Optimization method and device of power system and electronic equipment

Abstract

The invention discloses an optimization method and device of a power system and electronic equipment. The method relates to the technical field of elastic power system planning, and comprises the following steps: obtaining operation parameters of a power system in a fault scene; based on the operation parameters, constructing a two-stage robust optimization model and constraint conditions of the power system; the power system is optimized based on the two-stage robust optimization model and the constraint condition, an optimization result of the power system is obtained, an outer-layer variable of the two-stage robust optimization model is an energy storage system distribution variable of the power system, a middle-layer variable of the two-stage robust optimization model is a fault variable of the power system, and an inner-layer variable of the two-stage robust optimization model is an operation variable of the power system; the optimization result is used for representing that the power system can keep the basic function to continue to operate in the fault scene. According to the invention, the technical problem of high construction cost of the power system in a fault scene in the prior art is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of power system planning considering resilience, and in particular, to an optimization method, device, and electronic device for a power system. Background Technique

[0002] With the intensification of global climate change, extreme weather events have become more frequent and intense. The impact of these events on the power system is particularly significant. Therefore, it is particularly important to improve the ability of the power system to resist external shocks, reduce the impact of external shock capabilities, quickly resume normal operation, and adapt to future potential risks in the fault scenario in the technical field of power system planning considering resilience. Most of them resist various external shocks or disasters by strengthening system design and building a solid infrastructure, or redundant design to maintain basic functions when the main system fails, reducing the possibility of power grid interruption. However, these measures will lead to a high construction cost of the power system in the fault scenario.

[0003] In response to the above problems, no effective solution has been proposed yet. Summary of the Invention

[0004] Embodiments of the present invention provide an optimization method, device, and electronic device for a power system to at least solve the technical problem of high construction cost of the power system in the fault scenario in the related art.

[0005] According to an aspect of an embodiment of the present invention, an optimization method for a power system is provided, including: obtaining operation parameters of the power system in a fault scenario, where the operation parameters at least include: the comprehensive operation cost of the power system, the minimum energy consumption, and the failure rate; based on the operation parameters, constructing a two-stage robust optimization model and constraint conditions of the power system, where the two-stage robust optimization model is used to characterize the matching relationship between the operation parameters and the comprehensive operation cost of the power system, and between the operation parameters and the minimum energy consumption of the power system, and the constraint conditions are used to constrain the power balance, the maximum installable capacity of the power supply, the charge and discharge power and state of charge of the energy storage, the power flow, the voltage deviation, the mobile energy storage, and the power supply to important loads of the power system; optimizing the power system based on the two-stage robust optimization model and the constraint conditions to obtain an optimization result of the power system, where the outer variable of the two-stage robust optimization model is the energy storage system allocation variable of the power system, the middle layer variable is the fault variable of the power system, the inner layer variable is the operation variable of the power system, and the optimization result is used to characterize that the power system can maintain basic functions and continue to operate in the fault scenario.

[0006] Optionally, the operating costs include: the comprehensive cost of wind turbines in the power system, the comprehensive cost of photovoltaic, the comprehensive cost of energy storage, and the comprehensive cost of interaction; the energy consumption includes: different levels of loss load and the fault duration; the failure rate includes: the component failure rate of the power system, the line failure rate, the road failure rate, and the distributed power source processing failure rate; based on the operating parameters, a two-stage robust optimization model of the power system is constructed, including: based on the comprehensive cost of wind turbines, the comprehensive cost of photovoltaic, the comprehensive cost of energy storage, and the comprehensive cost of interaction, a first objective function is constructed, where the first objective function is used to represent the minimum comprehensive cost of the power system under the fault scenario; based on different levels of loss load, the fault duration, and the weights corresponding to different levels of loss load, a second objective function is constructed, where the second objective function is used to represent the minimum energy consumption of the power system under the fault scenario; based on the component failure rate, the line failure rate, the road failure rate, and the distributed power source output failure rate, a fault state uncertainty set of the power system is constructed, where the fault state uncertainty set is used to represent the probability of the power system failing under the fault scenario; based on the first objective function, the second objective function, and the fault state uncertainty set, a two-stage robust optimization model is constructed.

[0007] Optionally, the operating cost further includes: the first comprehensive installation cost of the wind turbine generator set, the first discount rate, the first economic service life, the number of wind turbine generator sets, the first unit capacity investment cost coefficient, the first rated capacity, the first comprehensive operating cost, and the first unit capacity operating cost coefficient; the second comprehensive installation cost of the photovoltaic system, the second discount rate, the second economic service life, the number of photovoltaic systems, the second unit capacity investment cost coefficient, the second rated capacity, the second comprehensive operating cost, and the second unit capacity operating cost coefficient; the operating duration of the power system, the power at a preset moment, and the time interval step size; the fixed cost, depreciation cost, operation and maintenance price per unit capacity of the energy storage battery of the power system, the serviceable life of the energy storage, the energy storage battery discount rate, and the rated capacity of the energy storage battery; the first interaction unit cost between the power system and the power grid at a preset moment, the first power purchase and sale power of the power system and the power grid at a preset moment, the first power purchase time and the first power sale time of the power system and the power grid, the second interaction unit cost between the power system and other systems at a preset moment, the second power purchase and sale power of the power system and other systems at a preset moment, the second power purchase time and the second power sale time of the power system and other systems; based on the comprehensive cost of the wind turbine generator set, the comprehensive cost of the photovoltaic system, the comprehensive cost of the energy storage, and the comprehensive interaction cost, construct a first objective function, including: calculate the comprehensive cost of the wind turbine generator set based on the first comprehensive installation cost, the first discount rate, the first economic service life, the number of wind turbine generator sets, the first unit capacity investment cost coefficient, the first rated capacity, the first comprehensive operating cost, the first unit capacity operating cost coefficient, the operating duration, the power at the preset moment, and the time interval step size; calculate the comprehensive cost of the photovoltaic system based on the second comprehensive installation cost, the second discount rate, the second economic service life, the number of photovoltaic systems, the second unit capacity investment cost coefficient, the second rated capacity, the second comprehensive operating cost, the second unit capacity operating cost coefficient, the operating duration, the power at the preset moment, and the time interval step size; calculate the comprehensive cost of the energy storage based on the fixed cost, depreciation cost, operation and maintenance price per unit capacity of the energy storage battery, the serviceable life of the energy storage, the energy storage battery discount rate, and the rated capacity of the energy storage battery; calculate the comprehensive interaction cost based on the first interaction unit cost, the first power purchase power, the first power sale power, the first power purchase time, the first power sale time, the second interaction unit cost, the second power purchase power, the second power sale power, the second power purchase time, and the second power sale time; obtain the sum of the comprehensive cost of the wind turbine generator set, the comprehensive cost of the photovoltaic system, the comprehensive cost of the energy storage, and the comprehensive interaction cost to get the first objective function.

[0008] Optionally, a second objective function is constructed based on different levels of loss load, fault duration, and weights corresponding to different levels of loss load, including: obtaining the product of different levels of loss load and the corresponding weights to get multiple first products; respectively obtaining the product of multiple first products and the fault duration to get multiple second products; obtaining the sum value of multiple products to get a first sum value; obtaining the minimum value of the first sum value to get the second objective function.

[0009] Optionally, the operating parameters further include: the rated power of the distribution network, the allowable maximum power, the load power of the distribution network at a preset moment, the post-disaster photovoltaic power of the power system, the daily power generation efficiency, the photovoltaic capacity, and the failure rate further includes: a preset function of the component at a preset moment, the failure rate during normal operation of the distribution network, the failure rate of landslide roads, the failure rate of mountain flood roads, the failure rate of debris flow roads; based on the component failure rate, the line failure rate, the road failure rate, and the distributed power generation output failure rate, a fault state uncertainty set of the power system is constructed, including: obtaining the component failure rate based on the preset function, the standard deviation, and the mean value; obtaining the line failure rate based on the failure rate, the rated power, the allowable maximum power, and the load power; obtaining the road failure rate based on the failure rate of landslide roads, the failure rate of mountain flood roads, and the failure rate of debris flow roads; obtaining the distributed power generation output failure rate based on the post-disaster photovoltaic power, the daily power generation efficiency, the photovoltaic capacity, the first ratio, the second ratio, and the radiation intensity; summarizing the component failure rate, the line failure rate, the road failure rate, and the distributed power generation output failure rate to obtain the fault state uncertainty set.

[0010] Optionally, the operating parameters further include: the load power of the power system, the energy storage charging power, the wind power, the photovoltaic power, the energy storage discharging power, the power selling and buying powers between the power system and the large power grid, the interactive power selling and buying powers between the power system and other microgrids, the first installed capacity of the wind turbines in the power system, the first maximum installable capacity, the second installed capacity of the photovoltaic in the power system, the second maximum installable capacity, the maximum state of charge, the minimum state of charge, the state of charge of the energy storage battery at a preset moment, the rated energy storage capacity of the power system, the maximum transmission capacity of the line, the active power and reactive power of the line at a preset moment, the minimum voltage value, the maximum voltage value, and the voltage value of the nodes of the power system, the distance between the mobile energy storage of the power system between two adjacent nodes, the preset vehicle speed of the power system under zero traffic flow, the traffic congestion degree of the power system under a fault scenario, the output of the mobile energy storage of the power system, the energy storage output, the wind turbine output, the photovoltaic output, and the power supply required for the important loads of the power system within a preset time period under a fault scenario; based on the operating parameters, constraint conditions of the power system are constructed, including: based on the load power, the energy storage charging power, the wind power, the photovoltaic power, the energy storage discharging power, the power selling power, the power buying power, the interactive power selling power, and the interactive power buying power, a power balance constraint of the power system is constructed; based on the first installed capacity, the first maximum installable capacity, the second installed capacity, and the second maximum installable capacity, a maximum installable capacity constraint of the power supply of the power system is constructed; based on the maximum state of charge, the minimum state of charge, the state of charge, the rated energy storage capacity, the unit time, and the self-discharge rate, a constraint on the energy storage charging and discharging power and the state of charge of the power system is constructed; based on the maximum transmission capacity, the active power, the reactive power, and the line impedance, a power flow constraint of the power system is constructed; based on the minimum voltage value, the maximum voltage value, and the voltage value, a voltage deviation constraint of the power system is constructed; based on the distance, the preset vehicle speed, the traffic congestion degree, and the output of the mobile energy storage, a mobile energy storage constraint of the power system is constructed; based on the energy storage output, the wind turbine output, the photovoltaic output, and the power supply required for the important loads, an important load power supply constraint of the power system is constructed; the power balance constraint, the maximum installable capacity constraint of the power supply, the constraint on the energy storage charging and discharging power and the state of charge, the power flow constraint, the voltage deviation constraint, the mobile energy storage constraint, and the important load power supply constraint are summarized to obtain the constraint conditions of the power system.

[0011] Optionally, optimize the power system based on the two-stage robust optimization model and constraint conditions to obtain the optimization result of the power system, including: establishing a particle swarm of the particle swarm algorithm for the two-stage robust optimization model and initializing the particle swarm, where a particle is the basic component of the particle swarm algorithm and is used to find the optimal solution in the search space. The particle swarm at least includes: the position and velocity of the particle, the inertia weight, the learning factor, and the maximum number of iterations; determining the input variables of the power system and establishing a matrix set of the input variables, and globally searching through the particle swarm algorithm to determine the current position of the power system on the particles of the matrix set, where the input variables at least include: the transmission power between the power system and the external power grid, the interaction power between each system of the power system, the thermal power output, the basic economic attributes of each variable, and the battery state of charge; introducing power flow anomalies, line damage conditions, road congestion conditions, and abnormal component output conditions to the current position of each particle, and at the same time outputting the corresponding disaster intensity and related meteorological data to obtain the target scenario, where the target scenario is used to characterize the fault scenario under the extreme disaster with the highest probability; solving the inner-layer optimization problem for the target scenario and minimizing the load loss of different levels of lost load under the target scenario, determining the information summary and the number of photovoltaic units quickly, obtaining the optimal value of the fault variable, and calculating the fitness value; updating the velocity and position of each particle based on preset rules, and repeating the steps of initializing the particle swarm, determining the current position of the power system on the particles of the matrix set, obtaining the target scenario, obtaining the optimal value of the fault variable, and calculating the fitness value until the number of iterations of the particle reaches the maximum number of iterations or the fitness value reaches the convergence criterion, to obtain the optimization result of the power system.

[0012] According to another aspect of the embodiments of the present invention, there is also provided an optimization device for a power system, including: an acquisition module for acquiring the operating parameters of the power system in a fault scenario, where the operating parameters at least include: the operating cost, energy consumption, and failure rate of the power system; a construction module for constructing a two-stage robust optimization model and constraint conditions of the power system based on the operating parameters, where the two-stage robust optimization model is used to characterize the matching relationship between the operating parameters and the comprehensive operating cost of the power system, and between the operating parameters and the minimum energy consumption of the power system, and the constraint conditions are used to constrain the power balance, the maximum installable capacity of the power supply, the charge and discharge power and state of charge of the energy storage, power flow, voltage deviation, mobile energy storage, and power supply to important loads of the power system; an optimization module for optimizing the power system based on the two-stage robust optimization model and constraint conditions to obtain the optimization result of the power system, where the outer-layer variable of the two-stage robust optimization model is the energy storage system allocation variable of the power system, the middle-layer variable is the fault variable of the power system, and the inner-layer variable is the operation variable of the power system, and the optimization result is used to characterize that the power system can continue to operate with basic functions in a fault scenario.

[0013] According to another aspect of the embodiments of the present invention, an electronic device is further provided, including: a memory storing an executable program; a processor for running the program, wherein when the program runs, it executes the methods in the various embodiments of the present invention.

[0014] According to another aspect of the embodiments of the present invention, a computer-readable storage medium is further provided. The computer-readable storage medium includes a stored executable program, wherein when the executable program runs, it controls the device where the computer-readable storage medium is located to execute the methods in the various embodiments of the present invention.

[0015] According to another aspect of the embodiments of the present invention, a computer program product is further provided, including a computer program which, when executed by a processor, implements the methods in the various embodiments of the present invention.

[0016] According to another aspect of the embodiments of the present invention, a computer program product is further provided, including a non-volatile computer-readable storage medium storing a computer program, and the computer program, when executed by a processor, implements the methods in the various embodiments of the present invention.

[0017] According to another aspect of the embodiments of the present invention, a computer program is further provided, and the computer program, when executed by a processor, implements the methods in the various embodiments of the present invention.

[0018] In the embodiments of the present invention, the method includes: obtaining the operating parameters of the power system in a fault scenario; constructing a two-stage robust optimization model and constraint conditions of the power system based on the operating parameters; optimizing the power system based on the two-stage robust optimization model and the constraint conditions to obtain an optimization result of the power system, wherein the outer-layer variable of the two-stage robust optimization model is the allocation variable of the energy storage system of the power system, the middle-layer variable is the fault variable of the power system, and the inner-layer variable is the operation variable of the power system, and the optimization result is used to characterize the way in which the power system can maintain its basic functions and continue to operate in a fault scenario. It is easy to note that the two-stage robust optimization model is constructed through the operating parameters of the power system. By optimizing the power system through the two-stage robust optimization model, there is no need to strengthen the design of the power system and build the infrastructure. By reasonably allocating resources, it is possible to ensure the lowest comprehensive operating cost and the minimum energy consumption of the power system in a fault scenario. Further, it achieves the technical effect of being able to reduce the construction cost of the power system in a fault scenario, thus realizing the technical effect of reducing the construction cost of the power system in a fault scenario, and further solving the technical problem of the relatively high construction cost of the power system in a fault scenario in the related art. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The accompanying drawings described herein are used to provide a further understanding of the present invention and form a part of this application. The schematic embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0020] Figure 1 is a flowchart of an optimization method for a power system according to an embodiment of the present invention;

[0021] Figure 2 is a schematic diagram of the solution process of an optional two-stage robust model system based on disaster prediction according to an embodiment of the present invention;

[0022] Figure 3 is a schematic diagram of an optional electrical and traffic structure of a power system according to an embodiment of the present invention;

[0023] Figure 4 is an optional operating curve graph of Scenario 1 according to an embodiment of the present invention;

[0024] Figure 5 is an optional operating curve graph of Scenario 2 according to an embodiment of the present invention;

[0025] Figure 6 is an optional operating curve graph of Scenario 3 according to an embodiment of the present invention;

[0026] Figure 7 is an optional probability graph of line damage conditions according to an embodiment of the present invention;

[0027] Figure 8 is an optional probability graph of road damage conditions according to an embodiment of the present invention;

[0028] Figure 9 is an optional probability graph of abnormal power flow distribution conditions according to an embodiment of the present invention;

[0029] Figure 10 is an optional display graph of the damaged distribution of photovoltaic power output according to an embodiment of the present invention;

[0030] Figure 11 is an optional schematic diagram of the electrical and traffic structure of the system under extreme disasters according to an embodiment of the present invention;

[0031] Figure 12 is an optional graph of load power and recovery ratio at each time period according to an embodiment of the present invention;

[0032] Figure 13 is an optional graph of the charging and discharging power of electric vehicles at each time period according to an embodiment of the present invention;

[0033] Figure 14Schematic diagram of an optimization device for a power system according to an embodiment of the present invention. Detailed implementation manners

[0034] In order to enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0035] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and do not have to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present invention described here can be implemented in an order different from those illustrated or described here. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0036] Embodiment 1

[0037] According to an embodiment of the present invention, an embodiment of an optimization method for a power system is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0038] Figure 1 It is a flowchart of an optimization method for a power system according to an embodiment of the present invention. As Figure 1 shown, the method includes the following steps:

[0039] Step S102, obtaining the operating parameters of the power system in a fault scenario, where the operating parameters at least include: the comprehensive operating cost of the power system, the minimum energy consumption, and the failure rate.

[0040] The above-mentioned power system can be any resilient power system that needs to be optimized to ensure that it can still maintain its basic functions and continue to operate in the event of a fault scenario. The above-mentioned fault scenarios can include, but are not limited to: natural disasters, lightning faults, human operation errors, etc. In this embodiment, natural disasters are taken as an example for illustration, but it is not limited thereto. The above-mentioned comprehensive operation cost can include, but is not limited to: the comprehensive cost of wind turbines in the power system, the comprehensive cost of photovoltaic, the comprehensive cost of energy storage, and the comprehensive cost of interaction. The above-mentioned energy consumption (which can also be referred to as the weighted load shedding amount) can include, but is not limited to: different levels of lost load and fault duration. The above-mentioned failure rate can include, but is not limited to: the component failure rate of the power system, the line failure rate, the road failure rate, and the distributed power generation processing failure rate. It should be noted that the above-mentioned resilient power system refers to a power system that can maintain stable operation in the face of external disturbances (such as natural disasters, human attacks, etc.) and can quickly return to the normal state after being disturbed. The above-mentioned weighted load shedding amount refers to that in a resilient power system, when the power system encounters an emergency or the power supply is insufficient to support all loads, different weights are given to different loads according to the importance or priority of the loads.

[0041] In an alternative embodiment, in order to ensure that the power system can still maintain its basic functions and continue to operate in the event of a fault, the power system needs to be optimized. However, there is currently a technical problem that the construction cost of the power system in the fault scenario is relatively high. To solve the above technical problem, in this embodiment, first, the operating parameters of the power system can be obtained. Among them, the operating parameters of the power system can include, but are not limited to: the comprehensive operation cost of the power system, the minimum energy consumption, and the failure rate.

[0042] Step S104, based on the operating parameters, construct a two-stage robust optimization model and constraint conditions for the power system. Among them, the two-stage robust optimization model is used to characterize the matching relationship between the operating parameters and the comprehensive operation cost of the power system, and between the operating parameters and the minimum energy consumption of the power system. The constraint conditions are used to constrain the power balance of the power system, the maximum installable capacity of the power supply, the charge and discharge power and state of charge of the energy storage, the power flow, the voltage deviation, the mobile energy storage, and the power supply to important loads.

[0043] The main function of the above two-stage robust optimization model is to provide a systematic and forward-looking optimization framework for the multi-factor configuration of a new type of power distribution system under extreme conditions. Through the two-stage robust optimization model, a configuration plan that achieves a better balance between economy and resilience can be obtained. It not only considers the cost-benefit of normal operation but also fully takes into account the response ability of the power system under extreme disaster conditions, ensuring the efficient operation and rapid recovery of the power system in the face of potential future risks. Among them, the model objective of the first stage of the two-stage robust optimization model is to minimize the comprehensive system cost, including the investment cost and operation cost of each component of the power system. Through the optimization of this stage, a device capacity configuration plan with the optimal cost-benefit during normal operation can be determined, which considers the installation and operation costs of wind turbines, photovoltaics, energy storage, etc., as well as the interaction cost with the external power system. The model objective of the second stage is to minimize the weighted load shedding amount of the system under the expected fault scenarios based on the optimization results of the first stage, which is related to the stability and recovery ability of the power system under extreme disaster conditions. Through this stage, the response of the power system to disasters can be evaluated and optimized to ensure that even under the worst fault scenarios, the power system can minimize the impact on the power supply to users and quickly restore power supply.

[0044] In an alternative embodiment, when the operating parameters of the power system are obtained, the two-stage robust optimization model and constraint conditions of the power system can be constructed based on the operating parameters. For example, the two-stage robust optimization model can be constructed through the comprehensive operating cost, minimum energy consumption, and failure rate of the power system. The constraint conditions of the power system can also be constructed through power balance, maximum installable capacity of the power source, charge and discharge power and state of charge of the energy storage, power flow (also known as power flow), voltage deviation, mobile energy storage constraints, and important loads in the operating parameters. When the two-stage robust model and constraint conditions of the power system are obtained, through the two-stage robust model and constraint conditions, the reasonable allocation of resources in the power system under extreme conditions (i.e., the optimization of the power system) can be achieved without strengthening the design of the power system and constructing infrastructure.

[0045] Step S106, optimize the power system based on the two-stage robust optimization model and constraint conditions to obtain the optimization result of the power system. Among them, the outer variable of the two-stage robust optimization model is the allocation variable of the energy storage system of the power system, the middle variable is the fault variable of the power system, and the inner variable is the operation variable of the power system. The optimization result is used to characterize that the power system can maintain its basic functions and continue to operate under fault scenarios.

[0046] The above optimization result can be the equipment capacity configuration strategy of the power system based on disaster prediction. Based on this configuration strategy, it can be ensured that the power system can still maintain the most basic functions and continue to operate during natural disasters. Further, it can ensure the efficient operation and rapid recovery of the power system in the face of potential future risks.

[0047] In an alternative embodiment, when the two-stage robust optimization model and constraint conditions of the power system are obtained, the power system can be optimized through the two-stage robust optimization model and constraint conditions to obtain the equipment capacity configuration strategy of the power system based on disaster prediction. Based on this equipment capacity configuration strategy, it can be ensured that the power system can maintain the basic functions and continue to operate in the fault scenario. For example, through the two-stage robust optimization model and constraint conditions, based on the Particle Swarm Optimization (PSO) algorithm and a high-performance mathematical programming (Gurobi) solver, the operating parameters can be solved to obtain the optimization result of the power system.

[0048] Among them, the calculation steps of the particle swarm algorithm are: initializing the particle swarm, calculating the fitness value, updating the particles, boundary condition and constraint processing, iteration and convergence, and result output. Among them, initializing the particles includes: the particle swarm algorithm first generates a particle swarm composed of random positions and velocities, and each particle represents a possible solution. For the scenario in this embodiment, the position of the particle can be understood as the configuration parameters of the equipment (such as wind turbines, photovoltaics, energy storage), and the velocity of the particle represents the update direction and speed of these parameters. Calculating the fitness value includes: the performance or quality of each particle, that is, the fitness value, is calculated by evaluating the objective function (for example, minimizing the comprehensive cost of the system). The fitness value determines the "flight" direction of the particle in the search space, that is, which configuration parameters are better for the optimization objective. Updating the particles includes: the particles in the particle swarm will update their positions and velocities according to their own historical best positions (pbest) and the group historical best position (gbest). The velocity update formula usually includes an inertia weight, cognitive (self) and social (group) learning factors, and a random factor. Boundary condition and constraint processing includes: the position of the particle is restricted by the range of the search space, and the particle position beyond the range needs to be adjusted back to the valid range. For this embodiment, this involves the constraint conditions of the equipment capacity to ensure that the configuration scheme after particle update is still valid. Iteration and convergence include: the particle swarm algorithm repeats steps such as updating the particle position and velocity, calculating the fitness value, etc., until a predetermined number of iterations is reached or the convergence condition is met. In each iteration, the particle swarm continuously explores the space of the optimization solution and gradually approaches the global optimal solution or a high-quality approximate solution. Result output includes: the finally output is the particle with the highest fitness value in the particle swarm, that is, the equipment capacity configuration strategy it represents, which is usually the global optimal solution or a configuration scheme close to the optimal solution.

[0049] It should be noted that, in this embodiment, PSO is used in combination with the Gurobi solver. PSO is responsible for formulating a basic planning scheme on the preselected nodes, while Gurobi is used to solve the inner-layer optimization problem, that is, to minimize the load loss under specific fault scenarios. The combination of the global search ability of PSO and the precise solution ability of Gurobi can efficiently solve the multi-variable optimization problem, avoid falling into local optimal solutions, and at the same time reduce the calculation time and resource consumption. Through the collaborative work of the particle swarm algorithm and Gurobi, an economical and flexible device configuration strategy can be found to cope with the power demand under extreme conditions.

[0050] Through the above steps, it is possible to obtain the operating parameters of the power system under fault scenarios; based on the operating parameters, construct a two-stage robust optimization model and constraint conditions of the power system; optimize the power system based on the two-stage robust optimization model and constraint conditions to obtain the optimization results of the power system. Among them, the outer-layer variable of the two-stage robust optimization model is the energy storage system allocation variable of the power system, the middle-layer variable is the fault variable of the power system, and the inner-layer variable is the operation variable of the power system. The optimization results are used to characterize the way in which the power system can continue to operate with basic functions maintained under fault scenarios. It is easy to notice that the two-stage robust optimization model is constructed through the operating parameters of the power system. By optimizing the power system using the two-stage robust optimization model, there is no need to strengthen the design of the power system and build infrastructure. By reasonably allocating resources, it is possible to ensure the lowest comprehensive operating cost and minimum energy consumption of the power system under fault scenarios. Furthermore, it achieves the technical effect of being able to reduce the construction cost of the power system under fault scenarios, thus realizing the technical effect of reducing the construction cost of the power system under fault scenarios, and further solving the technical problem of the relatively high construction cost of the power system under fault scenarios in the related art.

[0051] Optionally, the operating costs include: the comprehensive cost of wind turbines in the power system, the comprehensive cost of photovoltaic, the comprehensive cost of energy storage, and the comprehensive interaction cost; the energy consumption includes: different levels of lost load and the fault duration; the failure rate includes: the component failure rate of the power system, the line failure rate, the road failure rate, and the distributed power source processing failure rate; based on the operating parameters, a two-stage robust optimization model of the power system is constructed, including: based on the comprehensive cost of wind turbines, the comprehensive cost of photovoltaic, the comprehensive cost of energy storage, and the comprehensive interaction cost, a first objective function is constructed, where the first objective function is used to represent the minimum comprehensive cost of the power system under the fault scenario; based on different levels of lost load, the fault duration, and the weights corresponding to different levels of lost load, a second objective function is constructed, where the second objective function is used to represent the minimum energy consumption of the power system under the fault scenario; based on the component failure rate, the line failure rate, the road failure rate, and the distributed power source output failure rate, a fault state uncertainty set of the power system is constructed, where the fault state uncertainty set is used to represent the probability of the power system failing under the fault scenario; based on the first objective function, the second objective function, and the fault state uncertainty set, a two-stage robust optimization model is constructed.

[0052] In an alternative embodiment, based on the comprehensive cost of wind turbines, the comprehensive cost of photovoltaic, the comprehensive cost of energy storage, and the comprehensive interaction cost, a first objective function is constructed. For example, the sum value of the comprehensive cost of wind turbines, the comprehensive cost of photovoltaic, the comprehensive cost of energy storage, and the comprehensive interaction cost can be obtained to get the first objective function. Wherein, the first objective function is used to represent the minimum comprehensive cost of the power system under the fault scenario; alternatively, based on different levels of lost load, the fault duration, and the weights corresponding to different levels of lost load, a second objective function can be constructed. For example, first, the product of different levels of lost load and the corresponding weights can be obtained to get a plurality of first products. Secondly, the product of a plurality of first products and the fault duration can be obtained to get a plurality of second products. Then, the sum value of the plurality of products can be obtained to get a first sum value. Finally, the minimum value of the first sum value can be obtained to get the second objective function. Wherein, the second objective function is used to represent the minimum energy consumption of the power system under the fault scenario; alternatively, based on the component failure rate, the line failure rate, the road failure rate, and the distributed power source output failure rate, a fault state uncertainty set of the power system can be constructed, where the fault state uncertainty set is used to represent the probability of the power system failing under the fault scenario; finally, based on the first objective function, the second objective function, and the fault state uncertainty set, a two-stage robust optimization model can be constructed.

[0053] Optionally, the operating cost further includes: the first comprehensive installation cost of the wind turbine generator set, the first discount rate, the first economic service life, the number of wind turbine generator sets, the first unit capacity investment cost coefficient, the first rated capacity, the first comprehensive operating cost, and the first unit capacity operating cost coefficient; the second comprehensive installation cost of the photovoltaic, the second discount rate, the second economic service life, the number of photovoltaics, the second unit capacity investment cost coefficient, the second rated capacity, the second comprehensive operating cost, and the second unit capacity operating cost coefficient; the operating duration of the power system, the power at a preset moment, and the time interval step size; the fixed cost, depreciation cost, storage unit capacity operation and maintenance price, storage serviceable life, storage battery discount rate, and storage rated capacity of the energy storage battery of the power system; the first interaction unit cost between the power system and the power grid at a preset moment, the first power purchase and power sale powers of the power system and the power grid at a preset moment, the first power purchase time and the first power sale time of the power system and the power grid, the second interaction unit cost between the power system and other systems at a preset moment, the second power purchase and power sale powers of the power system and other systems at a preset moment, the second power purchase time and the second power sale time of the power system and other systems; based on the comprehensive cost of the wind turbine generator set, the comprehensive cost of the photovoltaic, the comprehensive cost of the energy storage, and the comprehensive interaction cost, a first objective function is constructed, including: calculating the comprehensive cost of the wind turbine generator set based on the first comprehensive installation cost, the first discount rate, the first economic service life, the number of wind turbine generator sets, the first unit capacity investment cost coefficient, the first rated capacity, the first comprehensive operating cost, the first unit capacity operating cost coefficient, the operating duration, the power at the preset moment, and the time interval step size; calculating the comprehensive cost of the photovoltaic based on the second comprehensive installation cost, the second discount rate, the second economic service life, the number of photovoltaics, the second unit capacity investment cost coefficient, the second rated capacity, the second comprehensive operating cost, the second unit capacity operating cost coefficient, the operating duration, the power at the preset moment, and the time interval step size; calculating the comprehensive cost of the energy storage based on the fixed cost, depreciation cost, storage unit capacity operation and maintenance price, storage serviceable life, storage battery discount rate, and storage rated capacity of the energy storage battery; calculating the comprehensive interaction cost based on the first interaction unit cost, the first power purchase power, the first power sale power, the first power purchase time, the first power sale time, the second interaction unit cost, the second power purchase power, the second power sale power, the second power purchase time, and the second power sale time; obtaining the sum value of the comprehensive cost of the wind turbine generator set, the comprehensive cost of the photovoltaic, the comprehensive cost of the energy storage, and the comprehensive interaction cost to obtain the first objective function.

[0054] In an alternative embodiment, the comprehensive cost C of the wind power and the photovoltaic can be obtained through the following formula PV / WT :

[0055]

[0056] wherein, C PV / WTis the comprehensive cost of wind power and photovoltaic, C IN are the first comprehensive installation cost and the second comprehensive installation cost, C OM are the first comprehensive operation cost and the second comprehensive operation cost, r P / W are the first discount rate and the second discount rate, n P / W are the first economic service life and the second economic service life, N P / W are the number of wind turbines and the number of photovoltaic (the number of wind power stations or the number of photovoltaic panels), C T are the first unit capacity investment cost coefficient and the second unit capacity investment cost coefficient, P T are the first rated capacity and the second rated capacity. T is the operation duration of the power system, C OM,P / W are the first unit capacity operation cost coefficient and the second unit capacity operation cost coefficient, P P / W (t) is the power at time t (i.e., the preset time), and Δt is the time interval step size.

[0057] Optionally, the comprehensive cost of energy storage C can be obtained through the following formula BAT :

[0058] C BAT = C BATre + C BATo&m + C BATs ;

[0059]

[0060] where C BAT is the comprehensive cost of energy storage, C BATre is the fixed investment cost of energy storage battery installation (i.e., the fixed cost), C BATo&m is the operation and maintenance cost of energy storage battery, C BATs is the depreciation cost of energy storage battery, r bat is the discount rate of energy storage battery, nb at is the serviceable life of energy storage, C o&m represents the operation and maintenance price per unit capacity of energy storage (i.e., the operation price per unit capacity of energy storage), E bat is the rated capacity of energy storage.

[0061] Optionally, the interactive comprehensive cost C can also be obtained through the following formula GR&IN :

[0062]

[0063] where C GR&IN is the interactive comprehensive cost, C GRID is the comprehensive cost of interaction with the power grid, C INTER represents the interaction cost with other systems, C grid(t) represents the unit cost of interacting with the power grid at time t (i.e., the first interaction unit cost), P buy,gr (t), P sell,gr (t) represent the power purchase rate from the power grid at time t (i.e., the first power purchase rate) and the power selling rate (i.e., the first power selling rate) respectively, Δt buy，gr , Δt sell，gr represent the time of purchasing power from the power grid (i.e., the first power purchase time) and the time of selling power (i.e., the first power selling time) respectively. C inter (t) represents the unit cost of interacting with other systems at time t (i.e., the second interaction unit cost), P buy,in (t), P sell,in (t) represent the power purchase rate from other systems at time t (i.e., the second power purchase rate) and the power selling rate (i.e., the second power selling rate) respectively, Δt buy,in , Δt sell,in represent the time of purchasing power from other systems (i.e., the second power purchase time) and the time of selling power (i.e., the second power selling time) respectively.

[0064] Optionally, the first objective function F1 can be obtained through the following formula:

[0065] F1 = C WT + C PV + C BAT + C GR&IN ;

[0066] where F1 is the first objective function, that is, the minimum economic cost of the system (i.e., the minimum comprehensive cost of the power system in the fault scenario), C WT is the comprehensive cost of the wind turbine, C PV is the total comprehensive cost of the photovoltaic, C BAT is the comprehensive cost of the energy storage, C GR&IN is the comprehensive cost of the interaction.

[0067] Optionally, based on different levels of lost load, fault duration, and the weights corresponding to different levels of lost load, a second objective function is constructed, including: obtaining the product of different levels of lost load and the corresponding weights to get multiple first products; respectively obtaining the product of multiple first products and the fault duration to get multiple second products; obtaining the sum value of multiple products to get the first sum value; obtaining the minimum value of the first sum value to get the second objective function.

[0068] In an optional embodiment, the second objective function F2 can be obtained through the following formula:

[0069]

[0070] Among them, F2 is the second objective function, which represents that in the extreme disaster weather fault scenario, the weighted load shedding amount of the self-consistent energy system (i.e., the power system) is minimized. η is the weight corresponding to different levels of lost load, representing the magnitudes of different levels of lost load, T dis represents the fault duration, and min is to find the minimum value.

[0071] Optionally, the operating parameters further include: the rated power of the distribution network, the allowable maximum power, the load power of the distribution network at a preset moment, the post-disaster photovoltaic power of the power system, the daily power generation efficiency, the photovoltaic capacity. The failure rates further include: the preset function of the component at a preset moment, the failure rate when the distribution network is operating normally, the failure rate of the landslide road, the failure rate of the mountain flood road, the failure rate of the debris flow road; based on the component failure rate, the line failure rate, the road failure rate, and the distributed power generation output failure rate, a fault state uncertainty set of the power system is constructed, including: obtaining the component failure rate based on the preset function, the standard deviation, and the mean value; obtaining the line failure rate based on the failure rate, the rated power, the allowable maximum power, and the load power; obtaining the road failure rate based on the failure rate of the landslide road, the failure rate of the mountain flood road, and the failure rate of the debris flow road; obtaining the distributed power generation output failure rate based on the post-disaster photovoltaic power, the daily power generation efficiency, the photovoltaic capacity, the first ratio, the second ratio, and the radiation intensity; summarizing the component failure rate, the line failure rate, the road failure rate, and the distributed power generation output failure rate to obtain the fault state uncertainty set.

[0072] In the operation of the power system, in the face of complex environments and uncertainties, multiple fault scenarios must be comprehensively considered. Component failures may lead to local power supply interruptions or cascading reactions in the entire system; line failures will cut off regional power supply and affect a larger number of users. At the same time, external environmental factors will also exacerbate the fault risk. Road congestion may delay the dispatch of emergency repair personnel and equipment, prolonging the power outage time. In addition, the impairment of distributed power generation output (such as a sharp reduction in solar power generation due to bad weather or equipment failures) will increase the system's dependence on traditional power generation, further challenging the power system's dispatch and stability. Therefore, these fault scenarios must be comprehensively considered in the grid design to ensure that the power system has sufficient resilience and recovery capabilities.

[0073] It should be noted that in this embodiment, the probability of components suffering different degrees of damage is reflected according to the vulnerability of the line, which is described by the cumulative lognormal distribution function. In order to more intuitively reflect the probability of line damage in the system caused by extreme disaster events, weather data is applied to the component vulnerability analysis and stochastic simulation of extreme events to establish a corresponding component failure rate model.

[0074] In an alternative embodiment, the component failure rate R a (t) can be obtained through the following formula:

[0075]

[0076] Among them, R a (t) is the component failure rate, is for solving the definite integral, π is a constant, ε T is the standard deviation, exp is the cumulative lognormal distribution function (i.e., the preset function), s is the damage degree, ln is for solving the logarithm of the damage degree, λ T is the mean value, and d is a constant.

[0077] Optionally, the line failure rate R f (t) can be obtained through the following formula:

[0078]

[0079] Among them, R f (t) is the line failure rate, R f,o is the failure rate when the distribution network operates normally, represents the load power of the distribution network at time t, P r and P max represent the rated power and the allowable maximum power of the distribution network.

[0080] It should be noted that the corresponding load increases in high-temperature weather, which in turn leads to changes in the power grid flow. Therefore, when studying the resilience assessment in high-temperature weather, it is necessary to consider the impact of the power grid flow changes caused by high-temperature weather on the failure rate. Based on the load rate estimated by the previous impact load degree method and the load data determined by the system load prediction, it can be expressed as Thus, a failure rate impact model due to the power grid flow changes caused by high temperature is established.

[0081] Optionally, the road failure rate R r.ij (t) can be obtained through the following formula:

[0082] R r.ij (t) = 1 - (1 - R ij.mf (t))(1 - R ij.mf (t))(1 - R ij.df (t));

[0083] Among them, R r.ij (t) is the road failure rate, R ij.mf (t) is the mountain flood road failure rate, R ij.df (t) is the mudstone road failure rate, and 1 is a constant.

[0084] Optionally, the output situation of distributed power sources can be calculated through the following formula:

[0085]

[0086] Among them, P flooded (t) represents the post-disaster photovoltaic power, η normal represents the daily power generation efficiency, A1 represents the photovoltaic capacity, β A1 (t) is the efficiency loss ratio caused by floods (i.e., the first ratio), β B1 (t) is the covered area ratio (i.e., the second ratio), and G(t) represents the solar radiation intensity.

[0087] Finally, the failure rates of components, lines, roads, and distributed power generation output can be summarized to obtain the uncertain set of failure states.

[0088] It should be noted that after generating the failure scenarios based on the above mathematical model, an uncertain set of failure states is established. Combining the Monte Carlo probability sampling method, the set of pre-fault scenarios of the distribution network under compound disaster weather is generated and the probabilities of occurrence of each scenario are calculated. At the same time, to reduce the calculation pressure and ensure the effectiveness of the configuration results, the K-means clustering method is used to select representative scenarios, and the number of scenarios is determined based on the elbow method. The corresponding scenario with the largest compound probability is selected to provide typical scenarios for the upper and lower layers for mobile energy storage planning.

[0089] Optionally, the operating parameters further include: the load power of the power system, the energy storage charging power, the wind power, the photovoltaic power, the energy storage discharging power, the power selling and buying power between the power system and the large power grid, the interactive power selling and buying power between the power system and other microgrids, the first installed capacity of the wind turbines in the power system, the first maximum installable capacity, the second installed capacity of the photovoltaic in the power system, the second maximum installable capacity, the maximum state of charge, the minimum state of charge of the energy storage battery in the power system, the state of charge of the energy storage battery at a preset moment, the rated energy storage capacity, the maximum transmission capacity of the lines in the power system, the active power and reactive power of the lines at a preset moment, the minimum voltage value, the maximum voltage value of the nodes in the power system, and the voltage value of the nodes, the distance between adjacent two nodes of the mobile energy storage in the power system, the preset vehicle speed of the power system under zero traffic flow, the traffic congestion degree of the power system in a fault scenario, the output of the mobile energy storage in the power system, the energy storage output, the wind turbine output, the photovoltaic output and the power supply required for the important loads of the power system in a preset time period under the fault scenario; based on the operating parameters, constraint conditions of the power system are constructed, including: constructing a power balance constraint of the power system based on the load power, the energy storage charging power, the wind power, the photovoltaic power, the energy storage discharging power, the power selling power, the power buying power, the interactive power selling power and the interactive power buying power; constructing a maximum installable capacity constraint of the power sources of the power system based on the first installed capacity, the first maximum installable capacity, the second installed capacity and the second maximum installable capacity; constructing an energy storage charge-discharge power and state-of-charge constraint of the power system based on the maximum state of charge, the minimum state of charge, the state of charge, the rated energy storage capacity, the unit time and the self-discharge rate; constructing a power flow constraint of the power system based on the maximum transmission capacity, the active power, the reactive power and the line impedance; constructing a voltage deviation constraint of the power system based on the minimum voltage value, the maximum voltage value and the voltage value; constructing a mobile energy storage constraint of the power system based on the distance, the preset vehicle speed, the traffic congestion degree and the output of the mobile energy storage; constructing an important load power supply constraint of the power system based on the energy storage output, the wind turbine output, the photovoltaic output and the power supply required for the important loads; aggregating the power balance constraint, the maximum installable capacity constraint of the power sources, the energy storage charge-discharge power and state-of-charge constraint, the power flow constraint, the voltage deviation constraint, the mobile energy storage constraint and the important load power supply constraint to obtain the constraint conditions of the power system.

[0090] In an alternative embodiment, the power balance constraint can be obtained through the following formula:

[0091] P LOAD (t)+P ch (t)+P SELL (t)+P sell,m (t)=P WT (t)+P PV (t)+P dis(t) + P BUY (t) +

[0092] P buy,m (t);

[0093] wherein, P LOAD (t) is the load power of the power system, P ch (t) is the energy storage charging power, P SELL (t) is the power selling power, P sell,m (t) is the interactive power selling power, P WT (t) is the wind power, P PV (t) is the photovoltaic power, P dis (t) is the energy storage discharging power, P BUY (t) is the power purchase power, P buy,m (t) is the interactive power purchase power.

[0094] Optionally, the maximum installable capacity constraint of the power source can be obtained through the following formula:

[0095]

[0096] wherein, N WT is the installed capacity of the wind turbine (i.e., the first installed capacity), N PV is the installed capacity of the photovoltaic (i.e., the second installed capacity), N WT,MAX is the maximum installable capacity of the wind turbine limited by the installation area (i.e., the first maximum installable capacity), N PV,MAX is the maximum installable capacity of the photovoltaic limited by the installation area (i.e., the second maximum installable capacity).

[0097] Optionally, the energy storage charging and discharging power and state of charge constraint can be obtained through the following formula:

[0098]

[0099] wherein, SOC(t + 1) is the state of charge of the energy storage battery at time t + 1, w is the self-discharge rate, P c (t) is the charging power at time t, Δt is the unit time, η c is the charging efficiency, P d (t) is the discharging power at time t, E BAT is the rated capacity of the energy storage, η d is the discharging efficiency, SOC min and SOC max are respectively the minimum state of charge and the maximum state of charge of the energy storage battery.

[0100] Optionally, the power flow constraint can be obtained through the following formula:

[0101]

[0102] Among them, Z ij,t is the impedance of line ij at the corresponding time t, is the maximum transmission capacity of line ij, and P ij,t , Q ij,t are the active power and reactive power of the line at time t, respectively.

[0103] Optionally, to ensure equipment safety, improve power quality, maintain system stability, and optimize power transmission efficiency. Maintaining the voltage within a reasonable range can reduce losses, ensure the safe and reliable operation of the power grid, and at the same time meet regulatory and standard requirements. The voltage deviation constraint can be obtained through the following formula:

[0104]

[0105] Among them, and are the minimum voltage value and maximum voltage value of node j, respectively, and U j is the voltage value of node j.

[0106] Optionally, disasters often lead to fluctuations in power demand and local power supply interruptions. Reasonable scheduling of mobile energy storage devices can quickly fill the power gap and maintain power supply in key areas. However, the movement and deployment of energy storage devices need to be restricted to avoid inefficiencies or system imbalances caused by over-concentration of resources or improper scheduling. The mobile energy storage constraint can be obtained through the following formula:

[0107]

[0108] Among them, is the travel time of mobile energy storage i between nodes j and k, is the output of mobile energy storage, is the actual vehicle speed, D i,t is the distance between nodes j and k, D i,0 is the location of mobile energy storage i at node j, V i,0 is the ideal vehicle speed (i.e., the preset vehicle speed) under zero traffic flow conditions, c is the traffic congestion degree, and k, a, and b are custom parameter values, and e is a constant.

[0109] Optionally, to ensure the reliability of power supply to important loads, during disasters, the output of energy storage and new energy should ensure that the power supply event for important loads lasts for more than 6 hours. Therefore, the important load power supply constraint can be obtained through the following formula:

[0110]

[0111] Among them, is the output of energy storage within six hours after the disaster, The wind turbine output within six hours after the disaster occurs, The photovoltaic output within six hours after the disaster occurs, The power supply required for important loads within six hours after the disaster occurs.

[0112] Optionally, optimize the power system based on the two-stage robust optimization model and constraint conditions to obtain the optimization result of the power system, including: establishing a particle swarm of the particle swarm algorithm for the two-stage robust optimization model and initializing the particle swarm. Among them, a particle is the basic component of the particle swarm algorithm and is used to find the optimal solution in the search space. The particle swarm at least includes: the position and velocity of the particle, the inertia weight, the learning factor, and the maximum number of iterations; determining the input variables of the power system and establishing a matrix set of the input variables. Through the global search of the particle swarm algorithm, determine the current position of the power system on the particles in the matrix set. Among them, the input variables at least include: the transmission power between the power system and the external power grid, the interaction power between each system of the power system, the thermal power output, the basic economic attributes of each variable, and the battery state level; introduce abnormal power flow conditions, line damage conditions, road congestion conditions, and abnormal component output conditions to the current position of each particle, and at the same time output the corresponding disaster intensity and relevant meteorological data to obtain the target scenario, where the target scenario is used to represent the fault scenario under the extreme disaster with the highest probability; solve the inner-layer optimization problem for the target scenario and minimize the load loss of different levels of lost loads under the target scenario, determine the information summary and the number of photovoltaics quickly, obtain the optimal value of the fault variable, and calculate the fitness value; update the velocity and position of each particle based on the preset rules, and repeat the steps of initializing the particle swarm, determining the current position of the power system on the particles in the matrix set, obtaining the target scenario, obtaining the optimal value of the fault variable, and calculating the fitness value until the iteration number of the particle reaches the maximum iteration number or the fitness value reaches the convergence standard to obtain the optimization result of the power system.

[0113] In an alternative embodiment, the two-stage robust optimization model can be obtained through the following formula:

[0114]

[0115] Among them, the first-stage objective function F1 is to minimize the investment cost and operating cost of each component in the system; the second-stage objective function F2 is to minimize the load loss of the system under the worst fault scenario. s is the set of continuous variables in the first stage, representing the planning decision set of each part in the system; x is the set of continuous variables in the second stage, representing the load power and source power of the system during a fault, and is the corresponding system operation set; z is a binary variable, representing the state of each component; u represents the fault scenario situation, U represents the fault uncertainty set, max is to solve the maximum value, and min is to solve the minimum value.

[0116] Based on the planning decision set, the fault uncertainty set, and the system operation set, a two-stage robust optimization model is established. In the two-stage robust energy storage system optimal configuration model, the first-stage optimization is to formulate an energy storage system allocation plan on the preselected nodes based on the allocation results of the outer-layer min() problem in the first stage. On this basis, in the second stage of optimization, the middle-layer max0 is used to find the worst-case scenario and maximize the load loss in the uncertain fault set, and then the inner-layer min0 is used to minimize the load loss under the worst fault scenario of the power grid. To sum up, the optimal configuration model of the two-stage robust energy storage system can be modeled by a three-layer "min-max-min" function. The outer layer is the planning decision set, and the variable is the energy storage system allocation variable; the middle layer is the line fault uncertainty set, and the variable is the fault status scenario; the inner layer is the system operation set, and the variable is the system operation variable.

[0117] When there are many system variables, directly using the Gurobi solver will cause the problem of too long solution time or unable to obtain a feasible solution. Combining the particle swarm optimization (PSO) and Gurobi in this paper can give full play to the advantages of both. In the MP master problem with multiple variables, for each particle, its fitness value is evaluated. To solve the problem that it is difficult to find the global optimal solution and get trapped in the local optimal solution in the multi-variable optimization problem. In the SP sub-problem, the fitness value is determined by the result of the inner-layer optimization problem solved by Gurobi.

[0118] Step 1: Determine the basic initialization parameters of the system, input the corresponding predicted wind and light, and construct the uncertainty set. At the same time of inputting the load prediction and the load uncertainty set, establish the corresponding particle population. Initialize the particle swarm of PSO, including the position and velocity of the particles, the inertia weight, the learning factor, the maximum number of iterations, etc.

[0119] Step 2: Determine the system input variables, including the transmission power with the external power grid, the interactive power between systems, the thermal power output, the basic economic attributes of each variable, the SOC level, etc. Establish the corresponding matrix set, and through the global search of PSO, formulate the basic system planning plan on the preselected nodes.

[0120] Step 3: For the current position (basic planning plan) of each particle, introduce four disaster situations: abnormal power flow situation, line damage situation, road congestion situation, and abnormal component output situation. At the same time, output the corresponding disaster intensity and relevant meteorological data to find the fault scenario under the extreme disaster with the highest probability. For the worst fault scenario, use Gurobi to solve the inner-layer optimization problem, minimize the load loss under this fault scenario, determine the basic MESS and the number of photovoltaics, quickly obtain the optimal value of the second-stage decision variable y, and calculate the fitness value.

[0121] Step 4: Use the update rules of PSO to update the velocity and position of each particle. Repeat the above steps until the maximum number of iterations or the convergence criterion is reached. Through continuous iteration, gradually approach the global optimal solution.

[0122] Figure 2 It is a schematic diagram of the solution process of an optional two-stage robust model system based on disaster prediction according to an embodiment of the present invention. As Figure 2 shown, the solution process mainly includes: wind power and photovoltaic prediction, system load prediction, import of initial parameters, construction of uncertainty sets, construction of optimized uncertainty sets for impact loads, construction of a min-max-min two-stage robust optimization model, MP corresponding constraint conditions, setting initial values for iteration, judgment of convergence conditions, output of planning results, and simulation operation results.

[0123] Wind power and photovoltaic prediction includes: First, the system needs to predict the output of wind power generation and photovoltaic power generation. This step is based on weather forecasts and historical data to estimate the power generation of renewable energy under different meteorological conditions.

[0124] System load prediction includes: Next, the system predicts the power demand at each time point to understand the load conditions of the power grid under different weather conditions and optimize energy allocation and scheduling.

[0125] Import of initial parameters includes: Input relevant initial parameters, including meteorological data, equipment capacity, cost data, etc. This is the basis for the operation of the model.

[0126] Construction of uncertainty sets includes: Based on weather data and system predictions, construct uncertainty sets, which involves analyzing possible fault scenarios, including but not limited to component failures, line failures, road congestion, and reduced output of distributed power sources.

[0127] Construction of optimized uncertainty sets for impact loads includes: Further optimize the uncertainty sets, taking into account the impact of impact loads, which are loads that suddenly increase or decrease within a short period of time and may affect the stability and reliability of the power grid.

[0128] Construction of a min-max-min two-stage robust optimization model includes: This is the core part of the entire process. The model is divided into three steps: First, minimize the system investment and operating costs (min); then, find the worst fault scenario in the uncertainty set to maximize the load loss (max). This step is to solve the problem of the system under the most unfavorable conditions; finally, minimize the load loss in this worst scenario (min) to optimize the system configuration and scheduling.

[0129] The MP corresponding constraint conditions include: setting the constraint conditions of the master problem (MP), including but not limited to system power balance, maximum installable capacity, charge and discharge power of energy storage, and state of charge, etc., to ensure the feasibility of the optimization decision. Solve the MP to obtain the updated value of the master problem solution, and transfer the planning result to the SP: After solving the master problem, the updated configuration result is transferred to the sub-problem (SP) for further optimization analysis.

[0130] Setting initial values for iteration includes: The optimization model starts the iteration process to gradually improve the configuration plan. First, input relevant data such as region and meteorology into the model, and confirm the fault conditions: Confirm various fault conditions, including abnormal power flow, damaged lines, road congestion, and abnormal output of components, which is completed through model prediction and data analysis. Determine the minimum power requirements for the source and storage (source-storage) in the sub-problem: Based on the confirmed fault conditions, determine the minimum power configuration requirements for the source and energy storage (source-storage) in the sub-problem to ensure that the system can still operate basically during faults.

[0131] Convergence condition judgment: Check whether the optimization iteration meets the convergence criteria, that is, whether the configuration plan reaches a stable state or whether the predetermined number of iterations is reached.

[0132] Output the planning result: When the model converges, output the optimized distribution network configuration plan, including equipment capacity, energy storage quantity, scheduling strategy, etc.

[0133] Simulate the operation result: Simulate the output configuration plan to verify its performance and reliability under actual conditions.

[0134] End: After completing all processes, the process ends.

[0135] This flow chart reflects the complexity and systematicness of optimizing the configuration of multiple elements of a new distribution network under extreme weather conditions, and ensures the stability and economy of the power grid when facing natural disasters through prediction and simulation. The design of the two-stage robust optimization model takes into account uncertainties, and through multiple iterations and worst-case scenario analysis, finds the optimal configuration plan that balances cost and resilience.

[0136] To verify the correctness of the method for optimizing the capacity configuration of a wind-solar-thermal-energy storage system considering the flexible peak shaving transformation of thermal power in the embodiments of the present invention, a specific example is provided in this embodiment for verification: The basic parameters and corresponding data of the system are shown in Table 1 as follows:

[0137] Table 1 Basic parameters and corresponding data of the system

[0138]

[0139] In this system, if any line is damaged, the corresponding distribution node load loses power supply. Figure 3It is a schematic diagram of the electrical and transportation structure of an optional power system according to an embodiment of the present invention. At the same time, it can be seen from Figure 3 that the load nodes involve three types of weights, and their restoration order is allocated in sequence according to the corresponding weights. In addition, the actual highway corresponds to an 8-line area as shown in Figure 3 . Based on this, three scenarios are set to compare the superiority of this method.

[0140] Scenario 1: Taking the traditional distribution network model as the classic scenario, a typical plan with better economy is taken as the goal.

[0141] Scenario 2: Considering disaster prediction, a distribution network system plan for highways with elasticity and economy as dual goals.

[0142] Scenario 3: Without considering the disaster prediction situation, a distribution network plan with the maximum load restoration amount as the goal.

[0143] First of all, to meet the national requirements for the ratio of new energy to energy storage in the system and the relevant self-consistency rate requirements. The results of a typical day in three scenarios are selected for simulation analysis, and the simulation results are shown in Table 2:

[0144] Table 2 System planning capacity under different scenarios

[0145]

[0146]

[0147] It can be seen from Table 1 that compared with the traditional configuration method, the number of wind turbines and photovoltaics corresponding to Scenario 2 will increase to a certain extent compared with Scenario 1, but the number of energy storages only increases by 1 compared with the original. This is because the self-consistency rate of the system is restricted during the coordinated planning, and the operation and maintenance costs of wind and light and the converted installation costs are used to replace the costs of purchasing electricity from the external power grid by the distribution network and the operation and maintenance costs of the converter station. This part can offset the installation cost of the energy storage. At the same time, the configuration numbers of Scenario 2 are all smaller than those of Scenario 3, which obviously reduces the system installation and operation and maintenance costs. And the corresponding dispatching scenarios such as Figure 4 、 5 、6, Figure 4 is an optional operating curve diagram of Scenario 1 according to an embodiment of the present invention; Figure 5 is an optional operating curve diagram of Scenario 2 according to an embodiment of the present invention; Figure 6 is an optional operating curve diagram of Scenario 3 according to an embodiment of the present invention. It can be seen that because the configuration number of Scenario 3 is too large, it will significantly increase the power sales part, and the phenomenon of selling electricity to the main grid is significantly reduced in the optimal dispatching of Scenario 2.

[0148] Table 3 System self-consistency rate under different scenarios

[0149]

[0150] As can be seen from Table 3, the self-consistency rate of the system in Scenario 1 is 25.31%, which is lower than the design requirement of more than 30%. The self-consistency rates of Scenario 2 and Scenario 3 are 35.32% and 40.31% respectively, both higher than the design requirement. However, too high a proportion of wind and light will lead to relative waste of energy storage resources. At the same time, since wind and light are grid-connected devices, problems such as voltage instability may occur with too high a proportion. Therefore, under the current technical means, it is most appropriate to be between 30% and 40% of the annual output, and the self-consistency rate can fluctuate up and down by 5% with the change of seasons.

[0151] When a disaster occurs, the fault conditions of the lines and roads are as Figure 7 , 8 shown, Figure 7 is an optional probability diagram of line damage conditions according to an embodiment of the present invention, Figure 8 is an optional probability diagram of road damage conditions according to an embodiment of the present invention.

[0152] It can be seen that the predicted line fault probability and road fault probability of disasters will increase with the increase of disaster intensity. Among them, the line is more affected by the disaster intensity. When the disaster intensity is above 1.5, obvious damage or interruption will occur to the lines and roads. By quantifying the impact degree of extreme disasters on distribution lines and depicting the vulnerability of roads under disasters, effective early warning of risks before disasters is realized. And power flow faults and source-load damage generally occur in extreme disaster situations, and the daily situation is generally within the scope of system reliability consideration. Their distribution conditions are as Figure 9 shown, Figure 9 is an optional probability diagram of abnormal power flow distribution conditions according to an embodiment of the present invention.

[0153] The source-load damage conditions are as Figure 10 shown, Figure 10 is an optional display diagram of photovoltaic output damage distribution according to an embodiment of the present invention. It can be seen from Figure 10 that there is a power flow fault at Point 1 during the disaster occurrence time, and the voltage at its corresponding point is abnormal, resulting in the failure of nearby lines; some components of the photovoltaic modules at Position 2 are damaged at the 3rd moment of the disaster occurrence. Generally, some additional energy is generated quickly based on new energy regulation at the moment of disaster occurrence. However, due to the impact of the disaster, the output of some photovoltaic components decreases, and only 20.45% and 41.36% of the output at the highest point are available starting from the 4th moment. This also means that the fault recovery performance of photovoltaic in the system will be greatly restricted.

[0154] Finally, the typical disaster scenarios are obtained by clustering as Figure 11 shown, Figure 11 is an optional schematic diagram of the electrical and traffic structures of the system under extreme disasters according to an embodiment of the present invention. It can be seen from Figure 11It is known that four lines, namely 4-5, 13-14, 17-18, and 22-23, in the power distribution system have failed, resulting in a certain degree of congestion and interruption in road areas 2 and 6. PV2 has suffered partial failures due to weather, and abnormal power flow at Point 1 has caused nearby lines to fail. The disaster lasted for 8 hours in total. The affected situation of the system after the disaster is shown in the figure.

[0155] Mobile energy storage is an important component during disasters, and its corresponding discharge efficiency is as Figure 12 shown. Figure 12 It is an optional load power and recovery ratio diagram for each time period according to an embodiment of the present invention. It can be seen from Figure 12 that the recovery effect of the combined recovery of MESS and photovoltaic cannot be reflected in the pre-disaster stage. Therefore, during natural disasters, when the distribution network is disconnected from the upper-level main network, the uninterrupted power supply of MESS discharge to important load nodes and the rapid recovery of interrupted loads result in the corresponding overall elastic power output situation as Figure 13 shown. Figure 13 It is an optional charging and discharging power diagram of electric vehicles for each time period according to an embodiment of the present invention. It can be seen from Figure 13 that within the first hour after the fault occurs, MESS1 first discharges in Road Area 7, and the discharge power is the maximum power 200 Kw under the constraint. Due to road conditions, MESS2 outputs power at the position of Road 2 in the second hour. At the same time, due to partial line damage, the discharge power is only 46.21%. There are more key loads in Road Area 5. Therefore, in the third hour, both MESS1 and MESS2 reach Road Area 5 to supply power. In the fourth hour, MESS2 returns to Road Area 6 to supply power. During the first four hours, the affected parts have all received a certain supply. Therefore, in the subsequent four hours, both MESS1 and MESS2 follow the instructions to supply power to the corresponding areas according to the load deficit.

[0156] In addition, the combined output of MESS and photovoltaic ensures that the weighted load shedding amount of the system is minimized. It can be seen that the system load shedding amount shows a decreasing result over time within the 8 hours of the disaster. In the second hour, due to the slow start of photovoltaic output, the load shedding amount increases. After that, from the 3rd to the 5th hour, due to photovoltaic damage, MESS fails to suppress this part of the source loss. After 5 hours, the damaged areas and important loads have all received a certain compensation, and at the same time, the photovoltaic reaches the variable threshold. Therefore, the load shedding amount gradually decreases after 3 hours. In addition, during the entire disaster period, the guarantee rate of important loads is 100%, and the guarantee rate of non-important loads is above 85%, which generally meets the system requirement of 100% supply of important loads within 6 hours.

[0157] The entire process is assumed to be one operating cycle. To quantify the impact of disasters on the power system, the cost of load loss is added to the calculations of fixed investment costs, fixed operation and maintenance costs, grid interaction costs, and mobile energy storage layout costs, enabling a more comprehensive assessment of the economic consequences brought about by disasters. As shown in Table 4:

[0158] Table 4 Costs within One Operating Cycle under Different Scenarios

[0159]

[0160] It can be seen that without considering the load loss, the cost of the traditional planning method is the lowest. However, the large power grid of the distribution network is not perfect, and the construction cost involving the large power grid will increase significantly, so it is not feasible. When the cost of load loss is considered, taking into account the disaster prediction results reduces a lot of one-time investment costs compared to Scenario 3 and avoids many additional problems brought about by a lot of one-time investments. At the same time, the load loss cost is 15.20039 million yuan less than that in Scenario 1, which indicates that the grid resilience is greatly enhanced, and the comprehensive total cost is Scenario 2 < Scenario 3 < Scenario 1, fully demonstrating the necessity of enhancing grid resilience with a clear scope and goals.

[0161] After new energy is incorporated into the grid, the fault transient characteristics of the grid have changed significantly, and the interaction between each wind turbine in the wind farm also makes the transient characteristics more complex and variable. This application has the following characteristics:

[0162] 1) Construct a power system operating cost model. Based on the basic operating mode of the power system, obtain the basic capacity configuration results of the system, reduce the interaction frequency with the large power grid, and improve the stability of the entire system.

[0163] 2) Construct four mathematical models according to the impact caused by disasters, conduct corresponding scenario simulations, and obtain the system damage conditions under typical fault scenarios.

[0164] 3) According to the system damage conditions, analyze the dispatching and discharging conditions in the fault scenarios of MESS, and further explore that under different scenarios, the system comprehensive cost of the predicted disaster scenarios will be significantly reduced compared to the other two scenarios.

[0165] Embodiment 2

[0166] According to another aspect of the embodiments of the present invention, there is also provided an optimization device for a power system. This device can execute the optimization method for the power system provided in the above Embodiment 1. The specific implementation method and preferred application scenarios are the same as those in the above Embodiment 1 and will not be elaborated here.

[0167] Figure 14 is a schematic diagram of an optimization device for a power system according to an embodiment of the present invention, as Figure 14As shown in the figure, the device includes: an acquisition module 1402, configured to acquire the operating parameters of the power system in a fault scenario, where the operating parameters at least include: the operating cost of the power system, energy consumption, and failure rate; a construction module 1404, configured to construct a two-stage robust optimization model and constraint conditions of the power system based on the operating parameters, where the two-stage robust optimization model is used to characterize the matching relationship between the operating parameters and the comprehensive operating cost of the power system, and between the operating parameters and the minimum energy consumption of the power system, and the constraint conditions are used to constrain the power balance of the power system, the maximum installable capacity of the power supply, the charge and discharge power and state of charge of the energy storage, power flow, voltage deviation, mobile energy storage, and power supply to important loads; an optimization module 1406, configured to optimize the power system based on the two-stage robust optimization model and constraint conditions to obtain an optimization result of the power system, where the outer-layer variable of the two-stage robust optimization model is the energy storage system allocation variable of the power system, the middle-layer variable is the fault variable of the power system, the inner-layer variable is the operation variable of the power system, and the optimization result is used to characterize that the power system can continue to operate with basic functions in a fault scenario.

[0168] Optionally, the operating cost includes: the comprehensive cost of wind turbines of the power system, the comprehensive cost of photovoltaic, the comprehensive cost of energy storage, and the comprehensive cost of interaction; the energy consumption includes: different levels of lost load and fault duration; the failure rate includes: the component failure rate of the power system, the line failure rate, the road failure rate, and the distributed power generation handling failure rate; the construction module includes: a first construction unit, configured to construct a first objective function based on the comprehensive cost of wind turbines of the power system, the comprehensive cost of photovoltaic, the comprehensive cost of energy storage, and the comprehensive cost of interaction, where the first objective function is used to characterize the minimum comprehensive cost of the power system in a fault scenario; a second construction unit, configured to construct a second objective function based on different levels of lost load, fault duration, and the weights corresponding to different levels of lost load, where the second objective function is used to characterize the minimum energy consumption of the power system in a fault scenario; a third construction unit, configured to construct an uncertain set of fault states of the power system based on the component failure rate, the line failure rate, the road failure rate, and the distributed power generation output failure rate, where the uncertain set of fault states is used to characterize the probability of the power system failing in a fault scenario; a fourth construction unit, configured to construct a two-stage robust optimization model based on the first objective function, the second objective function, and the uncertain set of fault states.

[0169] Optionally, the operating cost further includes: the first comprehensive installation cost of the wind turbine generator set, the first discount rate, the first economic service life, the number of wind turbine generator sets, the first unit capacity investment cost coefficient, the first rated capacity, the first comprehensive operating cost, and the first unit capacity operating cost coefficient; the second comprehensive installation cost of the photovoltaic, the second discount rate, the second economic service life, the number of photovoltaics, the second unit capacity investment cost coefficient, the second rated capacity, the second comprehensive operating cost, and the second unit capacity operating cost coefficient; the operating duration of the power system, the power at a preset moment, and the time interval step size; the fixed cost, depreciation cost, storage unit capacity operation and maintenance price, storage serviceable life, storage battery discount rate, and storage rated capacity of the energy storage battery of the power system; the first interaction unit cost between the power system and the power grid at a preset moment, the first power purchase and power sale power of the power system and the power grid at a preset moment, the first power purchase time and the first power sale time of the power system and the power grid; the second interaction unit cost between the power system and other systems at a preset moment, the second power purchase and power sale power of the power system and other systems at a preset moment, the second power purchase time and the second power sale time of the power system and other systems; the first construction unit includes: a first calculation subunit, configured to calculate the comprehensive cost of the wind turbine generator set based on the first comprehensive installation cost, the first discount rate, the first economic service life, the number of wind turbine generator sets, the first unit capacity investment cost coefficient, the first rated capacity, the first comprehensive operating cost, the first unit capacity operating cost coefficient, the operating duration, the power at the preset moment, and the time interval step size; a second calculation subunit, configured to calculate the comprehensive cost of the photovoltaic based on the second comprehensive installation cost, the second discount rate, the second economic service life, the number of photovoltaics, the second unit capacity investment cost coefficient, the second rated capacity, the second comprehensive operating cost, the second unit capacity operating cost coefficient, the operating duration, the power at the preset moment, and the time interval step size; a third calculation subunit, configured to calculate the comprehensive cost of the energy storage based on the fixed cost, depreciation cost, storage unit capacity operation and maintenance price, storage serviceable life, storage battery discount rate, and storage rated capacity of the energy storage battery; a fourth calculation subunit, configured to calculate the comprehensive interaction cost based on the first interaction unit cost, the first power purchase power, the first power sale power, the first power purchase time, the first power sale time, the second interaction unit cost, the second power purchase power, the second power sale power, the first power purchase time, and the first power sale time; a summation subunit, configured to obtain the sum of the comprehensive cost of the wind turbine generator set, the comprehensive cost of the photovoltaic, the comprehensive cost of the energy storage, and the comprehensive interaction cost, and obtain the first objective function.

[0170] Optionally, the second construction unit includes: a first acquisition subunit, configured to acquire the product of different levels of loss loads and corresponding weights to obtain a plurality of first products; a second acquisition subunit, configured to respectively acquire the product of each of the plurality of first products and the fault duration to obtain a plurality of second products; a third acquisition subunit, configured to acquire the sum value of the plurality of products to obtain a first sum value; and a fourth acquisition subunit, configured to acquire the minimum value of the first sum value to obtain a second objective function.

[0171] Optionally, the operating parameters further include: the rated power of the distribution network, the allowable maximum power, the load power of the distribution network at a preset moment, the post-disaster photovoltaic power of the power system, the daily power generation efficiency, the photovoltaic capacity, and the failure rates further include: a preset function of the component at a preset moment, the failure rate of the distribution network during normal operation, the failure rate of landslide roads, the failure rate of mountain flood roads, the failure rate of debris flow roads; the third construction unit includes: a first processing subunit, configured to obtain the component failure rate based on the preset function, the standard deviation, and the mean value; a second processing subunit, configured to obtain the line failure rate based on the failure rate, the rated power, the allowable maximum power, and the load power; a third processing subunit, configured to obtain the road failure rate based on the failure rate of landslide roads, the failure rate of mountain flood roads, and the failure rate of debris flow roads; a fourth processing subunit, configured to obtain the failure rate of distributed power generation output based on the post-disaster photovoltaic power, the daily power generation efficiency, the photovoltaic capacity, the first ratio, the second ratio, and the radiation intensity; and a summarizing subunit, configured to summarize the component failure rate, the line failure rate, the road failure rate, and the failure rate of distributed power generation output to obtain a set of uncertain fault states.

[0172] Optionally, the operating parameters further include: the load power of the power system, the energy storage charging power, the wind power, the photovoltaic power, the energy storage discharging power, the power selling and buying power between the power system and the large power grid, the interactive power selling and buying power between the power system and other microgrids, the first installed capacity of the wind turbines in the power system, the first maximum installable capacity, the second installed capacity of the photovoltaic in the power system, the second maximum installable capacity, the maximum state of charge, the minimum state of charge of the energy storage battery in the power system, the state of charge of the energy storage battery at a preset moment, the rated energy storage capacity, the maximum transmission capacity of the power system lines, the active power and reactive power of the lines at a preset moment, the minimum voltage value, the maximum voltage value of the nodes of the power system, and the voltage value of the nodes, the distance between adjacent two nodes of the mobile energy storage in the power system, the preset vehicle speed of the power system under zero traffic flow, the traffic congestion degree of the power system in a fault scenario, the output of the mobile energy storage in the power system, the energy storage output, the fan output, the photovoltaic output and the power supply required for the important loads of the power system in a preset time period under the fault scenario; The construction module further includes: a fifth construction unit for constructing the power balance constraint of the power system based on the load power, the energy storage charging power, the wind power, the photovoltaic power, the energy storage discharging power, the power selling power, the power buying power, the interactive power selling power and the interactive power buying power; a sixth construction unit for constructing the maximum installable capacity constraint of the power supply of the power system based on the first installed capacity, the first maximum installable capacity, the second installed capacity and the second maximum installable capacity; a seventh construction unit for constructing the energy storage charge and discharge power and state of charge constraint of the power system based on the maximum state of charge, the minimum state of charge, the state of charge, the rated energy storage capacity, the unit time and the self-discharge rate; an eighth construction unit for constructing the power flow constraint of the power system based on the maximum transmission capacity, the active power, the reactive power and the line impedance; a ninth construction unit for constructing the voltage deviation constraint of the power system based on the minimum voltage value, the maximum voltage value and the voltage value; a tenth construction unit for constructing the mobile energy storage constraint of the power system based on the distance, the preset vehicle speed, the traffic congestion degree and the output of the mobile energy storage; an eleventh construction unit for constructing the important load power supply constraint of the power system based on the energy storage output, the fan output, the photovoltaic output and the power supply required for the important loads; a summarizing unit for summarizing the power balance constraint, the maximum installable capacity constraint of the power supply, the energy storage charge and discharge power and state of charge constraint, the power flow constraint, the voltage deviation constraint, the mobile energy storage constraint and the important load power supply constraint to obtain the constraint conditions of the power system.

[0173] Optionally, the optimization module includes: a construction unit for constructing a particle swarm of a particle swarm algorithm for a two-stage robust optimization model and initializing the particle swarm, where a particle is the basic component unit of the particle swarm algorithm and is used to find the optimal solution in the search space. The particle swarm includes at least: the position and velocity of the particle, the inertia weight, the learning factor, and the maximum number of iterations; a first determination unit for determining the input variables of the power system and constructing a matrix set of the input variables, and globally searching through the particle swarm algorithm to determine the current position of the power system on the particles of the matrix set, where the input variables include at least: the transmission power between the power system and the external power grid, the interaction power between each system of the power system, the thermal power output, the basic economic attributes of each variable, and the battery state of charge; a processing unit for introducing power flow anomalies, line damage conditions, road congestion conditions, and abnormal component output conditions to the current position of each particle, and simultaneously outputting the corresponding disaster intensity and related meteorological data to obtain a target scenario, where the target scenario is used to represent the fault scenario under the most probable extreme disaster; a second determination unit for solving the inner-layer optimization problem for the target scenario and minimizing the load loss of different levels of lost load under the target scenario, determining the information summary and the number of photovoltaic units quickly, obtaining the optimal value of the fault variable, and calculating the fitness value; a repetition unit for updating the velocity and position of each particle based on a preset rule, and repeating the initialization of the particle swarm, determining the current position of the power system on the particles of the matrix set, obtaining the target scenario, obtaining the optimal value of the fault variable, and calculating the fitness value until the number of iterations of the particle reaches the maximum number of iterations or the fitness value reaches the convergence criterion, to obtain the optimization result of the power system.

[0174] Embodiment 3

[0175] An embodiment of the present application further provides an electronic device, including: a memory storing an executable program; a processor for running the program, where when the program runs, it executes the methods in the various embodiments of the present invention.

[0176] Embodiment 4

[0177] An embodiment of the present application further provides a computer-readable storage medium. The computer-readable storage medium includes a stored executable program, where when the executable program runs, it controls the device where the computer-readable storage medium is located to execute the methods in the various embodiments of the present invention.

[0178] Embodiment 5

[0179] An embodiment of the present application further provides a computer program product, including a computer program, where when the computer program is executed by a processor, it implements the methods in the various embodiments of the present invention.

[0180] Embodiment 6

[0181] Embodiments of the present application also provide a computer program product, including a non-volatile computer-readable storage medium for storing a computer program, where the computer program, when executed by a processor, implements the methods in various embodiments of the present invention.

[0182] Embodiment 7

[0183] Embodiments of the present application also provide a computer program, where the computer program, when executed by a processor, implements the methods in various embodiments of the present invention described above.

[0184] The serial numbers of the above embodiments of the present invention are only for description and do not represent the superiority or inferiority of the embodiments.

[0185] In the above embodiments of the present invention, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0186] In the several embodiments provided by the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only illustrative. For example, the division of the units can be a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the units or modules can be in an electrical or other form.

[0187] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0188] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above integrated units can be implemented in the form of hardware or in the form of software functional units.

[0189] When the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as USB flash drives, read-only memories (ROMs), random access memories (RAMs), mobile hard disks, magnetic disks, or optical discs.

[0190] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. An optimization method for a power system, characterized in that, Including: Obtaining the operating parameters of the power system under a fault scenario, where the operating parameters at least include: the comprehensive operating cost of the power system, the minimum energy consumption, and the failure rate; Based on the operating parameters, constructing a two-stage robust optimization model and constraint conditions for the power system. Among them, the two-stage robust optimization model is used to characterize the matching relationship between the operating parameters and the comprehensive operating cost of the power system, and between the operating parameters and the minimum energy consumption of the power system. The constraint conditions are used to constrain the power balance, the maximum installable capacity of the power source, the charge and discharge power and state of charge of the energy storage, the power flow, the voltage deviation, the mobile energy storage, and the power supply to important loads of the power system; Optimizing the power system based on the two-stage robust optimization model and the constraint conditions to obtain the optimization result of the power system. Among them, the outer-layer variable of the two-stage robust optimization model is the allocation variable of the energy storage system of the power system, the middle-layer variable is the fault variable of the power system, and the inner-layer variable is the operation variable of the power system. The optimization result is used to characterize that the power system can continue to operate with basic functions under the fault scenario.

2. The method according to claim 1, wherein The operating cost includes: the comprehensive cost of the wind turbines of the power system, the comprehensive cost of the photovoltaic, the comprehensive cost of the energy storage, and the comprehensive cost of the interaction. The energy consumption includes: different levels of lost loads and the fault duration. The failure rate includes: the component failure rate of the power system, the line failure rate, the road failure rate, and the distributed power source processing failure rate. Based on the operating parameters, constructing the two-stage robust optimization model of the power system includes: Based on the comprehensive cost of the wind turbines, the comprehensive cost of the photovoltaic, the comprehensive cost of the energy storage, and the comprehensive cost of the interaction, constructing a first objective function, where the first objective function is used to characterize the minimum comprehensive cost of the power system under the fault scenario; Based on the different levels of lost loads, the fault duration, and the weights corresponding to the different levels of lost loads, constructing a second objective function, where the second objective function is used to characterize the minimum energy consumption of the power system under the fault scenario; Based on the component failure rate, the line failure rate, the road failure rate, and the distributed power source output failure rate, constructing an uncertain set of the fault states of the power system, where the uncertain set of the fault states is used to characterize the probability of the power system failing under the fault scenario; Based on the first objective function, the second objective function, and the uncertain set of the fault states, constructing the two-stage robust optimization model.

3. The method according to claim 2, wherein The operating cost further includes: the first comprehensive installation cost of the wind turbine generator set, the first discount rate, the first economic service life, the number of wind turbine generator sets, the first unit capacity investment cost coefficient, the first rated capacity, the first comprehensive operating cost, and the first unit capacity operating cost coefficient; the second comprehensive installation cost of the photovoltaic, the second discount rate, the second economic service life, the number of photovoltaics, the second unit capacity investment cost coefficient, the second rated capacity, the second comprehensive operating cost, and the second unit capacity operating cost coefficient; the operating duration of the power system, the power at a preset moment, and the time interval step size; the fixed cost, depreciation cost, storage unit capacity operation and maintenance price, storage serviceable life, storage battery discount rate, and storage rated capacity of the energy storage battery of the power system; the first interaction unit cost between the power system and the power grid at the preset moment, the first power purchase and power sale powers of the power system and the power grid at the preset moment, the first power purchase time and the first power sale time of the power system and the power grid, the second interaction unit cost between the power system and other systems at the preset moment, the second power purchase and power sale powers of the power system and other systems at the preset moment, the second power purchase time and the second power sale time of the power system and other systems; based on the comprehensive cost of the wind turbine generator set, the comprehensive cost of the photovoltaic, the comprehensive cost of the energy storage, and the comprehensive interaction cost, construct a first objective function, including: Based on the first comprehensive installation cost, the first discount rate, the first economic service life, the number of wind turbine generator sets, the first unit capacity investment cost coefficient, the first rated capacity, the first comprehensive operating cost, the first unit capacity operating cost coefficient, the operating duration, the power at the preset moment, and the time interval step size, calculate the comprehensive cost of the wind turbine generator set; Based on the second comprehensive installation cost, the second discount rate, the second economic service life, the number of photovoltaics, the second unit capacity investment cost coefficient, the second rated capacity, the second comprehensive operating cost, the second unit capacity operating cost coefficient, the operating duration, the power at the preset moment, and the time interval step size, calculate the comprehensive cost of the photovoltaic; Based on the fixed cost, the depreciation cost, the storage unit capacity operation and maintenance price, the storage serviceable life, the storage battery discount rate, and the storage rated capacity of the energy storage battery, calculate the comprehensive cost of the energy storage; Based on the first interaction unit cost, the first power purchase power, the first power sale power, the first power purchase time, the first power sale time, the second interaction unit cost, the second power purchase power, the second power sale power, the second power purchase time, and the second power sale time, calculate the comprehensive interaction cost; Obtain the sum value of the comprehensive cost of the wind turbine generator set, the comprehensive cost of the photovoltaic, the comprehensive cost of the energy storage, and the comprehensive interaction cost to obtain the first objective function.

4. The method according to claim 2, characterized in that, Construct a second objective function based on the different levels of loss loads, the fault duration, and the weights corresponding to the different levels of loss loads, including: Obtain the product of the different levels of loss loads and the corresponding weights to get a plurality of first products; Respectively obtain the product of each of the plurality of first products and the fault duration to get a plurality of second products; Obtain the sum value of the plurality of products to get a first sum value; Obtain the minimum value of the first sum value to get the second objective function.

5. The method according to claim 2, wherein The operating parameters further include: the rated power of the distribution network, the allowable maximum power, the load power of the distribution network at a preset moment, the post-disaster photovoltaic power of the power system, the daily power generation efficiency, the photovoltaic capacity, and the failure rate further includes: a preset function of the component at the preset moment, the failure rate when the distribution network operates normally, the failure rate of landslide roads, the failure rate of mountain flood roads, the failure rate of debris flow roads; construct the uncertain set of the fault state of the power system based on the component failure rate, the line failure rate, the road failure rate, and the distributed power generation output failure rate, including: Obtain the component failure rate based on the preset function, the standard deviation, and the mean value; Obtain the line failure rate based on the failure rate, the rated power, the allowable maximum power, and the load power; Obtain the road failure rate based on the failure rate of landslide roads, the failure rate of mountain flood roads, and the failure rate of debris flow roads; Obtain the distributed power generation output failure rate based on the post-disaster photovoltaic power, the daily power generation efficiency, the photovoltaic capacity, the first ratio, the second ratio, and the radiation intensity; Summarize the component failure rate, the line failure rate, the road failure rate, and the distributed power generation output failure rate to obtain the uncertain set of the fault state.

6. The method according to claim 1, wherein The operating parameters further include: the load power of the power system, the energy storage charging power, the wind power, the photovoltaic power, the energy storage discharging power, the power selling and power purchasing powers between the power system and the large power grid, the interactive power selling and interactive power purchasing powers between the power system and other microgrids, the first installed capacity of the wind turbines of the power system, the first maximum installable capacity, the second installed capacity of the photovoltaic of the power system, the second maximum installable capacity, the maximum state of charge and the minimum state of charge of the energy storage battery of the power system, the state of charge of the energy storage battery at a preset moment, the rated energy storage capacity, the maximum transmission capacity of the lines of the power system, the active power and reactive power of the lines at the preset moment, the minimum voltage value and the maximum voltage value of the nodes of the power system, and the voltage value of the nodes, the distance between adjacent nodes of the mobile energy storage of the power system, the preset vehicle speed of the power system under zero traffic flow, the traffic congestion degree of the power system in the fault scenario, the output of the mobile energy storage of the power system, the energy storage output, the wind turbine output, the photovoltaic output, and the power supply required for the important loads of the power system within a preset time period in the fault scenario; construct the constraint conditions of the power system based on the operating parameters, including: Based on the load power, the energy storage charging power, the wind power, the photovoltaic power, the energy storage discharging power, the power selling, the power purchasing, the interactive power selling, and the interactive power purchasing, construct the power balance constraint of the power system; Based on the first installed capacity, the first maximum installable capacity, the second installed capacity, and the second maximum installable capacity, construct the maximum installable capacity constraint of the power sources in the power system; Based on the maximum state of charge, the minimum state of charge, the state of charge, the rated capacity of the energy storage, the unit time, and the self-discharge rate, construct the energy storage charging and discharging power and state of charge constraint of the power system; Based on the maximum transmission capacity, the active power, the reactive power, and the line impedance, construct the power flow constraint of the power system; Based on the minimum voltage value, the maximum voltage value, and the voltage value, construct the voltage deviation constraint of the power system; Based on the distance, the preset vehicle speed, the traffic congestion degree, and the output of the mobile energy storage, construct the mobile energy storage constraint of the power system; Based on the output of the energy storage, the output of the wind turbine, the output of the photovoltaic, and the power supply required for the important load, construct the important load power supply constraint of the power system; Summarize the power balance constraint, the maximum installable capacity constraint of the power sources, the energy storage charging and discharging power and state of charge constraint, the power flow constraint, the voltage deviation constraint, the mobile energy storage constraint, and the important load power supply constraint to obtain the constraint conditions of the power system.

7. The method according to claim 1, wherein Optimize the power system based on the two-stage robust optimization model and the constraint conditions to obtain the optimization results of the power system, including: Establish a particle swarm of the particle swarm algorithm for the two-stage robust optimization model and initialize the particle swarm. Here, the particle is the basic component of the particle swarm algorithm, which is used to find the optimal solution in the search space. The particle swarm at least includes: the position and velocity of the particle, the inertia weight, the learning factor, and the maximum number of iterations; Determine the input variables of the power system and establish a matrix set of the input variables. Through the global search of the particle swarm algorithm, determine the current position of the power system on the particles in the matrix set. Here, the input variables at least include: the transmission power between the power system and the external power grid, the interactive power between the various systems of the power system, the thermal power output, the basic economic attributes of each variable, and the battery state level; Introduce power flow anomalies, line damage conditions, road congestion conditions, and abnormal component output conditions to the current position of each particle, and at the same time output the corresponding disaster intensity and relevant meteorological data to obtain the target scenario. Here, the target scenario is used to represent the fault scenario under the extreme disaster with the highest probability; Solve the inner-layer optimization problem for the target scenario and minimize the load loss of different levels of lost loads under the target scenario. Determine the information summary and the number of photovoltaic units quickly to obtain the optimal value of the fault variable and calculate the fitness value; Update the velocity and position of each particle based on preset rules, and repeat initializing the particle swarm. Determine the current position of the power system on the particles in the matrix set to obtain a target scenario, obtain the optimal value of the fault variable, and calculate the fitness value until the iteration number of the particles reaches the maximum iteration number or the fitness value reaches the convergence criterion, so as to obtain the optimization result of the power system.

8. An optimization device for a power system, characterized in that, Comprising: An acquisition module, configured to acquire the operation parameters of the power system in a fault scenario, where the operation parameters at least include: the operation cost, energy consumption, and failure rate of the power system; A construction module, configured to construct a two-stage robust optimization model and constraint conditions of the power system based on the operation parameters, where the two-stage robust optimization model is used to characterize the matching relationship between the operation parameters and the comprehensive operation cost of the power system, and between the operation parameters and the minimum energy consumption of the power system, and the constraint conditions are used to constrain the power balance, maximum installable capacity of the power supply, charge and discharge power and state of charge of the energy storage, power flow, voltage deviation, mobile energy storage, and power supply to important loads of the power system; An optimization module, configured to optimize the power system based on the two-stage robust optimization model and the constraint conditions to obtain the optimization result of the power system, where the outer-layer variable of the two-stage robust optimization model is the energy storage system allocation variable of the power system, the middle-layer variable is the fault variable of the power system, and the inner-layer variable is the operation variable of the power system, and the optimization result is used to characterize that the power system can continue to operate with basic functions in the fault scenario.

9. An electronic device, characterized in that, Comprising: A memory storing an executable program; A processor, configured to run the program, where the program, when running, executes the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, where, when the executable program runs, it controls the device where the storage medium is located to execute the method according to any one of claims 1 to 7.

11. A computer program product, characterized in that, Including a computer program, where the computer program, when executed by a processor, implements the method according to any one of claims 1 to 7.

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