Method and related device for risk assessment of power supply and demand in different stages driven by cold wave evolution

Abstract

The present application belongs to the technical field of power system operation and extreme weather disaster response, and discloses a cold wave evolution driven power supply and demand phased risk assessment method and related device; wherein, the power supply and demand phased risk assessment method comprises: obtaining the stage identification and division result of the whole life cycle of the cold wave; based on the stage identification and division result, constructing a new energy output model and a load response model; based on the new energy output model and the load response model, the risk assessment index is calculated in stages and compared with the corresponding preset threshold value, forming the power supply and demand phased risk assessment result. The present application is a new energy generation and load supply and demand balance two-way risk assessment scheme starting from the stage characteristics before, during and after the cold wave, which can run through the whole life cycle of the cold wave, can realize the dynamic assessment of simultaneously identifying the early stage consumption pressure and the late stage power shortage risk, and improves the accuracy and foresight of the power system operation risk assessment under the cold wave weather.

Description

Technical Field

[0001] This invention belongs to the field of power system operation and extreme weather disaster response technology, and specifically relates to a phased risk assessment method and related device for power supply and demand driven by cold wave evolution. Background Technology

[0002] With the increasing proportion of new energy installed capacity, the impact of cold waves on the power system has changed from a simple "sudden increase in load" to "severe fluctuations on both the power source and load sides".

[0003] Currently, existing technical solutions often have the following problems when assessing the impact of cold waves: First, the temporal and delayed nature of the impact of cold waves is overlooked. Existing methods are mostly static assessments based on a single time segment, failing to identify the characteristic differences of different stages of a cold wave. Specifically, this is reflected in neglecting the risk of power absorption in the early stages and neglecting the risk of supply-demand "scissors difference" in the later stages. The specific explanation for neglecting the risk of power absorption in the early stages is as follows: when the cold wave front arrives, it is often accompanied by strong winds. At this time, the temperature has not yet dropped to its lowest point, which may lead to an instantaneous oversupply due to "wind power surge and load ramp-up lag," triggering the risk of wind curtailment or increased grid frequency. The specific explanation for neglecting the risk of supply-demand "scissors difference" in the later stages is as follows: in the later stages of a cold wave (i.e., the period of cold high pressure control), wind speed often rapidly decreases to low wind speeds or even calm winds. At this time, the temperature remains at an extremely low level due to thermal inertia (heating load is at its peak), and photovoltaic modules may still be covered by snow. This peak-shifting phenomenon of "sharp drop in wind and solar output" and "maintaining high load" can easily lead to the depletion of system reserves.

[0004] Second, there is a lack of a two-way risk assessment mechanism. Traditional assessments focus more on the risk of "power shortage" (i.e., supply is less than demand), and lack a quantitative assessment of the "difficulty in absorbing" (i.e., supply exceeds demand) of high-proportion renewable energy grids under extreme weather conditions. The reverse peak-shaving characteristics of wind power output during cold waves (i.e., strong winds in the early stage and no wind in the later stage) exacerbate the amplitude of this two-way fluctuation.

[0005] In summary, existing technical solutions cannot cover the entire life cycle of a cold wave and cannot achieve dynamic and accurate assessment of both the initial power absorption pressure and the subsequent power shortage risk. There is an urgent need to develop new assessment solutions. Summary of the Invention

[0006] The purpose of this invention is to provide a method and related apparatus for phased risk assessment of power supply and demand driven by cold wave evolution, in order to solve one or more of the aforementioned technical problems. The technical solution disclosed in this invention is a two-way risk assessment scheme for the balance of new energy power generation and load supply and demand, based on the phased characteristics before, during, and after a cold wave. This scheme can cover the entire life cycle of a cold wave, enabling dynamic assessment that simultaneously identifies both the initial absorption pressure and the subsequent power shortage risk, thus improving the accuracy and foresight of power system operation risk assessment during cold wave weather.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, this invention provides a method for phased risk assessment of power supply and demand driven by cold wave evolution, comprising the following steps: Obtain the stage identification and segmentation results of the entire life cycle of cold waves; Based on the stage identification and division results, a new energy output model and a load response model are constructed; wherein, the new energy output model is constructed in stages based on the stage identification and division results, and some stages of the new energy output model contain meteorological correction factors; thermal inertia is considered in the construction process of the load response model. Based on the new energy output model and the load response model, the risk assessment index is calculated in stages and compared with the corresponding preset threshold to form the phased risk assessment results of power supply and demand.

[0008] A further improvement to the technical solution of this invention lies in the fact that the steps for obtaining the stage identification and division results of the entire life cycle of a cold wave include: Obtain weather forecast data; Based on the meteorological forecast data, and according to the meteorological dynamics of cold waves, the entire life cycle of cold waves is divided into three stages: the leading wind period, the passing strong convection period, and the static and stable deep cold period. Among them, the period of strong winds at the front is defined as the wind speed rise rate being greater than the wind speed rise threshold, and the temperature being higher than the sum of the predicted minimum temperature of the cold wave and the temperature difference margin; the period of strong convection passing through is defined as the wind speed being greater than or equal to the high wind speed threshold, or the presence of any precipitation phenomenon such as snowfall, rain, or freezing rain; the period of calm and deep cold is defined as the wind speed decrease rate being less than the wind speed decrease threshold, and the difference between the temperature and the predicted minimum temperature of the cold wave being within the preset range.

[0009] A further improvement of the technical solution of the present invention is that, in the new energy output model, wind power during the strong wind period is calculated normally according to the power curve; wind power during the strong convection period is introduced with the cut-out loss coefficient and icing efficiency correction function; wind power during the calm and deep cold period is exponentially decayed according to the wind speed attenuation factor, and photovoltaic power is corrected with the snow accumulation shading factor.

[0010] A further improvement to the technical solution of this invention is that the new energy output model, specifically the wind power output model, is expressed as follows: ; In the formula, for Total output of the wind farm cluster at any given time; The theoretical output is calculated based on the power curve of the wind farm group; for Predicted wind speed at any time; The cut-out loss coefficient, with a value of 0 to 1, represents the proportion of units that are cut off due to overspeed. The icing efficiency correction function varies with icing thickness. It increases but decreases non-linearly, with a value ranging from 0 to 1; The wind power output level at the beginning of the static and cold period; The wind speed decay time constant; This marks the beginning of the static, deep cryogenic phase. In the aforementioned new energy output model, the photovoltaic output model is expressed as: ; In the formula, for The output power of the photovoltaic system at any given time; for The irradiance on the surface of the photovoltaic panel at any given time; The reference conversion efficiency for photovoltaic modules; Temperature power coefficient; For component temperature; for The snow cover factor at any given time ranges from 0 to 1.

[0011] A further improvement to the technical solution of this invention lies in the step of calculating the risk assessment index in stages based on the new energy output model and the load response model, and comparing it with the corresponding preset threshold to form the staged risk assessment result of power supply and demand. The renewable energy consumption pressure index is calculated during the period of strong winds ahead, and a consumption warning is issued when the calculated renewable energy consumption pressure index exceeds the consumption warning threshold. During periods of stable and deep cold, calculate the supply-demand gap risk index and issue a power shortage warning when the calculated result of the supply-demand gap risk index exceeds the power shortage warning threshold. in, The formula for calculating the pressure index of renewable energy consumption is: ; In the formula, for The pressure index of new energy consumption at all times; ; ; The minimum stable output of thermal power units for grid-connected operation; for Total load power of the power system at any given time; The formula for calculating the supply-demand gap risk index is: ; In the formula, for Supply and demand gap risk index at all times; for The system's maximum available power generation capacity at any given time; The system's preset rotational reserve capacity; This refers to the risk weighting coefficient for the scissors difference. This represents the rate of change in total power generation. This represents the load change rate.

[0012] A further improvement to the technical solution of the present invention is that the load response model is expressed as: ; In the formula, for Total load power of the power system at any given time; The base load is independent of temperature; Maximum design heating load; The starting temperature threshold for heating load; Design minimum temperature for heating; for Always consider the effective temperature of thermal inertia; This is an index representing the load's sensitivity to temperature. in, ; In the formula, This is the thermal inertia weighting coefficient, with a value ranging from 0 to 1. The smaller the value, the more delayed the load's response to temperature changes; for Predicted temperature at any time; for The effective temperature should always take thermal inertia into account.

[0013] In a second aspect, the present invention provides a phased risk assessment system for power supply and demand driven by cold wave evolution, comprising: The phase identification and segmentation result acquisition unit is used to obtain the phase identification and segmentation results of the entire life cycle of the cold wave; The model building unit is used to build a new energy output model and a load response model based on the stage identification and division results; wherein, the new energy output model is built in stages based on the stage identification and division results, and the new energy output model built in some stages contains a meteorological correction factor; thermal inertia is considered in the construction process of the load response model. The calculation and comparison evaluation unit is used to calculate the risk assessment index in stages based on the new energy output model and the load response model, and compare it with the corresponding preset threshold to form the phased risk assessment results of power supply and demand.

[0014] A further improvement to the technical solution of the present invention is that, in the stage identification and division result acquisition unit, the step of acquiring the stage identification and division results of the entire life cycle of the cold wave includes: Obtain weather forecast data; Based on the meteorological forecast data, and according to the meteorological dynamics of cold waves, the entire life cycle of cold waves is divided into three stages: the leading wind period, the passing strong convection period, and the static and stable deep cold period. Among them, the period of strong winds at the front is defined as the wind speed rise rate being greater than the wind speed rise threshold, and the temperature being higher than the sum of the predicted minimum temperature of the cold wave and the temperature difference margin; the period of strong convection passing through is defined as the wind speed being greater than or equal to the high wind speed threshold, or the presence of any precipitation phenomenon such as snowfall, rain, or freezing rain; the period of calm and deep cold is defined as the wind speed decrease rate being less than the wind speed decrease threshold, and the difference between the temperature and the predicted minimum temperature of the cold wave being within the preset range.

[0015] A further improvement of the technical solution of the present invention is that, in the new energy output model, wind power during the strong wind period is calculated normally according to the power curve; wind power during the strong convection period is introduced with the cut-out loss coefficient and icing efficiency correction function; wind power during the calm and deep cold period is exponentially decayed according to the wind speed attenuation factor, and photovoltaic power is corrected with the snow accumulation shading factor.

[0016] A further improvement to the technical solution of this invention is that the new energy output model, specifically the wind power output model, is expressed as follows: ; In the formula, for Total output of the wind farm cluster at any given time; The theoretical output is calculated based on the power curve of the wind farm group; for Predicted wind speed at any time; The cut-out loss coefficient, with a value of 0 to 1, represents the proportion of units that are cut off due to overspeed. The icing efficiency correction function varies with icing thickness. It increases but decreases non-linearly, with a value ranging from 0 to 1; The wind power output level at the beginning of the static and cold period; The wind speed decay time constant; This marks the beginning of the static, deep cryogenic phase. In the aforementioned new energy output model, the photovoltaic output model is expressed as: ; In the formula, for The output power of the photovoltaic system at any given time; for The irradiance on the surface of the photovoltaic panel at any given time; The reference conversion efficiency for photovoltaic modules; Temperature power coefficient; For component temperature; for The snow cover factor at any given time ranges from 0 to 1.

[0017] A further improvement to the technical solution of this invention lies in the step of the calculation and comparison evaluation unit, which involves calculating the risk assessment index in stages based on the new energy output model and the load response model, and comparing it with the corresponding preset thresholds to form a staged risk assessment result for power supply and demand. The renewable energy consumption pressure index is calculated during the period of strong winds ahead, and a consumption warning is issued when the calculated renewable energy consumption pressure index exceeds the consumption warning threshold. During periods of stable and deep cold, calculate the supply-demand gap risk index and issue a power shortage warning when the calculated result of the supply-demand gap risk index exceeds the power shortage warning threshold. in, The formula for calculating the pressure index of renewable energy consumption is: ; In the formula, for The pressure index of new energy consumption at all times; ; ; The minimum stable output of thermal power units for grid-connected operation; for Total load power of the power system at any given time; The formula for calculating the supply-demand gap risk index is: ; In the formula, for Supply and demand gap risk index at all times; for The system's maximum available power generation capacity at any given time; The system's preset rotational reserve capacity; This refers to the risk weighting coefficient for the scissors difference. This represents the rate of change in total power generation. This represents the load change rate.

[0018] A further improvement to the technical solution of the present invention is that the load response model is expressed as: ; In the formula, for Total load power of the power system at any given time; The base load is independent of temperature; Maximum design heating load; The starting temperature threshold for heating load; Design minimum temperature for heating; for Always consider the effective temperature of thermal inertia; This is an index representing the load's sensitivity to temperature. in, ; In the formula, This is the thermal inertia weighting coefficient, with a value ranging from 0 to 1. The smaller the value, the more delayed the load's response to temperature changes; for Predicted temperature at any time; for The effective temperature should always take thermal inertia into account.

[0019] In a third aspect, the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements a phased risk assessment method for power supply and demand driven by cold wave evolution as described in any one of the first aspects of the present invention.

[0020] In a fourth aspect, the present invention provides a non-transitory computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements a phased risk assessment method for power supply and demand driven by cold wave evolution as described in any one of the first aspects of the present invention.

[0021] In a fifth aspect, the present invention provides a computer program product comprising computer instructions which, when executed by a processor, implement the steps of the phased risk assessment method for power supply and demand driven by cold wave evolution as described in any one of the first aspects of the present invention.

[0022] The present invention has the following beneficial effects: This invention discloses a phased risk assessment method for power supply and demand driven by cold wave evolution. Through the technical concept of dividing the entire life cycle of a cold wave into stages, modeling source and load in stages, and assessing risk indices in stages, it solves the technical problems of existing technologies that neglect the temporal stages and lags of cold waves and lack a two-way risk assessment mechanism, achieving dynamic and accurate assessment of the entire life cycle of cold waves. Specifically, this invention first obtains the stage identification and division results of the entire life cycle of a cold wave, transforming the cold wave from a single static assessment object into a dynamic assessment object with stages throughout the entire cycle. This can identify the differences in meteorological and power characteristics at different stages of a cold wave, laying the foundation for subsequent phased analysis of source and load response and risk assessment, thus solving the problem of existing technologies neglecting the temporal stages from an assessment perspective. Based on the stage division results, a new energy output model is constructed in stages, with some stages including meteorological correction factors. This accurately depicts the rapid decrease in wind speed and the sharp drop in wind and solar output caused by snow cover on photovoltaic modules in the later stages of a cold wave, matching the physical scenario in the later stages of a cold wave. This solves the problem that existing technologies cannot accurately capture the sudden changes in wind and solar output in the later stages, providing accurate generation-side data support for identifying the risk of supply and demand scissors differences in the later stages. The load response model is constructed by taking thermal inertia into account, which can accurately reproduce the characteristics of high load maintenance during cold waves, where the temperature remains at an extremely low level due to thermal inertia and the heating load is at its peak. This solves the problem that existing technologies ignore the lag of load thermal inertia and cannot match the actual load status in the later stages, providing accurate load-side data support for identifying the risk of supply-demand scissors difference in the later stages. Based on the phased construction of the source-load model, the risk assessment index is calculated in stages, which can assess the corresponding risks for the stage characteristics of "wind power surge and delayed load ramp-up" in the early stage of the cold wave, solving the problem of missed assessment of the risk of absorption in the early stage; and assess the corresponding risks for the stage characteristics of "sharp drop in wind and solar power output and high load maintenance" in the later stage, solving the problem of missed assessment of the risk of supply-demand scissors difference in the later stage, and solving the defect of existing static assessment technology that cannot adapt to the time sequence characteristics of cold waves. In summary, compared with the static assessment and single-risk dimension analysis of existing technologies, the phased risk assessment results of power supply and demand formed by the technical solution of this invention can accurately reflect the risk characteristics of power supply and demand at different stages of cold waves, improve the accuracy and foresight of power supply and demand risk assessment under the influence of cold waves, and provide a scientific and reliable basis for the power grid to make dispatch decisions in response to extreme weather conditions caused by cold waves.

[0023] In the preferred embodiment of this invention, differentiated new energy power generation and load models are established for the pre-, mid-, and post-cold wave phases. Specifically, for the pre-cold wave phase, the absorption pressure caused by increased wind power generation is assessed, enabling early warning of wind power absorption risks. For the post-cold wave phase, the supply-demand imbalance risk resulting from the superposition of "sharp wind speed drop - solar photovoltaic snow cover - low temperature and high load" is quantified. This invention constructs a characterization model including wind speed attenuation factors, snow cover factors, and load thermal inertia factors, forming a two-way supply-demand risk indicator covering both new energy output and load response, thereby improving the accuracy and foresight of power system operation risk assessment during cold wave weather. Attached Figure Description

[0024] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of a phased risk assessment method for power supply and demand driven by cold wave evolution in an embodiment of the present invention. Figure 2 This is a schematic diagram of a two-way risk assessment method for the balance between new energy power generation and load supply and demand, based on the phased characteristics before, during and after a cold wave, in a specific embodiment of the present invention. Figure 3 This is a schematic diagram of a phased risk assessment system for power supply and demand driven by cold wave evolution, as described in an embodiment of the present invention. Detailed Implementation

[0026] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0027] Please see Figure 1 The present invention provides a method for phased risk assessment of power supply and demand driven by cold wave evolution, comprising the following steps: Step 1: Obtain the stage identification and division results of the entire life cycle of cold waves.

[0028] In a specific exemplary technical solution, this step first obtains meteorological forecast data, and then divides the entire life cycle of the cold wave into three stages based on the meteorological dynamics characteristics of the cold wave: the leading wind period, the passing strong convection period, and the stagnant and deep cold period.

[0029] In a preferred embodiment of the present invention, the specific criteria for the above three stages are as follows: when the wind speed rise rate is greater than the wind speed rise threshold and the temperature is higher than the sum of the predicted minimum temperature and the temperature difference margin, it is classified as the frontal gale period; when the wind speed is greater than or equal to the wind speed high threshold, or when there is any precipitation phenomenon of snowfall, rain or freezing rain, it is classified as the transit strong convection period; when the wind speed fall rate is less than the wind speed fall threshold and the temperature is close to the predicted minimum temperature of this cold wave (i.e., the difference between the two is within a preset range), it is classified as the calm and stable deep cold period.

[0030] In this step, based on the meteorological dynamics of the cold wave, the entire process is divided into the period of strong winds at the front, the period of strong convection during the passage, and the period of calm and deep cold. Wind speed change rate, temperature, and precipitation are used as clear criteria for automatic identification, realizing dynamic tracking of the entire process, and no longer using a single time section.

[0031] Step 2: Based on the stage identification and division results obtained in Step 1, construct a new energy output model with meteorological correction factors and a load response model with thermal inertia in stages.

[0032] In a specific exemplary technical solution, this step is used to quantify the dynamic response of the power generation side and the load side; wherein, wind power during the strong wind period is calculated normally according to the power curve; wind power during the strong convection period is introduced with the cut-out loss coefficient and icing efficiency correction function; wind power during the calm and deep cold period is exponentially attenuated according to the wind speed attenuation factor, and photovoltaic power is corrected with the snow shading factor.

[0033] In this step, phased modeling is used to restore the real hysteresis response of wind and solar power output and load, which can truly restore the source and load hysteresis and abrupt change characteristics of the entire cold wave process.

[0034] Step 3: Based on the new energy output model and load response model constructed in Step 2, calculate the risk assessment index in stages and compare it with the corresponding preset threshold to form the phased risk assessment results of power supply and demand.

[0035] This invention discloses a phased risk assessment method for power supply and demand driven by cold wave evolution. Through step 1, a three-stage cold wave division; step 2, phased source-load modeling; and step 3, phased index calculation and comparison, it achieves dynamic assessment of the entire cold wave lifecycle. This method eliminates the need for static judgment based on a single cross-section, accurately identifying early-stage absorption risks and later-stage supply-demand gap risks. It solves the technical problems of existing technologies that neglect the phased and lagging nature of cold waves, rely on static assessment, and fail to assess early-stage absorption and / or later-stage gap risks. Furthermore, through improved methods of calculating the absorption index during the leading wind period and the gap index during the stable and deep cold period, a two-way quantitative assessment system is established, fully covering the two-sided fluctuation risks brought about by cold wave counter-peak regulation. This solves the technical problem of lacking a two-way risk assessment mechanism and only preventing power shortages without quantifying absorption difficulties. In summary, the technical solution of this invention, starting from the physical mechanism of cold waves and the response characteristics of the power system, can simultaneously identify early-stage absorption pressure and later-stage power shortage risks, achieving dynamic and accurate assessment throughout the entire process.

[0036] Please see Figure 2 This invention demonstrates a complete technical path from cold wave phase identification, quantification of the impact on the power generation and load sides, construction of comprehensive assessment indicators, to real-time assessment and response. The embodiments of this invention provide a two-way risk assessment method for the supply and demand balance of new energy power generation and load, based on the phased characteristics before, during, and after a cold wave. Its core technical route includes cold wave life cycle phase division and characteristic identification, phased analysis of new energy output and load response, construction of two-way supply and demand risk assessment indicators, and the formation of a real-time assessment and dynamic early warning process. The specific process is as follows: Step 1: Identification of the entire life cycle stages of cold waves.

[0037] This step, based on meteorological forecast data, defines three key stages in the evolution of the cold wave, including: 1) Stage I (frontal gale period): During this stage, wind speeds rise sharply, and temperatures begin to drop but do not reach extreme values. Specific criteria are as follows: and ; In the formula, To predict wind speed, m / s; This is the wind speed rise threshold, for example, 5 m / s / h; For the predicted temperature, ℃; The predicted minimum temperature for the cold wave is shown in °C. This is a temperature margin, such as 5°C, used to determine whether the coldest moment has not yet been reached.

[0038] 2) Stage II (Passing Severe Convection Period): During this stage, wind speeds remain high or fluctuate, accompanied by snowfall, rain, or freezing rain, and temperatures drop sharply. Specific criteria are as follows: or ; In the formula, The high-level threshold for wind speed is given in m / s. This refers to the amount of snowfall, rainfall, or freezing rain.

[0039] 3) Stage III (Stable and Deep Cold Period): During this stage, after the cold front passes, the area is controlled by a cold high-pressure system, resulting in a sharp drop in wind speed, extremely low temperatures, and no snow melt. Specific criteria for judgment: and ; In the formula, This is the threshold for wind speed reduction, for example, 4 m / s / h.

[0040] Step 2: Phased analysis of dynamic response of renewable energy output to load.

[0041] Step 2A, dynamic response analysis of new energy power generation, i.e. time-series evolution analysis of new energy power generation.

[0042] 1) The wind power output model incorporates late-stage attenuation and icing corrections, expressed as: ; In the formula, for Total output of the wind farm cluster at any given time, in MW; The theoretical output, in MW, is calculated based on the power curve of the wind farm cluster. for Predicted wind speed at any time; The cut-out loss coefficient, with a value of 0 to 1, represents the proportion of units that are cut off due to overspeed. The icing efficiency correction function varies with icing thickness. It increases but decreases non-linearly, with a value ranging from 0 to 1; The wind power output level at the beginning of the static and cold period; The wind speed decay time constant represents the rate at which wind speed decays under the control of cold high pressure, including the effect of icing maintenance. This marks the beginning of the static, deep cryogenic phase.

[0043] Explanatoryly, Phase III focuses on describing the calm wind effect under high-voltage control, where wind power output may drop sharply in a short period of time.

[0044] 2) Introducing snowmelt lag into the photovoltaic output model: This focuses on the "light but no electricity" phenomenon in Stage III, where the weather clears (irradiance recovers) but the snow has not melted, which is represented as: ; In the formula, for The output power of the photovoltaic system at any given time; for Irradiance on the surface of the photovoltaic panel at any given time, W / m 2 ; The reference conversion efficiency for photovoltaic modules; This is the temperature power coefficient, which is usually negative, such as -0.0035 / ℃; The component temperature is typically extremely low during cold waves, which theoretically is beneficial for power generation. for The snow cover factor at any given time ranges from 0 to 1. In stage III, although the weather clears up, Recovery is underway, but due to the low temperatures preventing the snow from melting, the factor may still be close to 1 (i.e., no output).

[0045] Step 2B, load-side dynamic response analysis, i.e. load-side thermal inertia hysteresis analysis.

[0046] Considering the thermal inertia of buildings, the peak load often lags behind the lowest temperature point. Furthermore, during the later stages of a cold wave (Stage III), due to the sustained low temperatures, the load not only does not decrease but may continue to rise due to the cumulative effect, as expressed as: ; In the formula, for Total load power of the power system at any given time; For the base load, which is independent of temperature, MW; For the maximum design heating load, MW; The starting temperature threshold for heating load, for example, 10℃; Design minimum temperature for heating; for Always consider the effective temperature of thermal inertia; This is the load's sensitivity index to temperature, typically taken as 1.2 to 1.5; in, Defined as: ; In the formula, This is the thermal inertia weighting coefficient, with a value ranging from 0 to 1. The smaller the value, the better the building's thermal insulation performance, and the more delayed the load's response to temperature changes; for Predicted temperature at any time; for The effective temperature should always take thermal inertia into account.

[0047] Step 3: Calculate the supply and demand risk indicators.

[0048] In this step of the present invention embodiment, two independent risk indicators are constructed to address the risk characteristics of different stages. The new energy consumption pressure index is obtained by subtracting the load from the sum of wind power output, photovoltaic power output, and minimum stable thermal power output, and then dividing by the load. It is used to quantify the risk of oversupply during the leading wind period. In addition, the supply and demand gap risk index is composed of a weighted static gap term and a dynamic scissor difference term. The static gap term reflects the degree of power shortage, and the dynamic scissor difference term reflects the difference in the rate of power generation decline and load increase. It is used to quantify the source-load scissor difference risk during the static, stable, and cold period.

[0049] The Renewable Energy Consumption Pressure Index (RCPI, for Phase I) is used to assess the peak-shaving difficulties caused by the surge in wind power in the lead-up to a cold wave. The calculation formula is as follows: ; In the formula, for The pressure index of new energy consumption at all times; for Wind power output at any time, MW; for Solar power output at all times, MW; The minimum stable output of the thermal power unit connected to the grid, in MW; for Total system load at any time; Among them, if This indicates that even when the thermal power is reduced to its minimum, power generation still exceeds the load, posing a risk of wind and solar power curtailment.

[0050] The Supply-Demand Gap Risk Index (SDGRI, for Phase III) is used to assess the risk of reserve depletion caused by the "source reduction and load increase scissors difference" in the later stages of a cold wave. The calculation formula is as follows: ; In the formula, for Supply and demand gap risk index at all times; for The system's maximum available power generation capacity at any given time, in MW, including the maximum output of thermal power and the current output of new energy sources; The system's preset spinning reserve capacity, in MW; This is the risk weighting coefficient for the scissors difference, used to unify and weight the dimensions of the power change rate. This represents the rate of change in total power generation, expressed in MW / min. It is typically negative during the later stages of a cold wave (due to a sharp decrease in wind power). This is the load change rate, expressed in MW / min, and is usually positive or zero during the later stages of a cold wave. The second item describes the rate of change of the supply-demand gap. If wind power drops rapidly while load increases rapidly, this item will be very large, indicating the risk of insufficient ramp-up resources.

[0051] The Supply-Demand Gap Risk Index (SDGRI) is a composite indicator comprising a static gap term and a dynamic scissor difference term. In the dynamic scissor difference term, a relevant mathematical model is constructed to calculate the difference between the rate of decrease in power generation (negative gradient) and the rate of increase in power load (positive gradient), thereby quantifying the system's adjustment pressure per unit time. Unlike traditional assessment methods that only focus on power balance, this invention introduces a power change rate dimension. It can not only identify whether there is a power shortage but also whether there is sufficient time to adjust, effectively providing early warning of the risk of system frequency collapse due to insufficient ramp-up rates.

[0052] Step 4: Real-time evaluation and dynamic strategy generation.

[0053] In this embodiment of the invention, future RCPI and SDGRI indices are calculated based on rolling weather forecasts; wherein, 1) Early Warning Mode A (Emergency Response Warning): When the forecast enters Stage I and Issue a power consumption warning; furthermore, provide response strategies such as deep peak shaving of thermal power, activation of pumped storage and hydropower, and power transmission through market transactions, etc.

[0054] 2) Warning Mode B (Power Shortage Warning): When the forecast enters Phase III and In such cases, early warnings can be issued; furthermore, response strategies can be provided, including maintaining reservoir water levels in advance at the end of Phase II, arranging for thermal power units to complete grid connection and preheating before the end of Phase II, and preparing demand-side response, etc.

[0055] In this embodiment of the invention, the current stage is automatically identified based on real-time meteorological characteristics, and the evaluation indicators and early warning strategies are dynamically switched: when identified as Stage I, the Renewable Energy Consumption Pressure Index (RCPI) is automatically invoked to focus on monitoring the risk of wind and solar curtailment, and a deep peak-shaving strategy can be generated; when identified as Stage III, the Supply and Demand Gap Risk Index (SDGRI) is automatically switched to focus on monitoring the risk of reserve depletion, and an early start-up and demand response strategy can be generated. The improved approach of this invention solves the problem of the traditional early warning mechanism's "one-size-fits-all" approach under extreme weather conditions (usually only preventing power shortages), and realizes a two-way closed-loop management of the entire cold wave process, "preventing consumption difficulties in the early stage and preventing supply shortages in the later stage," significantly improving the flexibility and economy of the power grid in dealing with complex meteorological disasters.

[0056] The technical solutions disclosed in the embodiments of the present invention have significant advancements in the following aspects: It fills the gap in the impact assessment of "windless, snowy, and persistently low temperatures" in the later stages of a cold wave, and can identify the risks of the "cold high-pressure control period" that are easily missed by traditional methods. This includes the supply and demand imbalance caused by a significant decrease in wind speed and abnormal photovoltaic output while the load remains high, which helps to prevent the risk of power shortages in the later stages. It solves the problem of early warning for the consumption of new energy during periods of high energy generation, and provides early warning of consumption pressure in the lead-up to cold waves. This can avoid the waste of wind curtailment caused by blindly increasing reserves and help improve the economic efficiency of the system. It enhances the ability to cope with ramp rates by introducing a "scissors difference" rate term, which not only focuses on power balance but also on the instantaneous rate of change of power, helping dispatchers cope with the rapid ramping demand when wind power suddenly decreases.

[0057] A specific exemplary scenario of this invention is as follows: In December 202X, a strong cold wave struck a major new energy province.

[0058] T=0h (Cold Wave Front): Weather forecast indicates strong winds and temperature drops lasting 24 hours; Calculation (Supply exceeds demand); the system action command thermal power units to perform deep peak shaving to 30% of rated output, and notify neighboring provincial power grids to prepare to receive external wind power.

[0059] T=24h (cold wave passing through): Wind speeds in some areas reach cutoff speeds, some units cut off, some units shut down due to icing, snowfall begins in some areas, and snow cover begins to form on solar panels.

[0060] T=48h (late cold wave): Weather forecasts indicate that the cold front has passed, and under the control of a cold high-pressure system, wind speed will plummet from 18m / s to 5m / s within 6 hours. Temperatures will remain at -10℃. Wind power icing and solar photovoltaic snow accumulation will persist, and loads will continue to climb. Calculations It is expected to decline by 80%, while Because the temperature only rose by 15%, The index surged above the warning threshold. The system's actions included urgently starting the gas turbines in cold standby, stopping the pumping operation of the pumped storage units and adjusting them to standby power generation status, and issuing an orderly power consumption warning for the next 6 hours to high-energy-consuming enterprises.

[0061] The implementation results of the technical solution of this invention are as follows: when the wind power output suddenly drops by 3 million kilowatts within 2 hours and the photovoltaic output continues to be abnormal due to snow cover, the intervention measures effectively avoid the risk of low-frequency load reduction of the power grid caused by the sudden drop in the output of new energy sources.

[0062] In summary, this invention discloses a phased analysis framework based on the evolution characteristics of cold waves. It divides the cold wave process into several stages with significant differences in meteorological dynamics, and within each stage, characterizes the dynamic response relationship between renewable energy output and power load to key meteorological driving factors, thereby achieving refined analysis of the coupled response of renewable energy output and load under cold wave conditions. Specifically, this invention segments the cold wave evolution process temporally and introduces specific correction factors from renewable energy generation and load models, such as the wind speed decay time constant and icing efficiency correction function for wind power models, the snow shading factor for photovoltaic models, and the thermal inertia weighting coefficient for load models, etc. This invention overcomes the limitations of traditional models that rely solely on real-time meteorological data. By introducing "attenuation" and "hysteresis" parameters, it restores the source-load physical characteristics under the crucial condition of "wind stops, snow remains, and cold persists" in the later stages of a cold wave, filling the gap in existing technology for modeling extreme scenarios in the later stages of cold waves.

[0063] The following are embodiments of the apparatus of the present invention, which can be used to execute embodiments of the method of the present invention. For details not disclosed in the apparatus embodiments, please refer to the embodiments of the method of the present invention.

[0064] Please see Figure 3 In this embodiment of the invention, a phased risk assessment system for power supply and demand driven by cold wave evolution is provided, comprising: The phase identification and segmentation result acquisition unit is used to obtain the phase identification and segmentation results of the entire life cycle of the cold wave; The model building unit is used to build a new energy output model and a load response model based on the stage identification and division results; wherein, the new energy output model is built in stages based on the stage identification and division results, and the new energy output model built in some stages contains a meteorological correction factor; thermal inertia is considered in the construction process of the load response model. The calculation and comparison evaluation unit is used to calculate the risk assessment index in stages based on the new energy output model and the load response model, and compare it with the corresponding preset threshold to form the phased risk assessment results of power supply and demand.

[0065] In one embodiment of the present invention, a computer device is provided, comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the memory. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions from a computer storage medium to achieve a corresponding method flow or function. The processor described in this embodiment of the present invention can be used to execute the operation of a phased risk assessment method for power supply and demand driven by cold wave evolution.

[0066] In one embodiment of the present invention, a storage medium is provided, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the computer device and extended storage media supported by the computer device. The computer-readable storage medium provides storage space that stores the operating system of the terminal. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM (Random Access Memory) or non-volatile memory, such as at least one disk storage device. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the phased risk assessment method for power supply and demand driven by cold wave evolution in the above embodiments.

[0067] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, etc.) containing computer-usable program code.

[0068] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0069] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0070] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0071] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A phased risk assessment method for power supply and demand driven by cold wave evolution, characterized in that, Includes the following steps: Obtain the stage identification and segmentation results of the entire life cycle of cold waves; Based on the stage identification and division results, a new energy output model and a load response model are constructed; wherein, the new energy output model is constructed in stages based on the stage identification and division results, and some stages of the new energy output model contain meteorological correction factors; thermal inertia is considered in the construction process of the load response model. Based on the new energy output model and the load response model, the risk assessment index is calculated in stages and compared with the corresponding preset threshold to form the phased risk assessment results of power supply and demand.

2. The method for phased risk assessment of power supply and demand driven by cold wave evolution according to claim 1, characterized in that, The steps to obtain the stage identification and segmentation results of the entire life cycle of a cold wave include: Obtain weather forecast data; Based on the meteorological forecast data, and according to the meteorological dynamics of cold waves, the entire life cycle of cold waves is divided into three stages: the leading wind period, the passing strong convection period, and the static and stable deep cold period. Among them, the period of strong winds at the front is defined as the wind speed rise rate being greater than the wind speed rise threshold, and the temperature being higher than the sum of the predicted minimum temperature of the cold wave and the temperature difference margin; the period of strong convection passing through is defined as the wind speed being greater than or equal to the high wind speed threshold, or the presence of any precipitation phenomenon such as snowfall, rain, or freezing rain; the period of calm and deep cold is defined as the wind speed decrease rate being less than the wind speed decrease threshold, and the difference between the temperature and the predicted minimum temperature of the cold wave being within the preset range.

3. The method for phased risk assessment of power supply and demand driven by cold wave evolution according to claim 2, characterized in that, In the aforementioned new energy output model, wind power during the strong wind period is calculated normally according to the power curve; wind power during the strong convection period is adjusted by introducing the cut-out loss coefficient and the icing efficiency correction function; wind power during the calm and deep cold period is adjusted by the wind speed attenuation factor, and photovoltaic power is adjusted by introducing the snow shading factor.

4. The method for phased risk assessment of power supply and demand driven by cold wave evolution according to claim 3, characterized in that, The aforementioned new energy output model, specifically the wind power output model, is expressed as follows: ； In the formula, for Total output of wind farm cluster at all times; The theoretical output is calculated based on the power curve of the wind farm group; for Predicted wind speed at any time; The cut-out loss coefficient, with a value of 0 to 1, represents the proportion of units that are cut off due to overspeed. The icing efficiency correction function varies with icing thickness. It increases but decreases non-linearly, with a value ranging from 0 to 1; The wind power output level at the beginning of the static and cold period; The wind speed decay time constant; This marks the beginning of the static, deep cryogenic phase. In the aforementioned new energy output model, the photovoltaic output model is expressed as: ； In the formula, for The output power of the photovoltaic system at any given time; for The irradiance on the surface of the photovoltaic panel at any given time; The reference conversion efficiency for photovoltaic modules; Temperature power coefficient; For component temperature; for The snow cover factor at any given time ranges from 0 to 1.

5. The method for phased risk assessment of power supply and demand driven by cold wave evolution according to claim 3, characterized in that, In the step of calculating risk assessment indices in stages based on the aforementioned new energy output model and load response model, and comparing them with corresponding preset thresholds to form phased risk assessment results for power supply and demand,... The renewable energy consumption pressure index is calculated during the period of strong winds ahead, and a consumption warning is issued when the calculated renewable energy consumption pressure index exceeds the consumption warning threshold. During periods of stable and deep cold, calculate the supply-demand gap risk index and issue a power shortage warning when the calculated result of the supply-demand gap risk index exceeds the power shortage warning threshold. in, The formula for calculating the pressure index of renewable energy consumption is: ； In the formula, for The pressure index of new energy consumption at all times; ; ; The minimum stable output of thermal power units for grid-connected operation; for Total load power of the power system at any given time; The formula for calculating the supply-demand gap risk index is: ； In the formula, for Supply and demand gap risk index at all times; for The system's maximum available power generation capacity at any given time; The system's preset rotational reserve capacity; This refers to the risk weighting coefficient for the scissors difference. This represents the rate of change in total power generation. This represents the load change rate.

6. The method for phased risk assessment of power supply and demand driven by cold wave evolution according to claim 1, characterized in that, The load response model is expressed as follows: ； In the formula, for Total load power of the power system at any given time; The base load is independent of temperature; Maximum design heating load; The starting temperature threshold for heating load; Design minimum temperature for heating; for Always consider the effective temperature of thermal inertia; This is an index representing the load's sensitivity to temperature. in, ; In the formula, This is the thermal inertia weighting coefficient, with a value ranging from 0 to 1. The smaller the value, the more delayed the load's response to temperature changes; for Predicted temperature at any time; for The effective temperature should always take thermal inertia into account.

7. A phased risk assessment system for power supply and demand driven by cold wave evolution, characterized in that, include: The phase identification and segmentation result acquisition unit is used to obtain the phase identification and segmentation results of the entire life cycle of the cold wave; The model building unit is used to build a new energy output model and a load response model based on the stage identification and division results; wherein, the new energy output model is built in stages based on the stage identification and division results, and the new energy output model built in some stages contains a meteorological correction factor; thermal inertia is considered in the construction process of the load response model. The calculation and comparison evaluation unit is used to calculate the risk assessment index in stages based on the new energy output model and the load response model, and compare it with the corresponding preset threshold to form the phased risk assessment results of power supply and demand.

8. The phased risk assessment system for power supply and demand driven by cold wave evolution according to claim 7, characterized in that, The step of obtaining the stage identification and segmentation results in the stage identification and segmentation result acquisition unit includes: Obtain weather forecast data; Based on the meteorological forecast data, and according to the meteorological dynamics of cold waves, the entire life cycle of cold waves is divided into three stages: the leading wind period, the passing strong convection period, and the static and stable deep cold period. Among them, the period of strong winds at the front is defined as the wind speed rise rate being greater than the wind speed rise threshold, and the temperature being higher than the sum of the predicted minimum temperature of the cold wave and the temperature difference margin; the period of strong convection passing through is defined as the wind speed being greater than or equal to the high wind speed threshold, or the presence of any precipitation phenomenon such as snowfall, rain, or freezing rain; the period of calm and deep cold is defined as the wind speed decrease rate being less than the wind speed decrease threshold, and the difference between the temperature and the predicted minimum temperature of the cold wave being within the preset range.

9. A phased risk assessment system for power supply and demand driven by cold wave evolution as described in claim 8, characterized in that, In the aforementioned new energy output model, wind power during the strong wind period is calculated normally according to the power curve; wind power during the strong convection period is adjusted by introducing the cut-out loss coefficient and the icing efficiency correction function; wind power during the calm and deep cold period is adjusted by the wind speed attenuation factor, and photovoltaic power is adjusted by introducing the snow shading factor.

10. A phased risk assessment system for power supply and demand driven by cold wave evolution according to claim 9, characterized in that, The aforementioned new energy output model, specifically the wind power output model, is expressed as follows: ； In the formula, for Total output of wind farm cluster at all times; The theoretical output is calculated based on the power curve of the wind farm group; for Predicted wind speed at any time; The cut-out loss coefficient, with a value of 0 to 1, represents the proportion of units that are cut off due to overspeed. The icing efficiency correction function varies with icing thickness. It increases but decreases non-linearly, with a value ranging from 0 to 1; The wind power output level at the beginning of the static and cold period; The wind speed decay time constant; This marks the beginning of the static, deep cryogenic phase. In the aforementioned new energy output model, the photovoltaic output model is expressed as: ； In the formula, for The output power of the photovoltaic system at any given time; for The irradiance on the surface of the photovoltaic panel at any given time; The reference conversion efficiency for photovoltaic modules; Temperature power coefficient; For component temperature; for The snow cover factor at any given time ranges from 0 to 1.

11. A phased risk assessment system for power supply and demand driven by cold wave evolution according to claim 9, characterized in that, In the calculation and comparison evaluation unit, the step of calculating the risk assessment index in stages based on the new energy output model and the load response model, and comparing it with the corresponding preset thresholds to form the staged risk assessment results of power supply and demand is as follows: The renewable energy consumption pressure index is calculated during the period of strong winds ahead, and a consumption warning is issued when the calculated renewable energy consumption pressure index exceeds the consumption warning threshold. During periods of stable and deep cold, calculate the supply-demand gap risk index and issue a power shortage warning when the calculated result of the supply-demand gap risk index exceeds the power shortage warning threshold. in, The formula for calculating the pressure index of renewable energy consumption is: ； In the formula, for The pressure index of new energy consumption at all times; ; ; The minimum stable output of thermal power units for grid-connected operation; for Total load power of the power system at any given time; The formula for calculating the supply-demand gap risk index is: ； In the formula, for Supply and demand gap risk index at all times; for The system's maximum available power generation capacity at any given time; The system's preset rotational reserve capacity; This refers to the risk weighting coefficient for the scissors difference. This represents the rate of change in total power generation. This represents the load change rate.

12. The phased risk assessment system for power supply and demand driven by cold wave evolution according to claim 7, characterized in that, The load response model is expressed as follows: ； In the formula, for Total load power of the power system at any given time; The base load is independent of temperature; Maximum design heating load; The starting temperature threshold for heating load; Design minimum temperature for heating; for Always consider the effective temperature of thermal inertia; This is an index representing the load's sensitivity to temperature. in, ; In the formula, This is the thermal inertia weighting coefficient, with a value ranging from 0 to 1. The smaller the value, the more delayed the load's response to temperature changes; for Predicted temperature at any time; for The effective temperature should always take thermal inertia into account.

13. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the phased risk assessment method for power supply and demand driven by cold wave evolution as described in any one of claims 1 to 6.

14. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the phased risk assessment method for power supply and demand driven by the evolution of cold waves as described in any one of claims 1 to 6.

15. A computer program product, characterized in that, It includes computer instructions that, when executed by a processor, implement the steps of the phased risk assessment method for power supply and demand driven by cold wave evolution as described in any one of claims 1 to 6.

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