US-20260127332-A1 - MULTI-FACTOR BENEFIT ANALYSIS METHOD AND SYSTEM FOR RESILIENCE-ORIENTED POWER SYSTEM PLANNING SCHEME

US20260127332A1US 20260127332 A1US20260127332 A1US 20260127332A1US-20260127332-A1

Abstract

Disclosed is a multi-factor benefit analysis method and system for resilience-oriented power system planning schemes, including acquiring new power system planning schemes; evaluating the abilities of the acquired schemes to withstand extreme events within a set period of years, to obtain resilience indicators; estimating the acquired schemes by using the carbon emission flow method, to obtain carbon emission indicators; calculating the cost of constructing power facilities and the cost of reinforcing or renovating substations and lines, to obtain economic indicators; and evaluating and comparing the planning schemes under the evaluation and decision-making framework of new power system planning schemes for resilience enhancement by comprehensively considering the resilience indicators, carbon emission indicators, and economic indicators, to form a closed loop of decision-making, and give feedback to and correct the planning schemes, thereby achieving adaptive planning for the construction of new power systems under climate changes.

Inventors

Zhaohong BIE
Bingkai Huang
Siyuan SUN
Yiheng BIAN
Zhengkun XIN
Gengfeng LI
Haipeng Xie
Liyin ZHANG
Qianwen HU
Wei Fu

Assignees

XI'AN JIAOTONG UNIVERSITY

Dates

Publication Date: 20260507
Application Date: 20251231
Priority Date: 20240910

Claims (10)

1 . A multi-factor benefit analysis method for resilience-oriented power system planning schemes, comprising the following steps: acquiring new power system planning schemes; evaluating the ability of each of the acquired new power system planning schemes to withstand extreme events within a set period of years by extreme event simulation, component vulnerability analysis, system response behavior analysis, and resilience indicator calculation, to obtain resilience indicators; estimating each of the acquired new power system planning schemes in terms of low-carbon costs of power system construction, at the source side, at the load side, and at the grid side during a planning period by using a carbon emission flow method, to obtain carbon emission indicators; calculating the cost of constructing power facilities and the cost of reinforcing or renovating substations and lines, with respect to the implementation cost of each of the acquired new power system planning schemes, to obtain economic indicators; and evaluating and comparing the planning schemes under an evaluation and decision-making framework of new power system planning schemes for resilience enhancement by comprehensively considering the resilience indicators, the carbon emission indicators, and the economic indicators, to form a closed loop of decision-making, and give feedback to and correct the planning schemes, thereby achieving adaptive planning for new power system construction under climate changes.
2 . The multi-factor benefit analysis method for resilience-oriented power system planning scheme according to claim 1 , wherein acquiring new power system planning schemes is specifically to acquire: planning periods considered in the planning schemes; planned capacities, integration locations, and years of manufacturing and commissioning of generator units of different types; planned capacities, integration locations, and years of manufacturing and commissioning of energy storage systems of different types; planned routes, line capacities, and years of construction and commissioning of power transmission line corridors; planned locations and years of construction and commissioning of substations; typical load data estimation during the planning years; and typical daily operation modes of the power system during the planning years.
3 . The multi-factor benefit analysis method for resilience-oriented power system planning schemes according to claim 1 , wherein evaluating each of the new power system planning schemes to obtain resilience indicators specifically comprises: for extreme event simulation, simulating the impact of the extreme events based on the characteristics thereof such as frequency, location, duration, and hazard intensity to generate a set of computer-simulated extreme event scenarios, and analyzing and calculating hazard intensities at sites of interest for each simulated extreme event scenario; analyzing and selecting key parameters that characterize the extreme events based on historical disaster data, establishing probability distribution models or extreme value regression models for key parameters of the extreme events to describe the uncertainty of the extreme events, and performing hypothesis testing on the models to verify goodness of fit; and sampling the number of disaster occurrences for each simulation year during the planning period based on annual frequency distribution, sampling other key parameters for each disaster scenario to obtain initial states of simulated disasters, and converting the parameters into dynamic disaster scenarios with spatiotemporal characteristics by using meteorological or geographical models; for component vulnerability analysis, based on disaster scenarios generated in extreme event analysis and vulnerability curves of power system components, calculating failure rates of power system components, and generating random numbers to sample the fault states of the components, to determine whether the power system components will fail in the corresponding disaster scenarios; for system response behavior analysis, modeling the response process of the power system to extreme disasters, establishing an emergency control and restoration model for the power system to respond to extreme events, and simulating the system power outage and restoration process; and for resilience indicator calculation, defining risk loss as the weighted load loss and the maintenance cost of the system in an entire disaster, choosing the value at risk and the tail value at risk of loss as specific resilience indicators, using a Monte Carlo method to simulate economic loss caused each year, and achieving the convergence of the indicators by this year-by-year simulation during the planning period.
4 . The multi-factor benefit analysis method for resilience-oriented power system planning schemes according to claim 3 , wherein the quantitative resilience indicator VaR a (X) and the tail value at risk TVaR a (X) are calculated as follows: VaR α ⁢ ( X ) = inf ⁢ { x : P ⁢ ( X ≤ x ) ≥ α } TVaR α ⁢ ( X ) = 1 1 - α ⁢ ∫ α 1 VaR u ⁢ ( X ) ⁢ du where inf{⋅} is the infimum, P(⋅) is the probability of an event, α is the set confidence level, X is the annual risk loss, and X is an auxiliary parameter for risk loss under a given confidence level α.
5 . The multi-factor benefit analysis method for resilience-oriented power system planning schemes according to claim 4 , wherein the annual risk loss X is calculated as follows: X = ∑ r - 1 Λ [ X = ∑ k ∈ F r L ⋃ F r N L k repair + X = ∑ i ∈ N L i outage ( d i , r ) ] where Λ is the total number of disasters in the simulation year, F r L ⁢ and ⁢ F r N are the set or lines in fault and the set of nodes in fault in the r-th disaster respectively, L k repair is the maintenance or reconstruction cost of the corresponding device of the component k, L i outage is the economic loss of the lost load of the node i, and d i,r is the duration of load loss of the nodei in the r-th disaster.
6 . The multi-factor benefit analysis method for resilience-oriented power system planning schemes according to claim 1 , wherein estimating each of the new power system planning schemes to obtain carbon emission indicators specifically comprises: for each planning year during the planning period, determining the construction and commissioning conditions of generator units, energy storage systems, transmission lines, and substations in that year, and converting carbon emission levels of raw materials, production, and installation during the construction of power facilities; characterizing direct carbon emissions of power generation at the source side by direct carbon emissions of different power plants at the source side of the power system under a typical daily operation mode, and converting the carbon emissions per unit of power generation of each unit based on the carbon emission factor of power generation; characterizing indirect carbon emissions of power utilization at the load side by direct carbon emissions at the source side corresponding to customer power utilization behaviors under the typical daily operation mode, and converting the indirect carbon emissions per unit of power utilization of each customer corresponding to each node based on the carbon emission factor of the node; characterizing indirect carbon emissions of grid loss at the grid side by accumulated carbon emissions coupled in power flows corresponding to the grid loss under the typical daily operation mode, and converting the accumulated carbon emissions based on carbon flow rate, including branch carbon flow rate and grid loss carbon flow rate, wherein the branch carbon flow rate is the indirect carbon emissions of the branches along with the power flow per unit of time, and the grid loss carbon flow rate represents the indirect carbon emissions of grid loss along with the power flow per unit of time; summing carbon emissions of power facility construction, the direct carbon emissions of power generation at the source side, the indirect carbon emissions of power utilization at the load side, and the indirect carbon emissions of grid loss at the grid side of each year during the planning period, to obtain the carbon emission levels of the new power system planning schemes during the planning period; and quantifying low-carbon costs, including a low-carbon investment cost and a low-carbon loss cost, wherein the low-carbon investment cost is estimated based on the initial investment in equipment and technology and the costs of related operating activities, and the low-carbon loss cost is the payment for carbon dioxide emissions.
7 . The multi-factor benefit analysis method for resilience-oriented power system planning schemes according to claim 6 , wherein the low-carbon loss cost C CO 2 is calculated as follows: C CO 2 = ( E power - D G ) · p CO 2 where D G is the carbon emission quota, p CO 2 is the price of carbon trading, and E power is the carbon emissions generated by fuel consumption in the power system.
8 . The multi-factor benefit analysis method for resilience-oriented power system planning schemes according to claim 1 , wherein calculating the cost of constructing power facilities and the cost of reinforcing or renovating substations and lines to obtain economic indicators specifically comprises: for power facility construction and allocation, calculating the costs of commissioning, operation, maintenance, decommissioning, and disposal of the power facilities during a planning period, wherein the power facilities comprise generator units, energy storage systems, transmission lines, and substations; and for the reinforcement, strengthening, upgrading, or construction standard improvement of substations and lines, calculating fixed investment costs and operation and maintenance costs.
9 . The multi-factor benefit analysis method for resilience-oriented power system planning schemes according to claim 1 , wherein evaluating and comparing the planning schemes to form a closed loop of decision-making specifically comprises: analyzing impact of the extreme events based on a study on evolution trends of the extreme events, to form the planning schemes; evaluating each of the planning schemes in terms of the resilience indicators, the carbon emission indicators, and the economic indicators; and comparing the planning schemes based on a given economic budget, choosing a Pareto optimal scheme for implementation, monitoring implemented strategies during system operation, and performing post-evaluation of effects, to form a closed loop of decision-making.
10 . A multi-factor benefit analysis system for resilience-oriented power system planning schemes, comprising: a planning module, configured to acquire new power system planning schemes; a resilience module, configured to evaluate the ability of each of the acquired new power system planning schemes to withstand extreme events within a set period of years by extreme event simulation, component vulnerability analysis, system response behavior analysis, and resilience indicator calculation, to obtain resilience indicators; a carbon emission module, configured to estimate each of the acquired new power system planning schemes in terms of low-carbon costs of power system construction, at the source side, at the load side, and at the grid side during a planning period by using a carbon emission flow method, to obtain carbon emission indicators; an economic module, configured to calculate the cost of constructing power facilities and the cost of reinforcing or renovating substations and lines, with respect to the implementation cost of each of the acquired new power system planning schemes, to obtain economic indicators; and an analysis module, configured to evaluate and compare the planning schemes under an evaluation and decision-making framework of new power system planning schemes for resilience enhancement by comprehensively considering the resilience indicators, the carbon emission indicators, and the economic indicators, to form a closed loop of decision-making, and give feedback to and correct the planning schemes, thereby achieving adaptive planning for new power system construction under climate changes.

Description

CROSS-REFERENCE TO RELATED APPLICATIONS The application claims priority to Chinese patent application No. 202411265084.1, filed on Sep. 10, 2024, the entire contents of which are incorporated herein by reference. TECHNICAL FIELD The present application relates to the technical field of power system planning and technical and economic analysis, and specifically relates to a multi-factor benefit analysis method and system for resilience-oriented power system planning schemes. BACKGROUND The power system is a national key core infrastructure concerning economic development, social stability, and national security. At present, China has the world's largest power system with the longest transmission lines, the highest voltage level, and the most complex grid structure. With the proposal of “carbon peaking and carbon neutrality” goals and the continuous advancement of new power system construction, power system security is facing new severe challenges under the influence of internal and external risks. First, the accelerated construction of a new power system will inevitably bring about major changes in the power supply structure, load characteristics, and grid morphology. Changes in essential characteristics of the system will lead to increased safety and stability risks. Second, global climate change is becoming more and more serious, and extreme weather and natural disasters, such as extreme cold, high temperatures, typhoons, and heavy rainfall, are showing a new normal of widespread, strong, frequent, and concurrent occurrences, making it much more difficult to ensure safe and reliable power supply. Third, the international situation is complex and changeable. Various types of man-made attacks such as strong electromagnetic pulses may become real threats to the power infrastructure during wartime, while China's power system still has shortcomings and a large gap with those of some other countries. Therefore, when we build a new power system, we shall take full consideration of improving the resilience of the system to extreme events with high impact and low probability, such as extreme natural disasters and man-made attacks, at the system planning stage, to ensure the safe and reliable power supply of the new power system from the source, which is of great significance for promoting the construction of the new power system. At present, the study on the evaluation and decision-making for new power system planning schemes is not in-depth enough, and there is even a lack of decision-making on resilience-oriented planning schemes while taking into account normal operation and impact of extreme events. The conventional decisions on power system planning schemes are mainly made by economic and technical analysis under conventional operation scenarios. When we make a new power system planning scheme for resilience enhancement, we need to take into account both normal operation and impact of extreme events. Making reasonable decisions in planning scheme analysis is a key issue to be solved in the planning of the new power system for resilience enhancement. Therefore, it is necessary to develop new cost-benefit analysis and evaluation decision-making methods for resilience-oriented new power system planning schemes. SUMMARY In view of the above-mentioned shortcomings in the prior art, this application provides a multi-factor benefit analysis method and system for resilience-oriented power system planning schemes, in which new power system planning schemes are evaluated by comprehensively considering multiple factors such as resilience indicators, carbon emission indicators, and economic indicators, to solve the technical problem that conventional robust or stochastic planning methods cannot simultaneously consider multiple complex scenarios involving resilience and carbon benefits, thereby providing a scientific decision-making basis for improving investment decision-making. The technical solution adopted by the present application is as follows: a multi-factor benefit analysis method for resilience-oriented power system planning schemes, including the following steps:acquiring new power system planning schemes;evaluating the ability of each of the acquired new power system planning schemes to withstand extreme events within a set period of years by extreme event simulation, component vulnerability analysis, system response behavior analysis, and resilience indicator calculation, to obtain resilience indicators;estimating each of the acquired new power system planning schemes in terms of low-carbon costs of power system construction, at the source side, at the load side, and at the grid side during a planning period by using a carbon emission flow method, to obtain carbon emission indicators;calculating the cost of constructing power facilities and the cost of reinforcing or renovating substations and lines, with respect to the implementation cost of each of the acquired new power system planning schemes, to obtain ec