A high renewable energy penetration power system planning method considering the impact of extreme weather
By constructing a typical scenario set that includes both regular and extreme weather conditions, and employing dynamic time warping and clustering methods, the problem of incorporating the impact of extreme weather into power system planning was solved, achieving a balance between the reliability and economy of the power system under extreme weather conditions.
Patent Information
- Application Number
- CN202610489220.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-14
- Publication Date
- 2026-06-23
AI Technical Summary
Existing power system planning methods cannot effectively incorporate the impact of extreme weather, making it difficult to guarantee the reliability of power supply under extreme weather conditions. Furthermore, existing methods ignore the persistence and cumulative effects of extreme weather events, leading to inaccurate planning results.
By constructing a typical scenario set including both regular and extreme weather scenarios, dynamic time warping and clustering methods are used to distinguish scenario types. Combined with objective functions and energy balance constraints, a power system planning model is constructed to minimize annualized costs and alleviate energy imbalances caused by extreme weather.
It enables optimized power system planning under extreme weather conditions, ensuring both economic efficiency and control over the risks of extreme weather events, thereby guaranteeing the reliability and energy balance of the power system.
Smart Images
Figure CN122267749A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of system planning technology, and more specifically, to a planning method for power systems with high renewable energy penetration that takes into account the impact of extreme weather. Background Technology
[0002] Current clustering methods inherently converge to the mean, failing to represent extreme weather events with anomalous characteristics and thus ignoring their impact on system planning decisions. While some have attempted to incorporate extreme cases into representative scenario sets, most methods rely on clustering typical days. This approach fails to preserve the persistence and cumulative effects of long-term extreme events, as multi-day events are compressed into a single day. Threshold-based methods also disrupt the temporal continuity between normal and extreme periods. This discontinuity distorts energy balance and long-term storage operations across time, leading to inaccurate planning results.
[0003] Furthermore, most existing planning methods address power imbalances by minimizing the expected load shedding. Although load shedding under extreme weather conditions is more severe than under normal conditions within the same time window, the proportion of load shedding caused by extreme weather events remains small due to their high impact and low probability. Optimization models designed to minimize expected load shedding often completely forgo load supply under extreme weather scenarios to reduce overall investment costs, but this makes it difficult to ensure reliable power supply when these events occur. This is because expected value is a measure of central tendency, and optimization models based on expected load shedding significantly underestimate the risks posed by extreme weather events. Summary of the Invention
[0004] The purpose of this invention is to provide a planning method for power systems with high renewable energy penetration that takes into account the impact of extreme weather, thereby solving the problem in existing power system planning technologies that make it difficult to incorporate the impact of extreme weather into the planning model, resulting in planning results that cannot guarantee power supply under extreme weather conditions.
[0005] This invention is achieved through the following technical solution:
[0006] A planning method for power systems with high renewable energy penetration that takes into account the impact of extreme weather includes: Obtain annual data for the target planning area, and construct a typical scenario set including both regular and extreme weather scenarios based on the obtained annual data for the target planning area; A planning model is constructed based on a set of typical scenarios with the goal of minimizing the annualized cost. The planning model is then solved to obtain the planning results.
[0007] Preferably, the typical scenario set constructed based on the acquired annual data of the target planning area, including both regular and extreme weather scenarios, includes: Extract morphological features from the annual data of the target planning area, and generate typical scene weeks based on morphological feature clustering; By distinguishing between regular typical scenarios and extreme weather scenarios through the aforementioned typical scenario week, a typical scenario set is constructed based on the regular typical scenarios and extreme weather scenarios.
[0008] Preferably, the extraction of morphological features from the annual data of the target planning area includes:
[0009] in, For dynamic time-normalized distance, For the first Weekly data, For the first Weekly data, This is the morphological feature matrix.
[0010] Preferably, the step of distinguishing between typical scenarios and extreme weather scenarios through the typical scenario week includes:
[0011] in, For the first A typical scenario and This represents a set of typical weeks for extreme weather and typical weeks for regular weather scenarios. For the first Cluster centers, This is the global average of the weekly running data. The median of all dynamic time-normalized distances. For robust discrimination coefficients, A set of typical scenarios, This represents the median absolute deviation of all dynamic time-normalized distances.
[0012] Preferably, the construction planning model includes a construction objective function, the objective function including:
[0013] in, This represents the total annualized cost. This indicates the annualized investment cost. Indicates annual operating costs. This is a penalty for annual load shedding.
[0014] Preferably, the annualized investment cost includes:
[0015] in, This is the investment recovery factor. The total investment cost, For the first One generator, For the first One energy storage device, For the first One transmission line, Indicates service life. Indicates discounting. , , These represent collections of generators, energy storage, and transmission lines, respectively. and This indicates the installed capacity of various generators and energy storage devices. The binary variables represent the transmission lines The construction status, They represent transmission lines Construction costs, The unit investment cost of the generator This refers to the unit investment cost of energy storage devices.
[0016] Preferably, the annual operating cost includes:
[0017] in, Annual operating costs, For the bus set of the planning model, The weighting coefficients for each typical scenario week. and These represent the unit start-up cost and unit fuel cost of a synchronous generator, respectively. Indicates the output power of the synchronous machine. This indicates the startup capacity of the synchronous machine.
[0018] Preferably, the annual load shedding penalty in the conventional scenario includes:
[0019] in, Penalty for unit load shedding, These represent load shedding power under normal scenarios and load shedding power under extreme weather scenarios, respectively. This indicates a penalty for load shedding in a normal scenario. This refers to the weighting coefficient for the risk item. Weights for all extreme weather scenarios, Conditional Value at Risk (VaR) This refers to the load shedding caused by extreme weather. This represents the confidence level of the conditional value of risk.
[0020] Preferably, it also includes setting energy balance constraints, which include energy balance constraints for normal scenarios and energy balance constraints for extreme weather scenarios: Energy balance constraints in typical scenarios include:
[0021] in, These refer to the output power of synchronous machines, wind turbines, photovoltaic systems, and hydropower, respectively. These are the discharge and charging power for short-term energy storage, respectively. These are the discharge and charging power for long-term energy storage, respectively. For load, , The lines are respectively Candidate routes power flow, For from the busbar The set of existing transmission lines from which to start. For from the busbar The collection of existing transmission lines that arrive at the destination. busbar The initial set of candidate transmission lines, busbar The set of candidate transmission lines that have ended.
[0022] Preferably, the energy balance constraints of the extreme weather scenario include: .
[0023] The technical solution of the present invention has at least the following advantages and beneficial effects: The method provided by this invention mainly includes acquiring annual data of the target planning area, constructing a typical scenario set including both conventional and extreme weather scenarios based on the acquired annual data, building a planning model based on the typical scenario set with the objective of minimizing the annualized cost, and solving the planning model to obtain the planning result. This method considers the risk of extreme weather events. Long-term and short-term energy storage systems are incorporated into the planning model, achieving the optimal annualized economic cost while controlling the tail risk introduced by extreme weather events within an acceptable range. This solves the problem of minimizing annualized cost while mitigating the energy imbalance problem in the power system caused by extreme weather. Attached Figure Description
[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.
[0025] Figure 1 This is a schematic diagram of the process of the present invention. Detailed Implementation
[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.
[0027] Please refer to Figure 1 A planning method for power systems with high renewable energy penetration that takes into account the impact of extreme weather, comprising: S1: Obtain the timing operation data of the power system.
[0028] Specifically, obtain annual data for the target planning area, including node loads, wind turbine output power, and photovoltaic unit output power at several points in time.
[0029] S2: Construct the morphological feature matrix of the running data based on the shape dynamic event regularization method.
[0030]
[0031] in, For dynamic time-normalized distance, For the first Weekly data, For the first Weekly data, This is the morphological feature matrix.
[0032] Indicates the dynamic time-warped distance. , Indicates the first Zhou's raw data, The first derivative of the original data. For a set of paths, Indicates a typical week Time index, Indicates a typical week Time index.
[0033] S3: Based on the morphological feature matrix, perform typical scene clustering according to the clustering method to generate typical scene clusters.
[0034] ; in, Indicates the first The center of each cluster, For the first A sample set of cluster centers For the c-th typical scenario, Let be the objective function. For typical weekly totals, For the first A typical week, For dynamic time-normalized distance.
[0035] S4: Based on the typical scene weeks after clustering, the typical scenes after clustering are classified into regular typical scenes and extreme weather typical scenes according to the outlier detection method, and typical scene sets are constructed respectively.
[0036]
[0037] in, for…, and This represents a set of typical weeks for extreme weather and typical weeks for regular weather scenarios. For the first Cluster centers, This is the global average of the weekly running data. This is the median of all dynamic time-normalized distances. For robust discrimination coefficients, A set of typical scenarios, This represents the median absolute deviation of all dynamic time-normalized distances.
[0038] S5: Perform secondary clustering for typical scenarios based on the difference function.
[0039]
[0040] in, For difference function, For the first Timeframe:
[0041] in, For difference function, and The adjacent clusters to be clustered. Time series The center. This represents typical operational data for adjacent hours within a week. It is a shape feature vector described by mean, variance, skewness, and kurtosis. As a weighting factor, Time series The mean, variance, skewness, and kurtosis.
[0042] S6: Construct a planning model based on the objective function and constraints in the planning.
[0043] Constructing a planning model includes constructing an objective function, which includes:
[0044] in, This represents the total annualized cost. This indicates the annualized investment cost. Indicates annual operating costs. This is a penalty for annual load shedding.
[0045] Annualized investment costs include:
[0046] in, This is the investment recovery factor. The total investment cost, For the first One generator, For the first One energy storage device, For the first One transmission line, Indicates service life. Indicates discounting. , , These represent collections of generators, energy storage, and transmission lines, respectively. and This indicates the installed capacity of various generators and energy storage devices. The binary variables represent the transmission lines The construction status, They represent transmission lines Construction costs, The unit investment cost of the generator This refers to the unit investment cost of energy storage devices.
[0047] Annual operating costs include:
[0048] in, Annual operating costs, For the bus set of the planning model, The weighting coefficients for each typical scenario week. and These represent the unit start-up cost and unit fuel cost of a synchronous generator, respectively. Indicates the output power of the synchronous machine. This indicates the startup capacity of the synchronous machine.
[0049] Typical annual load shedding penalties include:
[0050] in, Penalty for unit load shedding, These represent load shedding power under normal scenarios and load shedding power under extreme weather scenarios, respectively. This indicates a penalty for load shedding in a normal scenario. This refers to the weighting coefficient for the risk item. Weights for all extreme weather scenarios, Conditional Value at Risk (VaR) This refers to the load shedding caused by extreme weather. This represents the confidence level of the conditional value of risk.
[0051] It also includes setting energy balance constraints, which include energy balance constraints for normal scenarios and energy balance constraints for extreme weather scenarios: Energy balance constraints in typical scenarios include:
[0052] in, These refer to the output power of synchronous machines, wind turbines, photovoltaic systems, and hydropower, respectively. These are the discharge and charging power for short-term energy storage, respectively. These are the discharge and charging power for long-term energy storage, respectively. For load, , The lines are respectively Candidate routes power flow, For from the busbar The set of existing transmission lines from which to start. For from the busbar The collection of existing transmission lines that arrive at the destination. busbar The initial set of candidate transmission lines, busbar The set of candidate transmission lines that have ended.
[0053] Energy balance constraints in extreme weather scenarios include: .
[0054] S7: Solve the planning model to obtain the planning scheme.
[0055] This invention first acquires annual operational data for the target planning area; then constructs typical operational scenarios based on the acquired data, including both routine and extreme weather scenarios; finally, it constructs a power system planning model based on these typical scenarios and solves the model to obtain the planning results. This model aims to minimize annualized costs while mitigating power system energy imbalances caused by extreme weather.
[0056] This invention can solve the problem that power system planning models cannot easily incorporate the impact of extreme weather events into the planning process, resulting in planning results that are unable to cope with energy supply under extreme weather events.
[0057] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.
[0058] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. This computer software product, stored in a storage medium, includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.
[0059] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.
Claims
1. A planning method for power systems with high renewable energy penetration that takes into account the impact of extreme weather, characterized in that, include: Obtain annual data for the target planning area, and construct a typical scenario set including both regular and extreme weather scenarios based on the obtained annual data for the target planning area; A planning model is constructed based on a set of typical scenarios with the goal of minimizing the annualized cost. The planning model is then solved to obtain the planning results.
2. The planning method for a high renewable energy penetration power system taking into account the impact of extreme weather, as described in claim 1, is characterized in that... The typical scenario set, which includes both routine and extreme weather scenarios, is constructed based on the acquired annual data of the target planning area. Extract morphological features from the annual data of the target planning area, and generate typical scene weeks based on morphological feature clustering; By distinguishing between regular typical scenarios and extreme weather scenarios through the aforementioned typical scenario week, a typical scenario set is constructed based on the regular typical scenarios and extreme weather scenarios.
3. The planning method for a high renewable energy penetration power system taking into account the impact of extreme weather, as described in claim 2, is characterized in that... The extraction of morphological features from the annual data of the target planning area includes: in, For dynamic time-normalized distance, For the first Weekly data, For the first Weekly data, This is the morphological feature matrix.
4. The planning method for a high renewable energy penetration power system taking into account the impact of extreme weather, as described in claim 3, is characterized in that... The distinction between typical scenarios and extreme weather scenarios based on the typical scenario week includes: in, For the first A typical scenario and This represents a set of typical weeks for extreme weather and typical weeks for regular weather scenarios. For the first Cluster centers, This is the global average of the weekly running data. The median of all dynamic time-normalized distances. For robust discrimination coefficients, A set of typical scenarios, This represents the median absolute deviation of all dynamic time-normalized distances.
5. A planning method for a high renewable energy penetration power system taking into account the impact of extreme weather, as described in claim 4, is characterized in that... The construction planning model includes a construction objective function, which includes: in, This represents the total annualized cost. This indicates the annualized investment cost. Indicates annual operating costs. This is a penalty for annual load shedding.
6. A planning method for a high renewable energy penetration power system taking into account the impact of extreme weather, as described in claim 5, is characterized in that... The annualized investment cost includes: in, This is the investment recovery factor. The total investment cost, For the first One generator, For the first One energy storage device, For the first One transmission line, Indicates service life. Indicates discounting. , , These represent collections of generators, energy storage, and transmission lines, respectively. and This indicates the installed capacity of various generators and energy storage devices. The binary variables represent the transmission lines The construction status, They represent transmission lines Construction costs, The unit investment cost of the generator This refers to the unit investment cost of energy storage devices.
7. A planning method for a high renewable energy penetration power system taking into account the impact of extreme weather, as described in claim 6, is characterized in that... The annual operating costs include: in, Annual operating costs, For the bus set of the planning model, The weighting coefficients for each typical scenario week. and These represent the unit start-up cost and unit fuel cost of a synchronous generator, respectively. Indicates the output power of the synchronous machine. This indicates the startup capacity of the synchronous machine.
8. A planning method for a high renewable energy penetration power system taking into account the impact of extreme weather, as described in claim 7, is characterized in that... The annual load shedding penalty in the typical scenario includes: in, Penalty for unit load shedding, These represent load shedding power under normal scenarios and load shedding power under extreme weather scenarios, respectively. This indicates a penalty for load shedding in a normal scenario. This refers to the weighting coefficient for the risk item. Weights for all extreme weather scenarios, Conditional Value at Risk (VaR) This refers to the load shedding caused by extreme weather. This represents the confidence level of the conditional value of risk.
9. A planning method for a high renewable energy penetration power system taking into account the impact of extreme weather, as described in claim 8, is characterized in that, It also includes setting energy balance constraints, which include energy balance constraints for normal scenarios and energy balance constraints for extreme weather scenarios: Energy balance constraints in typical scenarios include: in, These refer to the output power of synchronous machines, wind turbines, photovoltaic systems, and hydropower, respectively. These are the discharge and charging power for short-term energy storage, respectively. These are the discharge and charging power for long-term energy storage, respectively. For load, , The lines are respectively Candidate routes power flow, For from the busbar The set of existing transmission lines from which to start. For from the busbar The collection of existing transmission lines that arrive at the destination. busbar The initial set of candidate transmission lines, busbar The set of candidate transmission lines that have ended.
10. A planning method for a high renewable energy penetration power system taking into account the impact of extreme weather, as described in claim 9, is characterized in that... The energy balance constraints for the extreme weather scenarios include: 。