Consider extreme weather scenarios and carbon emission rights of pumping and storage capacity configuration method and device

Abstract

This invention discloses a method and apparatus for configuring pumped storage capacity considering extreme weather scenarios and carbon emission rights. The method includes: calculating the daily power supply adequacy index of a microgrid system; constructing a power supply adequacy sample set and dividing it into datasets with various power supply adequacy levels; constructing a baseline scenario set and sets of extreme weather scenarios of different degrees and clustering them to obtain typical scenarios; calculating the carbon emission rights revenue of the microgrid system under typical scenarios based on the configured capacity of distributed pumped storage stations; calculating the electricity purchase and sale revenue between the microgrid system and the upper-level grid under typical scenarios; and calculating the variable costs after configuring distributed pumped storage stations in the microgrid system; establishing a distributed pumped storage station capacity configuration model with the goal of maximizing net revenue; and determining the target configuration capacity of the distributed pumped storage stations based on the maximum net revenue. This invention maximizes the absorption of new energy sources and reduces carbon emissions, while improving power supply reliability under extreme weather conditions, while ensuring economic benefits.

Description

Technical Field

[0001] This invention relates to the field of microgrids in power systems, and more particularly to a method and apparatus for configuring pumped storage capacity that takes into account extreme weather scenarios and carbon emission rights. Background Technology

[0002] New energy sources such as wind power are characterized by volatility and intermittency, and the integration of a high proportion of new energy sources places higher demands on the flexibility of the power grid. Meanwhile, against the backdrop of global climate change, extreme temperature events are showing a trend of increasing frequency, intensity, and duration. Extreme high temperatures and low temperatures, along with cold waves, are constantly impacting the power grid, further exacerbating the contradiction of "random supply and demand," making the guarantee of power supply more complex. Energy storage, as a flexible two-way regulation means, is an inevitable way to solve the problem of high-penetration new energy integration. Currently, pumped storage power stations, due to their moderate construction costs and mature technology, have achieved large-scale application and play an extremely important role in peak shaving of modern power grids, becoming the most reliable flexible regulation resource for building new power systems. To accelerate the development of pumped storage power stations, the current power system has transitioned from the traditional centralized power supply mode to a power supply mode combining centralized and decentralized approaches. Microgrids are an effective way to achieve large-scale, high-proportion new energy integration, and the proportion of new, efficient, and green small independent power sources in the power system is increasing. Distributed pumped storage power stations have advantages such as small capacity, low head requirements, low investment, and short construction period. They are also easier to site and can make full use of existing water resources. They can form a complementary development pattern with large and medium-sized pumped storage power stations and have broad application prospects.

[0003] Reasonable capacity configuration is crucial for maximizing the operational efficiency of pumped storage power stations. While there is considerable research on capacity configuration for large pumped storage power stations in large power grids under normal meteorological conditions to fulfill their peak-shaving role, the capacity configuration of small distributed pumped storage power stations in microgrids has largely neglected extreme weather scenarios. Microgrids, as small power systems comprising distributed power sources, energy storage systems, loads, and control devices, are significantly affected by extreme weather events (such as cold waves and heat waves) from multiple dimensions, including the power source and load sides, impacting their operational balance (including power balance and energy balance). Given insufficient self-regulation resources within the microgrid system, its susceptibility to extreme weather is significantly greater than that of the large power grid. Therefore, it is necessary to study the capacity configuration of distributed pumped storage power stations in wind-powered microgrid systems that consider extreme weather scenarios.

[0004] Furthermore, the scope and scale of carbon market trading will gradually increase in the future, and considering carbon emission rights trading in the capacity configuration of distributed pumped storage power stations in microgrid systems has important practical significance. Summary of the Invention

[0005] The purpose of this invention is to overcome the above-mentioned defects and problems in the prior art and to provide a pumped storage capacity configuration method and device that takes into account extreme weather scenarios and carbon emission rights, so as to maximize the absorption of new energy sources and reduce carbon emissions, and improve the power supply reliability under extreme weather conditions while ensuring economic benefits.

[0006] To achieve the above objectives, the technical solution of the present invention is:

[0007] In a first aspect, the present invention provides a method for configuring pumped storage capacity that takes into account extreme weather scenarios and carbon emission rights, comprising:

[0008] Calculate the daily power supply adequacy index of the microgrid system and construct a power supply adequacy sample set; divide the power supply adequacy sample set into datasets with multiple power supply adequacy levels, and construct a benchmark scenario set and extreme weather scenario sets of different degrees; use median clustering technology to cluster the benchmark scenario set and extreme weather scenario sets of different degrees to obtain typical scenarios;

[0009] Based on the configuration capacity of distributed pumped storage power stations, we calculate the carbon emission rights quota and actual carbon emissions of microgrid systems in typical scenarios, as well as the carbon emission rights revenue of microgrid systems, the electricity purchase and sale revenue between microgrid systems and the upper-level power grid in typical scenarios, and the variable costs after configuring distributed pumped storage power stations in microgrid systems.

[0010] A capacity configuration model for distributed pumped storage power stations in a microgrid system is established with the goal of maximizing net revenue, and the target configuration capacity of distributed pumped storage power stations is determined based on the maximum net revenue. The net revenue is obtained through the electricity purchase and sale revenue between the microgrid system and the upper-level power grid, the carbon emission rights revenue of the microgrid system, and the variable costs after configuring distributed pumped storage power stations in the microgrid system.

[0011] Preferably, the power supply adequacy index includes capacity adequacy and power supply adequacy, respectively:

[0012] ;

[0013] ;

[0014] In the formula, For the scene The corresponding microgrid system capacity adequacy; For thermal power units Maximum output; This represents the number of thermal power units. For the scene The next micro-network system At the moment of maximum load, thermal power units contribution; For microgrid system wind power Maximum power output; For the scene The next micro-network system The output of wind power generation at the moment of maximum load; For the scene The maximum load of the microgrid system; For the scene The corresponding power adequacy of the microgrid system; This refers to the runtime interval of the microgrid system. For the scene The next micro-network system Heavenly thermal power units contribution; For the scene The next micro-network system Heavenly Moment Wind Power The output power of electricity generation; For the scene Next Heavenly Load values ​​of the microgrid system at any given time; Total number of time periods;

[0015] The power supply adequacy sample set is as follows:

[0016] ;

[0017] In the formula, This is a sample set for the capacity adequacy of microgrid systems. This is a sample set of power adequacy for microgrid systems. The number of scenes.

[0018] Preferably, the baseline scenario set and the extreme weather scenario set of different degrees are as follows:

[0019] ;

[0020] ;

[0021] ;

[0022] In the formula, As a set of baseline scenarios; A collection of mild extreme weather scenarios; This is a collection of moderate extreme weather scenarios; A collection of severe extreme weather scenarios; A collection of extremely severe weather scenarios; This is the first type of dataset with sufficient capacity; This is a dataset of the second level of sufficient capacity; This is a dataset representing the third level of sufficient capacity. For the capacity of the interconnection line between the microgrid system and the upper-level power grid; This represents the maximum load of the microgrid system. This is a dataset representing the fourth level of sufficiency. This is the dataset for the first level of sufficient power supply; This is a dataset representing the second level of sufficient battery power. This is a dataset representing the third level of sufficient power supply. This is the dataset for the fourth level of sufficient power supply.

[0023] Preferably, the typical scenario is as follows:

[0024] ;

[0025] ;

[0026] In the formula, This is the set of baseline scenarios after clustering; The capacity adequacy of a typical capacity adequacy level scenario for a microgrid system under the benchmark scenario after clustering. The probability of a typical capacity adequacy level scenario for a microgrid system under the baseline scenario; The power adequacy level of a typical power adequacy scenario for a microgrid system under the benchmark scenario after clustering. The probability of a typical power adequacy level scenario for a microgrid system under the baseline scenario; This is a clustered set of mild extreme weather scenarios; The capacity adequacy of a microgrid system under typical capacity adequacy levels in mildly extreme scenarios after clustering. The probability of a typical capacity adequacy level scenario for a microgrid system under mild extreme weather conditions after clustering; The power adequacy level of a microgrid system under a typical power adequacy scenario in a mild extreme weather condition after clustering. The probability of a typical power adequacy level scenario for a microgrid system under mild extreme weather conditions after clustering; This is a clustered set of moderately extreme weather scenarios; The capacity adequacy of a typical capacity adequacy level scenario for a microgrid system under moderate extreme weather conditions after clustering. The probability of a typical capacity adequacy level scenario for a microgrid system under moderate extreme weather conditions after clustering; The power adequacy level of a microgrid system under a typical scenario of moderate extreme weather after clustering. The probability of a typical power sufficiency level scenario for a microgrid system under moderate extreme weather conditions after clustering; This is a clustered set of severe extreme weather scenarios; The capacity adequacy of a microgrid system under a typical capacity adequacy scenario in a severe extreme weather scenario after clustering. The probability of a typical capacity adequacy level scenario for a microgrid system under severe extreme weather conditions after clustering; The power adequacy level of a microgrid system under a typical power adequacy scenario in a severe extreme weather scenario after clustering. The probability of a typical power adequacy level scenario for a microgrid system under severe extreme weather conditions after clustering; This is a clustered set of extremely severe weather scenarios. The capacity adequacy of a microgrid system under a typical capacity adequacy scenario in an extremely severe weather scenario after clustering. The probability of a typical capacity adequacy level scenario for a microgrid system under extremely severe weather conditions after clustering; The power adequacy level of a microgrid system under a typical scenario of extremely severe weather after clustering. The probability of a typical power adequacy level scenario for a microgrid system under extremely severe weather conditions after clustering; The sufficiency of the sample size for the baseline scenario set; The battery adequacy of the baseline scenario set samples; The sufficiency of the sample size for mild extreme weather scenarios; Battery adequacy for a sample set of mild extreme weather scenarios; The adequacy of the sample size for moderate extreme weather scenarios; The power adequacy of a sample dataset representing moderate extreme weather scenarios; The sufficiency of the sample size for severe extreme weather scenarios; The battery adequacy of a sample dataset representing severe extreme weather scenarios; The sufficiency of the sample size for extremely severe weather scenarios; The battery adequacy of a sample dataset representing extremely severe weather scenarios.

[0027] Preferably, the carbon emission rights revenue of the microgrid system is:

[0028] ;

[0029] ;

[0030] ;

[0031] ;

[0032] ;

[0033] ;

[0034] In the formula, For carbon emission rights revenue of microgrid systems; and These are the reservoir capacity of the distributed pumped storage power station and the rated power of the reversible generator unit, respectively. The trading price of carbon emission rights in the carbon market; For carbon emission credits; This refers to actual carbon emissions; For the carbon emission allowances of thermal power units in the microgrid system; Carbon emission credits for wind power in microgrid systems; Carbon emission allowances for distributed pumped storage power stations in microgrid systems; Carbon emission credits per unit of electricity generated by thermal power units; This represents the number of thermal power units. Typical scenario The probability of typical capacity sufficiency scenarios in microgrid systems; Typical scenario The typical capacity adequacy scenario of a microgrid system The active power of thermal power units at all times; Typical scenario The probability of typical power sufficiency scenarios in a microgrid system; Typical scenario The typical scenario of sufficient power supply in a microgrid system The active power of thermal power units at all times; Carbon emission credits per unit of wind power output; Typical scenario The typical capacity adequacy scenario of a microgrid system The active power of wind power at any given time; Typical scenario The typical scenario of sufficient power supply in a microgrid system The active power of wind power at any given time; Carbon emission credits per unit of electricity generated by distributed pumped storage power stations; Typical scenario The typical capacity adequacy scenario of a microgrid system Active power of distributed pumped storage power stations at all times; Typical scenario The typical scenario of sufficient power supply in a microgrid system Active power of distributed pumped storage power stations at all times; Carbon emissions per unit of thermal power generation.

[0035] Preferably, the revenue from the purchase and sale of electricity between the microgrid system and the upper-level power grid is as follows:

[0036] ;

[0037] ;

[0038] In the formula, The revenue from the purchase and sale of electricity between the microgrid system and the upper-level power grid; This refers to the electricity purchased and sold between the microgrid system and the upper-level power grid. The unit electricity price for a microgrid system to purchase and sell electricity from its superior power grid; Typical scenario The probability of typical capacity sufficiency scenarios in microgrid systems; Typical scenario The typical capacity adequacy scenario of a microgrid system The active power of thermal power units at all times; Typical scenario The typical capacity adequacy scenario of a microgrid system The active power of wind power at any given time; Typical scenario The typical capacity adequacy scenario of a microgrid system Active power of distributed pumped storage power stations at all times; Typical scenario The typical capacity adequacy scenario of a microgrid system Load of the microgrid system at any time; Typical scenario The probability of typical power sufficiency scenarios in a microgrid system; Typical scenario The typical scenario of sufficient power supply in a microgrid system The active power of thermal power units at all times; Typical scenario The typical scenario of sufficient power supply in a microgrid system The active power of wind power at any given time; Typical scenario The typical scenario of sufficient power supply in a microgrid system Active power of distributed pumped storage power stations at all times; Typical scenario The typical scenario of sufficient power supply in a microgrid system Load of the microgrid system at any time.

[0039] Preferably, the variable cost after configuring distributed pumped storage power stations in the microgrid system for:

[0040] ;

[0041] ;

[0042] ;

[0043] In the formula, Investment and operation costs for distributed pumped storage power stations; Fuel cost of thermal power units in a microgrid system; The unit investment and operation and maintenance cost of the reservoir; The unit investment and operation and maintenance cost of the generator set; The fixed annual operating fee rate for the reservoir; The fixed annual operating rate for generator sets; The discount rate; The lifespan of a distributed pumped storage power station; and These are the reservoir capacity of the distributed pumped storage power station and the rated power of the reversible generator unit, respectively. Typical scenario The probability of typical capacity sufficiency scenarios in microgrid systems; Typical scenario The typical capacity adequacy scenario of a microgrid system Fuel costs for thermal power units at all times; Typical scenario The probability of typical power sufficiency scenarios in a microgrid system; Typical scenario The typical scenario of sufficient power supply in a microgrid system Fuel costs for thermal power units at all times.

[0044] Secondly, the present invention provides a pumped storage capacity configuration device that considers extreme weather scenarios and carbon emission rights, the device being used to implement the method described above, the device comprising:

[0045] The typical scenario construction module is used to calculate the daily power supply adequacy index of the microgrid system and construct a power supply adequacy sample set. The power supply adequacy sample set is divided into datasets with multiple power supply adequacy levels, and a baseline scenario set and extreme weather scenario sets of different degrees are constructed. The median clustering technique is used to cluster the baseline scenario set and extreme weather scenario sets of different degrees to obtain typical scenarios.

[0046] The revenue and cost calculation module is used to calculate the carbon emission rights quota and actual carbon emissions of the microgrid system under typical scenarios based on the configuration capacity of the distributed pumped storage station, as well as to calculate the carbon emission rights revenue of the microgrid system, the electricity purchase and sale revenue between the microgrid system and the upper-level power grid under typical scenarios, and the variable costs after configuring the distributed pumped storage station in the microgrid system.

[0047] The target configuration capacity acquisition module is used to establish a capacity configuration model for distributed pumped storage power stations in a microgrid system with the goal of maximizing net revenue, and to determine the target configuration capacity of distributed pumped storage power stations based on the maximum net revenue; the net revenue is obtained through the electricity purchase and sale revenue between the microgrid system and the upper-level power grid, the carbon emission rights revenue of the microgrid system, and the variable costs after configuring distributed pumped storage power stations in the microgrid system.

[0048] Thirdly, the present invention provides a pumped storage capacity configuration device that takes into account extreme weather scenarios and carbon emission rights, including a memory and a processor;

[0049] The memory is used to store computer program code and transmit the computer program code to the processor;

[0050] The processor is configured to execute the method described above according to instructions in the computer program code.

[0051] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0053] This invention discloses a method and apparatus for configuring pumped storage capacity considering extreme weather scenarios and carbon emission rights. It constructs typical extreme weather scenarios by clustering data based on historical power supply adequacy indicators under extreme weather conditions. Adapting to the current trend of increasing frequency, intensity, and duration of extreme temperature events, it introduces a carbon emission trading mechanism and establishes a carbon emission rights revenue model for distributed pumped storage power stations in a microgrid system. By comprehensively considering the investment and operation costs of distributed pumped storage, system operating costs, and revenue from power purchases and sales with the upper-level grid, it determines the configuration capacity of distributed pumped storage power stations based on maximizing net revenue, thereby maximizing the absorption of new energy sources and reducing carbon emissions. Therefore, this invention maximizes the absorption of new energy sources and reduces carbon emissions while improving power supply reliability under extreme weather conditions, all while ensuring economic benefits. Attached Figure Description

[0054] Figure 1 This is a flowchart of a pumped storage capacity configuration method that takes into account extreme weather scenarios and carbon emission rights according to the present invention.

[0055] Figure 2 This is a structural block diagram of a pumped storage capacity configuration device that takes into account extreme weather scenarios and carbon emission rights according to the present invention.

[0056] Figure 3 This is a structural block diagram of a pumped storage capacity configuration device that takes into account extreme weather scenarios and carbon emission rights according to the present invention.

[0057] Figure 4 This is a flowchart illustrating the determination of the target configuration capacity of a distributed pumped storage power station based on the maximum net revenue in an embodiment of the present invention.

[0058] Figure 5 This is a schematic diagram illustrating the annual cost of a microgrid system under different pumped storage power station configuration capacities in embodiments of the present invention. Detailed Implementation

[0059] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0060] See Figure 1 This invention provides a method for configuring pumped storage capacity that considers extreme weather scenarios and carbon emission rights, comprising:

[0061] S1. Calculate the daily power supply adequacy index of the microgrid system and construct a power supply adequacy sample set; divide the power supply adequacy sample set into datasets with multiple power supply adequacy levels, and construct a benchmark scenario set and extreme weather scenario sets of different degrees; use median clustering technology to cluster the benchmark scenario set and extreme weather scenario sets of different degrees to obtain typical scenarios;

[0062] S2. Based on the configuration capacity of the distributed pumped storage power station, calculate the carbon emission rights quota and actual carbon emissions of the microgrid system in a typical scenario, calculate the carbon emission rights revenue of the microgrid system, and calculate the electricity purchase and sale revenue between the microgrid system and the upper-level power grid in a typical scenario and the variable cost after configuring the distributed pumped storage power station in the microgrid system.

[0063] S3. Establish a capacity configuration model for distributed pumped storage power stations in a microgrid system with the goal of maximizing net revenue, and determine the target configuration capacity of distributed pumped storage power stations based on the maximum net revenue; the net revenue is obtained through the electricity purchase and sale revenue between the microgrid system and the upper-level power grid, the carbon emission rights revenue of the microgrid system, and the variable costs after configuring distributed pumped storage power stations in the microgrid system.

[0064] This invention constructs typical extreme weather scenarios by clustering historical power supply adequacy indicators of the system under extreme weather conditions. It introduces a carbon emission trading mechanism and establishes a carbon emission rights revenue model for distributed pumped storage power stations in a microgrid system, used for the optimization analysis of distributed pumped storage power station capacity configuration. The model comprehensively considers the investment and operation costs of distributed pumped storage, system operating costs, revenue from power purchase and sale with the upper-level grid, and system carbon emission rights revenue. Under various system operating constraints, it determines the configuration capacity of distributed pumped storage power stations based on maximizing net revenue. This invention maximizes the absorption of new energy sources and reduces carbon emissions, while improving power supply reliability under extreme weather conditions, all while ensuring economic benefits.

[0065] Furthermore, the power supply adequacy index includes capacity adequacy and power supply adequacy, respectively:

[0066] ;

[0067] ;

[0068] In the formula, For the scene The corresponding microgrid system capacity adequacy; For thermal power units Maximum output; This represents the number of thermal power units. For the scene The next micro-network system At the moment of maximum load, thermal power units contribution; For microgrid system wind power Maximum power output; For the scene The next micro-network system The output of wind power generation at the moment of maximum load; For the scene The maximum load of the microgrid system; For the scene The corresponding power adequacy of the microgrid system; This refers to the runtime interval of the microgrid system, typically 1 hour. For the scene The next micro-network system Heavenly thermal power units contribution; For the scene The next micro-network system Heavenly Moment Wind Power The output power of electricity generation; For the scene Next Heavenly Load values ​​of the microgrid system at any given time; Total number of time periods;

[0069] The power supply adequacy sample set is as follows:

[0070] ;

[0071] In the formula, This is a sample set for the capacity adequacy of microgrid systems. This is a sample set of power adequacy for microgrid systems. The number of scenes.

[0072] The probability density function and cumulative probability distribution function of the power supply capacity adequacy and power adequacy of the microgrid system during the statistical analysis period are calculated using the following formulas:

[0073] ; ;

[0074] In the formula, Let be the probability density function of capacity adequacy. Let be the cumulative probability distribution function of capacity adequacy; Let be the probability density function of battery adequacy. Let be the cumulative probability distribution function of battery adequacy.

[0075] Based on the power supply capacity adequacy of the microgrid system, the capacity adequacy dataset is divided into four data sets with different capacity adequacy levels, including a baseline scenario that meets conventional reserve requirements. The microgrid system's power supply can meet the electricity demand, but it does not meet the backup requirements. The microgrid system's supply cannot meet the electricity demand, but the capacity of the interconnection lines with the upper-level power grid can meet the shortfall. The capacity of the upstream power grid interconnection lines is also insufficient to meet the power shortage. Specifically:

[0076] ;

[0077] Based on the power supply adequacy of the microgrid system, the power adequacy dataset is divided into four data sets with different power adequacy levels, including a baseline scenario that meets regular power reserve requirements. The backup power requirement is not met, but the microgrid system can meet the power demand. The microgrid system's power supply cannot meet the electricity demand, but the power transmission through the interconnection lines with the upper-level power grid can make up the shortfall. The upstream power grid interconnection lines also cannot meet the power shortage. Specifically:

[0078] ;

[0079] ;

[0080] ;

[0081] ;

[0082] Based on datasets of capacity sufficiency and power sufficiency levels, a baseline scenario set and a set of extreme weather scenarios of varying degrees are constructed, specifically:

[0083] ;

[0084] ;

[0085] ;

[0086] ;

[0087] ;

[0088] In the formula, As a set of baseline scenarios; A collection of mild extreme weather scenarios; This is a collection of moderate extreme weather scenarios; A collection of severe extreme weather scenarios; A collection of extremely severe weather scenarios; This is the first type of dataset with sufficient capacity; This is a dataset of the second level of sufficient capacity; This is a dataset representing the third level of sufficient capacity. For the capacity of the interconnection line between the microgrid system and the upper-level power grid; This represents the maximum load of the microgrid system. This is a dataset representing the fourth level of sufficiency. This is the dataset for the first level of sufficient power supply; This is a dataset representing the second level of sufficient battery power. This is a dataset representing the third level of sufficient power supply. This is the dataset for the fourth level of sufficient power supply.

[0089] The baseline scenario set and the sets of extreme weather scenarios of varying degrees can be represented as:

[0090] ;

[0091] Specifically, based on the visibility of the characteristics and patterns of extreme weather scenarios and the ease of computation, median clustering is used to cluster the baseline scenario and sets of extreme weather scenarios of different degrees. The typical scenarios after clustering are as follows:

[0092] ;

[0093] ;

[0094] In the formula, This is the set of baseline scenarios after clustering; The capacity adequacy of a typical capacity adequacy level scenario for a microgrid system under the benchmark scenario after clustering. The probability of a typical capacity adequacy level scenario for a microgrid system under the baseline scenario; The power adequacy level of a typical power adequacy scenario for a microgrid system under the benchmark scenario after clustering. The probability of a typical power adequacy level scenario for a microgrid system under the baseline scenario; This is a clustered set of mild extreme weather scenarios; The capacity adequacy of a microgrid system under typical capacity adequacy levels in mildly extreme scenarios after clustering. The probability of a typical capacity adequacy level scenario for a microgrid system under mild extreme weather conditions after clustering; The power adequacy level of a microgrid system under a typical power adequacy scenario in a mild extreme weather condition after clustering. The probability of a typical power adequacy level scenario for a microgrid system under mild extreme weather conditions after clustering; This is a clustered set of moderately extreme weather scenarios; The capacity adequacy of a typical capacity adequacy level scenario for a microgrid system under moderate extreme weather conditions after clustering. The probability of a typical capacity adequacy level scenario for a microgrid system under moderate extreme weather conditions after clustering; The power adequacy level of a microgrid system under a typical scenario of moderate extreme weather after clustering. The probability of a typical power sufficiency level scenario for a microgrid system under moderate extreme weather conditions after clustering; This is a clustered set of severe extreme weather scenarios; The capacity adequacy of a microgrid system under a typical capacity adequacy scenario in a severe extreme weather scenario after clustering. The probability of a typical capacity adequacy level scenario for a microgrid system under severe extreme weather conditions after clustering; The power adequacy level of a microgrid system under a typical power adequacy scenario in a severe extreme weather scenario after clustering. The probability of a typical power adequacy level scenario for a microgrid system under severe extreme weather conditions after clustering; This is a clustered set of extremely severe weather scenarios. The capacity adequacy of a microgrid system under a typical capacity adequacy scenario in an extremely severe weather scenario after clustering. The probability of a typical capacity adequacy level scenario for a microgrid system under extremely severe weather conditions after clustering; The power adequacy level of a microgrid system under a typical scenario of extremely severe weather after clustering. The probability of a typical power adequacy level scenario for a microgrid system under extremely severe weather conditions after clustering; The sufficiency of the sample size for the baseline scenario set; The battery adequacy of the baseline scenario set samples; The sufficiency of the sample size for mild extreme weather scenarios; Battery adequacy for a sample set of mild extreme weather scenarios; The adequacy of the sample size for moderate extreme weather scenarios; The power adequacy of a sample dataset representing moderate extreme weather scenarios; The sufficiency of the sample size for severe extreme weather scenarios; The battery adequacy of a sample dataset representing severe extreme weather scenarios; The sufficiency of the sample size for extremely severe weather scenarios; The battery adequacy of a sample dataset representing extremely severe weather scenarios.

[0095] Taking the benchmark scenario set as an example, two typical target scenarios and The calculation steps are as follows:

[0096] First, the sample Belonging to or The rules are as follows:

[0097] ;

[0098] Two typical target scenarios and The calculation formula is as follows:

[0099] ;

[0100] In the formula, for The number of random assignments at that time.

[0101] Furthermore, the carbon emission rights revenue of the microgrid system is as follows:

[0102] ;

[0103] ;

[0104] ;

[0105] ;

[0106] ;

[0107] ;

[0108] In the formula, For carbon emission rights revenue of microgrid systems; and These are the reservoir capacity of the distributed pumped storage power station and the rated power of the reversible generator unit, respectively. The trading price of carbon emission rights in the carbon market; For carbon emission credits; This refers to actual carbon emissions; For the carbon emission allowances of thermal power units in the microgrid system; Carbon emission credits for wind power in microgrid systems; Carbon emission allowances for distributed pumped storage power stations in microgrid systems; Carbon emission credits per unit of electricity generated by thermal power units; This represents the number of thermal power units. Typical scenario The probability of typical capacity sufficiency scenarios in microgrid systems; Typical scenario The typical capacity adequacy scenario of a microgrid system The active power of thermal power units at all times; Typical scenario The probability of typical power sufficiency scenarios in a microgrid system; Typical scenario The typical scenario of sufficient power supply in a microgrid system The active power of thermal power units at all times; Carbon emission credits per unit of wind power output; Typical scenario The typical capacity adequacy scenario of a microgrid system The active power of wind power at any given time; Typical scenario The typical scenario of sufficient power supply in a microgrid system The active power of wind power at any given time; Carbon emission credits per unit of electricity generated by distributed pumped storage power stations; Typical scenario The typical capacity adequacy scenario of a microgrid system Active power of distributed pumped storage power stations at all times; Typical scenario The typical scenario of sufficient power supply in a microgrid system Active power of distributed pumped storage power stations at all times; Carbon emissions per unit of thermal power generation.

[0109] The constraints include power output constraints for thermal power units, power output constraints for wind power, and operational constraints for distributed pumped storage power stations:

[0110] ;

[0111] In the formula, For thermal power units Rated power; For thermal power units Minimum technical output; This refers to the total capacity of wind power. For distributed pumped storage power stations in typical scenarios The typical capacity adequacy scenario of a microgrid system The amount of water used for power generation in the upper reservoir at any given time; For distributed pumped storage power stations in typical scenarios The typical scenario of sufficient power supply in a microgrid system The amount of water used for power generation in the upper reservoir at any given time; This refers to the runtime interval of the microgrid system, typically 1 hour. The efficiency of the generator units in a distributed pumped storage power station; This refers to the head height of a distributed pumped storage power station.

[0112] Furthermore, the revenue from electricity purchase and sale between the microgrid system and the upper-level power grid is as follows:

[0113] ;

[0114] ;

[0115] In the formula, The revenue from the purchase and sale of electricity between the microgrid system and the upper-level power grid; This refers to the electricity purchased and sold between the microgrid system and the upper-level power grid. The unit electricity price for a microgrid system to purchase and sell electricity from its superior power grid; Typical scenario The probability of typical capacity sufficiency scenarios in microgrid systems; Typical scenario The typical capacity adequacy scenario of a microgrid system The active power of thermal power units at all times; Typical scenario The typical capacity adequacy scenario of a microgrid system The active power of wind power at any given time; Typical scenario The typical capacity adequacy scenario of a microgrid system Active power of distributed pumped storage power stations at all times; Typical scenario The typical capacity adequacy scenario of a microgrid system Load of the microgrid system at any time; Typical scenario The probability of typical power sufficiency scenarios in a microgrid system; Typical scenario The typical scenario of sufficient power supply in a microgrid system The active power of thermal power units at all times; Typical scenario The typical scenario of sufficient power supply in a microgrid system The active power of wind power at any given time; Typical scenario The typical scenario of sufficient power supply in a microgrid system Active power of distributed pumped storage power stations at all times; Typical scenario The typical scenario of sufficient power supply in a microgrid system Load of the microgrid system at any time.

[0116] Furthermore, the variable cost after configuring distributed pumped storage power stations in the microgrid system. for:

[0117] ;

[0118] ;

[0119] ;

[0120] In the formula, Investment and operation costs for distributed pumped storage power stations; Fuel cost of thermal power units in a microgrid system; The unit investment and operation and maintenance cost of the reservoir; The unit investment and operation and maintenance cost of the generator set; The fixed annual operating fee rate for the reservoir; The fixed annual operating rate for generator sets; The discount rate; The lifespan of a distributed pumped storage power station; and These are the reservoir capacity of the distributed pumped storage power station and the rated power of the reversible generator unit, respectively. Typical scenario The probability of typical capacity sufficiency scenarios in microgrid systems; Typical scenario The typical capacity adequacy scenario of a microgrid system Fuel costs for thermal power units at all times; Typical scenario The probability of typical power sufficiency scenarios in a microgrid system; Typical scenario The typical scenario of sufficient power supply in a microgrid system Fuel costs for thermal power units at all times.

[0121] Furthermore, the objective function of the distributed pumped storage power station capacity configuration model in the microgrid system is:

[0122] ;

[0123] ;

[0124] ;

[0125] ;

[0126] In the formula, Net income.

[0127] For details, see Figure 4 The target configuration capacity of distributed pumped storage power stations is determined based on the maximum net income, including:

[0128] Step 1: Initialize the reservoir capacity of the distributed pumped storage power station ;

[0129] Step 2: Initialize the rated power of the reversible generator set of the distributed pumped storage power station. ;

[0130] Step 3: Construct typical scenarios and obtain a set of typical scenarios, including benchmark scenarios. Collection of mild extreme weather scenarios Collection of moderate extreme weather scenarios Collection of severe extreme weather scenarios Collection of extremely severe weather scenarios ;

[0131] Step 4: Calculate carbon emission allowances for microgrid systems under different typical scenarios. And the purchase and sale of electricity from the upper-level power grid ;

[0132] Step 5: Calculate the carbon emission rights revenue of the microgrid system. Revenue from purchasing and selling electricity Variable costs of configuring distributed pumped storage power stations Then calculate the distributed pumped storage configuration capacity. Corresponding system net income ;

[0133] Step 6: Determine the rated power of the distributed pumped-storage reversible generator set. Does iterate to the set maximum value? This refers to the maximum system load. Has an inflection point been reached to determine the maximum value? If so, proceed to the next step; otherwise, let... Then proceed to step 3;

[0134] Step 7: Select storage capacity The power of the distributed pumped-storage generator unit that generates the largest net income is denoted as . ;

[0135] Step 8: Determine the capacity of the distributed pumped storage reservoir. Does iterate to the set maximum value? This refers to the reservoir capacity corresponding to 12 hours of continuous power generation by distributed pumped-storage generator units. If yes, proceed to the next step; otherwise, let... Then proceed to step 2;

[0136] Step 9: Select the distributed pumped storage capacity corresponding to the scheme with the largest net benefit, i.e., the target configuration capacity.

[0137] This invention selects a distributed microgrid system in an industrial park. This area has a complex climate, prone to extreme low temperatures in winter (accompanied by a sharp drop in wind power output) and extreme rainstorms in summer (leading to interruptions in photovoltaic power output). Extreme weather events affect the system for an average of about 35 days annually, posing a significant threat to the system's power supply adequacy. The microgrid includes distributed photovoltaic, wind power, gas turbines, loads, and distributed pumped storage power stations, interconnected with the 110kV upper-level grid via tie lines, and possesses dual modes of "grid-connected operation and islanded standby." To improve power supply reliability under extreme weather conditions and introduce a carbon emission trading mechanism, the proposed method optimizes the capacity configuration of the distributed pumped storage power stations. Specific parameters are as follows:

[0138]

[0139] Based on historical daily meteorological data (temperature, wind speed, and rainfall) and microgrid operation data for the region, daily power supply capacity adequacy (CSA) and power adequacy (ESA) are calculated. Following the indicator classification criteria in the methodology, original datasets of the baseline scenario and mild, moderate, severe, and extremely severe extreme weather scenarios are obtained. Median clustering is employed, using CSA and ESA as the core clustering indicators. Balancing scenario visibility and computational convenience, the original scenario set is clustered and deduplicated, ultimately yielding five typical scenario categories. The parameters and probabilities of each scenario are shown in the table below.

[0140]

[0141] After clustering, the scene covers more than 98% of the features of the original data, which can accurately reflect the power supply sufficiency pattern of the system under different extreme weather conditions, and provide scene input for subsequent capacity configuration optimization.

[0142] By solving a pumped-storage power station capacity configuration model, the combination of pumped-storage capacity (0.5–6 MW) and maximum pumping duration (1–8 h) is simultaneously optimized to obtain the system's annual net benefit and support capacity in extreme scenarios corresponding to different combinations (with the gap filling rate for extremely severe weather scenarios as the core indicator). See [link / reference] Figure 5 The optimal configuration remains 2.0MW of pumped storage capacity + 6h of maximum pumping time, at which point the system’s annual net revenue is maximized (RMB 1.863 million), and the power shortage compensation rate reaches 92% under extremely severe weather scenarios, balancing economic benefits and power supply reliability.

[0143] The traditional configuration scheme, whose capacity configuration model excludes the impact of extreme weather and does not calculate carbon emission rights benefits, focuses solely on daily peak-valley arbitrage and has no requirements for power supply reliability or environmental assessment. A comparison with the configuration scheme of this invention is shown in the table below:

[0144]

[0145] Compared to traditional configuration schemes, under the same capacity, by optimizing the extraction time from 4 hours to 6 hours, it avoids the redundancy cost of 155,000 yuan / year and reduces emergency costs by 143,000 yuan. Combined with carbon emission rights benefits, the net benefit is nearly doubled, resulting in a significantly better return on investment than traditional schemes. Simultaneously, it enables continuous power supply support under extreme weather conditions, increasing the gap filling rate to 92% and the average power supply reliability to 99.8%. It also significantly reduces the start-up and shutdown frequency of gas turbines, reduces equipment wear, and further improves system operational stability. This capacity configuration scheme is more suitable for scenarios with large load fluctuations, including distributed power sources such as wind / solar power, frequent extreme weather events, and high requirements for power supply reliability. Typical projects include industrial park microgrids, new energy supporting microgrids, and remote area backup microgrids—projects with high self-balancing requirements—and have broad application value.

[0146] See Figure 2 The present invention also provides a pumped storage capacity configuration device that considers extreme weather scenarios and carbon emission rights. The device is used to implement the above-described pumped storage capacity configuration method that considers extreme weather scenarios and carbon emission rights. The device includes:

[0147] The typical scenario construction module is used to calculate the daily power supply adequacy index of the microgrid system and construct a power supply adequacy sample set. The power supply adequacy sample set is divided into datasets with multiple power supply adequacy levels, and a baseline scenario set and extreme weather scenario sets of different degrees are constructed. The median clustering technique is used to cluster the baseline scenario set and extreme weather scenario sets of different degrees to obtain typical scenarios.

[0148] The revenue and cost calculation module is used to calculate the carbon emission rights quota and actual carbon emissions of the microgrid system under typical scenarios based on the configuration capacity of the distributed pumped storage station, as well as to calculate the carbon emission rights revenue of the microgrid system, the electricity purchase and sale revenue between the microgrid system and the upper-level power grid under typical scenarios, and the variable costs after configuring the distributed pumped storage station in the microgrid system.

[0149] The target configuration capacity acquisition module is used to establish a capacity configuration model for distributed pumped storage power stations in a microgrid system with the goal of maximizing net revenue, and to determine the target configuration capacity of distributed pumped storage power stations based on the maximum net revenue; the net revenue is obtained through the electricity purchase and sale revenue between the microgrid system and the upper-level power grid, the carbon emission rights revenue of the microgrid system, and the variable costs after configuring distributed pumped storage power stations in the microgrid system.

[0150] See Figure 3 The present invention also provides a pumped storage capacity configuration device that takes into account extreme weather scenarios and carbon emission rights, including a memory and a processor;

[0151] The memory is used to store computer program code and transmit the computer program code to the processor;

[0152] The processor is configured to execute, according to instructions in the computer program code, the pumped storage capacity configuration method as described above, taking into account extreme weather scenarios and carbon emission rights.

[0153] The present invention also provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the pumped storage capacity configuration method considering extreme weather scenarios and carbon emission rights as described above.

[0154] Generally, the computer instructions for implementing the method of the present invention can be carried on any combination of one or more computer-readable storage media. Non-transitory computer-readable storage media can include any computer-readable medium except for the signal itself, which is temporarily propagating.

[0155] Computer-readable storage media can be, for example, but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatuses, or any combination thereof. More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EKROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0156] Computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. These programming languages ​​include object-oriented programming languages—such as Java, Smalltalk, and C++—as well as conventional procedural programming languages—such as the "C" language or similar programming languages. In particular, Python, suitable for neural network computation, and platform frameworks such as TensorFlow and PyTorch can be used. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer or to an external computer (e.g., via the Internet using an Internet service provider) through any type of network, including a local area network (LAN) or a wide area network (WAN).

[0157] The aforementioned equipment and non-transitory computer-readable storage media can be found in the detailed description of a pumped storage power station capacity configuration method that takes into account extreme weather scenarios and carbon emission rights, as well as its beneficial effects, which will not be repeated here.

[0158] Although embodiments of the present invention have been shown and described above, it should be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for configuring pumped storage capacity considering extreme weather scenarios and carbon emission rights, characterized in that, include: Calculate the daily power supply adequacy index of the microgrid system and construct a power supply adequacy sample set; The power supply sufficiency sample set was divided into datasets with multiple power supply sufficiency levels, and a set of benchmark scenarios and sets of extreme weather scenarios of different degrees were constructed. Median clustering was used to cluster the baseline scene set and the extreme weather scene set of different degrees to obtain typical scenes; Based on the configuration capacity of distributed pumped storage power stations, we calculate the carbon emission rights quota and actual carbon emissions of microgrid systems in typical scenarios, as well as the carbon emission rights revenue of microgrid systems, the electricity purchase and sale revenue between microgrid systems and the upper-level power grid in typical scenarios, and the variable costs after configuring distributed pumped storage power stations in microgrid systems. A capacity configuration model for distributed pumped storage power stations in a microgrid system is established with the goal of maximizing net revenue, and the target configuration capacity of distributed pumped storage power stations is determined based on the maximum net revenue. The net revenue is obtained through the electricity purchase and sale revenue between the microgrid system and the upper-level power grid, the carbon emission rights revenue of the microgrid system, and the variable costs after configuring distributed pumped storage power stations in the microgrid system.

2. The pumped storage capacity configuration method considering extreme weather scenarios and carbon emission rights according to claim 1, characterized in that, The power supply adequacy index includes capacity adequacy and power supply adequacy, which are respectively: ； ； In the formula, For the scene The corresponding microgrid system capacity adequacy; For thermal power units Maximum output; This represents the number of thermal power units. For the scene The next micro-network system At the moment of maximum load, thermal power units contribution; For microgrid system wind power Maximum power output; For the scene The next micro-network system The output of wind power generation at the moment of maximum load; For the scene The maximum load of the microgrid system; For the scene The corresponding power adequacy of the microgrid system; This refers to the runtime interval of the microgrid system. For the scene The next micro-network system Heavenly thermal power units contribution; For the scene The next micro-network system Heavenly Moment Wind Power The output power of electricity generation; For the scene Next Heavenly Load values ​​of the microgrid system at any given time; Total number of time periods; The power supply adequacy sample set is as follows: ； In the formula, This is a sample set for the capacity adequacy of microgrid systems. This is a sample set of power adequacy for microgrid systems. The number of scenes.

3. The pumped storage capacity configuration method considering extreme weather scenarios and carbon emission rights according to claim 2, characterized in that, The baseline scenario set and the extreme weather scenario sets of different degrees are as follows: ； ； ； In the formula, As a set of baseline scenarios; A collection of mild extreme weather scenarios; This is a collection of moderate extreme weather scenarios; A collection of severe extreme weather scenarios; A collection of extremely severe weather scenarios; This is the first type of dataset with sufficient capacity; This is a dataset of the second level of sufficient capacity; This is a dataset representing the third level of sufficient capacity. For the capacity of the interconnection line between the microgrid system and the upper-level power grid; This represents the maximum load of the microgrid system. This is a dataset representing the fourth level of sufficiency. This is the dataset for the first level of sufficient power supply; This is a dataset representing the second level of sufficient battery power. This is a dataset representing the third level of sufficient power supply. This is the dataset for the fourth level of sufficient power supply.

4. The pumped storage capacity configuration method considering extreme weather scenarios and carbon emission rights according to claim 3, characterized in that, The typical scenario is as follows: ； ； In the formula, This is the set of baseline scenarios after clustering; The capacity adequacy of a typical capacity adequacy level scenario for a microgrid system under the benchmark scenario after clustering. The probability of a typical capacity adequacy level scenario for a microgrid system under the baseline scenario; The power adequacy level of a typical power adequacy scenario for a microgrid system under the benchmark scenario after clustering. The probability of a typical power adequacy level scenario for a microgrid system under the baseline scenario; This is a clustered set of mild extreme weather scenarios; The capacity adequacy of a microgrid system under typical capacity adequacy levels in mildly extreme scenarios after clustering. The probability of a typical capacity adequacy level scenario for a microgrid system under mild extreme weather conditions after clustering; The power adequacy level of a microgrid system under a typical power adequacy scenario in a mild extreme weather condition after clustering. The probability of a typical power adequacy level scenario for a microgrid system under mild extreme weather conditions after clustering; This is a clustered set of moderately extreme weather scenarios; The capacity adequacy of a typical capacity adequacy level scenario for a microgrid system under moderate extreme weather conditions after clustering. The probability of a typical capacity adequacy level scenario for a microgrid system under moderate extreme weather conditions after clustering; The power adequacy level of a microgrid system under a typical scenario of moderate extreme weather after clustering. The probability of a typical power sufficiency level scenario for a microgrid system under moderate extreme weather conditions after clustering; This is a clustered set of severe extreme weather scenarios; The capacity adequacy of a microgrid system under a typical capacity adequacy scenario in a severe extreme weather scenario after clustering. The probability of a typical capacity adequacy level scenario for a microgrid system under severe extreme weather conditions after clustering; The power adequacy level of a microgrid system under a typical power adequacy scenario in a severe extreme weather scenario after clustering. The probability of a typical power adequacy level scenario for a microgrid system under severe extreme weather conditions after clustering; This is a clustered set of extremely severe weather scenarios. The capacity adequacy of a microgrid system under a typical capacity adequacy scenario in an extremely severe weather scenario after clustering. The probability of a typical capacity adequacy level scenario for a microgrid system under extremely severe weather conditions after clustering; The power adequacy level of a microgrid system under a typical scenario of extremely severe weather after clustering. The probability of a typical power adequacy level scenario for a microgrid system under extremely severe weather conditions after clustering; The sufficiency of the sample size for the baseline scenario set; The battery adequacy of the baseline scenario set samples; The sufficiency of the sample size for mild extreme weather scenarios; Battery adequacy for a sample set of mild extreme weather scenarios; The adequacy of the sample size for moderate extreme weather scenarios; The power adequacy of a sample dataset representing moderate extreme weather scenarios; The sufficiency of the sample size for severe extreme weather scenarios; The battery adequacy of a sample dataset representing severe extreme weather scenarios; The sufficiency of the sample size for extremely severe weather scenarios; The battery adequacy of a sample dataset representing extremely severe weather scenarios.

5. A method for configuring pumped storage capacity considering extreme weather scenarios and carbon emission rights according to claim 2, characterized in that, The carbon emission rights revenue of the microgrid system is: ； ； ； ； ； ； In the formula, For carbon emission rights revenue of microgrid systems; and These are the reservoir capacity of the distributed pumped storage power station and the rated power of the reversible generator unit, respectively. The trading price of carbon emission rights in the carbon market; For carbon emission credits; This refers to actual carbon emissions; For the carbon emission allowances of thermal power units in the microgrid system; Carbon emission credits for wind power in microgrid systems; Carbon emission allowances for distributed pumped storage power stations in microgrid systems; Carbon emission credits per unit of electricity generated by thermal power units; This represents the number of thermal power units. Typical scenario The probability of typical capacity sufficiency scenarios in microgrid systems; Typical scenario The typical capacity adequacy scenario of a microgrid system The active power of thermal power units at all times; Typical scenario The probability of typical power sufficiency scenarios in a microgrid system; Typical scenario The typical scenario of sufficient power supply in a microgrid system The active power of thermal power units at all times; Carbon emission credits per unit of wind power output; Typical scenario The typical capacity adequacy scenario of a microgrid system The active power of wind power at any given time; Typical scenario The typical scenario of sufficient power supply in a microgrid system The active power of wind power at any given time; Carbon emission credits per unit of electricity generated by distributed pumped storage power stations; Typical scenario The typical capacity adequacy scenario of a microgrid system Active power of distributed pumped storage power stations at all times; Typical scenario The typical scenario of sufficient power supply in a microgrid system Active power of distributed pumped storage power stations at all times; Carbon emissions per unit of thermal power generation.

6. A method for configuring pumped storage capacity considering extreme weather scenarios and carbon emission rights according to claim 2, characterized in that, The revenue from the purchase and sale of electricity between the microgrid system and the upper-level power grid is as follows: ； ； In the formula, The revenue from the purchase and sale of electricity between the microgrid system and the upper-level power grid; This refers to the electricity purchased and sold between the microgrid system and the upper-level power grid. The unit electricity price for a microgrid system to purchase and sell electricity from its superior power grid; Typical scenario The probability of typical capacity sufficiency scenarios in microgrid systems; Typical scenario The typical capacity adequacy scenario of a microgrid system The active power of thermal power units at all times; Typical scenario The typical capacity adequacy scenario of a microgrid system The active power of wind power at any given time; Typical scenario The typical capacity adequacy scenario of a microgrid system Active power of distributed pumped storage power stations at all times; Typical scenario The typical capacity adequacy scenario of a microgrid system Load of the microgrid system at any time; Typical scenario The probability of typical power sufficiency scenarios in a microgrid system; Typical scenario The typical scenario of sufficient power supply in a microgrid system The active power of thermal power units at all times; Typical scenario The typical scenario of sufficient power supply in a microgrid system The active power of wind power at any given time; Typical scenario The typical scenario of sufficient power supply in a microgrid system Active power of distributed pumped storage power stations at all times; Typical scenario The typical scenario of sufficient power supply in a microgrid system Load of the microgrid system at any time.

7. The pumped storage capacity configuration method considering extreme weather scenarios and carbon emission rights according to claim 1, characterized in that, The variable cost after configuring distributed pumped storage stations in the microgrid system for: ； ； ； In the formula, The investment and operation costs of distributed pumped storage power stations; Fuel cost of thermal power units in a microgrid system; The unit investment and operation and maintenance cost of the reservoir; The unit investment and operation and maintenance cost of the generator set; The fixed annual operating fee rate for the reservoir; The fixed annual operating rate for generator sets; The discount rate; The lifespan of a distributed pumped storage power station; and These are the reservoir capacity of the distributed pumped storage power station and the rated power of the reversible generator unit, respectively. Typical scenario The probability of typical capacity sufficiency scenarios in microgrid systems; Typical scenario The typical capacity adequacy scenario of a microgrid system Fuel costs for thermal power units at all times; Typical scenario The probability of typical power sufficiency scenarios in a microgrid system; Typical scenario The typical scenario of sufficient power supply in a microgrid system Fuel costs for thermal power units at all times.

8. A pumped storage capacity configuration device considering extreme weather scenarios and carbon emission rights, characterized in that, The apparatus is used to implement the method according to any one of claims 1 to 7, the apparatus comprising: The typical scenario construction module is used to calculate the daily power supply adequacy index of the microgrid system and construct a power supply adequacy sample set. The power supply adequacy sample set is divided into datasets with multiple power supply adequacy levels, and a baseline scenario set and extreme weather scenario sets of different degrees are constructed. The median clustering technique is used to cluster the baseline scenario set and extreme weather scenario sets of different degrees to obtain typical scenarios. The revenue and cost calculation module is used to calculate the carbon emission rights quota and actual carbon emissions of the microgrid system under typical scenarios based on the configuration capacity of the distributed pumped storage station, as well as to calculate the carbon emission rights revenue of the microgrid system, the electricity purchase and sale revenue between the microgrid system and the upper-level power grid under typical scenarios, and the variable costs after configuring the distributed pumped storage station in the microgrid system. The target configuration capacity acquisition module is used to establish a capacity configuration model for distributed pumped storage power stations in a microgrid system with the goal of maximizing net revenue, and to determine the target configuration capacity of distributed pumped storage power stations based on the maximum net revenue; the net revenue is obtained through the electricity purchase and sale revenue between the microgrid system and the upper-level power grid, the carbon emission rights revenue of the microgrid system, and the variable costs after configuring distributed pumped storage power stations in the microgrid system.

9. A pumped storage capacity configuration device considering extreme weather scenarios and carbon emission rights, characterized in that, Including memory and processor; The memory is used to store computer program code and transmit the computer program code to the processor; The processor is configured to execute the method as described in any one of claims 1 to 7 according to instructions in the computer program code.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the method as described in any one of claims 1 to 7.

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