Multi-element benefit analysis method and system for elastic planning scheme of power system

Abstract

The invention discloses a multi-element benefit analysis method and system for an elastic planning scheme of a power system. The method comprises the steps of obtaining a novel power system planning scheme; carrying out extreme event coping capability evaluation on the novel power system planning scheme within a set age limit to obtain an elastic index; carrying out accounting estimation on the obtained novel power system planning scheme by adopting a carbon emission flow method to obtain a carbon emission benefit index; aiming at the implementation cost of the obtained novel power system planning scheme, calculating the construction cost of power facilities and the reinforcement or reconstruction cost of station buildings and lines to obtain economic indexes; according to the method, an elastic index, carbon emission and an economic index are integrated, and under an elastic improvement-oriented novel power system planning scheme evaluation decision framework, different planning schemes are evaluated, compared and selected, a decision closed loop is formed, and the planning schemes are fed back and corrected, so that novel power system construction and adaptive planning under climate change are realized. And a scientific decision-making basis is provided for improving investment decision-making.

Description

Background Art

[0002] At present, the evaluation and decision-making research on the planning scheme of new power systems is not in-depth enough, and the decision-making of planning schemes for improving resilience and taking into account both conventional operation and response to extreme events is even more blank. The decision-making of traditional power system planning schemes is mainly carried out from the economic and technical analysis level for conventional operation scenarios. The planning scheme of new power systems for improving resilience needs to take into account both conventional operation and response to extreme events. How to make reasonable planning scheme analysis and decision-making is the key problem to be solved in the planning of new power systems for improving resilience. Therefore, it is necessary to study new cost-benefit analysis and evaluation decision-making methods for the resilience planning schemes of new power systems. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a multi-factor benefit analysis method and system for power system resilience planning schemes in response to the above-mentioned deficiencies in the prior art, which comprehensively evaluates new power system planning schemes based on multiple indicators such as resilience, carbon emission reduction, and economy, and is used to solve the technical problem that the robust / random planning of traditional planning cannot simultaneously consider multiple complex scenarios such as resilience and carbon benefits, thereby providing a scientific decision-making basis for improving investment decisions.

[0005] The present invention adopts the following technical solutions:

[0006] A multi-factor benefit analysis method for a power system flexibility planning scheme comprises the following steps:

[0007] Obtain new power system planning solutions;

[0008] Through extreme event simulation, component vulnerability analysis, system response behavior analysis, and elasticity index calculation, the new power system planning scheme is evaluated for its ability to cope with extreme events within a set period of time, and elasticity benefit indicators are obtained;

[0009] The carbon emission flow method is used to calculate and estimate the low-carbon costs of power system construction, source measurement, load side, and grid side during the planning period to obtain the new power system planning scheme and obtain the carbon emission index;

[0010] Based on the implementation cost of the new power system planning scheme, the construction cost of power facilities and the reinforcement or transformation cost of station buildings and lines are calculated to obtain economic indicators;

[0011] Based on the comprehensive resilience indicators, carbon emissions and economic indicators, different planning schemes are evaluated and compared under the new power system planning scheme evaluation and decision-making framework for improving resilience, forming a closed decision-making loop, and providing feedback and corrections to the planning schemes to achieve the construction of new power systems and adaptive planning under climate change.

[0012] Preferably, obtaining a new power system planning scheme is specifically as follows:

[0013] The planning cycle considered in the planning scheme;

[0014] The planned capacity, connection location, and year of construction and commissioning of each type of generating unit;

[0015] The planned capacity, access location, and construction and commissioning year of each type of energy storage device;

[0016] The planned location of the transmission corridor, the capacity of the line, and the year in which the line will be constructed and put into operation;

[0017] The planned location of the substation and the year in which the substation will be constructed and put into operation;

[0018] Typical load data estimated during the planning year;

[0019] Plan the typical daily operation mode of the power system during the year.

[0020] Preferably, the new power system planning scheme is evaluated to obtain the elasticity index as follows:

[0021] For extreme event simulation, the impact of extreme events is simulated through the frequency, location, duration, and disaster intensity characteristics of extreme events, and a set of computer-simulated extreme event scenarios is generated. For each simulated extreme event scenario, the disaster intensity is analyzed and calculated at each disaster-stricken location. Based on historical disaster data, key parameters characterizing extreme events are analyzed and selected, and a probability distribution model or extreme value regression model of key parameters of extreme events is established to characterize the uncertainty of extreme events, and hypothesis testing of the model is performed to verify the goodness of fit. For each simulated year within the planning period, the number of disaster occurrences is sampled based on the annual frequency distribution. For each disaster scenario, other key parameters are sampled to obtain the initial state of the simulated disaster, and the parameters are converted into dynamic disaster scenarios with spatiotemporal characteristics through meteorological or geographical models.

[0022] For component vulnerability analysis, based on the disaster scenarios generated in extreme event simulation and the vulnerability curves of power system components, the failure rate of components in the power system is calculated, random numbers are generated to sample the fault status of each component, and it is determined whether each component of the power system fails under the corresponding disaster scenario;

[0023] Analyze the system response behavior, describe the response process of the power system to extreme disasters, establish the emergency control and emergency power restoration model of the power system to extreme events, and simulate the system power outage and restoration process;

[0024] For the calculation of elasticity indicators, the risk loss is defined as the weighted loss of load and maintenance cost of the system during the entire disaster. The specific elasticity indicators are the risk value of the loss and the tail value at risk. The Monte Carlo method is used to simulate the economic losses caused each year, and the convergence of the indicators is achieved through year-by-year simulation within the planning year.

[0025] Preferably, the elasticity quantitative indicator VaR α (X) and tail value at risk TVaR α (X) is calculated as follows:

[0026] VaR α (X)=inf{x:P(X≤x)≥α}

[0027]

[0028] Among them, inf{·} is the infimum, P(·) is the probability of the event, α is the set confidence level, X is the annual risk loss, and x is the risk loss auxiliary parameter under the given confidence level α.

[0029] Preferably, the annual risk loss X is:

[0030]

[0031] Among them, Λ is the total number of passing typhoons, is the set of fault lines and fault nodes under the rth disaster, is the repair or reconstruction cost of the equipment corresponding to component k, is the economic loss of load loss at node i, d i,r is the load loss time of node i during the rth typhoon.

[0032] Preferably, the new power system planning scheme is calculated and estimated to obtain the carbon emission index as follows:

[0033] For each planning year within the planning cycle, determine the construction and commissioning of units, energy storage, lines, and stations within that year, and calculate the carbon emission levels of raw materials, production, and installation during the construction of power facilities;

[0034] For the direct carbon emissions from power generation on the source side, the direct carbon emissions of different power plants on the source side of the power system under typical daily operation modes are used to characterize the carbon emissions generated by the unit power generation through the power generation carbon emission factor;

[0035] For the indirect carbon emissions from electricity consumption on the load side, the direct carbon emissions generated by the user's electricity consumption behavior on the source side under a typical daily operation mode are used to represent it, and the indirect carbon emissions per unit of electricity consumption of the user belonging to each node are converted through the node carbon emission factor;

[0036] Regarding the indirect carbon emissions from grid-side network losses: the cumulative amount of coupled carbon emissions in the tidal current corresponding to the network loss under the daily operation mode is converted by the carbon flow rate, including the branch carbon flow rate and the network loss carbon flow rate. The branch carbon flow rate is the indirect carbon emissions flowing through the branch with the tidal current per unit time, and the network loss carbon flow rate represents the indirect carbon emissions attached to the tidal current network loss per unit time;

[0037] Add the carbon emissions from power facility construction, direct carbon emissions from power generation on the source side, indirect carbon emissions from power consumption on the load side, and indirect carbon emissions from network losses on the grid side in each year of the planning period to obtain the carbon emission level of the new power system planning scheme within the planning period.

[0038] Quantify low-carbon costs, including low-carbon input costs and low-carbon loss costs. Low-carbon input costs are estimated based on the initial investment in equipment, technology and related operating activities, and low-carbon loss costs are the costs paid for carbon dioxide emissions.

[0039] Preferably, low carbon loss cost for:

[0040]

[0041] Among them, D G For carbon emission quotas, is the carbon trading price, E power Carbon emissions from fuel consumption in the electricity system.

[0042] Preferably, the construction cost of power facilities and the reinforcement or transformation cost of station buildings and lines are calculated to obtain the economic indicators as follows:

[0043] For the construction and configuration of power facilities, calculate their costs of commissioning, operation, maintenance, and decommissioning during the planning period. Power facilities include generators, energy storage, lines, and stations. For the reinforcement, enhancement, renovation, or improvement of construction standards of station buildings and lines, calculate the fixed investment costs and operation and maintenance costs.

[0044] Preferably, different planning schemes are evaluated and compared to form a closed decision loop as follows:

[0045] Based on the analysis of the evolution trend of extreme events, a planning scheme is formulated through the analysis of the impact of extreme events;

[0046] Evaluate the proposed planning schemes on resilience, carbon emission and economic performance indicators;

[0047] Under a given economic budget, the proposed planning schemes are compared and selected, the Pareto optimal scheme is screened for implementation, and the implemented strategies are monitored and the effects are post-evaluated during system operation to form a decision-making closed loop.

[0048] In a second aspect, an embodiment of the present invention provides a multi-factor benefit analysis system for a power system flexibility planning scheme, including:

[0049] Planning module, to obtain new power system planning solutions;

[0050] The resilience module evaluates the ability of the new power system planning scheme to cope with extreme events within a set period of time through extreme event simulation, component vulnerability analysis, system response behavior analysis, and resilience index calculation to obtain resilience benefit indicators;

[0051] The carbon emission module uses the carbon emission flow method to calculate and estimate the low-carbon costs of power system construction, source measurement, load side, and grid side during the planning period for the new power system planning scheme to obtain carbon emission indicators;

[0052] The economic module calculates the cost of implementing the new power system planning scheme, the cost of power facility construction, and the cost of strengthening or renovating station buildings and lines to obtain economic indicators;

[0053] The analysis module integrates resilience indicators, carbon emissions and economic indicators, and evaluates and compares different planning schemes under the new power system planning scheme evaluation and decision-making framework for resilience improvement, forming a decision-making closed loop, and provides feedback and correction to the planning schemes to achieve new power system construction and adaptive planning under climate change.

[0054] In a third aspect, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above-mentioned method for analyzing the multi-factor benefit of a power system resilience planning scheme when executing the computer program.

[0055] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for multi-factor benefit analysis of power system resilience planning schemes.

[0056] Compared with the prior art, the present invention has at least the following beneficial effects:

[0057] A multi-factor benefit analysis method for power system resilience planning schemes can quantitatively characterize the advantages and disadvantages of different planning schemes, take into account both conventional operation scenarios and extreme event scenarios, balance economic efficiency and various benefit indicators, and provide a scientific reference for practical issues such as the access of new energy to the system, the arrangement of expansion planning, the optimization of operation mode, and the improvement of investment decisions.

[0058] Furthermore, a complete process of elastic benefit evaluation of planning schemes considering the occurrence and development uncertainty of long-term extreme events was established, including extreme event simulation, component vulnerability analysis, system response behavior analysis, and elasticity index calculation. In the extreme event simulation stage, the method characterizes the uncertainty of characteristic parameters such as the frequency, location, duration, and disaster intensity of extreme events in each simulation year by establishing a probability distribution model or extreme value regression model of key parameters of extreme events. The parameters are converted into dynamic disaster scenarios with spatiotemporal characteristics through meteorological or geographical models. The convergence of indicators is achieved through year-by-year simulations in the planning year, and the simulation of extreme event scenarios in the long term is achieved. In the system response behavior analysis stage, the method considers emergency measures such as emergency control and emergency repair and power restoration, and can accurately simulate the system response and recovery process. In the index calculation stage, the method uses the risk value of annual losses and the tail value at risk as quantitative indicators. The convergence of indicators is achieved through year-by-year simulations in the planning year, and the probabilistic assessment of the system disaster risk under the planning scheme is achieved.

[0059] Furthermore, a process for carbon emission benefit assessment of planning schemes was established, and carbon emission accounting methods for different levels of sources, grids and loads were provided, taking into account low-carbon input costs and low-carbon loss costs, to achieve accurate and comprehensive carbon emission benefit calculations.

[0060] Furthermore, an evaluation process for the economic feasibility of the planning scheme was established, taking into account the construction costs of power facilities such as generators, energy storage, and lines, as well as the costs of station buildings and line reinforcement or transformation, which can more comprehensively quantify the economic costs of the new power system planning scheme.

[0061] It can be understood that the beneficial effects of the second aspect mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0062] In summary, the method of the present invention establishes a scientific and complete evaluation method for multiple factors including planning scheme elasticity, carbon emissions, and economic efficiency, taking into account the uncertainty of the occurrence and development of extreme events in the long term and the probabilistic evaluation of the elastic benefits of the system response recovery process, and comprehensively quantifies the carbon emissions at all levels of the source grid and load. This method can quantitatively characterize the advantages and disadvantages of different planning schemes, taking into account both conventional operation scenarios and extreme event scenarios, balancing economic efficiency and various benefit indicators, and providing a scientific reference basis for practical issues such as the access of new energy to the system, the arrangement of expansion planning, the optimization of operation mode, and the improvement of investment decisions.

[0063] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A flow chart for the resilience benefit analysis of new power system planning options;

[0065] Figure 2 A visual display of the typhoon disaster process simulation;

[0066] Figure 3 This is a schematic diagram of power system component failure sampling in disaster scenarios;

[0067] Figure 4 Schematic diagram of a decision framework for evaluating new power system planning options for improving resilience;

[0068] Figure 5 A schematic diagram of a computer device provided by an embodiment of the present invention;

[0069] Figure 6 The present invention is a block diagram of an electronic device provided according to an embodiment. DETAILED DESCRIPTION

[0070] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0071] In the description of the present invention, it should be understood that the terms “include” and “comprises” indicate the presence of described features, wholes, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or collections thereof.

[0072] It should also be understood that the terms used in the present specification are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the present specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0073] It should be further understood that the term "and / or" used in the present specification and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, and includes these combinations. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in the present invention generally indicates that the associated objects are in an "or" relationship.

[0074] It should be understood that, although the terms first, second, third, etc. may be used to describe preset ranges, etc. in the embodiments of the present invention, these preset ranges should not be limited to these terms. These terms are only used to distinguish preset ranges from each other. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0075] The word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to determining" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)", depending on the context.

[0076] Various structural schematic diagrams of the embodiments disclosed in the present invention are shown in the accompanying drawings. These figures are not drawn to scale, and some details are magnified and some details may be omitted for the purpose of clear expression. The shapes of various regions and layers shown in the figures and the relative sizes and positional relationships therebetween are only exemplary, and may deviate in practice due to manufacturing tolerances or technical limitations, and those skilled in the art may additionally design regions / layers with different shapes, sizes, and relative positions according to actual needs.

[0077] The invention provides a multi-factor benefit analysis method for a power system flexibility planning scheme, which adopts.

[0078] The present invention provides a multi-factor benefit analysis method for a power system flexibility planning scheme, comprising the following steps:

[0079] S1. Obtain the new power system planning scheme, clarify the planning period considered in the planning scheme, the construction and commissioning plan of the units, energy storage, lines, stations, etc. within the planning scheme, the typical load data estimated within the planning year, and the typical daily operation mode within the planning year;

[0080] Obtain a new power system planning scheme and organize the following data:

[0081] S101. Planning cycle considered in planning schemes;

[0082] S102: Planned capacity, access location, and construction and commissioning year of various types of generating units including new energy and flexible thermal power;

[0083] S103. Planned capacity, access location, and construction and commissioning year of various types of energy storage devices such as pumped storage and batteries;

[0084] S104. The planned location of the transmission corridor, the line capacity, and the year in which the line is constructed and put into operation;

[0085] S105. The planned location of the substation and the year in which the substation was constructed and put into operation;

[0086] S106. Typical load data estimated during the planning year;

[0087] S107. Plan the typical daily operation mode of the power system within the year.

[0088] S2. The resilience improvement benefits of the planning scheme are evaluated through four steps: extreme event simulation, component vulnerability analysis, system response behavior analysis, and resilience index calculation;

[0089] S201. For extreme event simulation, the impact of extreme events is simulated through the characteristics of extreme event frequency, location, duration, disaster intensity, etc., to generate a large number of computer-simulated extreme event scene sets, and for each simulated extreme event scene, the disaster intensity is analyzed and calculated at each disaster-stricken location. Based on historical disaster data, analyze and select key parameters that can characterize extreme events (frequency, intensity, duration, etc.), establish a probability distribution model or extreme value regression model of key parameters of extreme events to characterize the uncertainty of extreme events, and perform hypothesis testing on the model to verify the goodness of fit. For each simulated year in the planning period, the number of disaster occurrences is sampled based on the annual frequency distribution. For each disaster scene, other key parameters are sampled to obtain the initial state of the simulated disaster, and the parameters are converted into dynamic disaster scenes with spatiotemporal characteristics (typhoon movement path, heavy rainfall process, etc.) through meteorological or geographical models.

[0090] Taking typhoon disasters as an example, we first use the best path dataset of tropical cyclones in the northwest Pacific to perform moment estimation, maximum likelihood estimation, and interval estimation on the parameters in the probability distribution of five key typhoon parameters: annual occurrence rate, shortest distance, central pressure difference, moving speed, and moving direction. We also use the goodness of fit test method to perform hypothesis testing on the probability model.

[0091] Secondly, the probability distribution model of key parameters and the typhoon wind field and filling model are integrated to build a typhoon simulation generator, which can generate typhoon transit scenarios with specific disaster intensity according to certain probability distribution. Subsequent model examples are based on typhoon disaster scenarios, such as Figure 2 shown.

[0092] S202. For component vulnerability analysis, based on the disaster scenarios generated in the extreme event simulation and the vulnerability curves of the power system components, the failure rate of the components in the power system is calculated, and random numbers are generated to sample the fault status of each component to determine whether each component of the power system fails under the corresponding disaster scenario.

[0093] Taking the vulnerability analysis of power cables under typhoon disasters as an example, the reliability function R(t) of the line during the typhoon is an exponential function with the failure rate λ(t) integral over time as the exponent. Since the typhoon simulation generator samples and generates typhoon information data with a time resolution of hours, it is considered that the failure rate of the line in each hour is a certain constant. Therefore, the availability function of the line during the typhoon is in the form of a piecewise exponential function. Combined with the continuity property of the reliability curve, the availability function of the line during the typhoon can be written as:

[0094]

[0095] The constant of integration is given by C k =R(t k ) is used to determine the forced outage rate function of the line during the typhoon period:

[0096]

[0097] Among them, C k =1-F(t k ). Figure 3 A schematic diagram of the forced outage rate function curve during the typhoon passage is given.

[0098] The random suspension time of the line in the typhoon passing scenario is determined according to the inverse function method. The specific method is: randomly generate uniformly distributed random numbers u~U[0,1] in the interval [0,1], and determine u=F(t down ) corresponds to the time t down This is the random suspension time of the line, as shown by the dotted line in the figure below. For each line, the random suspension time during the typhoon can be determined according to the above method. If the suspension time of the line is greater than the departure time of the typhoon, the line will not be affected by the typhoon and will not be out of service due to failure in this typhoon scenario.

[0099] S203. Analyze the system response behavior, describe the response process of the power system to extreme disasters, establish the emergency control and emergency power restoration model of the power system to extreme events, and simulate the system power outage and restoration process, including:

[0100] While the disaster is ongoing, control measures such as fault identification, isolation and load restoration are taken to mitigate the impact of the fault; after the disaster is over, emergency repair personnel are dispatched to find and eliminate the fault and quickly restore power supply.

[0101] Taking the response process of the new distribution system under typhoon disaster as an example, the specific process is as follows:

[0102] 1) During the disaster, manual on-site operation is not possible due to safety issues, and control is completed through the distribution automation system, including FTU, DTU and other components to collect fault information and operate the status of the remote switch;

[0103] 2) After the disaster is over, the line inspection task or emergency repair task is assigned to the standby operation and maintenance personnel. The line inspection task is to patrol the fault area according to the fault indication and confirm the area status. The emergency repair task is to repair the faulty equipment and eliminate the fault.

[0104] 3) When a zone is confirmed to be fault-free, the operation and maintenance personnel restore the power supply to the zone and reduce the scope of the fault through manual switches or remote switches;

[0105] 4) When the fault point in the fault section is cleared, the remote switch and manual switch will operate to restore the power supply to the faulty area, and this process will be repeated until the system resumes normal power supply.

[0106] S204. For elasticity index calculation, risk loss is defined as the weighted load loss and maintenance cost of the system in the entire disaster. The specific elasticity indexes are the Value at Risk (VaR) and the Tail Value at Risk (TVaR). The Monte Carlo method is used to simulate the economic losses caused each year, and the convergence of the indicators is achieved through year-by-year simulation within the planning year.

[0107] The calculation formula for annual risk loss X is as follows:

[0108]

[0109] Among them, Λ is the total number of passing typhoons, is the set of fault lines and fault nodes under the rth disaster, is the repair or reconstruction cost of the equipment corresponding to component k, is the economic loss of load loss at node i, d i,r is the load loss time of node i during the rth typhoon.

[0110] The calculation formulas for the annual risk loss, elasticity quantitative indicators VaR and TVaR are as follows:

[0111] VaR α (X)=inf{x:P(X≤x)≥α} (4)

[0112]

[0113] Among them, inf{·} represents the infimum, P(·) represents the probability of event occurrence, and α represents the set confidence level.

[0114] S3. Based on the carbon emission reduction benefits of the planning scheme, the carbon emission flow method is used to calculate and estimate the low-carbon costs of power system construction, source measurement, load side, and grid side during the planning period;

[0115] S301. For each planning year within the planning cycle, determine the construction and commissioning of units, energy storage, lines, stations, etc. within that year, and calculate the carbon emission levels of raw materials, production, and installation during the construction of power facilities;

[0116] S302. For the direct carbon emissions from power generation on the source side, the direct carbon emissions of different power plants on the source side of the power system under typical daily operation modes are used to characterize the carbon emissions generated by the unit power generation of the unit through the power generation carbon emission factor;

[0117] S303: For the indirect carbon emissions from electricity consumption on the load side, the direct carbon emissions generated by the user's electricity consumption behavior on the source side under a typical daily operation mode are used to represent it, and the indirect carbon emissions per unit of electricity consumption of the user belonging to each node are converted by the node carbon emission factor;

[0118] S304. For indirect carbon emissions from grid-side network losses: the cumulative amount of coupled carbon emissions in the tidal current corresponding to the network loss under the daily operation mode is converted by the carbon flow rate, including the branch carbon flow rate and the network loss carbon flow rate. The branch carbon flow rate is the indirect carbon emissions flowing through the branch with the tidal current per unit time, and the network loss carbon flow rate represents the indirect carbon emissions attached to the tidal current network loss per unit time;

[0119] S305. Add the carbon emissions from power facility construction, direct carbon emissions from power generation on the source side, indirect carbon emissions from power consumption on the load side, and indirect carbon emissions from network losses on the grid side in each year during the planning period to obtain the carbon emission level of the new power system planning scheme during the planning period.

[0120] S306. Quantify the low-carbon cost, which consists of two parts: low-carbon input cost and low-carbon loss cost. The low-carbon input cost can be estimated based on the initial investment in equipment and technology and the costs of related operating activities. The low-carbon loss cost is the cost paid for carbon dioxide emissions.

[0121] The calculation method is as follows:

[0122]

[0123] in, is the low carbon loss cost, D G For carbon emission quotas, The carbon trading price.

[0124] S4. The implementation cost of the planning scheme includes the construction cost of power facilities such as computer groups, energy storage, lines, stations, and the cost of strengthening or renovating station buildings and lines;

[0125] S401. For the construction and configuration of power facilities such as generators, energy storage, lines, and stations, calculate the costs of commissioning, operation, maintenance, and decommissioning within the planning period;

[0126] S402. Calculate the fixed investment costs and operation and maintenance costs for station building and line reinforcement, enhancement, renovation or improvement of construction standards.

[0127] S5. Comprehensively consider the three indicators of resilience, carbon emissions and economy, and evaluate and compare different planning schemes under the new power system planning scheme evaluation and decision-making framework for improving resilience, thus forming a closed decision-making loop.

[0128] S501. Based on the analysis of the evolution trend of extreme events, formulate planning schemes through analysis of the impact of extreme events;

[0129] S502, evaluate the three indicators of flexibility, carbon emissions and economy of the proposed planning scheme according to steps S1 to S4;

[0130] S503. Compare and select the proposed planning schemes under a given economic budget, select the Pareto optimal scheme for implementation, and monitor the implemented strategies and post-evaluate their effects during system operation, thus forming a decision-making closed loop.

[0131] It will be appreciated by those skilled in the art that various aspects of the present invention may be implemented as systems, methods or program products. Therefore, various aspects of the present invention may be specifically implemented in the following forms, namely: complete hardware implementation, complete software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, which may be collectively referred to herein as "circuits", "modules" or "platforms".

[0132] In another embodiment of the present invention, a multi-factor benefit analysis system for a power system resilience planning scheme is provided. The system can be used to implement the above-mentioned multi-factor benefit analysis method for a power system resilience planning scheme. Specifically, the multi-factor benefit analysis system for a power system resilience planning scheme includes a planning module, a resilience module, a carbon emission module, an economic module and an analysis module.

[0133] Among them, the planning module obtains the new power system planning scheme;

[0134] The resilience module evaluates the ability of the new power system planning scheme to cope with extreme events within a set period of time through extreme event simulation, component vulnerability analysis, system response behavior analysis, and resilience index calculation to obtain resilience benefit indicators;

[0135] The carbon emission module uses the carbon emission flow method to calculate and estimate the low-carbon costs of power system construction, source measurement, load side, and grid side during the planning period for the new power system planning scheme to obtain carbon emission indicators;

[0136] The economic module calculates the cost of implementing the new power system planning scheme, the cost of power facility construction, and the cost of strengthening or renovating station buildings and lines to obtain economic indicators;

[0137] The analysis module integrates resilience indicators, carbon emissions and economic indicators, and evaluates and compares different planning schemes under the new power system planning scheme evaluation and decision-making framework for resilience improvement, forming a decision-making closed loop, and provides feedback and correction to the planning schemes to achieve new power system construction and adaptive planning under climate change.

[0138] In another embodiment of the present invention, a terminal device is provided, which includes a processor and a memory, wherein the memory is used to store a computer program, the computer program includes program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, and is specifically suitable for loading and executing one or more instructions to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used for the operation of the multi-factor benefit analysis method of the power system elastic planning scheme, including:

[0139] Obtain a new power system planning scheme; evaluate the extreme event response capability of the obtained new power system planning scheme within a set period through extreme event simulation, component vulnerability analysis, system response behavior analysis, and elasticity index calculation, and obtain elasticity benefit indicators; use the carbon emission flow method to estimate the low-carbon costs of power system construction, source measurement, load side, and grid side during the planning period for the obtained new power system planning scheme, and obtain carbon emission indicators; calculate the cost of power facility construction and the cost of station building and line reinforcement or transformation for the implementation cost of the obtained new power system planning scheme, and obtain economic indicators; comprehensively consider elasticity indicators, carbon emissions, and economic indicators, and evaluate and compare different planning schemes under the new power system planning scheme evaluation and decision-making framework for elasticity improvement, form a decision-making closed loop, and provide feedback and correction to the planning scheme to achieve new power system construction and adaptive planning under climate change.

[0140] In another embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a terminal device for storing programs and data. It is understandable that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and the extended storage medium supported by the terminal device, and can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more computer programs (including program codes). It should be noted that more specific examples (non-exhaustive list) of the computer-readable storage medium here include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0141] Computer readable storage media also include data signals propagated in baseband or as part of a carrier wave, which carry readable program codes. Such propagated data signals can take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The readable storage medium can also be any readable medium other than a readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, device, or device. The program code contained on the readable storage medium can be transmitted using any appropriate medium, including but not limited to wireless, wired, optical cable, RF, etc., or any suitable combination of the above.

[0142] Program code for performing the operations of the present invention may be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computing device (e.g., through the Internet using an Internet service provider).

[0143] One or more instructions stored in a computer-readable storage medium may be loaded and executed by a processor to implement the corresponding steps of the multi-factor benefit analysis method for a power system flexibility planning scheme in the above-mentioned embodiment; one or more instructions in the computer-readable storage medium are loaded and executed by the processor as follows:

[0144] Obtain a new power system planning scheme; evaluate the extreme event response capability of the obtained new power system planning scheme within a set period through extreme event simulation, component vulnerability analysis, system response behavior analysis, and elasticity index calculation, and obtain elasticity benefit indicators; use the carbon emission flow method to estimate the low-carbon costs of power system construction, source measurement, load side, and grid side during the planning period for the obtained new power system planning scheme, and obtain carbon emission indicators; calculate the cost of power facility construction and the cost of station building and line reinforcement or transformation for the implementation cost of the obtained new power system planning scheme, and obtain economic indicators; comprehensively consider elasticity indicators, carbon emissions, and economic indicators, and evaluate and compare different planning schemes under the new power system planning scheme evaluation and decision-making framework for elasticity improvement, form a decision-making closed loop, and provide feedback and correction to the planning scheme to achieve new power system construction and adaptive planning under climate change.

[0145] See also Figure 5 , the terminal device is a computer device, and the computer device 60 of this embodiment includes: a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When the computer program 63 is executed by the processor 61, the method for calculating the composition of fluid in the reservoir transformation wellbore in the embodiment is realized. To avoid repetition, it is not described one by one here. Alternatively, when the computer program 63 is executed by the processor 61, the functions of each model / unit in the calculation system for the composition of fluid in the reservoir transformation wellbore in the embodiment are realized. To avoid repetition, it is not described one by one here.

[0146] The computer device 60 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will appreciate that Figure 5 This is only an example of the computer device 60 and does not constitute a limitation of the computer device 60. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the computer device may also include input and output devices, network access devices, buses, etc.

[0147] The processor 61 may be a central processing unit (CPU), or other general-purpose processors, central processing units, graphics processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, data processing logic based on quantum computing, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0148] The memory 62 may be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 may also be an external storage device of the computer device 60, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc., equipped on the computer device 60.

[0149] Furthermore, the memory 62 may include both an internal storage unit of the computer device 60 and an external storage device. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 may also be used to temporarily store data that has been output or is to be output.

[0150] Any reference to memory, database or other media used in the embodiments provided in the present application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0151] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0152] See also Figure 6 The terminal device 600 is an electronic device, and the electronic device is in the form of a general computing device. The components of the electronic device may include but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including the storage unit 620 and the processing unit 610), a display unit 640, etc.

[0153] The storage unit stores program codes, which can be executed by the processing unit 610, so that the processing unit 610 performs the steps according to various exemplary embodiments of the present invention described in the above method section of this specification. For example, the processing unit 610 can perform the following steps: Figure 1 Follow the steps shown in .

[0154] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache memory unit 6202 , and may further include a read-only memory unit (ROM) 6203 .

[0155] The storage unit 620 may also include a program / utility 6204 having a set (at least one) of program modules 6205, such program modules 6205 including but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which or some combination may include an implementation of a network environment.

[0156] Bus 630 may represent one or more of several types of bus structures, including a memory unit bus or memory unit controller, a peripheral bus, an accelerated graphics port, a processing unit, or a local bus using any of a variety of bus architectures.

[0157] The electronic device 600 may also communicate with one or more external devices 700 (e.g., keyboards, pointing devices, Bluetooth devices, etc.), may also communicate with one or more devices that enable a user to interact with the electronic device 600, and / or communicate with any device that enables the electronic device 600 to communicate with one or more other computing devices (e.g., routers, modems, etc.). Such communication may be performed via an input / output (I / O) interface 650. Furthermore, the electronic device 600 may also communicate with one or more networks (e.g., a local area network (LAN), a wide area network (WAN), and / or a public network, such as the Internet) via a network adapter 660. The network adapter 660 may communicate with other modules of the electronic device 600 via a bus 630. It should be understood that, although not shown in the figure, other hardware and / or software modules may be used in conjunction with the electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms, etc.

[0158] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. The components of the embodiments of the present invention described and shown in the drawings here can usually be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0159] In order to simulate the proposed multi-factor benefit analysis method for power system resilience planning, a simulation environment is designed to use computer software and algorithms to simulate the performance of different planning schemes in terms of extreme events, component vulnerability, system response, carbon emissions, and economy. The following is an example of a simplified simulation process:

[0160] 1. Initialization and data preparation

[0161] Define system parameters: including the basic structure of the power system (such as grid topology, type and distribution of power stations, load points, etc.), equipment parameters (such as generator capacity, transmission line capacity and impedance, etc.).

[0162] Planning scheme input: Prepare multiple new power system planning schemes, including different power supply configurations, expansion plans, energy storage configuration schemes, etc.

[0163] 2. Extreme event simulation

[0164] Simulate the impact of extreme events: simulate the impact of extreme events through the frequency, location, duration, disaster intensity and other characteristics of extreme events, generate a large number of computer-simulated extreme event scenario sets, and for each simulated extreme event scenario, analyze and calculate the disaster intensity at each disaster-stricken location.

[0165] 3. Component vulnerability analysis and system response

[0166] Vulnerability analysis: Based on historical data and expert knowledge, evaluate the failure probability and severity of consequences of each component under extreme events.

[0167] System response simulation: simulate the system's remote control and on-site repair response measures after extreme events (such as remote topology reconstruction, manual switch operation, fault repair, etc.), and record the system recovery process.

[0168] 4. Elasticity index calculation

[0169] Define resilience indicators: define risk loss as the weighted load loss and repair cost of the system during the entire disaster. Specific resilience indicators include the risk value of the loss and the tail risk value.

[0170] Calculate elasticity index: Use Monte Carlo method to simulate the economic losses caused each year, and achieve convergence of index through year-by-year simulation within the planning year.

[0171] 5. Carbon emissions and low-carbon cost accounting

[0172] Carbon emission flow model: Establish a carbon emission model for the entire life cycle of the power system, including carbon emissions during the construction, operation and decommissioning stages.

[0173] Low-carbon cost accounting: Based on the carbon emission flow model, combined with the carbon emission rights trading price or carbon tax policy, calculate the low-carbon cost of each planning scheme.

[0174] 6. Economic evaluation

[0175] Cost calculation: including the cost of power facility construction, station building and line reinforcement or renovation costs, operation and maintenance costs, etc.

[0176] Economic indicators: Calculate the total cost, investment payback period, net present value and other economic indicators of each planning scheme.

[0177] 7. Comprehensive evaluation and decision-making

[0178] Construct an evaluation framework: Combine resilience indicators, carbon emission indicators, and economic indicators to build a new power system planning scheme evaluation and decision-making framework for improving resilience.

[0179] Scheme comparison and selection: Use the Pareto multi-objective method to conduct a comprehensive evaluation and comparison of different planning schemes.

[0180] Feedback correction: Based on the evaluation results, feedback correction is made to the planning scheme to optimize the scheme design.

[0181] 8. Result output and visualization

[0182] Generate evaluation report: Organize the simulation results and generate a detailed evaluation report, including a comparison chart of the elasticity, carbon emissions and economic indicators of each planning scheme, and a comprehensive evaluation ranking.

[0183] Visual display: Use charts, maps and other forms to intuitively display key information such as the performance of different planning schemes under extreme events, carbon emission distribution and economic costs.

[0184] Through the above simulation processing flow, the comprehensive benefits of different new power system planning schemes in terms of resilience, carbon emissions and economy can be systematically evaluated, providing a scientific basis for decision makers.

[0185] In summary, the multi-factor benefit analysis method and system for power system flexibility planning schemes of the present invention can evaluate the planning schemes of new power systems based on three indicators: flexibility, carbon emissions, and economy, and provide a scientific reference for practical issues such as the access of new energy to the system, the arrangement of expansion planning, the optimization of operation modes, and the improvement of investment decisions.

[0186] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0187] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0188] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0189] In the embodiments provided by the present invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as 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 mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0190] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0191] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0192] If the integrated module / 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 present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium, etc. It should be noted that the content contained in the computer-readable medium can be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable media do not include electric carrier signals and telecommunication signals.

[0193] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1A device that provides the functions specified in a block or multiple blocks.

[0194] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0195] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0196] The above contents are only for explaining the technical idea of ​​the present invention and cannot be used to limit the protection scope of the present invention. Any changes made on the basis of the technical solution in accordance with the technical idea proposed by the present invention shall fall within the protection scope of the claims of the present invention.

Claims

1. A multi-factor benefit analysis method for power system flexibility planning scheme, characterized in that: The following steps are involved: Obtain new power system planning solutions; Through extreme event simulation, component vulnerability analysis, system response behavior analysis, and elasticity index calculation, the new power system planning scheme is evaluated for its ability to cope with extreme events within a set period of time, and elasticity benefit indicators are obtained; The carbon emission flow method is used to calculate and estimate the low-carbon costs of power system construction, source measurement, load side, and grid side during the planning period to obtain the new power system planning scheme and obtain the carbon emission index; Based on the implementation cost of the new power system planning scheme, the construction cost of power facilities and the reinforcement or transformation cost of station buildings and lines are calculated to obtain economic indicators; Based on comprehensive resilience indicators, carbon emissions and economic indicators, different planning schemes are evaluated and compared under the new power system planning scheme evaluation and decision-making framework for improving resilience, forming a closed decision-making loop, and providing feedback and corrections to the planning schemes to achieve new power system construction and adaptive planning under climate change.

2. The multi-factor benefit analysis method for power system resilience planning scheme according to claim 1 is characterized in that: The specific steps for obtaining the new power system planning scheme are as follows: The planning cycle considered in the planning scheme; The planned capacity, connection location, and year of construction and commissioning of each type of generating unit; The planned capacity, access location, and construction and commissioning year of each type of energy storage device; The planned location of the transmission corridor, the capacity of the line, and the year in which the line will be constructed and put into operation; The planned location of the substation and the year in which the substation will be constructed and put into operation; Typical load data estimated during the planning year; Plan the typical daily operation mode of the power system during the year.

3. The multi-factor benefit analysis method for power system resilience planning scheme according to claim 1 is characterized in that: The new power system planning scheme is evaluated, and the elasticity indicators are as follows: For extreme event simulation, the impact of extreme events is simulated through the frequency, location, duration, and disaster intensity characteristics of extreme events, and a set of computer-simulated extreme event scenarios is generated. For each simulated extreme event scenario, the disaster intensity is analyzed and calculated at each disaster-stricken location. Based on historical disaster data, key parameters characterizing extreme events are analyzed and selected, and a probability distribution model or extreme value regression model of key parameters of extreme events is established to characterize the uncertainty of extreme events, and hypothesis testing of the model is performed to verify the goodness of fit. For each simulated year within the planning period, the number of disaster occurrences is sampled based on the annual frequency distribution. For each disaster scenario, other key parameters are sampled to obtain the initial state of the simulated disaster, and the parameters are converted into dynamic disaster scenarios with spatiotemporal characteristics through meteorological or geographical models. For component vulnerability analysis, based on the disaster scenarios generated in extreme event analysis and the vulnerability curves of power system components, the failure rate of components in the power system is calculated, random numbers are generated to sample the fault status of each component, and it is determined whether each component of the power system fails under the corresponding disaster scenario; Analyze the system response behavior, describe the response process of the power system to extreme disasters, establish the emergency control and emergency power restoration model of the power system to extreme events, and simulate the system power outage and restoration process; For the calculation of elasticity indicators, the risk loss is defined as the weighted loss of load and maintenance cost of the system during the entire disaster. The specific elasticity indicators are the risk value of the loss and the tail risk value. The Monte Carlo method is used to simulate the economic losses caused each year, and the convergence of the indicators is achieved through year-by-year simulation within the planning year.

4. The multi-factor benefit analysis method for power system resilience planning scheme according to claim 3 is characterized in that: Elasticity quantitative indicator VaR α (X) and tail value at risk TVaR α (X) is calculated as follows: VaR α (X)=inf{x:P(X≤x)≥α} Among them, inf{·} is the infimum, P(·) is the probability of the event, α is the set confidence level, X is the annual risk loss, and x is the risk loss auxiliary parameter under the given confidence level α.

5. The multi-factor benefit analysis method for power system resilience planning scheme according to claim 4 is characterized in that: The annual risk loss X is: Among them, Λ is the total number of passing typhoons, is the set of fault lines and fault nodes under the rth disaster, is the repair or reconstruction cost of the equipment corresponding to component k, is the economic loss of load loss at node i, d i,r is the load loss time of node i during the rth typhoon.

6. The multi-factor benefit analysis method for power system resilience planning scheme according to claim 1 is characterized in that: The carbon emission indicators obtained by calculating and estimating the new power system planning scheme are as follows: For each planning year within the planning cycle, determine the construction and commissioning of units, energy storage, lines, and stations within that year, and calculate the carbon emission levels of raw materials, production, and installation during the construction of power facilities; For the direct carbon emissions from power generation on the source side, the direct carbon emissions of different power plants on the source side of the power system under typical daily operation modes are used to characterize the carbon emissions generated by the unit power generation through the power generation carbon emission factor; For the indirect carbon emissions from electricity consumption on the load side, the direct carbon emissions generated by the user's electricity consumption behavior on the source side under a typical daily operation mode are used to represent it, and the indirect carbon emissions per unit of electricity consumption of the user belonging to each node are converted through the node carbon emission factor; Regarding the indirect carbon emissions from grid-side network losses: the cumulative amount of coupled carbon emissions in the tidal current corresponding to the network loss under the daily operation mode is converted by the carbon flow rate, including the branch carbon flow rate and the network loss carbon flow rate. The branch carbon flow rate is the indirect carbon emissions flowing through the branch with the tidal current per unit time, and the network loss carbon flow rate represents the indirect carbon emissions attached to the tidal current network loss per unit time; Add the carbon emissions from power facility construction, direct carbon emissions from power generation on the source side, indirect carbon emissions from power consumption on the load side, and indirect carbon emissions from network losses on the grid side in each year of the planning period to obtain the carbon emission level of the new power system planning scheme within the planning period. Quantify low-carbon costs, including low-carbon input costs and low-carbon loss costs. Low-carbon input costs are estimated based on the initial investment in equipment, technology and related operating activities, and low-carbon loss costs are the costs paid for carbon dioxide emissions.

7. The multi-factor benefit analysis method for power system resilience planning scheme according to claim 6 is characterized in that: Low carbon loss cost for: Among them, D G For carbon emission quotas, is the carbon trading price, E power Carbon emissions from fuel consumption in the electricity system.

8. The multi-factor benefit analysis method for power system resilience planning scheme according to claim 1 is characterized in that: Calculate the construction cost of power facilities and the cost of station building and line reinforcement or transformation, and get the economic indicators as follows: For the construction and configuration of power facilities, calculate their costs of commissioning, operation, maintenance, and decommissioning during the planning period. Power facilities include generators, energy storage, lines, and stations. For the reinforcement, enhancement, renovation, or improvement of construction standards of station buildings and lines, calculate the fixed investment costs and operation and maintenance costs.

9. The multi-factor benefit analysis method for power system resilience planning scheme according to claim 1 is characterized in that: Evaluate and compare different planning schemes to form a closed decision loop: Based on the analysis of the evolution trend of extreme events, a planning scheme is formulated through the analysis of the impact of extreme events; Evaluate the proposed planning schemes on resilience, carbon emission and economic performance indicators; Under a given economic budget, the proposed planning schemes are compared and selected, the Pareto optimal scheme is screened for implementation, and the implemented strategies are monitored and the effects are post-evaluated during system operation to form a decision-making closed loop.

10. A multi-factor benefit analysis system for power system flexibility planning scheme, characterized in that: include: Planning module, to obtain new power system planning solutions; The resilience module evaluates the ability of the new power system planning scheme to cope with extreme events within a set period of time through extreme event simulation, component vulnerability analysis, system response behavior analysis, and resilience index calculation to obtain resilience benefit indicators; The carbon emission module uses the carbon emission flow method to calculate and estimate the low-carbon costs of power system construction, source measurement, load side, and grid side during the planning period for the new power system planning scheme to obtain carbon emission indicators; The economic module calculates the cost of implementing the new power system planning scheme, the cost of power facility construction, and the cost of strengthening or renovating station buildings and lines to obtain economic indicators; The analysis module integrates resilience indicators, carbon emissions and economic indicators, and evaluates and compares different planning schemes under the new power system planning scheme evaluation and decision-making framework for resilience improvement, forming a decision-making closed loop, and provides feedback and correction to the planning schemes to achieve new power system construction and adaptive planning under climate change.

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