reactive power generated by the distributed power on bus are constrained as follows:​ ; wherein, Pij(t) is the active / reactive power of the distributed generator connected to bus i at time t in scenario j; Pmax is the maximum capacity of the distributed generator; cos(φ) is the power factor; is a binary variable, which is used to represent whether the distributed generator is installed on bus i (0 represents not installed, and 1 represents installed).

[0044] The relationship (15) originally represented by the circular constraint is replaced by the relationship (16) of polygon approximation, and the GORUBI solver is called for efficient solving, as follows: ; In addition, the maximum number of distributed generation devices that can be applied in the microgrid formation process is constrained by formula (17) to ensure the rational allocation of resources, as follows: ; wherein, formula (17) is a circular plane constraint, in order to improve the solving efficiency, the polygon plane restriction is used to approximate the circular constraint in the inequality, so as to simplify the constraint condition and make the solving process more efficient.

[0045] In the microgrid formation process, the placement constraint formula (18) of the main distributed generator is shown to constrain at least one distributed generator as the main distributed generator in each formed microgrid to achieve the frequency control target: ; wherein, Pmax is the maximum number of DGs allowed to be installed in the system; is a binary variable, which represents whether the distributed generator is connected to bus i in the jth microgrid; is a binary variable, which represents whether the distributed generator is connected to bus i in the jth microgrid in island mode; is the power distribution coefficient of the jth microgrid in scenario j; is the voltage of bus i at time t in scenario j.

[0046] ​​​​​​​​​​​​​(6) Energy storage (ESS) constraints, the output characteristics of ESS, as follows: ; wherein, are the charging and discharging power of ESS in time period, respectively; , is a 0-1 variable of charging / discharging state, 1 for charging and 0 for discharging; , are the maximum charging and discharging power of ESS, respectively; are the capacity of ESS at and time, respectively; are the upper / lower limit of ESS capacity in time period, respectively; , are the charging / discharging efficiency, respectively; , are the initial and terminal state, respectively; indicates that ESS cannot simultaneously charge and discharge; indicates that the ESS capacity in the beginning and end of the scheduling period remains the same, thereby ensuring the continuity of the scheduling.

[0047] (7) Line failure probability and reinforcement model constraints; Unlike previous line damage scenarios, which are often based on a single factor, this section proposes a reinforcement strategy based on line failure probability. Due to the unpredictability of high-impact low-probability events (such as typhoons) and the lack of related information, it is more appropriate to use a multi-scenario disaster-based method to generate line damage scenarios.

[0048] The line failure probability and reinforcement model constraints are constructed in the following ways: establish a mapping relationship between the line failure rate and the disaster intensity parameter; define two reinforcement states for the line and associate a corresponding resilience index with each state; wherein the reinforcement states include two states of un-reinforced and reinforced; According to the mapping relationship and the resilience index corresponding to the current reinforcement state of the line, dynamically calculate the line failure probability of the line under the current disaster scenario; Compare the line failure probability with the predetermined threshold to determine the on-off state of the line.

[0049] Exemplarily, a convex probability model based on wind speed scenarios is established to adapt to different wind speed scenarios for more accurately evaluating the failure probability of lines under different disaster conditions and improving the solving efficiency and stability of the model through convex processing. The failure rate of lines and towers in the distribution system is comprehensively derived based on historical data, material properties, and environmental conditions, reflecting the reliability of towers and conductors under different conditions, as follows: ; wherein, is the line failure rate of the electric pole under the typhoon wind speed; is the line failure rate of the distribution overhead line under the typhoon wind speed; is the wind speed, and 5.173 are disaster intensity coefficients, which are parameters fitted based on historical data and physical properties.

[0050] The resilience index is an important indicator to measure the ability of towers to resist damage and maintain function under disaster conditions. By calculating the resilience index, important basis can be provided for line reinforcement decision-making. Formula (21) is used to calculate the resilience index of type 1 and type 2 towers, as follows: ; wherein, is the resilience index of type 1 electric pole (unreinforced) under the typhoon wind speed; is the resilience index of type 2 (reinforced) electric pole under the typhoon wind speed. is the mechanical strength of type 1 electric pole; is the mechanical strength of type 2 electric pole.

[0051] Considering the influence of line reinforcement, a total failure probability model of lines under various wind speed scenarios is established, and the improvement effect of different reinforcement schemes on the failure probability of lines is evaluated to select the optimal reinforcement strategy, as follows: ; wherein, is the predetermined threshold value of the electric pole failure rate; exemplarily, takes the value of 0.7.

[0052] Based on the predetermined threshold value of the line disconnection logic, i.e., in each disaster scenario, if the failure probability of the line exceeds the threshold value , the line is considered to be disconnected ( ) to constrain the actual operating state of the line under simulated disaster conditions.

[0053] ; wherein, is the threshold value of the electric pole failure rate. To construct a disaster scenario planning and operation joint optimization mathematical model of power distribution system with multi-dimensional constraints integrated for minimizing the total annual cost. The total failure probability (unit, ) of the line; ); Define the reinforcement decision variable , which is a binary variable, to represent whether the line is reinforced (1 represents reinforcement, 0 represents no reinforcement): ; Step S1 is to construct a disaster scenario planning and operation joint optimization mathematical model of power distribution system with multi-dimensional constraints integrated for minimizing the total annual cost.

[0054] Step S2, in particular.

[0055] Based on historical disaster data and meteorological prediction information, a plurality of disaster scenarios characterizing different disaster severity are generated to obtain the disaster key parameters of each disaster scenario; The disaster key parameters also include: Disaster intensity spatiotemporal distribution parameters for characterizing the intensity of the disaster at spatial locations and time periods; Load and renewable energy time series curve for characterizing the changes in load demand and wind and light output at each period under the influence of disasters; Line basic vulnerability parameters for characterizing the inherent failure characteristics of the line under different disaster intensities; The disaster key parameters are dynamically input into the corresponding part of the planning and operation joint optimization model, including: The disaster scenario occurrence probability weight is input into the objective function for calculating the load reduction cost; The disaster intensity spatiotemporal distribution parameters and the line basic vulnerability parameters are jointly input into the line failure probability and reinforcement coupling constraint for dynamically calculating the failure probability and on-off state of each line at each time period in each disaster scenario; The load and renewable energy time series curve is input into the load reduction constraint, distributed power output constraint, energy storage constraint, and power balance constraint in the AC power flow constraint for defining the real-time quantity and supply-demand balance boundary of the power distribution system at each time period in each disaster scenario.

[0056] Collect historical disaster data within a certain historical period, or use future disaster data with meteorological prediction information for a certain future period.

[0057] Exemplarily, the historical disaster data collection 5-10 years of typhoon / flood disaster records, including maximum wind speed, duration, impact range, actual line damage location, etc.; meteorological prediction information uses the future 72-hour prediction data of ECMWF, NCEP and other global weather forecast models.

[0058] Using Monte Carlo sampling or Latin hypercube sampling technology, continuous meteorological random variables are discretized into 3-5 representative scenarios (such as wind speed 30 m / s, 40 m / s, 50 m / s), each scenario is assigned a probability weight , meet , both quantify uncertainty and avoid computational explosion.

[0059] (1) Disaster scenario occurrence probability weight , directly used in formula (1) target function load reduction cost calculation; (2) Disaster intensity spatiotemporal distribution parameters, such as typhoon wind speed of typhoon disaster scenario , used in formula (20)-(24) calculation; (3) Line basic vulnerability parameters, such as line element (tower, line) failure rate, formula (20) and , formula (21) and ; (4) Load and renewable energy time series curve includes load curve, new energy curve, load curve, real-time quantity and operating boundary of power, load, and new energy under each disaster scenario. Corresponding to power balance constraint, load reduction constraint, load shedding constraint and distributed power output constraint.

[0060] in formula (2) and real-time quantity and boundary value; in formula (13) and upper limit; in formula (14) load active power upper limit ; in formula (15) distributed power maximum capacity , , upper limit; in formula (19) and .

[0061] The step S2 is used for converting uncertain natural disaster information into quantifiable key parameters through multi-scenario probabilistic and parameterized modeling, and systematically mapping the key parameters into corresponding constraints and targets of the joint optimization model, so as to provide accurate dynamic input and boundary conditions for planning-operation collaborative decision-making.

[0062] The step S3 is specifically.

[0063] The optimization solver is used for solving, and a hierarchical solving strategy is adopted, including: An upper-layer planning problem is solved to obtain a planning decision scheme, including a reinforcement scheme of a line and installation location and capacity schemes of a distributed power supply and energy storage; A lower-layer operation problem is solved in parallel to obtain operation scheduling schemes in multiple disaster scenarios under the constraint of the planning decision scheme, including a topological structure of a micro-grid, output plans of each distributed power supply and energy storage, and a load reduction scheme; The upper-layer planning problem and the lower-layer operation problem are iteratively coordinated to solve until the target function converges, so as to obtain an optimal planning scheme and an optimal operation scheme.

[0064] ‌(1) Parameter initialization, initialization of power grid topological parameters (such as node, branch, tie line information), disaster scenario data (including wind speed probability distribution and storm path) and device parameters (capacity and cost of DG, PV, WT and ESS), and setting of GUROBI solver parameters, such as an optimal gap of 0.01%, to ensure solving accuracy, on the basis of which, a mixed integer quadratic constraint programming MIQCP (General Algebraic Modeling System, modeling system for solving complex optimization problems) model, that is, a planning and operation joint optimization model, is imported and solved.

[0065] ‌(2) Constraint processing and solving strategy Firstly, the AC power flow equation is linearized, the power balance equation is introduced, and the voltage-current relationship constraint is considered; secondly, the micro-grid formation constraint is introduced to ensure that each bus is uniquely attributed to a micro-grid at any time and to ensure the internal connectivity of the micro-grid and the isolation between micro-grids; then, the radial topological constraint is introduced to ensure that the power grid topology is loop-free and connected. For the device constraint and the line fault probability, the convex technology is used to process the distributed power output constraint, and the line fault probability is processed by piecewise linearization; finally, a hierarchical solving strategy is adopted, the planning layer is responsible for the line reinforcement and device investment decision, the operation layer is used to solve the micro-grid scheduling problem in each disaster scenario in parallel, and the Benders decomposition method is used to improve the solving efficiency, so as to realize the rapid response to disaster changes.

[0066] ‌(3) Output of optimization results The optimal planning scheme includes: based on obtained to-be-reinforced line set, and based on obtained installation positions of the distributed power supply and energy storage corresponding to the busbar; The optimal operation scheme comprises: obtaining a corresponding dynamically formed micro-grid topology structure based on the on-off state of the disaster scenario, and obtaining obtained active load scheduling plans of each period.

[0067] The role of step S3 is to finally output a coordinated optimal scheme of load reduction cost, line reinforcement cost and distributed power supply and energy storage device installation cost, and operation resilience under multiple disaster scenarios by decomposing a complex joint optimization model into two levels of planning and operation, and adopting an iterative coordination and efficient solving strategy.

[0068] The present application is based on case studies and result analysis as follows: Simulation settings are performed, and simulation tests are performed based on an IEEE 33-node system as shown in Figure 4 To comprehensively evaluate the resilience and new energy carrying capacity of the distribution system, the present method designs multiple disaster severity scenarios, specifically including: scenario 1 (wind speed 30 m / s) and scenario 2 (wind speed 50 m / s) to simulate the impact of natural disasters of different intensities on the power grid, and introduces dynamic change curves of renewable energy and load based on wind and light load time series data, considers load priority and cost (including loss of load value VOLL and reinforcement cost), and sets a planning period.

[0069] To verify the effectiveness of the method of the present application, three comparative cases are designed: Case 1 only considers operation resilience (including intelligent distributed power supply DG and micro-grid formation, but no line reinforcement measures and wind turbines WT, photovoltaic PV, energy storage ESS); Case 2 increases WT, PV and ESS based on case 1 to further improve operation resilience, but still has no line reinforcement; Case 3 is a joint optimization case that combines all the measures of case 2 and adds line reinforcement planning. All cases are solved on the GAMS / GUROBI solving platform, and the optimal gap is set to 0.01% to ensure the solving accuracy, and the calculation time is explained in detail.

[0070] Due to the influence of different scenarios and case studies, the impact of wind disaster on the test distribution system is as shown in Figures 5 to 7 .

[0071] As Figure 5As shown, in the emergency mode, Case 1 reduces load shedding by forming microgrids containing distributed generators (DGs), which is effectively applied in both scenarios. In addition, on the basis of microgrids, Case 2 further optimizes the layout of renewable distributed generation (such as wind power and photovoltaic power) and energy storage, thereby significantly reducing load shedding and investment costs, as shown in Figure 6 By comparing the results of Case 1 and Case 2, it can be seen that the load shedding cost of Case 2 is reduced by 9.3%, and the total cost is reduced by 22.94%.

[0072] The scheme proposed in this paper, i.e. Case 3, further integrates line reinforcement planning to adapt to various wind disaster scenarios, as shown in Figure 7 and Figure 8 By comparing the results of Case 2 and Case 3, it can be seen that the load shedding cost of Case 3 is reduced by 40.63%, and the total cost is further reduced by 25.32%. In addition, compared with Case 1, Case 3 reduces load shedding and investment costs by 46.15% and 42.45%, respectively.

[0073] In all research cases and scenarios, the master-slave scheme, microgrid formation, and radial structure are comprehensively covered and considered, as shown in Figure 7 and Figure 8 As the severity of the wind disaster increases (Scenario 2), the line interruption situation becomes more severe, and the system effectively alleviates the impact and reduces load shedding by forming new microgrids. In addition, renewable distributed generation (RDGs) is placed close to the upper grid to effectively consume new energy. Given the large amount of result data, the results of Case 3 are used as the main analysis object to verify the effectiveness of the method proposed in this paper.

[0074] (1) Analysis of the interaction power between distributed generation and the upper grid, the active power and reactive power injected by the upper grid are shown in Figure 9 and Figure 10 In Case 1 (see Figure 7 ), because MG1 bears more power supply demand of users, the upper grid needs to inject more active and reactive power to maintain system balance in Scenario 1 and Scenario 2.

[0075] The active and reactive power injected by DGs are shown in Figure 11 and Figure 12DGs in bus 5, 16 and 33 fail to reach their rated values. However, as the weather scenario deteriorates to scenario 2 and new microgrids are formed, resulting in a significant increase in DGs generation, in case 3, as the number of microgrids increases, DGs gradually take on the role of the main power supply within the microgrid, and their active and reactive power support plays a prominent role, providing the necessary voltage support for the system.

[0076] Table 1: Objective function value in each case

[0077] As shown in Table 1, by comparing the key indicators of the three cases, the joint optimization method has significant advantages in improving the resilience of the distribution system and reducing costs. Among them, compared with case 1, the load reduction cost of case 3 decreased by 44.46%, and the total cost also decreased by 41.26%, which is obviously superior to the single operation resilience improvement strategy. In addition, compared with case 2, the total cost of case 3 decreased by 39.8%, and the load reduction cost also decreased significantly. Compared with case 1, although the total cost and load reduction cost of case 2 decreased, the improvement degree was 24.9% and 10.3% respectively, but this effect is still not as good as case 3, which shows the important role of wind-solar-storage configuration in improving operation resilience and reducing investment cost, and the comprehensive benefits of joint optimization strategy are more prominent.

[0078] (2) New energy power analysis, in Figure 13 and Figure 14 , the active power generated by the wind turbine located in bus 4 and 22, and the photovoltaic unit in bus 3 and 30 per hour within a day. In the dispatching period, all available power generated by wind turbines and photovoltaic units in scenario 1 is effectively dispatched and allocated. Further comparison shows that when wind energy and photovoltaic power are available, the power injected from the upper grid will decrease accordingly, and vice versa, when new energy generation is insufficient, the power injected from the upper grid will increase, that is, case 1 has the highest dependence on external grid, while case 3 has lower dependence on external grid than case 1 but higher than case 2; and case 2 further reduces the dependence on external grid by increasing wind energy, solar energy and storage configuration. In addition, under scenario 2, due to local damage leading to a decrease in system power supply capacity, the output of the upper grid increases in all cases to maintain stable operation of the system.

[0079] (3) Energy storage power supply analysis, as Figure 15The charge and discharge power variation of the energy storage units located at buses 4 and 28 in Case 3 is shown in a day, where positive values indicate that the energy storage units are in discharge mode, providing power support to the system, while negative values indicate that the energy storage units are charging to reserve energy. As shown in the figure, the charging activity of the energy storage units is mainly concentrated between 1:00 am to 11:00 am, when the wind speed is usually high and the power generation of the wind turbine is large, which reserves energy for the energy storage; from 12:00 noon to around 11:00 pm, the energy storage units enter the discharge mode to supply power to the system, making up for the power gap caused by insufficient wind power, ensuring the stable operation of the system, and improving the flexibility and resilience of the distribution system in two different disaster scenarios through the energy storage low-storage-high-generation strategy.

[0080] (4) Load reduction analysis, as shown in Figure 16 , is the power curve of the total active / reactive load reduction implemented by the distribution system under different disaster scenarios. To optimize system operation, load priority is considered, and more load reduction is applied to buses with low sensitivity and low priority (such as buses 25 and 30) to ensure stable power supply to critical loads. On the contrary, for sensitive loads (such as buses 10 and 23) or important loads (such as bus 31), the optimization algorithm tries to ensure their stable power supply and reduce or avoid load reduction. In emergency situations such as natural disasters, optimal allocation of loads is achieved through intelligent scheduling, thereby improving the overall resilience and operation efficiency of the distribution system.

[0081] (5) Node voltage analysis, as shown in Figure 17 , is the voltage distribution of the system at peak hours (6:00 pm), where in scenario 1, the voltage of buses 23, 24 and 25 drops to the minimum limit of 0.9 PU, indicating that these buses are under greater pressure under peak load, while the remaining buses maintain good voltage conditions. Due to the disconnection of some buses due to line disconnection, their voltage values are not included in the figure, resulting in discontinuity in the depicted voltage curve. Especially in extreme conditions (scenario 2), although some node voltages drop to the lower limit of 0.9 PU, the overall voltage distribution remains within a controllable range, demonstrating the voltage stability of the system in the face of natural disasters.

[0082] Through the above example analysis, case 3 has significant advantages in improving system resilience and new energy carrying capacity. Compared with case 1 and case 2, case 3 selects to reinforce the key line, and can still maintain more load power supply in the serious disaster scenario, and forms a more reasonable microgrid structure, thereby improving the system resilience. With the increase of disaster intensity, case 3 shows stronger adaptive ability, and effectively deals with physical damage by constructing more microgrids. In addition, the distributed renewable energy generation is optimally arranged near the upstream or key nodes, ensuring the sustainability of power supply. In the interaction of source, network, load and storage, although the wind and light output is affected by the resource and there are differences between scenarios, the system realizes full consumption of new energy, which reflects the high carrying capacity of new energy. Energy storage effectively reduces peak and fills valley through flexible charging and discharging strategy, and supports the operation of microgrid in serious disaster. The load reduction strategy prioritizes power supply to high-priority loads and implements reasonable reduction to low-priority loads, which meets the expected optimization goal. In summary, the proposed power distribution system planning and operation joint optimization method for multiple scenario natural disasters significantly improves the system resilience and new energy carrying capacity.

[0083] Those skilled in the art can understand that all or part of the processes of the above-mentioned embodiment methods can be completed by a computer program instructing related hardware. The program can be stored in a computer readable storage medium. The computer readable storage medium includes a magnetic disk, an optical disk, a read-only memory, a random access memory, etc.

[0084] The above merely describes the preferred embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or replacements within the technical scope disclosed by the present application, which should be covered within the protection scope of the present application.

Claims

1. A joint optimization method for power distribution system planning and operation under multiple natural disaster scenarios, characterized in that, Its features include: A joint optimization model for planning and operation under disaster scenarios is constructed with the objective of minimizing the annualized total cost of the power distribution system. The annualized total cost includes load reduction costs, line reinforcement costs, and installation costs of distributed power sources and energy storage devices. The model integrates a set of constraints to describe the behavior of the power distribution system under disaster scenarios. Based on historical data and meteorological forecast information, multiple disaster scenarios with different degrees of severity are generated. The key disaster parameters for each scenario are used as input parameters for the joint planning and operation optimization model. These key disaster parameters include the probability weights of the disaster scenario occurrence. ; The nonlinear constraints in the joint planning and operation optimization model are made convex, and the model is solved using an optimization solver to obtain the optimal planning scheme and optimal operation scheme of the power distribution system corresponding to the disaster scenario.

2. The method for joint optimization of power distribution system planning and operation under multiple natural disaster scenarios as described in claim 1, characterized in that, The objective function of the joint optimization model for planning and operation is as follows: ； in, This represents the annualized total cost. A collection of disaster scenarios; Weights representing the probability of disasters occurring; This is the annualized weighting factor for operating costs. For the value of load loss, busbar Load priority factor; In response to the corresponding disaster scenarios Down Time bus The amount of active power that has been reduced; The annualized weighting coefficient for line reinforcement This is the cost coefficient for line reinforcement. For the line A binary decision variable regarding whether to reinforce; For the line Length; This is the cost weighting coefficient for new energy equipment. These are the unit capacity installation cost coefficients for distributed generation devices, wind turbines, photovoltaic units, and energy storage, respectively. , , , They are respectively on the busbar The binary decision variables for whether to install synchronous distributed generators, wind turbines, photovoltaic units, and energy storage; To reduce costs by reducing load, Cost of reinforcing the line; The installation cost of distributed power sources and energy storage equipment.

3. The method for joint optimization of power distribution system planning and operation under multiple natural disaster scenarios as described in claim 2, characterized in that, , , , , For planning decision variables; For running decision variables.

4. The method for joint optimization of power distribution system planning and operation under multiple natural disaster scenarios as described in claim 2, characterized in that, The constraints of the joint optimization model for planning and operation include: The constraints include AC power flow constraints, microgrid dynamic formation constraints, radial operation constraints, load reduction constraints, distributed generation output constraints, energy storage constraints, and line fault and hardening model constraints; among them, AC power flow constraints and distributed generation output constraints are nonlinear constraints. The AC power flow constraints are used to construct power balance equations and voltage-current relationships based on DistFlow to describe the steady-state operating characteristics of the power distribution system. The microgrid dynamic constraint is used to dynamically divide the power distribution network into multiple electrically isolated microgrids in disaster scenarios. Each bus belongs to a unique microgrid, and the microgrids are interconnected internally and isolated from each other. The radial operating constraints are used to ensure that the power distribution network maintains a loop-free and connected radial structure in both normal operation and microgrid operation modes. Used for load reduction constraints, to limit the amount of active and reactive load reduction of each bus when power generation is insufficient in disaster scenarios, and to associate the load priority factor; The distributed power output constraint is used to limit the active and reactive power output range of various distributed power sources. The energy storage constraints are used to limit the charging and discharging power, capacity, and state transition logic of the energy storage. Line fault probability and reinforcement model constraints are used to calculate the line fault probability based on wind speed scenario parameters, and to determine the line's on / off status in disaster scenarios based on whether the line fault probability exceeds a predetermined threshold.

5. The method for joint optimization of power distribution system planning and operation under multiple natural disaster scenarios as described in claim 4, characterized in that, Based on historical disaster data and meteorological forecast information, multiple disaster scenarios representing different levels of disaster severity are generated. This allows us to obtain the key disaster parameters for each disaster scenario; The key disaster parameters also include: The spatiotemporal distribution parameters of disaster intensity are used to characterize the intensity of a disaster at different spatial locations and time periods. Load and renewable energy time-series curves are used to characterize the changes in load demand and wind and solar power output at different times under the impact of disasters; Line foundation vulnerability parameters are used to characterize the inherent fault characteristics of lines under different disaster intensities; The key disaster parameters are dynamically input into the corresponding part of the joint planning and operation optimization model, including: The probability weights of the disaster scenarios are input into the objective function to calculate the load reduction cost; The spatiotemporal distribution parameters of the disaster intensity and the vulnerability parameters of the line foundation are jointly input into the coupling constraint of the line fault probability and reinforcement, which is used to dynamically calculate the fault probability and on / off status of each line in each disaster scenario and time period. The load and renewable energy time-series curves are input into the power balance constraints in the load reduction constraints, distributed power generation output constraints, energy storage constraints, and AC power flow constraints to define the supply and demand balance boundaries of the power distribution system in various disaster scenarios and time periods.

6. The method for joint optimization of power distribution system planning and operation under multiple natural disaster scenarios as described in claim 4, characterized in that, The nonlinear constraints in the joint planning and operation optimization model are made convex, including: Replace the AC power flow constraint equations with linearized AC power flow constraints based on DistFlow. The circular feasible region constraint, which couples active and reactive power in the distributed power generation output constraint, is replaced with a polygonal approximation constraint.

7. The method for joint optimization of power distribution system planning and operation under multiple natural disaster scenarios as described in claim 4, characterized in that, The line fault probability and hardening model constraints are constructed in the following way: Establish a mapping relationship between line failure rate and disaster intensity parameters; Two reinforcement states are defined for the line, and a corresponding toughness index is associated with each state; wherein, the reinforcement states include two states: unreinforced state and reinforced state; Based on the mapping relationship and the structural strength coefficient corresponding to the current reinforcement status of the line, the probability of line failure under the current disaster scenario is dynamically calculated. The line fault probability is compared with a predetermined threshold to determine the line's on / off status.

8. The method for joint optimization of power distribution system planning and operation under multiple natural disaster scenarios as described in claim 1, characterized in that, The optimal planning scheme includes: based on The obtained set of lines to be reinforced, and based on , , , The obtained distributed power sources and energy storage are installed at the corresponding bus locations; The optimal operation scheme includes: obtaining a dynamically formed microgrid topology based on the on / off state of a disaster scenario, and based on... The active power load scheduling plan for each time period is obtained.

9. The method for joint optimization of power distribution system planning and operation under multiple natural disaster scenarios as described in claim 1, characterized in that, Solving using an optimized solver and employing a hierarchical solution strategy, including: The upper-level planning problem is solved to obtain planning decision schemes, including line reinforcement schemes and installation location and capacity schemes for distributed power sources and energy storage. The underlying operational issues involve solving multiple disaster scenarios in parallel, under the constraints of the planning and decision-making scheme, including the microgrid topology, the output plans of each distributed power source and energy storage, and load reduction schemes. By iteratively coordinating the solutions to the upper-level planning problem and the lower-level operational problem until the objective function converges, the optimal planning scheme and the optimal operational scheme are obtained.

10. The method for joint optimization of power distribution system planning and operation under multiple natural disaster scenarios according to any one of claims 1-9, characterized in that, The distributed power source includes at least one of synchronous distributed generators, wind turbines, and photovoltaic units.

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