CN-121998291-A - Regional energy power configuration optimization method and system for extreme weather uncertainty

CN121998291ACN 121998291 ACN121998291 ACN 121998291ACN-121998291-A

Abstract

The invention provides an area energy power configuration optimization method and system for extreme weather uncertainty, which comprises the steps of firstly determining an area history typical extreme scene based on history extreme weather and contemporaneous power data, then constructing an electric power gap optimization upper model considering load uncertainty disturbance and new energy fluctuation to obtain an electric power gap value of an area in a planning period, finally constructing an energy power low-carbon optimization configuration lower model with minimum economic cost and minimum carbon emission as targets on the premise of meeting area power supply safety requirements, and obtaining an area energy power configuration optimization scheme by solving the energy power low-carbon optimization configuration lower model. According to the invention, by considering the power supply and demand conditions under the influence of extreme weather, the safety guarantee and low-carbon optimal configuration of the regional energy power system under the extreme weather condition are realized, the capability of the power system for coping with regional extreme events is improved, and the structural optimization of the regional power supply and the improvement of the cleanliness of the power grid are also promoted.

Inventors

ZHANG HAOQIN
WANG YAWEN
SANG ZIXIA
WANG ZHIXUAN
ZHANG ZHEN
YANG ZHENYU
WANG YINGXIANG
HOU TINGTING
XIA FANGZHOU
LI SIWU
LEI HE
PENG JUNZHE
LIAO SHUANG
WANG PINGFAN

Assignees

国网湖北省电力有限公司经济技术研究院

Dates

Publication Date: 20260508
Application Date: 20251216

Claims (10)

1. The regional energy power configuration optimizing method for extreme weather uncertainty is characterized in that, The method comprises the following steps: s1, determining a regional historical typical extreme scene, which comprises a power load and a new energy relative output, based on historical extreme weather and contemporaneous power data; S2, constructing an electric power gap optimization upper model considering load uncertainty disturbance and new energy fluctuation based on a regional history typical extreme scene, and obtaining an electric power gap value of a region in a planning period; S3, based on the power gap value of the area in the planning period, constructing an energy power low-carbon optimal configuration lower model by taking the minimum economic cost and the minimum carbon emission as targets on the premise of meeting the power supply safety requirement of the area; S4, solving a lower model of low-carbon optimal configuration of the energy power to obtain an optimal scheme of regional energy power configuration, wherein the scheme comprises a capacity configuration strategy of thermal power and new energy.
2. The regional energy power configuration optimization method for extreme weather uncertainty as claimed in claim 1, wherein, The S1 comprises the following steps: s11, collecting history Historical load data meeting a high quantile threshold and corresponding new energy output data are selected based on experience quantile of a historical extremum sample to form a high load-corresponding new energy output scene, namely an HL scene, and historical new energy output data meeting a low quantile threshold and corresponding load data are selected to form a low new energy-corresponding load scene, namely an LR scene; S12, calculating the relative output of the average power load and the average new energy in each region in an HL scene and an LR scene each year; S13, determining the relative power output of the power load and the new energy source in the historical typical extreme scenes of each region based on the relative power output of the average power load and the average new energy source in the HL scene and the LR scene of each region every year, wherein the method comprises the following steps: If it is Area then In history 1 Electric load in a typical extreme scenario Relative output of new energy The method comprises the following steps of: ; ; If it is Area then In history 1 Electric load in a typical extreme scenario Relative output of new energy The method comprises the following steps of: ; ; In the above-mentioned method, the step of, Is a region In history 1 The average power load of an annual HL scenario, Is a region In history 1 The total number of annual HL scenes, Is a region In history 1 Year of the first year The new energy in the HL scene is actually output, Is a region In history 1 The average power load of the annual LR scenario, Is a region In history 1 The total number of annual LR scenes, Is a region In history 1 Year of the first year New energy actual output in the LR scenes.
3. The regional energy power configuration optimization method for extreme weather uncertainty as claimed in claim 1, wherein, In the step S2, the power notch optimization upper model is as follows: ; ; ; In the above-mentioned method, the step of, Is a region Planned year The annual power gap value of (a), Is a region Planned year Is set in the load demand planning value of (a), Is a region Is used for the safety margin parameter of the (a), For a load factor under extreme weather effects, In order to provide a new energy source relative output factor under extreme weather effects, Is a region Planned year The new energy available output planning value of (a), Is a region Planned year Is a function of the other adjustable power planning values of the power grid, Is a region Planned year Is used for the energy storage and discharge capacity planning value, For the total number of years of history, Is a region In history 1 The power load in a typical extreme scenario of the year, Is a region In history 1 The average power load over the years, Is a region In history 1 New energy sources in typical extreme scenes are relatively output.
4. The regional energy power configuration optimization method for extreme weather uncertainty as claimed in claim 3, wherein, In the upper-layer power notch optimization model, the safety margin parameters of the region are calculated by adopting the following formula: ; ; In the above-mentioned method, the step of, Is a region Is used for the safety margin parameter of the (a), Is a first-level regional safety margin parameter, The secondary region safety margin parameter, Three-level regional safety margin parameters meet > > , Is a region Is used for the toughness requirement parameter of the steel sheet, For the high toughness requirement threshold value, A low toughness requirement threshold value is provided, As a key load duty cycle weight, Is a region Is used for the control of the load of the (c), Is a region Is used for the control of the total power load of the vehicle, The new energy installation takes up the weight of the proportion, Is a region The new energy installation capacity of the utility model is set, Is a region Is a thermal power installed capacity of (a).
5. The regional energy power configuration optimization method for extreme weather uncertainty as claimed in claim 1, wherein, In the step S3, the objective function of the low-carbon optimal configuration lower model of the energy power is as follows: ; In the above-mentioned method, the step of, The objective function of the lower model is configured for low-carbon optimization of energy power, In order to plan the total number of years, In order to be of economical weight, As a discount rate for the time value of the funds, To plan for the year The annual net cost of thermal power, Is a region Planned year According to the new thermal power increment corresponding to extreme weather, To plan for the year The annual net cost of new energy sources, Is a region Planned year New energy is added according to extreme weather, As the weight of the carbon emissions to be weighed, The carbon emission intensity of the thermal power generating unit is; Constraint conditions of the lower model of the low-carbon optimal configuration of the energy power comprise extreme scene supply guarantee constraint, annual capacity increasing constraint and annual investment calculation upper limit constraint.
6. An regional energy power configuration optimizing system oriented to extreme weather uncertainty is characterized in that, The system comprises a typical extreme scene determining module, an electric power gap optimizing upper model constructing module, an energy power low-carbon optimizing configuration lower model constructing module and an energy power low-carbon optimizing configuration lower model solving module; The typical extreme scene determining module is used for determining a regional historical typical extreme scene based on historical extreme weather and contemporaneous power data, wherein the regional historical typical extreme scene comprises a power load and a new energy relative output; The power gap optimization upper model construction module is used for constructing a power gap optimization upper model considering load uncertainty disturbance and new energy fluctuation based on a regional history typical extreme scene to obtain a power gap value of a region in a planning period; The energy power low-carbon optimal configuration lower model construction module is used for constructing an energy power low-carbon optimal configuration lower model based on the power gap value of the region in the planning period and on the premise of meeting the regional power supply safety requirement by taking the minimum economic cost and the minimum carbon emission as targets; the energy power low-carbon optimal configuration lower model solving module is used for solving the energy power low-carbon optimal configuration lower model to obtain an area energy power configuration optimizing scheme which comprises a capacity configuration strategy of thermal power and new energy.
7. The extreme weather uncertainty oriented regional energy power configuration optimization system of claim 6, The typical extreme scene determining module comprises a scene forming unit, an energy average calculating unit under each scene and an energy determining unit under the extreme scene; The scene forming unit is used for collecting histories Historical load data meeting a high quantile threshold and corresponding new energy output data are selected based on experience quantile of a historical extremum sample to form a high load-corresponding new energy output scene, namely an HL scene, and historical new energy output data meeting a low quantile threshold and corresponding load data are selected to form a low new energy-corresponding load scene, namely an LR scene; The energy average calculation unit in each scene is used for calculating the relative output of average power load and average new energy in each region in each yearly HL scene and LR scene; the energy determining unit under the extreme scene is used for determining the relative power output of the power load and the new energy source under the historical typical extreme scene of each region based on the relative power output of the average power load and the average new energy source under the HL scene and the LR scene of each region every year, and comprises the following steps: If it is Area then In history 1 Electric load in a typical extreme scenario Relative output of new energy The method comprises the following steps of: ; ; If it is Area then In history 1 Electric load in a typical extreme scenario Relative output of new energy The method comprises the following steps of: ; ; In the above-mentioned method, the step of, Is a region In history 1 The average power load of an annual HL scenario, Is a region In history 1 The total number of annual HL scenes, Is a region In history 1 Year of the first year The new energy in the HL scene is actually output, Is a region In history 1 The average power load of the annual LR scenario, Is a region In history 1 The total number of annual LR scenes, Is a region In history 1 Year of the first year New energy actual output in the LR scenes.
8. The extreme weather uncertainty oriented regional energy power configuration optimization system of claim 6, In the power gap optimization upper model construction module, the power gap optimization upper model is as follows: ; ; ; In the above-mentioned method, the step of, Is a region Planned year The annual power gap value of (a), Is a region Planned year Is set in the load demand planning value of (a), Is a region Is used for the safety margin parameter of the (a), For a load factor under extreme weather effects, In order to provide a new energy source relative output factor under extreme weather effects, Is a region Planned year The new energy available output planning value of (a), Is a region Planned year Is a function of the other adjustable power planning values of the power grid, Is a region Planned year Is used for the energy storage and discharge capacity planning value, For the total number of years of history, Is a region In history 1 The power load in a typical extreme scenario of the year, Is a region In history 1 The average power load over the years, Is a region In history 1 New energy sources in typical extreme scenes are relatively output.
9. The extreme weather uncertainty oriented regional energy power configuration optimization system of claim 8, In the upper-layer power notch optimization model, the safety margin parameters of the region are calculated by adopting the following formula: ; ; In the above-mentioned method, the step of, Is a region Is used for the safety margin parameter of the (a), Is a first-level regional safety margin parameter, The secondary region safety margin parameter, Three-level regional safety margin parameters meet > > , Is a region Is used for the toughness requirement parameter of the steel sheet, For the high toughness requirement threshold value, A low toughness requirement threshold value is provided, As a key load duty cycle weight, Is a region Is used for the control of the load of the (c), Is a region Is used for the control of the total power load of the vehicle, The new energy installation takes up the weight of the proportion, Is a region The new energy installation capacity of the utility model is set, Is a region Is a thermal power installed capacity of (a).
10. The extreme weather uncertainty oriented regional energy power configuration optimization system of claim 6, In the energy power low-carbon optimal configuration lower model construction module, the objective function of the energy power low-carbon optimal configuration lower model is as follows: ; In the above-mentioned method, the step of, The objective function of the lower model is configured for low-carbon optimization of energy power, In order to plan the total number of years, In order to be of economical weight, As a discount rate for the time value of the funds, To plan for the year The annual net cost of thermal power, Is a region Planned year According to the new thermal power increment corresponding to extreme weather, To plan for the year The annual net cost of new energy sources, Is a region Planned year New energy is added according to extreme weather, As the weight of the carbon emissions to be weighed, The carbon emission intensity of the thermal power generating unit is; Constraint conditions of the lower model of the low-carbon optimal configuration of the energy power comprise extreme scene supply guarantee constraint, annual capacity increasing constraint and annual investment calculation upper limit constraint.

Description

Regional energy power configuration optimization method and system for extreme weather uncertainty Technical Field The invention belongs to the technical field of power planning, and particularly relates to an regional energy power configuration optimization method and system for extreme weather uncertainty. Background With the aggravation of global climate change, the occurrence frequency and intensity of extreme weather events (such as high temperature, cold tide, heavy rain, freezing and the like) are obviously increased, and the safety and stability operation of an energy power system are seriously threatened. The sudden, rare and strong destructive nature of extreme weather causes rapid increase of regional power load, the power generation capacity of new energy source is obviously fluctuated, and the high risk situation of 'load steep rise and new energy source attenuation' concurrency occurs, which directly affects resident life, key load supply and socioeconomic operation safety for an energy source power system with continuously-improved new energy source duty ratio. The traditional energy power planning method is mainly based on historical typical meteorological conditions and average working conditions, and often has the problems of insufficient adaptability, weak recovery capacity and the like in a real disaster scene. Meanwhile, the permeability of renewable energy sources in the novel energy source power system is continuously improved, the output of the novel energy source power system is obviously influenced by weather, and the sensitivity and vulnerability of the novel energy source power system to extreme weather are further amplified. Therefore, development of an energy power system optimization method capable of effectively embedding extreme weather uncertainty and combining economy and toughness is urgently needed. Disclosure of Invention The invention aims to provide an regional energy power configuration optimization method and system for extreme weather uncertainty aiming at the problems in the prior art. In order to achieve the above object, the technical scheme of the present invention is as follows: in a first aspect, the present invention provides an regional energy power configuration optimization method for extreme weather uncertainty, including: s1, determining a regional historical typical extreme scene, which comprises a power load and a new energy relative output, based on historical extreme weather and contemporaneous power data; S2, constructing an electric power gap optimization upper model considering load uncertainty disturbance and new energy fluctuation based on a regional history typical extreme scene, and obtaining an electric power gap value of a region in a planning period; S3, based on the power gap value of the area in the planning period, constructing an energy power low-carbon optimal configuration lower model by taking the minimum economic cost and the minimum carbon emission as targets on the premise of meeting the power supply safety requirement of the area; S4, solving a lower model of low-carbon optimal configuration of the energy power to obtain an optimal scheme of regional energy power configuration, wherein the scheme comprises a capacity configuration strategy of thermal power and new energy. The S1 comprises the following steps: s11, collecting history Historical load data meeting a high quantile threshold and corresponding new energy output data are selected based on experience quantile of a historical extremum sample to form a high load-corresponding new energy output scene, namely an HL scene, and historical new energy output data meeting a low quantile threshold and corresponding load data are selected to form a low new energy-corresponding load scene, namely an LR scene; S12, calculating the relative output of the average power load and the average new energy in each region in an HL scene and an LR scene each year; S13, determining the relative power output of the power load and the new energy source in the historical typical extreme scenes of each region based on the relative power output of the average power load and the average new energy source in the HL scene and the LR scene of each region every year, wherein the method comprises the following steps: If it is Area thenIn history 1Electric load in a typical extreme scenarioRelative output of new energyThe method comprises the following steps of: ; ; If it is Area thenIn history 1Electric load in a typical extreme scenarioRelative output of new energyThe method comprises the following steps of: ; ; In the above-mentioned method, the step of, Is a regionIn history 1The average power load of an annual HL scenario,Is a regionIn history 1The total number of annual HL scenes,Is a regionIn history 1Year of the first yearThe new energy in the HL scene is actually output,Is a regionIn history 1The average power load of the annual LR scenario,Is a regionIn history 1The total number of annual LR sce