Water-wind-light integrated base capacity optimization method considering extreme climate event risk

Abstract

The invention discloses a water-wind-light integrated base capacity optimization method considering extreme climate event risks. The method comprises the steps of obtaining multiple pieces of CMIP6 global climate mode data and meteorological and hydrological historical data; the CMIP6 global climate mode data comprises historical climate mode data and future climate mode data; based on the historical climate mode data and the meteorological and hydrological historical data, performing deviation correction on the future climate mode data to obtain corrected climate mode data; based on the corrected climate mode data, estimating future water, wind and light output and determining an extreme climate event; determining an extreme climate event risk based on the pre-estimated long-series power grid load and future water-wind-light output; taking extreme climate event risk minimization and full life cycle benefit maximization as targets, constructing a multi-target capacity optimization configuration model of the water-wind-light integrated base considering the extreme climate event risk, and determining a multi-target capacity optimization configuration strategy of the water-wind-light integrated base based on the capacity optimization configuration model.

Description

Technical Field

[0001] The present invention relates to the field of power system optimization and dispatching, and in particular to a method for optimizing the capacity of a water-wind-solar integrated base taking into account the risks of extreme climate events. Background Art

[0002] The impact of global warming on human health and ecosystems is increasing. If greenhouse gas emissions such as carbon dioxide are not reduced, global temperatures are expected to rise by at least 4°C by the end of the 21st century. As of 2023, the global average temperature is about 1.45°C higher than the pre-industrial level, approaching the limit set by the Paris Agreement on Climate Change. The intensification of the impact of climate change is driving the transformation of the power system to a low-carbon and clean one. Statistics from the National Energy Administration show that by the end of 2023, China's cumulative installed power generation capacity will be about 2.92 billion kW, and the cumulative installed power generation capacity of renewable energy will account for more than 50%. In the future, renewable energy will play an increasingly important role in the power system, but renewable energy is susceptible to changes in climate conditions. In addition to long-term changes in climate conditions, extreme climate events such as high temperatures, droughts, and cold waves caused by climate warming are frequent, widespread, strong, and concurrent. Renewable energy grids face the dual challenges of a surge in power load and insufficient power generation capacity. The resulting extreme climate events will seriously restrict the stability and reliability of grid operation.

[0003] As the power system becomes increasingly dependent on climate conditions, the system's operating characteristics are gradually changing. The development of renewable energy and the planning and construction of integrated water, wind and solar bases must take into account the power security constraints caused by the risks of future extreme climate events, and achieve multi-objective optimization of power resource capacity under the premise of ensuring climate safety. In recent years, considering the intermittent, volatile and random problems of wind and solar power generation, as well as the frequency-adjustable and peak-shaving characteristics of hydropower, domestic and foreign countries have focused on the research of integrated water, wind and solar power, using the complementarity of resources and the rapid adjustment capabilities of turbine units to offset the fluctuations of new energy power generation and form a high-quality and stable power supply, thereby meeting the grid's requirements for load stability.

[0004] However, most of the traditional capacity optimization configuration methods for integrated water, wind and solar bases are based on historical output data for capacity optimization configuration. Although historical data can reflect the operation status over a period of time in the past, it cannot accurately predict the future output change trend. In addition, the traditional capacity configuration method of multi-energy complementary systems does not fully consider the impact of climate change on system output and capacity configuration. This means that when facing extreme climate events, the existing capacity configuration may not meet the requirements for stable operation of the system, which poses certain risks.

[0005] Therefore, in order to improve the resilience of integrated water-wind-solar bases under climate risks and promote the large-scale development of renewable energy, a capacity optimization configuration method for integrated water-wind-solar bases that considers the risks of extreme climate events is urgently needed. Summary of the invention

[0006] In order to solve the above-mentioned problems existing in the prior art, the present invention provides a capacity optimization method for an integrated water-wind-solar base taking into account the risks of extreme climate events, which is intended to solve the technical problems in the design and implementation of renewable energy projects under complex climate conditions.

[0007] In order to achieve the above object, the technical solution of the embodiment of the present invention is:

[0008] In a first aspect, the present invention provides a method for optimizing the capacity of a water-wind-solar integrated base considering the risk of extreme climate events, the method comprising:

[0009] Acquire multiple CMIP6 global climate model data and meteorological and hydrological historical data; the CMIP6 global climate model data includes historical climate model data and future climate model data;

[0010] Based on the historical climate model data and the meteorological and hydrological historical data, the future climate model data is subjected to deviation correction to obtain corrected climate model data;

[0011] Based on the corrected climate model data, the future water, wind and solar power output is estimated;

[0012] determining extreme climate events based on the corrected climate model data;

[0013] Determine the risk of extreme climate events based on the long-term forecast of grid load and future hydropower, wind and solar output during the period of occurrence of the extreme climate events;

[0014] With the goal of minimizing the risk of extreme climate events and maximizing the benefits over the entire life cycle, a multi-objective capacity optimization configuration model for the integrated water, wind and solar base considering the risk of extreme climate events is constructed. Based on the capacity optimization configuration model, a multi-objective capacity optimization configuration strategy for the integrated water, wind and solar base is determined.

[0015] The first objective function for minimizing the risk of extreme climate events is: ; Where: I is the number of scenario modes; i is the time period; is the frequency of extreme climate events that lead to reduced output in period i; , , …, They are the probabilities of low output operation of hydropower, wind power and photovoltaic power under different extreme climate event conditions;

[0016] The second objective function of maximizing the benefits of the entire life cycle is:

[0017] ; Where: t is the time period; T is the project life cycle; r is the discount rate; For the revenue from electricity sales; for government subsidy revenue; For carbon trading revenue; Power station operating costs; Maintenance costs for power station facilities; The cost of the energy storage system; The construction cost of wind, solar and water storage facilities.

[0018] In a second aspect, the present invention provides a device for optimizing the capacity of a water-wind-solar integrated base taking into account the risk of extreme climate events, the device comprising:

[0019] An acquisition module is used to acquire a plurality of CMIP6 global climate model data and meteorological and hydrological historical data; the CMIP6 global climate model data includes historical climate model data and future climate model data;

[0020] A correction module, used for performing deviation correction on the future climate model data based on the historical climate model data and the meteorological and hydrological historical data to obtain corrected climate model data;

[0021] An estimation module, used for estimating future hydropower, wind power and photovoltaic power output based on the corrected climate model data;

[0022] A determination module, configured to determine an extreme climate event based on the corrected climate model data;

[0023] The determination module is further used to determine the risk of extreme climate events based on the estimated long series of grid loads and future water, wind and solar outputs during the period when the extreme climate events occur;

[0024] The determination module is also used to construct a multi-objective capacity optimization configuration model for a water-wind-solar integrated base taking into account the risk of extreme climate events with the goal of minimizing the risk of extreme climate events and maximizing the benefits over the entire life cycle, and determine a multi-objective capacity optimization configuration strategy for the water-wind-solar integrated base based on the capacity optimization configuration model;

[0025] The first objective function for minimizing the risk of extreme climate events is: ; Where: I is the number of scenario modes; i is the time period; is the frequency of extreme climate events that lead to reduced output in period i; , , …, They are the probabilities of low output operation of hydropower, wind power and photovoltaic power under different extreme climate event conditions;

[0026] The second objective function of maximizing the benefits of the entire life cycle is:

[0027] ; Where: t is the time period; T is the project life cycle; r is the discount rate; For the revenue from electricity sales; for government subsidy revenue; For carbon trading revenue; Power station operating costs; Maintenance costs for power station facilities; The cost of the energy storage system; The construction cost of wind, solar and water storage facilities.

[0028] In some embodiments, the correction module is also used to compare the historical climate model data with the historical meteorological and hydrological data to obtain meteorological data differences; based on the meteorological data differences, a quantile mapping method is used to perform bias correction on the future climate model data to obtain the corrected climate model data.

[0029] In some embodiments, the corrected climate model data includes target wind speed, target temperature, target solar radiation and target precipitation; the estimation module is further used to estimate future wind power output based on the target wind speed using a preset wind power output estimation model; estimate future photovoltaic output based on the target temperature and the target solar radiation using a preset photovoltaic output estimation model; estimate basin runoff based on the target precipitation and a preset monthly water balance model; and estimate future hydropower output based on the basin runoff using a preset water energy estimation model; wherein the preset wind power output estimation model is:

[0030] ; In the formula, is the wind power output in the i-th period; is the installed capacity of the wind farm; is the wind speed at the wind turbine in the i-th period; , and are the cut-in wind speed, cut-out wind speed and full-power wind speed of the wind turbine respectively; the preset photovoltaic output estimation model is: ; Where: is the actual average photovoltaic output in the i-th period; Installed capacity for photovoltaic power plants; is the solar radiation intensity in the i-th period; is the solar radiation intensity under standard test conditions; is the solar panel temperature; is the air temperature under standard test conditions; is the temperature-power conversion coefficient; the preset monthly water balance model is: ; In the formula, is the runoff in the nth month; is the soil moisture content at the end of last month; is the precipitation in the nth month; is the actual evaporation in the nth month; SC is the maximum water storage capacity of the basin; the preset water energy estimation model is: ; In the formula, is the efficiency coefficient of the hydropower station; H is the net water head of the hydropower station.

[0031] In some embodiments, the determination module is also used to determine the wind speed threshold, the air temperature threshold, the solar radiation threshold and the precipitation threshold based on the target wind speed, the target air temperature, the target solar radiation and the target precipitation; and determine the extreme climate event based on the wind speed threshold, the air temperature threshold, the solar radiation threshold and the precipitation threshold.

[0032] In some embodiments, the determination module is further used to calculate the occurrence frequency and duration of the extreme climate event; based on the occurrence frequency, the duration, the estimated long series grid load and the future water, wind and solar output, the extreme climate event risk is determined; wherein the calculation formula for the occurrence frequency is: , ; In the formula, is the number of occurrences of extreme climate events that meet the corresponding thresholds in the corresponding time period; To calculate the total duration; is the actual value of the target wind speed, target temperature, target solar radiation or target precipitation at time a; To determine whether the current climate at time a is the upper limit of extreme climate events; To determine whether the current climate at time a is the lower limit of an extreme climate event; F is the occurrence frequency of the corresponding extreme climate event; the calculation formula for the duration is: ; In the formula, is the duration of the kth extreme climate event; is the starting time of the kth extreme climate event; is the end time of the kth extreme climate event.

[0033] In some embodiments, the apparatus further comprises: a construction module, configured to construct constraints of the first objective function and the second objective function;

[0034] The constraints include: Wind power output constraints: ; In the formula, is the wind power output in the i-th period; The upper limit of wind power output;

[0035] Photovoltaic output constraints: ; In the formula, is the photovoltaic output in the i-th period; The upper limit of photovoltaic output;

[0036] Power abandonment rate constraints: ; In the formula, is the total wind and solar power generation in the period; is the total amount of wind and solar power abandoned during the period; is the wind and solar power abandonment rate allowed by the system;

[0037] Hydropower station output constraints: :In the formula, is the hydropower output in period i; , They are the lower and upper limits of the hydropower station’s output respectively;

[0038] Reservoir water level constraints: :In the formula, is the reservoir water level in the i-th period; , are the minimum and maximum limit water levels of the reservoir in the i-th period respectively;

[0039] Output balance constraints: :In the formula, The load demand of the power system;

[0040] Installed capacity constraints: :In the formula, , are the maximum capacity values ​​of wind turbines and photovoltaic power generation, respectively; , are the installed capacity of wind turbines and photovoltaic power generation respectively;

[0041] Power stability constraints: :In the formula, is the power generation at time a; is the power generation at time a-1; The maximum power change allowed in the power system.

[0042] In a third aspect, an embodiment of the present invention provides an electronic device, comprising: a memory for storing executable instructions; and a processor for implementing the above-mentioned method for optimizing the capacity of an integrated water-wind-solar base taking into account the risks of extreme climate events when executing the executable instructions stored in the memory.

[0043] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing executable instructions for causing a processor to execute the executable instructions to implement the above-mentioned method for optimizing the capacity of an integrated water-wind-solar base taking into account the risks of extreme climate events.

[0044] The method for optimizing the capacity of an integrated water-wind-solar base considering the risk of extreme climate events provided by the present invention first obtains a plurality of CMIP6 global climate model data and meteorological and hydrological historical data; based on the historical climate model data and the meteorological and hydrological historical data, bias correction is performed on future climate model data to obtain corrected climate model data; based on the corrected climate model data, future water-wind-solar output is estimated; based on the corrected climate model data, extreme climate events are determined; based on the estimated long series of power grid loads and future water-wind-solar outputs during the occurrence of extreme climate events, the risk of extreme climate events is determined; with the goal of minimizing the risk of extreme climate events and maximizing the benefits over the entire life cycle, a multi-objective capacity optimization configuration model for an integrated water-wind-solar base considering the risk of extreme climate events is constructed; based on the capacity optimization configuration model, a multi-objective capacity optimization configuration strategy for the integrated water-wind-solar base is determined. In this way, on the one hand, the water, wind and solar power output data calculated by the present invention using the future meteorological element data after deviation correction is reliable and can more accurately predict the future output change trend; on the other hand, the risk of extreme climate events is considered in the process of capacity optimization configuration, which improves the resistance of the power system under various climatic conditions and ensures the normal operation of the power system; on the third aspect, the capacity optimization configuration method of the water, wind and solar integrated base is oriented to different stakeholders such as power grids and power generation companies, and can maximize the benefits of the stakeholders while ensuring the safe and stable operation of the power system; on the fourth aspect, the present invention adopts a multi-objective optimization model, comprehensively considering the impact of future extreme climate event risks and full life cycle benefits, and realizes the optimization of the capacity configuration of the water, wind and solar integrated base. In addition, the scheme of the present invention is applicable to the capacity configuration problem of water, wind and solar integrated bases in different geographical locations and under different climatic conditions, and has strong versatility. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] Figure 1 is a schematic diagram of the structure of a water-wind-solar integrated base capacity optimization system considering the risk of extreme climate events provided by an embodiment of the present invention;

[0046] Figure 2 It is a flow chart of a method for optimizing the capacity of a water-wind-solar integrated base taking into account the risk of extreme climate events provided by an embodiment of the present invention;

[0047] Figure 3 It is a flow chart of a method for optimizing the capacity configuration of a water-wind-solar integrated base taking into account the risk of extreme climate events provided by an embodiment of the present invention;

[0048] Figure 4 It is a schematic diagram of the composition structure of a water-wind-solar integrated base capacity optimization device considering the risk of extreme climate events provided by an embodiment of the present invention;

[0049] Figure 5 It is a schematic diagram of the composition structure of a water-wind-solar integrated base capacity optimization device taking into account the risks of extreme climate events provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0050] In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings. The described embodiments should not be regarded as limiting the present invention. All other embodiments obtained by ordinary technicians in the field without making creative work are within the scope of protection of the present invention.

[0051] In the following description, reference is made to "some embodiments", which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict. Unless otherwise defined, all technical and scientific terms used in the embodiments of the present invention have the same meaning as those commonly understood by those skilled in the art to which the embodiments of the present invention pertain. The terms used in the embodiments of the present invention are only for the purpose of describing the embodiments of the present invention and are not intended to limit the present invention.

[0052] The following describes an exemplary application of a water-wind-solar integrated base capacity optimization device considering the risk of extreme climate events in an embodiment of the present invention. The water-wind-solar integrated base capacity optimization device considering the risk of extreme climate events provided by the embodiment of the present invention can be implemented as a terminal or a server. In one implementation, the water-wind-solar integrated base capacity optimization device considering the risk of extreme climate events provided by the embodiment of the present invention can be implemented as various types of terminals such as laptops, tablet computers, desktop computers, mobile devices, etc.; in another implementation, the water-wind-solar integrated base capacity optimization device considering the risk of extreme climate events provided by the embodiment of the present invention can also be implemented as a server, wherein the server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content distribution networks (CDN, Content Delivery Network), and big data and artificial intelligence platforms. The terminal and the server can be directly or indirectly connected by wired or wireless communication, which is not limited in the embodiment of the present invention. The following describes an exemplary application of the water-wind-solar integrated base capacity optimization device considering the risk of extreme climate events as a server.

[0053] See also Figure 1 , Figure 1 It is a structural schematic diagram of a water-wind-solar integrated base capacity optimization system 10 considering the risk of extreme climate events provided by an embodiment of the present invention. In order to optimize the capacity of a water-wind-solar integrated base considering the risk of extreme climate events, an embodiment of the present invention may provide a water-wind-solar integrated base capacity optimization platform considering the risk of extreme climate events, and the water-wind-solar integrated base capacity optimization platform considering the risk of extreme climate events may be implemented as a water-wind-solar integrated base capacity optimization application considering the risk of extreme climate events. The water-wind-solar integrated base capacity optimization system 10 considering the risk of extreme climate events provided by an embodiment of the present invention includes a terminal 110, a network 120 and a server 130, wherein the server 130 is a server of a water-wind-solar integrated base capacity optimization application considering the risk of extreme climate events. The server 130 may constitute a water-wind-solar integrated base capacity optimization device considering the risk of extreme climate events according to an embodiment of the present invention. The terminal 110 is connected to the server 130 via the network 120, and the network 120 may be a wide area network or a local area network, or a combination of the two.

[0054] In some embodiments, please refer to Figure 1 When optimizing the capacity of the integrated water, wind and solar base, the terminal 110 sends the capacity optimization task of the integrated water, wind and solar base to the server 130 through the network 120. The server 130 responds to the capacity optimization task of the integrated water, wind and solar base initiated by the terminal 110, obtains multiple CMIP6 global climate model data and meteorological and hydrological historical data; based on the historical climate model data and the meteorological and hydrological historical data, the future climate model data is bias-corrected to obtain the corrected climate model data; based on the corrected climate model, the future water, wind and solar output is estimated; based on the corrected climate model data, extreme climate events are calculated; based on the estimated long series of power grid loads and future water, wind and solar output during the occurrence of extreme climate events, the risk of extreme climate events is determined; with the goal of minimizing the power generation fluctuations caused by extreme climate events and maximizing the benefits of the entire life cycle, a multi-objective capacity optimization configuration model for the integrated water, wind and solar base considering the risk of extreme climate events is constructed, and based on the capacity optimization configuration model, a multi-objective capacity optimization configuration strategy for the integrated water, wind and solar base is determined. After obtaining the multi-objective capacity optimization configuration strategy for the integrated water-wind-solar base, the server 130 sends the multi-objective capacity optimization configuration strategy for the integrated water-wind-solar base to the terminal 110 through the network 120 .

[0055] The embodiment of the present invention provides a method for optimizing the capacity of a water-wind-solar integrated base considering the risk of extreme climate events, see Figure 2 , Figure 2This is a flow chart of a method for optimizing the capacity of a water-wind-solar integrated base considering the risk of extreme climate events provided by an embodiment of the present invention. Figure 2 The steps shown are explained.

[0056] Step S210, obtaining a plurality of CMIP6 global climate model data and meteorological and hydrological historical data; the CMIP6 global climate model data includes historical climate model data and future climate model data.

[0057] It should be noted that CMIP6 (Coupled Model Intercomparison Project Phase 6) is the sixth phase of the Global Climate Model Intercomparison Program, organized by the World Climate Research Program. CMIP6 provides historical and future climate simulation data from multiple global climate models for studying climate change and its impacts. These data include meteorological elements such as temperature, precipitation, wind speed, and solar radiation, covering the global range, and the time span from historical periods to different climate scenarios in the future.

[0058] In the present invention, the multiple CMIP6 global climate model data can be multiple sets of data from different scientific research institutions around the world (such as Beijing Climate Center (BCC), French National Meteorological Research Center (Centre Nationalde Recherches Météorologiques, CNRM), European Consortium-Earth System Model (EC-Earth), Goddard Institute for Space Studies (GISS), Institute of Numerical Mathematics (INM), Model for Interdisciplinary Research on Climate (MIROC)) and other institutional data.

[0059] In the embodiment of the present invention, the use of CMIP6 data can provide a reliable climate change background for the capacity optimization of the integrated water, wind and solar base. By using multiple CMIP6 model data, the uncertainty of a single model can be reduced and the accuracy of the prediction can be improved.

[0060] In some embodiments, meteorological and hydrological historical data refers to meteorological and hydrological observation data recorded in the past period of time, including precipitation, temperature, wind speed, runoff, reservoir water level, etc. These data are usually collected by observation equipment such as meteorological stations and hydrological stations, and are quality controlled and processed.

[0061] In the embodiment of the present invention, the historical meteorological and hydrological data are the basis for correcting the future climate model data, which can help eliminate the systematic deviations in the climate model and ensure the accuracy of future climate predictions.

[0062] Step S220, based on the historical climate model data and the meteorological and hydrological historical data, bias correction is performed on the future climate model data to obtain corrected climate model data.

[0063] In some embodiments, bias correction refers to comparing future climate model data with historical observation data to eliminate systematic biases in the climate model so that future climate predictions are closer to actual conditions. Here, the bias correction method includes at least linear scaling, quantile mapping, and the like.

[0064] Step S230, estimating future water, wind and solar power output based on the corrected climate model data.

[0065] In some embodiments, future hydropower output, future wind power output, and future photovoltaic output refer to outputs estimated based on corresponding hydropower output estimation models, wind power output estimation models, and photovoltaic output estimation models, respectively.

[0066] Step S240, determining extreme climate events based on the corrected climate model data.

[0067] In some embodiments, different extreme climate event types refer to extreme climates such as high temperature, drought and cold wave.

[0068] Step S250, determining the risk of extreme climate events based on the estimated long-series grid load and future water, wind and solar power output during the period when the extreme climate event occurs.

[0069] In some embodiments, the estimated long series grid load refers to the grid load estimated by a linear trend method.

[0070] In some embodiments, the risk of an extreme weather event refers to the ratio of the duration of power grid load loss during an extreme weather event to the total duration of the extreme weather event.

[0071] In some embodiments, extreme climate events can be determined by an extreme climate event risk assessment model, where the extreme climate event risk assessment model is a mathematical model for assessing the risk of extreme climate events based on historical climate pattern data, meteorological and hydrological historical data, and different extreme climate event types. The model generally includes parameters such as the probability of occurrence and impact of extreme climate events.

[0072] Step S260, with the goal of minimizing the risk of extreme climate events and maximizing the benefits over the entire life cycle, construct a multi-objective capacity optimization configuration model for the integrated water, wind and solar base taking into account the risk of extreme climate events, and determine the multi-objective capacity optimization configuration strategy for the integrated water, wind and solar base based on the capacity optimization configuration model.

[0073] Here, the first objective function for minimizing the risk of extreme climate events is: ; Where: I is the number of scenario modes; i is the time period; is the frequency of extreme climate events that lead to reduced output in period i; , , …, They are the probabilities of low output operation of hydropower, wind power and solar power under different extreme climate event conditions.

[0074] Here, the second objective function of maximizing the whole life cycle benefit is:

[0075] ; Where: t is the time period; T is the project life cycle; r is the discount rate; For the revenue from electricity sales; for government subsidy revenue; For carbon trading revenue; Power station operating costs; Maintenance costs for power station facilities; The cost of the energy storage system; The construction cost of wind, solar and water storage facilities.

[0076] In some embodiments, the multi-objective capacity optimization configuration model of the integrated water, wind and solar base is a mathematical model established with the goal of minimizing the risk of extreme climate events and maximizing the benefits of the entire life cycle. The model achieves the economy and reliability of the power system by optimizing the capacity configuration of water, wind, solar and energy storage.

[0077] In some embodiments, the first objective function aims to minimize the risk of extreme climate events and ensure the stability of the power system. The function quantifies the impact of power generation fluctuations by considering power generation output fluctuations under different extreme climate event scenarios.

[0078] In the present invention, the first objective function can effectively reduce the impact of extreme climate events on the power system and improve the system's ability to resist risks.

[0079] In some embodiments, the second objective function aims to maximize the full life cycle benefits of the integrated water, wind and solar base, including electricity sales revenue, government subsidy revenue, carbon trading revenue, etc., while taking into account the power station operation costs, maintenance costs, energy storage system costs and facility construction costs.

[0080] In the present invention, the second objective function can ensure the economic feasibility of the integrated water, wind and solar base, while also taking into account environmental effects and social benefits.

[0081] The method for optimizing the capacity of an integrated water-wind-solar base considering the risk of extreme climate events provided by the present invention first obtains a plurality of CMIP6 global climate model data and meteorological and hydrological historical data; based on the historical climate model data and the meteorological and hydrological historical data, bias correction is performed on future climate model data to obtain corrected climate model data; based on the corrected climate model data, future water-wind-solar output is estimated; based on the corrected climate model data, extreme climate events are determined; based on the estimated long series of power grid loads and future water-wind-solar outputs during the occurrence of extreme climate events, the risk of extreme climate events is determined; with the goal of minimizing the risk of extreme climate events and maximizing the benefits over the entire life cycle, a multi-objective capacity optimization configuration model for an integrated water-wind-solar base considering the risk of extreme climate events is constructed; based on the capacity optimization configuration model, a multi-objective capacity optimization configuration strategy for the integrated water-wind-solar base is determined. In this way, on the one hand, the water, wind and solar power output data calculated by the present invention using the future meteorological element data after deviation correction is reliable and can more accurately predict the future output change trend; on the other hand, the risk of extreme climate events is considered in the process of capacity optimization configuration, which improves the resistance of the power system under various climatic conditions and ensures the normal operation of the power system; on the third aspect, the capacity optimization configuration method of the water, wind and solar integrated base is oriented to different stakeholders such as power grids and power generation companies, and can maximize the benefits of the stakeholders while ensuring the safe and stable operation of the power system; on the fourth aspect, the present invention adopts a multi-objective optimization model, comprehensively considering the impact of future extreme climate event risks and full life cycle benefits, and realizes the optimization of the capacity configuration of the water, wind and solar integrated base. In addition, the scheme of the present invention is applicable to the capacity configuration problem of water, wind and solar integrated bases in different geographical locations and under different climatic conditions, and has strong versatility.

[0082] In some embodiments, the above step S220 can be implemented by following the steps S221 to S222:

[0083] Step S221, comparing the historical climate model data with the meteorological and hydrological historical data to obtain a meteorological data difference.

[0084] Step S222, based on the meteorological data difference, adopt the quantile mapping method to perform deviation correction on the future climate model data to obtain the corrected climate model data.

[0085] In some embodiments, the corrected climate model data includes target wind speed, target temperature, target solar radiation and target precipitation; the above step S230 can be implemented by the following steps S231 to S233:

[0086] Step S231 : based on the target wind speed, using a preset wind power output estimation model, estimating future wind power output.

[0087] Step S232, based on the target temperature and the target solar radiation, a preset photovoltaic output estimation model is used to estimate future photovoltaic output.

[0088] Step S233, based on the target precipitation and the preset monthly water balance model, the basin runoff is estimated; and based on the basin runoff, a preset water energy estimation model is used to estimate the future hydropower output.

[0089] Here, the preset wind power output estimation model is:

[0090] ; In the formula, is the wind power output in the i-th period; is the installed capacity of the wind farm; is the wind speed at the wind turbine in the i-th period; , and They are the cut-in wind speed, cut-out wind speed and full-power wind speed of the wind turbine respectively.

[0091] In the embodiment of the present invention, the preset wind power output estimation model takes into account the cut-in wind speed, cut-out wind speed and full-power wind speed of the wind turbine, and can more accurately reflect the output of the wind farm under different wind speed conditions. In particular, under extreme wind speed conditions, the model can effectively predict the output fluctuation of the wind farm and provide a reliable basis for capacity optimization.

[0092] Here, the preset photovoltaic output estimation model is: ; Where: is the actual average photovoltaic output in the i-th period; Installed capacity for photovoltaic power plants; is the solar radiation intensity in the i-th period; is the solar radiation intensity under standard test conditions; is the solar panel temperature; is the air temperature under standard test conditions; is the air temperature to power conversion coefficient.

[0093] In the embodiment of the present invention, the preset photovoltaic output estimation model not only takes into account the solar radiation intensity, but also introduces the solar panel temperature and air temperature power conversion coefficient, which can more accurately reflect the actual output of the photovoltaic power station. In particular, under extreme climate conditions of high or low temperature, the model can effectively predict the changes in photovoltaic output.

[0094] Here, the preset monthly water balance model is: ; In the formula, is the runoff in the nth month; is the soil moisture content at the end of last month; is the precipitation in the nth month; is the actual evaporation in the nth month; SC is the maximum water storage capacity of the basin.

[0095] In the embodiment of the present invention, the preset monthly water balance model can more accurately estimate the runoff of the basin by considering the soil moisture content, precipitation and actual evaporation at the end of the previous month. In particular, under extreme climate conditions such as drought or flood, the model can effectively reflect the change of runoff, thereby improving the estimation accuracy of hydropower output.

[0096] Here, the preset water energy estimation model is: ; In the formula, is the efficiency coefficient of the hydropower station; H is the net water head of the hydropower station.

[0097] In some embodiments, the above step S240 can be implemented by following the steps S241 to S242:

[0098] Step S241, determining a wind speed threshold, an air temperature threshold, a solar radiation threshold, and a precipitation threshold based on the target wind speed, the target air temperature, the target solar radiation, and the target precipitation.

[0099] Step S242, determining the extreme climate event based on the wind speed threshold, the air temperature threshold, the solar radiation threshold and the precipitation threshold.

[0100] In some embodiments, the above method further includes: calculating the occurrence frequency and duration of the extreme climate event. Based on the above embodiment, the above step S250 can also be implemented in the following manner:

[0101] The risk of extreme climate events is determined based on the occurrence frequency, the duration, the estimated long-term grid load and the future water, wind and solar power output.

[0102] In the present invention, the frequency and duration of extreme climate events refer to the number of extreme climate events such as drought, high temperature, cold wave and the duration of each event under future climate conditions. These parameters are important indicators for assessing the risk of extreme climate events.

[0103] In the present invention, data support can be provided for extreme climate event risk assessment by statistically analyzing the frequency and duration of extreme climate events.

[0104] Here, the calculation formula for the occurrence frequency is: , ; In the formula, is the number of occurrences of extreme climate events that meet the corresponding thresholds in the corresponding time period; To calculate the total duration; is the actual value of the target wind speed, target temperature, target solar radiation or target precipitation at time a; To determine whether the current climate at time a is the upper limit of extreme climate events; To determine whether the current climate at time a is the lower limit of an extreme climate event; F is the frequency of occurrence of the corresponding extreme climate event.

[0105] Here, the calculation formula of the duration is: ; In the formula, is the duration of the kth extreme climate event; is the starting time of the kth extreme climate event; is the end time of the kth extreme climate event.

[0106] In some embodiments, the method further includes constructing constraints for the first objective function and the second objective function. The constraints include:

[0107] Wind power output constraints: ; In the formula, is the wind power output in the i-th period; The upper limit of wind power output;

[0108] Photovoltaic output constraints: ; In the formula, is the photovoltaic output in the i-th period; The upper limit of photovoltaic output;

[0109] Power abandonment rate constraints: ; In the formula, is the total wind and solar power generation in the period; is the total amount of wind and solar power abandoned during the period; is the wind and solar power abandonment rate allowed by the system;

[0110] Hydropower station output constraints: :In the formula, is the hydropower output in period i; , They are the lower and upper limits of the hydropower station’s output respectively;

[0111] Reservoir water level constraints: :In the formula, is the reservoir water level in the i-th period; , are the minimum and maximum limit water levels of the reservoir in the i-th period respectively;

[0112] Output balance constraints: :In the formula, The load demand of the power system;

[0113] Installed capacity constraints: :In the formula, , are the maximum capacity values ​​of wind turbines and photovoltaic power generation, respectively; , are the installed capacity of wind turbines and photovoltaic power generation respectively;

[0114] Power stability constraints: :In the formula, is the power generation at time a; is the power generation at time a-1; The maximum power change allowed in the power system.

[0115] The following is an explanation of an exemplary application of an embodiment of the present application in a practical application scenario.

[0116] In order to improve the resistance of the integrated water-wind-solar base under climate risks and promote the large-scale development of renewable energy, the present invention proposes a flow chart of a method for optimizing the capacity of the integrated water-wind-solar base considering the risk of extreme climate events. Figure 3 As shown, the specific implementation steps of the method include:

[0117] Step S31, estimating water, wind and solar power output under CMIP6 climate model.

[0118] CMIP6 climate model data is the latest global climate model simulation result. First, by selecting the historical and future data of daily wind speed, temperature, solar radiation and precipitation elements in 5 CMIP6 global climate models, according to the historical meteorological and hydrological data of meteorological stations, the quantile mapping method (Quantile Mapping, QM) is used to correct the deviation of CMIP6 climate model data, eliminate the difference between model simulation and observation data, make it more consistent with local historical meteorological observation data, and ensure high-precision meteorological element prediction. Among them, the quantile mapping method is to correct the cumulative probability distribution of the predicted value according to the cumulative probability distribution of the observed value. In the present invention, the formula of the quantile mapping method is as follows:

[0119] ;

[0120] Where: is the corrected predicted sequence; is the predicted sequence before correction; is the specific value in the predicted sequence; The inverse function of the cumulative distribution function measured at the rate period is used to convert the cumulative probability back to the original value; is the cumulative distribution function of the rate measured periodically; Cumulative distribution function for the rate-periodic forecast series.

[0121] Based on the corrected data, the wind power output model and the photovoltaic output calculation model are used to estimate the wind power output process from 2035 to 2065. The wind power output calculation formula (i.e., the preset wind power output estimation model in the above embodiment) is as follows:

[0122] ;

[0123] Where: is the wind power output in the i-th period; is the installed capacity of the wind farm; is the wind speed at the wind turbine in the i-th period; , and They are the cut-in wind speed, cut-out wind speed and full-power wind speed of the wind turbine respectively.

[0124] The photovoltaic output calculation formula (i.e., the preset photovoltaic output estimation model in the above embodiment) is as follows:

[0125] ;

[0126] Where: is the actual average photovoltaic output in the i-th period; Installed capacity for photovoltaic power plants; is the solar radiation intensity in the i-th period; is the solar radiation intensity under standard test conditions; is the solar panel temperature; is the air temperature under standard test conditions; is the air temperature to power conversion coefficient.

[0127] The calculation of hydropower output can be done by using a two-parameter monthly water balance model to estimate the basin runoff, and then using the water energy formula to estimate the hydropower output. The core idea of ​​the monthly water balance model is to simulate the water balance process of the basin by considering factors such as precipitation, evaporation and the water storage capacity of the basin. The two main parameters of the monthly water balance model include parameters C and SC. C represents the water storage capacity or regulation capacity of the basin, which is related to the soil moisture and vegetation coverage of the basin. SC represents the maximum water storage capacity of the basin, which is closely related to the topography, geology and land use characteristics of the basin. The basic form of the monthly water balance model (i.e., the preset monthly water balance model in the above embodiment) is:

[0128] ;

[0129] In the formula, is the runoff in the nth month; is the soil moisture content at the end of last month; is the precipitation in the nth month; is the actual evaporation in the nth month; SC is the maximum water storage capacity of the basin.

[0130] The water energy formula (i.e., the preset water energy estimation model in the above embodiment) is as follows:

[0131] ;

[0132] In the formula, is the efficiency coefficient of the hydropower station; H is the net water head of the hydropower station.

[0133] Step S32, calculating the risk of extreme climate events in the integrated water, wind and solar base.

[0134] Based on the bias-corrected CMIP6 climate model wind speed, temperature, solar radiation and precipitation data from 2035 to 2065, the thresholds of wind speed, solar radiation and other data are determined to identify "no wind", "no sun" and "no wind and no sun" events, and calculate the frequency and duration of different extreme climate events. The calculation formulas for the frequency and duration of different extreme climate events are as follows:

[0135] The frequency calculation formula is: , ; In the formula, is the number of occurrences of extreme climate events that meet the corresponding thresholds in the corresponding time period; To calculate the total duration; is the actual value of the target wind speed, target temperature, target solar radiation or target precipitation at time a; To determine whether the current climate at time a is the upper limit of extreme climate events; To determine whether the current climate at time a is the lower limit of an extreme climate event; F is the frequency of occurrence of the corresponding extreme climate event.

[0136] The calculation formula for duration is: ; In the formula, is the duration of the kth extreme climate event; is the starting time of the kth extreme climate event; is the end time of the kth extreme climate event.

[0137] Afterwards, the long-term grid demand (grid load) is estimated by the linear trend method, and the risk of load loss is calculated by comparing the output at the corresponding time (i.e., the future hydropower output, future wind power output, and future photovoltaic output in the above embodiment) with the load. Combined with the frequency and duration of extreme climate events, different types of extreme climate events are identified and their occurrence probabilities are calculated, thereby achieving the purpose of quantifying the risks of extreme climate events.

[0138] Finally, a risk assessment model for extreme climate events is established by combining climate data, historical data, and the identification of extreme climate events. The frequency of occurrence of different extreme climate events (such as high temperature, drought, cold wave, etc.) is analyzed, and the risk of extreme climate events is estimated through extreme value theory (EVT).

[0139] It should be noted that extreme value theory is an important branch of statistics, which mainly focuses on the probability distribution and properties of extreme climate events. EVT can accurately measure the risk value of extreme climate events in the tail distribution and has been widely used in the field of natural disaster prediction.

[0140] In some embodiments, load loss risk refers to the risk that the power system cannot meet user demand due to insufficient power generation output or excessive grid load.

[0141] In the present invention, the risk of load loss is calculated by comparing future hydropower output, future wind power output and future photovoltaic output with the load, taking into account the volatility of power generation output and changes in grid load.

[0142] Step S33, multi-objective capacity optimization configuration of the integrated water-wind-solar base considering the risk of extreme climate events.

[0143] First, establish the capacity optimization configuration principle, including the feasibility rule, which can be understood as ensuring that the designed system is technically and environmentally feasible, including full utilization of resources, environmental impact assessment and technical feasibility verification; economic rule, which can be understood as ensuring that the design and operation of the system have good economic benefits, can achieve cost minimization and benefit maximization; volatility rule, which can be understood as considering the uncertainty and volatility of renewable energy output, the need to balance supply and demand through appropriate design; load matching rule, which can be understood as ensuring that the output of the power generation system can effectively meet the power needs of users and avoid supply and demand imbalance. After that, establish a multi-objective capacity optimization configuration model for the integrated water, wind and solar base considering the risk of climate events. The first objective function, that is, minimizing the risk of extreme climate events, is to quantify the operational risk caused by extreme climate events through the comprehensive risk rate.

[0144] here, ;

[0145] Where: I is the number of scenario modes; i is the time period; is the frequency of extreme climate events that lead to reduced output in period i; , , …, They are the probabilities of low output operation of hydropower, wind power and solar power under different extreme climate event conditions.

[0146] Maximizing the life cycle benefit objective function, that is, the second objective function is:

[0147] ;

[0148] Where: t is the time period; T is the life of the project cycle; r is the discount rate; For the revenue from electricity sales; for government subsidy revenue; For the revenue from electricity sales; for government subsidy revenue; For carbon trading revenue; Power station operating costs; Maintenance costs for power station facilities; The cost of the energy storage system; The construction cost of wind, solar and water storage facilities.

[0149] The constraints include:

[0150] Wind power output constraints: ; In the formula, is the wind power output in the i-th period; It is the upper limit of wind power output.

[0151] Photovoltaic output constraints: ; In the formula, is the photovoltaic output in the i-th period; It is the upper limit of photovoltaic output.

[0152] Power abandonment rate constraints: ; In the formula, is the total wind and solar power generation in the period; is the total amount of wind and solar power abandoned during the period; is the wind and solar power abandonment rate allowed by the system.

[0153] Hydropower station output constraints: :In the formula, is the hydropower output in period i; , It is the lower and upper limits of the hydropower station output.

[0154] Reservoir water level constraints: :In the formula, is the reservoir water level in the i-th period; , is the minimum and maximum water levels of the reservoir in the i-th period. Output balance constraint: :In the formula, The load demand of the power system.

[0155] Installed capacity constraints: :In the formula, , are the maximum capacity values ​​of wind turbines and photovoltaic power generation, respectively; , They are the installed capacity of wind turbines and photovoltaic power generation respectively.

[0156] Power stability constraints: :In the formula, is the power generation at time a; is the power generation at time a-1; The maximum power change allowed in the power system.

[0157] In the embodiment of the present invention, based on typical water, wind and solar power output data, the second generation non-dominated sorting genetic algorithm (NSGA-Ⅱ) is used to solve the Pareto solution set of the mathematical model of the water, wind and solar integrated operation system. Then, according to the model output scheme, the analytic hierarchy process (AHP) is used to consider the preferences of different decision makers and formulate different capacity optimization configuration schemes. The analytic hierarchy process divides various complex indicators into ordered and relevant indicators by establishing a multi-level structure. The analytic hierarchy process compares the importance of indicator factors pairwise, optimizes the weight assignment process between indicators, and increases the objectivity and logic of decision-making judgments between multiple indicators. Finally, by comparing the capacity ratios of other water, wind and solar integrated projects, the risk reduction and benefit improvement of the capacity optimization configuration of this study are calculated to achieve the purpose of optimal configuration.

[0158] The NSGA-Ⅱ algorithm is known for its efficiency in handling large populations and its ability to maintain solution diversity. The NSGA-Ⅱ algorithm uses a fast non-dominated sorting method, elitism, and crowding distance mechanisms to ensure a good distribution of the Pareto front. The basic steps of the NSGA-Ⅱ algorithm include: initializing the population, evaluating the fitness function, non-dominated sorting, calculating the crowding distance, selecting parents for crossover, crossover / mutation operations, and iteration.

[0159] Figure 4 is a schematic diagram of the composition structure of a water-wind-solar integrated base capacity optimization device considering the risk of extreme climate events provided by an embodiment of the present invention, such as Figure 4As shown, the water-wind-solar integrated base capacity optimization device 400 considering the risk of extreme climate events includes: an acquisition module 401, used to acquire multiple CMIP6 global climate model data and meteorological and hydrological historical data; the CMIP6 global climate model data includes historical climate model data and future climate model data; a correction module 402, used to perform deviation correction on the future climate model data based on the historical climate model data and the meteorological and hydrological historical data to obtain corrected climate model data; an estimation module 403, used to estimate the future water-wind-solar power output based on the corrected climate model data; a determination module 404, also The method is used to determine extreme climate events based on the corrected climate model data; the determination module 404 is also used to determine the risk of extreme climate events based on the estimated long-series power grid load and the future water, wind and solar output during the occurrence of the extreme climate event; the determination module 404 is also used to construct a multi-objective capacity optimization configuration model for a water-wind-solar integrated base considering the risk of extreme climate events with the goal of minimizing the risk of extreme climate events and maximizing the benefits of the entire life cycle, and determine the multi-objective capacity optimization configuration strategy for the water-wind-solar integrated base based on the capacity optimization configuration model; wherein the first objective function for minimizing the risk of extreme climate events is: ; Where: I is the number of scenario modes; i is the time period; is the frequency of extreme climate events that lead to reduced output in period i; , , …, They are the probabilities of low output operation of hydropower, wind power and photovoltaic power under different extreme climate event conditions; the second objective function for maximizing the benefits of the entire life cycle is: ; Where: t is the time period; T is the project life cycle; r is the discount rate; For the revenue from electricity sales; for government subsidy revenue; For carbon trading revenue; Power station operating costs; Maintenance costs for power station facilities; The cost of the energy storage system; The construction cost of wind, solar and water storage facilities.

[0160] It should be noted that the description of the device of the embodiment of the present invention is similar to the description of the above method embodiment, and has similar beneficial effects as the same method embodiment, so it will not be repeated. For technical details not disclosed in the embodiment of the device, please refer to the description of the method embodiment of the present invention for understanding.

[0161] It should be noted that in the embodiments of the present invention, if the above-mentioned water-wind-solar integrated base capacity optimization method considering the risk of extreme climate events is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the relevant technology, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions for a terminal to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM, Read Only Memory), a magnetic disk or an optical disk. In this way, the embodiments of the present invention are not limited to any specific combination of hardware and software.

[0162] Correspondingly, an embodiment of the present invention provides an electronic device, Figure 5 FIG. 1 is a schematic diagram of the structure of an electronic device provided by an embodiment of the present invention. Figure 5 As shown, the electronic device 500 at least includes: a processor 501 and a computer-readable storage medium 502 configured to store executable instructions, wherein the processor 501 generally controls the overall operation of the electronic device 500. The computer-readable storage medium 502 is configured to store instructions and applications executable by the processor 501, and can also cache data to be processed or processed by the processor 501 and each module in the electronic device 500, which can be implemented by flash memory (FLASH) or random access memory (RAM, Random Access Memory).

[0163] An embodiment of the present invention provides a storage medium storing executable instructions, wherein the executable instructions are stored. When the executable instructions are executed by a processor, the processor will be caused to execute the method provided by the embodiment of the present invention, for example, Figure 2 The method shown.

[0164] In some embodiments, the storage medium can be a computer-readable storage medium, for example, a ferroelectric random access memory (FRAM), a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a flash memory, a magnetic surface memory, an optical disk, or a compact disk read-only memory (CD-ROM); it can also be various devices including one or any combination of the above memories.

[0165] In some embodiments, executable instructions may be in the form of a program, software, software module, script or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and may be deployed in any form, including as a stand-alone program or as a module, component, subroutine or other unit suitable for use in a computing environment.

[0166] As an example, executable instructions may, but need not correspond to, a file in a file system, may be stored as part of a file storing other programs or data, such as one or more scripts in a Hyper Text Markup Language (HTML) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files storing one or more modules, subroutines, or code portions). As an example, executable instructions may be deployed to be executed on one electronic device, or on multiple electronic devices located at one location, or on multiple electronic devices distributed at multiple locations and interconnected by a communication network.

[0167] The above description is only an embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement and improvement made within the spirit and scope of the present invention are included in the protection scope of the present invention.

[0168] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present invention. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in various embodiments of the present invention, the size of the serial number of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present invention. The serial numbers of the above-mentioned embodiments of the present invention are only for description and do not represent the advantages and disadvantages of the embodiments.

[0169] It should be noted that, in this article, the terms "comprise", "include" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method or device. In the absence of further restrictions, an element defined by the statement "comprises one..." does not exclude the presence of other identical elements in the process, method, article or device including the element. In several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed.

[0170] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A method for optimizing the capacity of a water-wind-solar integrated base considering the risk of extreme climate events, characterized in that: The method comprises: Acquire multiple CMIP6 global climate model data and meteorological and hydrological historical data; the CMIP6 global climate model data includes historical climate model data and future climate model data; Based on the historical climate model data and the meteorological and hydrological historical data, the future climate model data is subjected to deviation correction to obtain corrected climate model data; Based on the corrected climate model data, the future water, wind and solar power output is estimated; determining extreme climate events based on the corrected climate model data; Determine the risk of extreme climate events based on the long-term forecast of grid load and future hydropower, wind and solar output during the period of occurrence of the extreme climate events; With the goal of minimizing the risk of extreme climate events and maximizing the benefits over the entire life cycle, a multi-objective capacity optimization configuration model for the integrated water, wind and solar base considering the risk of extreme climate events is constructed. Based on the capacity optimization configuration model, a multi-objective capacity optimization configuration strategy for the integrated water, wind and solar base is determined. The first objective function for minimizing the risk of extreme climate events is: ; Where: I is the number of scenario modes; i is the time period; is the frequency of extreme climate events that lead to reduced output in period i; , , …, They are the probabilities of low output operation of hydropower, wind power and photovoltaic power under different extreme climate event conditions; The second objective function of maximizing the benefits of the entire life cycle is: ; Where: t is the time period; T is the project life cycle; r is the discount rate; For the revenue from electricity sales; for government subsidy revenue; For carbon trading revenue; Power station operating costs; Maintenance costs for power station facilities; The cost of the energy storage system; The construction cost of wind, solar and water storage facilities.

2. The method according to claim 1, characterized in that The method of performing deviation correction on the future climate model data based on the historical climate model data and the meteorological and hydrological historical data to obtain corrected climate model data includes: Comparing the historical climate model data with the historical meteorological and hydrological data to obtain a meteorological data difference; Based on the meteorological data difference, the future climate model data is bias-corrected by using the quantile mapping method to obtain the corrected climate model data.

3. The method according to claim 1, characterized in that The corrected climate model data includes target wind speed, target temperature, target solar radiation and target precipitation; The prediction of future water, wind and solar power output based on the corrected climate model data includes: Based on the target wind speed, a preset wind power output estimation model is used to estimate future wind power output; Based on the target temperature and the target solar radiation, a preset photovoltaic output estimation model is used to estimate future photovoltaic output; Based on the target precipitation and the preset monthly water balance model, the basin runoff is estimated; and based on the basin runoff, the future hydropower output is estimated using the preset water energy estimation model; Wherein, the preset wind power output estimation model is: ; In the formula, is the wind power output in the i-th period; is the installed capacity of the wind farm; is the wind speed at the wind turbine in the i-th period; , and are the cut-in wind speed, cut-out wind speed and full-power wind speed of the wind turbine respectively; The preset photovoltaic output estimation model is: ; Where: is the actual average photovoltaic output in the i-th period; Installed capacity for photovoltaic power plants; is the solar radiation intensity in the i-th period; is the solar radiation intensity under standard test conditions; is the solar panel temperature; is the air temperature under standard test conditions; is the air temperature power conversion coefficient; The preset monthly water balance model is: ; In the formula, is the runoff in the nth month; is the soil moisture content at the end of last month; is the precipitation in the nth month; is the actual evaporation in the nth month; SC is the maximum water storage capacity of the basin; The preset water energy estimation model is: ; In the formula, is the efficiency coefficient of the hydropower station; H is the net water head of the hydropower station.

4. The method according to claim 3, characterized in that The step of determining an extreme climate event based on the corrected climate model data comprises: Determining a wind speed threshold, an air temperature threshold, a solar radiation threshold, and a precipitation threshold based on the target wind speed, the target air temperature, the target solar radiation, and the target precipitation; The extreme climate event is determined based on the wind speed threshold, the air temperature threshold, the solar radiation threshold and the precipitation threshold.

5. The method according to claim 4, characterized in that The method further comprises: Calculate the frequency and duration of the extreme climate events; The determination of the risk of extreme climate events based on the estimated long-term grid load and the future hydropower, wind power and solar power output during the period when the extreme climate events occur includes: Determine the risk of the extreme climate event based on the occurrence frequency, the duration, the estimated long-term grid load and the future water, wind and solar output; The calculation formula for the occurrence frequency is: , ; In the formula, is the number of occurrences of extreme climate events that meet the corresponding thresholds in the corresponding time period; To calculate the total duration; is the actual value of the target wind speed, target temperature, target solar radiation or target precipitation at time a; To determine whether the current climate at time a is the upper limit of extreme climate events; is to judge whether the current climate at time a is the lower limit of extreme climate events; F is the occurrence frequency of the corresponding extreme climate events; The calculation formula for the duration is: ; In the formula, is the duration of the kth extreme climate event; is the starting time of the kth extreme climate event; is the end time of the kth extreme climate event.

6. The method according to claim 1, characterized in that The method further comprises: Constructing constraints of the first objective function and the second objective function; The constraints include: Wind power output constraints: ; In the formula, is the wind power output in the i-th period; The upper limit of wind power output; Photovoltaic output constraints: ; In the formula, is the photovoltaic output in the i-th period; The upper limit of photovoltaic output; Power abandonment rate constraints: ; In the formula, is the total wind and solar power generation in the period; is the total amount of wind and solar power abandoned during the period; is the wind and solar power abandonment rate allowed by the system; Hydropower station output constraints: :In the formula, is the hydropower output in the i-th period; , They are the lower and upper limits of the hydropower station’s output respectively; Reservoir water level constraints: :In the formula, is the reservoir water level in the i-th period; , are the minimum and maximum limit water levels of the reservoir in the i-th period respectively; Output balance constraints: :In the formula, The load demand of the power system; Installed capacity constraints: :In the formula, , are the maximum capacity values ​​of wind turbines and photovoltaic power generation, respectively; , are the installed capacity of wind turbines and photovoltaic power generation respectively; Power stability constraints: :In the formula, is the power generation at time a; is the power generation at time a-1; The maximum power change allowed in the power system.

7. A device for optimizing the capacity of a water-wind-solar integrated base taking into account the risk of extreme climate events, characterized in that: The device comprises: An acquisition module is used to acquire a plurality of CMIP6 global climate model data and meteorological and hydrological historical data; the CMIP6 global climate model data includes historical climate model data and future climate model data; A correction module, used for performing deviation correction on the future climate model data based on the historical climate model data and the meteorological and hydrological historical data to obtain corrected climate model data; An estimation module, used for estimating future hydropower, wind power and photovoltaic power output based on the corrected climate model data; A determination module, configured to determine an extreme climate event based on the corrected climate model data; The determination module is further used to determine the risk of extreme climate events based on the estimated long series of grid loads and future water, wind and solar outputs during the period when the extreme climate events occur; The determination module is also used to construct a multi-objective capacity optimization configuration model for a water-wind-solar integrated base taking into account the risk of extreme climate events with the goal of minimizing the risk of extreme climate events and maximizing the benefits over the entire life cycle, and determine a multi-objective capacity optimization configuration strategy for the water-wind-solar integrated base based on the capacity optimization configuration model; The first objective function for minimizing the risk of extreme climate events is: ; Where: I is the number of scenario modes; i is the time period; is the frequency of extreme climate events that lead to reduced output in period i; , , …, They are the probabilities of low output operation of hydropower, wind power and photovoltaic power under different extreme climate event conditions; The second objective function of maximizing the benefits of the entire life cycle is: ; Where: t is the time period; T is the project life cycle; r is the discount rate; For the revenue from electricity sales; for government subsidy revenue; For carbon trading revenue; Power station operating costs; Maintenance costs for power station facilities; The cost of the energy storage system; The construction cost of wind, solar and water storage facilities.

8. An electronic device, characterized in that: include: A memory for storing executable instructions; The processor is used to implement the capacity optimization method of the integrated water-wind-solar base considering the risks of extreme climate events as described in any one of claims 1 to 6 when executing the executable instructions stored in the memory.

9. A computer-readable storage medium, characterized in that: Executable instructions are stored, which are used to cause the processor to execute the executable instructions to implement the water-wind-solar integrated base capacity optimization method considering the risks of extreme climate events as described in any one of claims 1 to 6.

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