Regional energy power configuration optimization method and system for extreme weather uncertainty

Abstract

The invention provides an extreme weather uncertainty-oriented regional energy power configuration optimization method and system, and the method comprises the steps: firstly determining a regional historical typical extreme scene based on historical extreme weather and synchronous power data; then, constructing a power gap optimization upper-layer model considering load uncertainty disturbance and new energy fluctuation, and obtaining a power gap value of the region in a planning period; finally, on the premise that the regional power supply safety requirement is met, the minimum economic cost and the minimum carbon emission serve as targets, and an energy power low-carbon optimization configuration lower-layer model is constructed; and a regional energy power configuration optimization scheme is obtained by solving the energy power low-carbon optimization configuration lower-layer model. By considering the power supply and demand conditions under the influence of the 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 a regional power supply and the improvement of the cleanliness of a power grid are promoted.

Description

Technical Field

[0001] This invention belongs to the field of power planning technology, specifically relating to a method and system for optimizing regional energy and power allocation in the face of uncertainties caused by extreme weather. Background Technology

[0002] With the intensification of global climate change, the frequency and intensity of extreme weather events (such as high temperatures, cold waves, torrential rains, and freezing rains) have increased significantly, posing a serious threat to the safe and stable operation of energy and power systems. The suddenness, rarity, and destructiveness of extreme weather events lead to rapid growth in regional power load and significant fluctuations in renewable energy generation output, resulting in a high-risk situation of simultaneous "sharp load increases and renewable energy decline." For energy and power systems where the proportion of renewable energy continues to increase, this will directly affect residents' lives, critical load supply, and the safety of socio-economic operations.

[0003] Traditional energy and power planning methods are primarily based on historical typical meteorological conditions and average operating conditions, often exhibiting insufficient adaptability and weak resilience under real disaster scenarios. Meanwhile, the increasing penetration of renewable energy in new energy and power systems means that their output is significantly affected by weather, further amplifying the system's sensitivity and vulnerability to extreme weather. Therefore, there is an urgent need to develop an energy and power system optimization method that can effectively incorporate the uncertainties of extreme weather while balancing economic efficiency and resilience. Summary of the Invention

[0004] The purpose of this invention is to address the aforementioned problems in the existing technology by providing a method and system for optimizing regional energy and power allocation in the face of uncertainties caused by extreme weather.

[0005] To achieve the above objectives, the technical solution of the present invention is as follows:

[0006] Firstly, this invention proposes a regional energy and power allocation optimization method for addressing the uncertainties of extreme weather, including:

[0007] S1. Based on historical extreme weather and power data from the same period, determine typical historical extreme scenarios in the region, including power load and relative output of new energy sources;

[0008] S2. Based on typical extreme scenarios in the region's history, construct an upper-level model for optimizing the power gap that considers load uncertainty disturbances and new energy fluctuations, and obtain the power gap value of the region during the planning period.

[0009] S3. Based on the power shortage value of the region during the planning period, and under the premise of meeting the regional power supply security requirements, a lower-level model for low-carbon energy and power allocation is constructed with the goals of minimizing economic costs and carbon emissions.

[0010] S4. Solve the lower-level model of low-carbon energy and power allocation to obtain the regional energy and power allocation optimization scheme, which includes capacity allocation strategies for thermal power and new energy.

[0011] S1 includes:

[0012] S11, Collection History Based on the empirical quantiles of historical extreme value samples, historical load data and corresponding renewable energy output data that meet the high quantile threshold are selected to form a high load-corresponding renewable energy output scenario, i.e., the HL scenario. Historical renewable energy output data and corresponding load data that meet the low quantile threshold are selected to form a low renewable energy-corresponding load scenario, i.e., the LR scenario.

[0013] S12. Calculate the average annual power load and average relative output of new energy sources for each region under the HL and LR scenarios respectively.

[0014] S13. Based on the average annual power load and average relative output of new energy sources under the HL and LR scenarios in each region, determine the power load and relative output of new energy sources under typical historical extreme scenarios in each region, including:

[0015] like Then the region In history Electricity load under typical extreme scenarios in 2018 Relative output of new energy They are respectively:

[0016] ;

[0017] ;

[0018] like Then the region In history Electricity load under typical extreme scenarios in 2018 Relative output of new energy They are respectively:

[0019] ;

[0020] ;

[0021] In the above formula, For the region In history Average power load in HL scenarios per year For the region In history The total number of HL scenarios per year For the region In history The year's first Actual output of new energy in HL scenarios For the region In history Average power load in the annual LR scenario For the region In history The total number of LR scenes per year, For the region In history The year's first Actual output of new energy in each LR scenario.

[0022] In S2, the upper-level model for optimizing the power shortage is:

[0023] ;

[0024] ;

[0025] ;

[0026] In the above formula, For the region In the planning year The annual power shortage value, For the region In the planning year The planned load demand value, For the region Safety margin parameters, The load factor under the influence of extreme weather. The relative output factor of new energy sources under the influence of extreme weather. For the region In the planning year The planned output value of new energy sources For the region In the planning year Other adjustable power planning values, For the region In the planning year The planned value of energy storage and discharge capacity, Total number of historical years For the region In history Electricity load under typical extreme scenarios in a year For the region In history Average annual electricity load For the region In history Relative output of new energy sources under typical extreme scenarios in a year.

[0027] In the upper-level power shortage optimization model, the regional safety margin parameter is calculated using the following formula:

[0028] ;

[0029] ;

[0030] In the above formula, For the region Safety margin parameters, This is a first-level regional safety margin parameter. Secondary area safety margin parameters, Level 3 area safety margin parameters, meet > > , For the region The toughness requirement parameters, For high resilience requirements threshold, Low resilience requirement threshold As a weighting of critical load percentage, For the region critical load, For the region Total power load Weighting of new energy installed capacity For the region New energy installed capacity, For the region The installed capacity of thermal power.

[0031] In S3, the objective function of the lower-level model for low-carbon energy and power allocation is:

[0032] ;

[0033] In the above formula, The objective function of the lower-level model is used to optimize the low-carbon configuration of energy and electricity. For the planned annual total, As an economic weight, The discount rate for the time value of money. In order to plan year Annualized net cost of thermal power For the region In the planning year New thermal power capacity added to cope with extreme weather In order to plan year The annualized net cost of new energy sources For the region In the planning year New energy installations to cope with extreme weather As carbon emission weights, Carbon emission intensity of thermal power units;

[0034] The constraints of the lower-level model for low-carbon optimization of energy and power allocation include supply guarantee constraints in extreme scenarios, annual new capacity constraints, and annual investment budget ceiling constraints.

[0035] Secondly, this invention proposes a regional energy and power allocation optimization system for extreme weather uncertainties, including a typical extreme scenario determination module, an upper-level model construction module for power gap optimization, a lower-level model construction module for low-carbon energy and power allocation, and a lower-level model solution module for low-carbon energy and power allocation.

[0036] The typical extreme scenario determination module is used to determine the typical historical extreme scenarios in the region based on historical extreme weather and power data of the same period, including power load and relative output of new energy sources;

[0037] The power gap optimization upper-level model construction module is used to construct a power gap optimization upper-level model that considers load uncertainty disturbances and new energy fluctuations based on typical extreme scenarios in the region's history, so as to obtain the power gap value of the region during the planning period.

[0038] The energy and power low-carbon optimization configuration lower-level model construction module is used to construct an energy and power low-carbon optimization configuration lower-level model based on the power shortage value of the region during the planning period, under the premise of meeting the regional power supply security requirements, with the goal of minimizing economic costs and carbon emissions.

[0039] The lower-level model solution module for low-carbon energy and power configuration is used to solve the lower-level model for low-carbon energy and power configuration to obtain a regional energy and power configuration optimization scheme, which includes capacity configuration strategies for thermal power and new energy sources.

[0040] The typical extreme scenario determination module includes a scenario formation unit, an energy average calculation unit for each scenario, and an energy determination unit for extreme scenarios.

[0041] The scene-forming unit is used to collect history. Based on the empirical quantiles of historical extreme value samples, historical load data and corresponding renewable energy output data that meet the high quantile threshold are selected to form a high load-corresponding renewable energy output scenario, i.e., the HL scenario. Historical renewable energy output data and corresponding load data that meet the low quantile threshold are selected to form a low renewable energy-corresponding load scenario, i.e., the LR scenario.

[0042] The energy average calculation unit for each scenario is used to calculate the average annual power load and average relative output of new energy sources for each region under the HL and LR scenarios respectively.

[0043] The energy determination unit under extreme scenarios is used to determine the power load and relative output of new energy sources under typical historical extreme scenarios in each region based on the average annual power load and average relative output of new energy sources under HL and LR scenarios in each region, including:

[0044] like Then the region In history Electricity load under typical extreme scenarios in 2018 Relative output of new energy They are respectively:

[0045] ;

[0046] ;

[0047] like Then the region In history Electricity load under typical extreme scenarios in 2018 Relative output of new energy They are respectively:

[0048] ;

[0049] ;

[0050] In the above formula, For the region In history Average power load in HL scenarios per year For the region In history The total number of HL scenarios per year For the region In history The year's first Actual output of new energy in HL scenarios For the region In history Average power load in the annual LR scenario For the region In history The total number of LR scenes per year, For the region In history The year's first Actual output of new energy in each LR scenario.

[0051] In the power shortage optimization upper-level model construction module, the power shortage optimization upper-level model is as follows:

[0052] ;

[0053] ;

[0054] ;

[0055] In the above formula, For the region In the planning year The annual power shortage value, For the region In the planning year The planned load demand value, For the region Safety margin parameters, The load factor under the influence of extreme weather. The relative output factor of new energy sources under the influence of extreme weather. For the region In the planning year The planned output value of new energy sources For the region In the planning year Other adjustable power planning values, For the region In the planning year The planned value of energy storage and discharge capacity, Total number of historical years For the region In history Electricity load under typical extreme scenarios in a year For the region In history Average annual electricity load For the region In history Relative output of new energy sources under typical extreme scenarios in a year.

[0056] In the upper-level power shortage optimization model, the regional safety margin parameter is calculated using the following formula:

[0057] ;

[0058] ;

[0059] In the above formula, For the region Safety margin parameters, This is a first-level regional safety margin parameter. Secondary area safety margin parameters, Level 3 area safety margin parameters, meet > > , For the region The toughness requirement parameters, For high resilience requirements threshold, Low resilience requirement threshold As a weighting of critical load percentage, For the region critical load, For the region Total power load Weighting of new energy installed capacity For the region New energy installed capacity, For the region The installed capacity of thermal power.

[0060] In the energy and power low-carbon optimization configuration lower-level model construction module, the objective function of the energy and power low-carbon optimization configuration lower-level model is:

[0061] ;

[0062] In the above formula, The objective function of the lower-level model is used to optimize the low-carbon configuration of energy and electricity. For the planned annual total, As an economic weight, The discount rate for the time value of money. In order to plan year Annualized net cost of thermal power For the region In the planning year New thermal power capacity added to cope with extreme weather In order to plan year The annualized net cost of new energy sources For the region In the planning year New energy installations to cope with extreme weather As carbon emission weights, Carbon emission intensity of thermal power units;

[0063] The constraints of the lower-level model for low-carbon optimization of energy and power allocation include supply guarantee constraints in extreme scenarios, annual new capacity constraints, and annual investment budget ceiling constraints.

[0064] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0065] 1. This invention proposes a method and system for optimizing regional energy and power allocation in the face of extreme weather uncertainties. The method first determines typical historical extreme scenarios for the region based on historical extreme weather and concurrent power data, including power load and relative output of new energy sources. Then, based on these typical historical extreme scenarios, an upper-level model for power gap optimization considering load uncertainty disturbances and new energy fluctuations is constructed to obtain the region's power gap value during the planning period. Finally, based on the region's power gap value during the planning period, and under the premise of meeting regional power supply security requirements, a lower-level model for low-carbon energy and power allocation is constructed with the objectives of minimizing economic costs and carbon emissions. By solving the lower-level model for low-carbon energy and power allocation, a regional energy and power allocation optimization scheme is obtained, which includes capacity allocation strategies for thermal power and new energy sources. On the one hand, this method constructs an upper-level model for optimizing the power gap based on typical historical extreme scenarios in the region, considering load uncertainty disturbances and new energy fluctuations. It optimizes the power gap under the influence of extreme weather year by year, effectively reflecting the combined impact of extreme weather on power load demand and new energy output, and ensuring the safety of the regional energy and power system under extreme weather conditions. On the other hand, under the premise of meeting the requirements of regional power supply security, this method constructs a lower-level model for low-carbon optimization of energy and power configuration with the goals of minimizing economic costs and carbon emissions. It comprehensively considers economic evaluation and carbon emission impact, and provides targeted capacity guarantee mechanisms to reduce power supply risks when extreme weather occurs. Under the background of "dual carbon" goals, it takes into account both power supply security and green and low-carbon development needs, and constructs a safe, reliable, and low-carbon energy and power system. This not only improves the power system's ability to cope with regional extreme events, but also promotes the optimization of regional power structure and the improvement of grid cleanliness.

[0066] 2. This invention proposes a method and system for optimizing regional energy and power allocation in the face of uncertainties caused by extreme weather. This method considers the power supply and demand situation under the influence of extreme weather, and is based on historical extreme weather and power data of the same period. It selects the load and relative output of the typical extreme scenarios with large supply and demand gaps in the region as the typical extreme scenario load and relative output of the region, so as to achieve a conservative characterization of the power supply and demand risks under extreme weather conditions and meet the requirements of regional power supply security.

[0067] 3. This invention proposes a method and system for optimizing regional energy and power allocation in the face of uncertainties in extreme weather. In the upper-level model of power gap optimization, this method sets a regional safety margin parameter, considers the regional resilience changes caused by differences in the penetration rate of new energy sources and spatial distribution, constructs a differentiated safety margin configuration, and forms a refined configuration strategy for extreme events, reflecting the necessity of regional power system to ensure power supply and the stability of power supply. Attached Figure Description

[0068] Figure 1This is an overall flowchart of the method described in this invention.

[0069] Figure 2 This is a structural diagram of the system described in this invention. Detailed Implementation

[0070] The present invention will now be described in further detail with reference to specific embodiments and accompanying drawings.

[0071] This invention proposes a method and system for optimizing regional energy and power allocation under extreme weather uncertainties. Based on historical extreme weather and concurrent power data, it forms typical historical extreme scenarios for the region under extreme weather conditions. It constructs an upper-level model for power gap optimization that considers load uncertainty disturbances and renewable energy fluctuations. Based on the proportion of critical loads and renewable energy within the region, it classifies the region into resilience levels and sets differentiated safety margin parameters for different levels, optimizing the power gap under the impact of extreme weather year by year. Under the premise of meeting regional power supply security requirements, and with the goals of minimizing economic costs and carbon emissions, it constructs a lower-level model for low-carbon optimization of energy and power allocation, optimizing the capacity allocation strategies for thermal power, renewable energy, and other power sources. By considering the power supply and demand situation under the influence of extreme weather, it achieves security assurance and low-carbon optimization of the regional energy and power system under extreme weather conditions, not only improving the power system's ability to cope with regional extreme events but also promoting the optimization of the regional power structure and the improvement of grid cleanliness.

[0072] Example 1:

[0073] This embodiment takes a single region A as the research object to optimize energy and power allocation. The parameter settings in the example include: a planning period of 1 year, an initial thermal power installed capacity of 100MW, an initial new energy installed capacity of 50MW, an annual average load of 150MW, a first-level regional safety margin parameter of 15%, a second-level regional safety margin parameter of 10%, and a third-level regional safety margin parameter of 5%.

[0074] like Figure 1 As shown, the regional energy and power allocation optimization method for extreme weather uncertainties is carried out in the following steps:

[0075] 1. Based on historical extreme weather and concurrent power data, identify typical historical extreme scenarios in the region, including power load and relative output of new energy sources;

[0076] Collection history Based on historical extreme value samples and empirical quantiles, annual meteorological, load, and renewable energy data are used to select historical load data that meets the high quantile threshold and its corresponding renewable energy output data to form a high load-corresponding renewable energy output scenario (HL scenario). Conversely, historical renewable energy output data that meets the low quantile threshold and its corresponding load data are selected to form a low renewable energy-corresponding load scenario (LR scenario). If the load data of a historical scenario meets the high quantile threshold and its corresponding renewable energy output data meets the low quantile threshold, the scenario can be included in both scenarios simultaneously. The HL scenario reflects the high load pressure caused by extreme high / low temperatures and retains the "actual renewable energy output level at the time of high load." The LR scenario reflects the severe deficiency of renewable energy output caused by extreme low radiation / low wind and retains the "actual load level corresponding to the time of low renewable energy," forming typical power supply and demand scenarios under extreme weather conditions.

[0077] Based on the empirical quantiles of historical extreme value samples, the formula for selecting historical loads that meet the high quantile threshold to form the HL scenario is as follows:

[0078] :

[0079] In the above formula, For the region In history HL scene collection of the year For the region In history The year's first Power load in an HL scenario For the region In history The year's first Actual output of new energy in HL scenarios For the region In history The year's first The new energy theory in the HL scenario can be used for power output. For quantile functions, For the region In history Annual electricity load The high quantile threshold;

[0080] Based on the empirical quantiles of historical extreme value samples, the formula for selecting historical renewable energy outputs that meet the low quantile threshold to form the LR scenario is as follows:

[0081] ;

[0082] In the above formula, For the region In history A collection of Lightroom scenes from 2010. For the region In history The year's first Power load in an LR scenario For the region In history The year's first Actual output of new energy in each LR scenario For the region In history The year's first The new energy theory in the LR scenario can be used for power output. For the region In history Actual output of new energy in 2018 For the region In history The theory of the year can be used to exert force. The threshold is the low quantile.

[0083] Calculate the average annual electricity load and average relative renewable energy output for each region under both the HL and LR scenarios, including:

[0084] The average annual power load and average relative output of new energy sources in each region under the HL scenario are calculated using the following formula:

[0085] ;

[0086] ;

[0087] In the above formula, For the region In history Average power load in HL scenarios per year For the region In history The total number of HL scenarios per year For the region In history The year's first Power load in an HL scenario For the region In history The average relative output of new energy in the annual HL scenario For the region In history The year's first Actual output of new energy in HL scenarios For the region In history The year's first The theoretically available power output of new energy sources in a single HL scenario;

[0088] The average annual electricity load and average relative output of new energy sources in each region under the LR scenario are calculated using the following formula:

[0089] ;

[0090] ;

[0091] In the above formula, For the region In history Average power load in the annual LR scenario For the region In history The total number of LR scenes per year, For the region In history The year's first Power load in an LR scenario For the region In history The average relative output of new energy sources in the annual LR scenario For the region In history The year's first Actual output of new energy in each LR scenario For the region In history The year's first The new energy theory available in the LR scenario can be used for power output.

[0092] Based on the average annual power load and average relative output of renewable energy under HL and LR scenarios in each region, the power load and relative output of renewable energy under typical historical extreme scenarios in each region are determined, including:

[0093] like Then the region In history Electricity load under typical extreme scenarios in 2018 Relative output of new energy They are respectively:

[0094] ;

[0095] ;

[0096] like Then the region In history Electricity load under typical extreme scenarios in 2018 Relative output of new energy They are respectively:

[0097] ;

[0098] ;

[0099] By comparing the supply and demand gap between average load and average relative output of renewable energy under two scenarios, the average load and average relative output of renewable energy with larger supply and demand gaps are selected as the typical extreme scenarios (power load and relative output of renewable energy) for the region in that year. These are then used for the extreme power load and renewable energy output values ​​in subsequent planning, thus achieving a conservative characterization of power supply and demand risks under extreme weather conditions.

[0100] In a certain historical year, Region A exhibited a typical HL scenario: average power load of 175MW, average actual output of renewable energy of 45MW, and relative output of renewable energy of 80%; and a typical LR scenario: average power load of 162MW, average actual output of renewable energy of 25MW, and relative output of renewable energy of 50%.

[0101] Therefore, the supply-demand gap between average load and average renewable energy output in the two scenarios is:

[0102] Typical HL scenario: 175-45=130MW;

[0103] Typical LR scenario: 162-25=137MW;

[0104] Therefore, the typical LR scenario is more severe. The typical extreme scenario for Region A in that year was an average power load of 162MW, an average actual output of renewable energy of 25MW, and a relative output of renewable energy of 50%.

[0105] 2. Based on the proportion of critical loads and the proportion of renewable energy in the region, the region is classified into resilience levels, and different safety margin parameters are set for different levels of regions.

[0106] The regional resilience demand parameter includes the proportion of critical loads and the proportion of renewable energy installed capacity within the region, reflecting the necessity for ensuring power supply and the stability of power supply in the region. Based on historical load data and renewable energy installed capacity data of the region, the regional resilience demand parameter is calculated using the following formula:

[0107] ;

[0108] In the above formula, For the region The toughness requirement parameters, As a weighting of critical load percentage, For the region Critical loads include regional primary and secondary loads. For the region Total power load Weighting of new energy installed capacity For the region New energy installed capacity, For the region The installed capacity of thermal power;

[0109] The region is classified into resilience levels based on the magnitude of the resilience requirement parameter. The higher the resilience level, the larger the safety margin parameter. The safety margin parameter of the region is calculated using the following formula:

[0110] ;

[0111] In the above formula, For the region Safety margin parameters, This is a first-level regional safety margin parameter. Secondary area safety margin parameters, Level 3 area safety margin parameters, meet > > , For high resilience requirements threshold, Low resilience requirement threshold; Region A meets the secondary region safety margin parameter of 10%.

[0112] 3. Based on typical extreme scenarios in the region's history, an upper-level model for optimizing the power gap is constructed, taking into account load uncertainty disturbances and new energy fluctuations, to obtain the power gap value of the region during the planning period;

[0113] Under extreme weather conditions, considering the combined effects of sudden load surges and sharp declines in renewable energy, the minimum shortfall that regional adjustable resources cannot meet demand is the annual power deficit. By utilizing the differences between historical typical extreme scenarios and baseline forecasts, a weighted average of the relative deviations of each scenario is calculated according to scenario probability, and this average is applied to the baseline forecasts for future planning years to obtain a conservative planning value, thus optimizing the power deficit under the influence of extreme weather year by year.

[0114] The upper-level model for optimizing the power shortage is:

[0115] ;

[0116] ;

[0117] ;

[0118] In the above formula, For the region In the planning year The annual power shortage value, For the region In the planning year The planned load demand value, For the region Safety margin parameters, The load factor under the influence of extreme weather. The relative output factor of new energy sources under the influence of extreme weather. For the region In the planning year The planned output value of new energy sources For the region In the planning year Other adjustable power planning values, including other adjustable power sources such as thermal power, For the region In the planning year The planned value of energy storage and discharge capacity, Total number of historical years For the region In history Electricity load under typical extreme scenarios in a year For the region In history Average annual electricity load For the region In history Relative output of new energy sources under typical extreme scenarios in a year.

[0119] Region A plans to have a planned load demand of 200MW, a planned renewable energy output of 80MW, and a planned adjustable power output of 120MW for the year. Under extreme weather conditions, the planned power load will be 237.6MW, renewable energy output will be 40MW, and the annual power shortage will be 77.6MW.

[0120] 4. Based on the power shortage value of the region during the planning period, and under the premise of meeting the regional power supply security requirements, a lower-level model for low-carbon optimization of energy and power allocation is constructed with the goals of minimizing economic costs and carbon emissions.

[0121] By deciding on the annual increase in thermal power and renewable energy capacity, we can meet the capacity guarantee constraints under extreme scenarios. At the same time, within the planning period, we can optimize the capacity allocation strategy of thermal power and renewable energy sources year by year with the goal of minimizing economic costs and carbon emissions.

[0122] The objective function of the lower-level model for low-carbon energy and power allocation optimization is:

[0123] ;

[0124] In the above formula, The objective function of the lower-level model is used to optimize the low-carbon configuration of energy and electricity. For the planned annual total, As an economic weight, The discount rate for the time value of money. In order to plan year Annualized net cost of thermal power For the region In the planning year New thermal power capacity added to cope with extreme weather In order to plan year The annualized net cost of new energy sources For the region In the planning year New energy installations to cope with extreme weather As carbon emission weights, Carbon emission intensity of thermal power units;

[0125] The constraints of the lower-level model for low-carbon optimization of energy and power allocation include supply guarantee constraints in extreme scenarios, annual new capacity constraints, and annual investment budget ceiling constraints.

[0126] Supply guarantee constraints in extreme scenarios are:

[0127] ;

[0128] The annual capacity increase constraint is:

[0129] ;

[0130] ;

[0131] The annual investment budget cap is:

[0132] ;

[0133] In the above formula, For the region In the planning year The annual power shortage value, For the region In the planning year The planned value of new thermal power capacity, For the region In the planning year The annual upper limit for new thermal power capacity, For the region In the planning year The planned value of new energy capacity, For the region In the planning year The annual upper limit for new energy capacity. Planning Year The unit capacity investment cost of thermal power plants Planning Year The investment cost per unit capacity of new energy sources Planning Year The upper limit of the investment budget.

[0134] 5. Solve the lower-level model of low-carbon energy and power allocation to obtain the regional energy and power allocation optimization scheme, which includes capacity allocation strategies for thermal power and new energy sources;

[0135] To ensure effective response to the impact of extreme weather, Region A has optimized its original planning configuration of "80MW of new energy and 120MW of thermal power". It has decided to add 50MW of thermal power on the original basis. Considering the impact of extreme scenarios on the output of new energy, an additional 35.2MW of new energy is needed.

[0136] Example 2:

[0137] like Figure 2 As shown, the regional energy and power allocation optimization system for extreme weather uncertainties includes a typical extreme scenario determination module, an upper-level model construction module for power gap optimization, a lower-level model construction module for low-carbon energy and power allocation, and a lower-level model solution module for low-carbon energy and power allocation.

[0138] The typical extreme scenario determination module is used to determine the typical historical extreme scenarios in the region based on historical extreme weather and power data of the same period, including power load and relative output of new energy sources;

[0139] The power gap optimization upper-level model construction module is used to construct a power gap optimization upper-level model that considers load uncertainty disturbances and new energy fluctuations based on typical extreme scenarios in the region's history, so as to obtain the power gap value of the region during the planning period.

[0140] The energy and power low-carbon optimization configuration lower-level model construction module is used to construct an energy and power low-carbon optimization configuration lower-level model based on the power shortage value of the region during the planning period, under the premise of meeting the regional power supply security requirements, with the goal of minimizing economic costs and carbon emissions.

[0141] The lower-level model solution module for low-carbon energy and power configuration is used to solve the lower-level model for low-carbon energy and power configuration to obtain a regional energy and power configuration optimization scheme, which includes capacity configuration strategies for thermal power and new energy sources.

[0142] The typical extreme scenario determination module includes a scenario formation unit, an energy average calculation unit for each scenario, and an energy determination unit for extreme scenarios.

[0143] The scene-forming unit is used to collect history. Based on the empirical quantiles of historical extreme value samples, historical load data and corresponding renewable energy output data that meet the high quantile threshold are selected to form a high load-corresponding renewable energy output scenario, i.e., the HL scenario. Historical renewable energy output data and corresponding load data that meet the low quantile threshold are selected to form a low renewable energy-corresponding load scenario, i.e., the LR scenario.

[0144] The energy average calculation unit for each scenario is used to calculate the average annual power load and average relative output of new energy sources for each region under the HL and LR scenarios respectively.

[0145] The energy determination unit under extreme scenarios is used to determine the power load and relative output of new energy sources under typical historical extreme scenarios in each region based on the average annual power load and average relative output of new energy sources under HL and LR scenarios in each region, including:

[0146] like Then the region In history Electricity load under typical extreme scenarios in 2018 Relative output of new energy They are respectively:

[0147] ;

[0148] ;

[0149] like Then the region In history Electricity load under typical extreme scenarios in 2018 Relative output of new energy They are respectively:

[0150] ;

[0151] ;

[0152] In the above formula, For the region In history Average power load in HL scenarios per year For the region In history The total number of HL scenarios per year For the region In history The year's first Actual output of new energy in HL scenarios For the region In history Average power load in the annual LR scenario For the region In history The total number of LR scenes per year, For the region In history The year's first Actual output of new energy in each LR scenario.

[0153] In the power shortage optimization upper-level model construction module, the power shortage optimization upper-level model is as follows:

[0154] ;

[0155] ;

[0156] ;

[0157] In the above formula, For the region In the planning year The annual power shortage value, For the region In the planning year The planned load demand value, For the region Safety margin parameters, The load factor under the influence of extreme weather. The relative output factor of new energy sources under the influence of extreme weather. For the region In the planning year The planned output value of new energy sources For the region In the planning year Other adjustable power planning values, For the region In the planning year The planned value of energy storage and discharge capacity, Total number of historical years For the region In history Electricity load under typical extreme scenarios in a year For the region In history Average annual electricity load For the region In history Relative output of new energy sources under typical extreme scenarios in a year.

[0158] In the upper-level power shortage optimization model, the regional safety margin parameter is calculated using the following formula:

[0159] ;

[0160] ;

[0161] In the above formula, For the region Safety margin parameters, This is a first-level regional safety margin parameter. Secondary area safety margin parameters, Level 3 area safety margin parameters, meet > > , For the region The toughness requirement parameters, For high resilience requirements threshold, Low resilience requirement threshold As a weighting of critical load percentage, For the region critical load, For the region Total power load Weighting of new energy installed capacity For the region New energy installed capacity, For the region The installed capacity of thermal power.

[0162] In the energy and power low-carbon optimization configuration lower-level model construction module, the objective function of the energy and power low-carbon optimization configuration lower-level model is:

[0163] ;

[0164] In the above formula, The objective function of the lower-level model is used to optimize the low-carbon configuration of energy and electricity. For the planned annual total, As an economic weight, The discount rate for the time value of money. In order to plan year Annualized net cost of thermal power For the region In the planning year New thermal power capacity added to cope with extreme weather In order to plan year The annualized net cost of new energy sources For the region In the planning year New energy installations to cope with extreme weather As carbon emission weights, Carbon emission intensity of thermal power units;

[0165] The constraints of the lower-level model for low-carbon optimization of energy and power allocation include supply guarantee constraints in extreme scenarios, annual new capacity constraints, and annual investment budget ceiling constraints.

Claims

1. A method for optimizing regional energy and power allocation in the face of extreme weather uncertainties, characterized in that: The method includes: S1. Based on historical extreme weather and concurrent power data, determine typical historical extreme scenarios in the region, including power load and relative output of new energy sources; S2. Based on typical extreme scenarios in the region's history, construct an upper-level model for optimizing the power gap that considers load uncertainty disturbances and new energy fluctuations, and obtain the power gap value of the region during the planning period. S3. Based on the power shortage value of the region during the planning period, and under the premise of meeting the regional power supply security requirements, a lower-level model for low-carbon energy and power allocation is constructed with the goals of minimizing economic costs and carbon emissions. S4. Solve the lower-level model of low-carbon energy and power allocation to obtain the regional energy and power allocation optimization scheme, which includes the capacity allocation strategy for thermal power and new energy.

2. The regional energy and power allocation optimization method for extreme weather uncertainties according to claim 1, characterized in that, S1 includes: S11, Collection History Based on the empirical quantiles of historical extreme value samples, historical load data and corresponding renewable energy output data that meet the high quantile threshold are selected to form a high load-corresponding renewable energy output scenario, i.e., the HL scenario. Historical renewable energy output data and corresponding load data that meet the low quantile threshold are selected to form a low renewable energy-corresponding load scenario, i.e., the LR scenario. S12. Calculate the average annual power load and average relative output of new energy sources for each region under the HL and LR scenarios respectively. S13. Based on the average annual power load and average relative output of new energy sources under the HL and LR scenarios in each region, determine the power load and relative output of new energy sources under typical historical extreme scenarios in each region, including: like Then the region In history Electricity load under typical extreme scenarios in 2018 Relative output of new energy They are respectively: ； ； like Then the region In history Electricity load under typical extreme scenarios in 2018 Relative output of new energy They are respectively: ； ； In the above formula, For the region In history Average power load in HL scenarios per year For the region In history The total number of HL scenarios per year For the region In history The year's first Actual output of new energy in HL scenarios For the region In history Average power load in the annual LR scenario For the region In history The total number of LR scenes per year, For the region In history The year's first Actual output of new energy in each LR scenario.

3. The regional energy and power allocation optimization method for extreme weather uncertainties according to claim 1, characterized in that, In S2, the upper-level model for optimizing the power shortage is: ； ； ； In the above formula, For the region In the planning year The annual power shortage value, For the region In the planning year The planned load demand value, For the region Safety margin parameters, The load factor under the influence of extreme weather. The relative output factor of new energy sources under the influence of extreme weather. For the region In the planning year The planned output value of new energy sources For the region In the planning year Other adjustable power planning values, For the region In the planning year The planned value of energy storage and discharge capacity, Total number of historical years For the region In history Electricity load under typical extreme scenarios in a year For the region In history Average annual electricity load For the region In history Relative output of new energy sources under typical extreme scenarios in a year.

4. The regional energy and power allocation optimization method for extreme weather uncertainties according to claim 3, characterized in that, In the upper-level power shortage optimization model, the regional safety margin parameter is calculated using the following formula: ； ； In the above formula, For the region Safety margin parameters, This is a first-level regional safety margin parameter. Secondary area safety margin parameters, Level 3 area safety margin parameters, meet > > , For the region The toughness requirement parameters, For high resilience requirements threshold, Low resilience requirement threshold As a weighting of critical load percentage, For the region critical load, For the region Total power load Weighting of new energy installed capacity For the region New energy installed capacity, For the region The installed capacity of thermal power.

5. The regional energy and power allocation optimization method for extreme weather uncertainties according to claim 1, characterized in that, In S3, the objective function of the lower-level model for low-carbon energy and power allocation is: ； In the above formula, The objective function of the lower-level model is used to optimize the low-carbon configuration of energy and electricity. For the planned annual total, As an economic weight, The discount rate for the time value of money. In order to plan year Annualized net cost of thermal power For the region In the planning year New thermal power capacity added to cope with extreme weather In order to plan year The annualized net cost of new energy sources For the region In the planning year New energy installations to cope with extreme weather For carbon emission weights, Carbon emission intensity of thermal power units; The constraints of the lower-level model for low-carbon optimization of energy and power allocation include supply guarantee constraints in extreme scenarios, annual new capacity constraints, and annual investment budget ceiling constraints.

6. A regional energy and power allocation optimization system for extreme weather uncertainties, characterized in that: The system includes a typical extreme scenario determination module, a power gap optimization upper-level model construction module, an energy and power low-carbon optimization configuration lower-level model construction module, and an energy and power low-carbon optimization configuration lower-level model solution module. The typical extreme scenario determination module is used to determine the typical historical extreme scenarios in the region based on historical extreme weather and power data of the same period, including power load and relative output of new energy sources; The power gap optimization upper-level model construction module is used to construct a power gap optimization upper-level model that considers load uncertainty disturbances and new energy fluctuations based on typical extreme scenarios in the region's history, so as to obtain the power gap value of the region during the planning period. The energy and power low-carbon optimization configuration lower-level model construction module is used to construct an energy and power low-carbon optimization configuration lower-level model based on the power shortage value of the region during the planning period, under the premise of meeting the regional power supply security requirements, with the goal of minimizing economic costs and carbon emissions. The lower-level model solution module for low-carbon energy and power configuration is used to solve the lower-level model for low-carbon energy and power configuration to obtain a regional energy and power configuration optimization scheme, which includes capacity configuration strategies for thermal power and new energy sources.

7. The regional energy and power allocation optimization system for extreme weather uncertainties according to claim 6, characterized in that, The typical extreme scenario determination module includes a scenario formation unit, an energy average calculation unit for each scenario, and an energy determination unit for extreme scenarios. The scene-forming unit is used to collect history. Based on the empirical quantiles of historical extreme value samples, historical load data and corresponding renewable energy output data that meet the high quantile threshold are selected to form a high load-corresponding renewable energy output scenario, i.e., the HL scenario. Historical renewable energy output data and corresponding load data that meet the low quantile threshold are selected to form a low renewable energy-corresponding load scenario, i.e., the LR scenario. The energy average calculation unit for each scenario is used to calculate the average annual power load and average relative output of new energy sources for each region under the HL and LR scenarios respectively. The energy determination unit under extreme scenarios is used to determine the power load and relative output of new energy sources under typical historical extreme scenarios in each region based on the average annual power load and average relative output of new energy sources under HL and LR scenarios in each region, including: like Then the region In history Electricity load under typical extreme scenarios in 2018 Relative output of new energy They are respectively: ； ； like Then the region In history Electricity load under typical extreme scenarios in 2018 Relative output of new energy They are respectively: ； ； In the above formula, For the region In history Average power load in HL scenarios per year For the region In history The total number of HL scenarios per year For the region In history The year's first Actual output of new energy in HL scenarios For the region In history Average power load in the annual LR scenario For the region In history The total number of LR scenes per year, For the region In history The year's first Actual output of new energy in each LR scenario.

8. The regional energy and power allocation optimization system for extreme weather uncertainties according to claim 6, characterized in that, In the power shortage optimization upper-level model construction module, the power shortage optimization upper-level model is as follows: ； ； ； In the above formula, For the region In the planning year The annual power shortage value, For the region In the planning year The planned load demand value, For the region Safety margin parameters, The load factor under the influence of extreme weather. The relative output factor of new energy sources under the influence of extreme weather. For the region In the planning year The planned output value of new energy sources For the region In the planning year Other adjustable power planning values, For the region In the planning year The planned value of energy storage and discharge capacity, Total number of historical years For the region In history Electricity load under typical extreme scenarios in a year For the region In history Average annual electricity load For the region In history Relative output of new energy sources under typical extreme scenarios in a year.

9. The regional energy and power allocation optimization system for extreme weather uncertainties according to claim 8, characterized in that, In the upper-level power shortage optimization model, the regional safety margin parameter is calculated using the following formula: ； ； In the above formula, For the region Safety margin parameters, This is a first-level regional safety margin parameter. Secondary area safety margin parameters, Level 3 area safety margin parameters, meet > > , For the region The toughness requirement parameters, For high resilience requirements threshold, Low resilience requirement threshold As a weighting of critical load percentage, For the region critical load, For the region Total power load Weighting of new energy installed capacity For the region New energy installed capacity, For the region The installed capacity of thermal power.

10. The regional energy and power allocation optimization system for extreme weather uncertainties according to claim 6, characterized in that, In the energy and power low-carbon optimization configuration lower-level model construction module, the objective function of the energy and power low-carbon optimization configuration lower-level model is: ； In the above formula, The objective function of the lower-level model is used to optimize the low-carbon configuration of energy and electricity. For the planned annual total, As an economic weight, The discount rate for the time value of money. In order to plan year Annualized net cost of thermal power For the region In the planning year New thermal power capacity added to cope with extreme weather In order to plan year The annualized net cost of new energy sources For the region In the planning year New energy installations to cope with extreme weather For carbon emission weights, Carbon emission intensity of thermal power units; The constraints of the lower-level model for low-carbon optimization of energy and power allocation include supply guarantee constraints in extreme scenarios, annual new capacity constraints, and annual investment budget ceiling constraints.

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