Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a water-wind-solar integrated base capacity optimization method considering the risk of extreme climate events, which aims to solve the technical problems in the design and implementation of renewable energy projects under complex climate conditions.
In order to achieve the above object, the technical solution of the embodiment of the present invention is:
in a first aspect, the present invention provides a method for optimizing the capacity of a water-wind-solar integrated base taking into account the risk of extreme weather events, the method comprising:
Acquiring a plurality of CMIP6 global climate mode data and meteorological hydrologic historical data, wherein the CMIP6 global climate mode data comprises historical climate mode data and future climate mode data;
performing deviation correction on the future climate pattern data based on the historical climate pattern data and the meteorological hydrology historical data to obtain corrected climate pattern data;
estimating future water wind power based on the corrected climate mode data;
Determining an extreme climate event based on the corrected climate pattern data;
Determining an extreme climate event risk based on the estimated long-series grid load and the future water-wind-light output during the occurrence of the extreme climate event;
Constructing a multi-target capacity optimization configuration model of the water-wind-solar integrated base taking the extreme climate event risk into consideration by taking the extreme climate event risk minimization and the full life cycle benefit maximization as targets, and determining a multi-target capacity optimization configuration strategy of the water-wind-solar integrated base based on the capacity optimization configuration model;
Wherein the first objective function of the extreme climate event risk minimization is: wherein I is the number of contextual models, I is the period; The occurrence frequency of extreme climate events which lead to reduced output in the period i is the same;、、...、 the probability of low-output running of the water, wind and light under the weather event conditions of different polar ends is respectively shown;
The second objective function of full life cycle benefit maximization is:
Wherein, T is time period, T is project period life, r is discount rate; the method is an electricity selling benefit; Benefit subsidized for government; Revenue for carbon trade; The running cost of the power station; Maintenance costs for the power plant facility; Is the cost of the energy storage system; is the construction cost of the wind-light water storage facility.
In a second aspect, the present invention provides a water and wind integrated base capacity optimization device taking into account the risk of extreme weather events, the device comprising:
The acquisition module is used for acquiring a plurality of CMIP6 global climate mode data and meteorological hydrologic historical data, wherein the CMIP6 global climate mode data comprises historical climate mode data and future climate mode data;
the correction module is used for carrying out deviation correction on the future climate pattern data based on the historical climate pattern data and the meteorological hydrology historical data to obtain corrected climate pattern data;
the estimating module is used for estimating the future water wind photoelectric output based on the corrected climate mode data;
a determination module for determining an extreme climate event based on the corrected climate pattern data;
the determining module is further used for determining the risk of the extreme weather event based on the estimated long-series power grid load and the future water-wind-light output force during the occurrence period of the extreme weather event;
The determining module is further used for constructing a multi-target capacity optimizing configuration model of the water-wind-solar integrated base taking the extreme climate event risk into consideration with the aim of minimizing the extreme climate event risk and maximizing the full life cycle benefit, and determining a multi-target capacity optimizing configuration strategy of the water-wind-solar integrated base based on the capacity optimizing configuration model;
Wherein the first objective function of the extreme climate event risk minimization is: wherein I is the number of contextual models, I is the period; The occurrence frequency of extreme climate events which lead to reduced output in the period i is the same;、、...、 the probability of low-output running of the water, wind and light under the weather event conditions of different polar ends is respectively shown;
The second objective function of full life cycle benefit maximization is:
Wherein, T is time period, T is project period life, r is discount rate; the method is an electricity selling benefit; Benefit subsidized for government; Revenue for carbon trade; The running cost of the power station; Maintenance costs for the power plant facility; Is the cost of the energy storage system; is the construction cost of the wind-light water storage facility.
In some embodiments, the correction module is further configured to compare the historical climate model data with the weather hydrologic historical data to obtain a weather data difference value, and based on the weather data difference value, correct the future climate pattern data by using a quantile mapping method to obtain the corrected climate pattern data.
In some embodiments, the corrected climate pattern data includes a target wind speed, a target air temperature, a target solar radiation and a target precipitation amount, the estimating module is further configured to estimate a future wind power output by using a preset wind power output estimating model based on the target wind speed, estimate a future photovoltaic output by using a preset photovoltaic output estimating model based on the target air temperature and the target solar radiation, estimate a river basin runoff amount by using a preset water power estimating model based on the target precipitation amount and a preset monthly water amount balancing model, and estimate a future water power output by using a preset water power estimating model based on the river basin runoff amount, wherein the preset wind power output estimating model is as follows:
In the formula (I), in the formula (II),Wind power generation is carried out for the ith period; is the installed capacity of the wind power plant; Wind speed at the wind turbine for the i-th period;、 AndThe wind turbine wind speed pre-set photovoltaic output estimation model comprises the following steps of: Wherein: the actual average output force of the photovoltaic in the ith period; the installed capacity of the photovoltaic power station; Solar radiation intensity for the i-th period; is the solar radiation intensity under standard test conditions; the temperature of the solar panel; Air temperature under standard test conditions; the preset lunar water balance model is as follows: In the formula (I), in the formula (II),Is the runoff of the nth month; the water content of the soil at the end of the last month; precipitation for the nth month; the method comprises the steps of obtaining a preset water energy prediction model, wherein the preset water energy prediction model is as follows: In the formula (I), in the formula (II),Is the efficiency coefficient of the hydropower station; H is the water purifying head of the hydropower station.
In some embodiments, the determining module is further configured to determine a wind speed threshold, a 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, and determine the extreme weather event based on the wind speed threshold, the air temperature threshold, the solar radiation threshold, and the precipitation threshold.
In some embodiments, the determining module is further configured to calculate an occurrence frequency and a duration of the extreme weather event, determine the risk of the extreme weather event based on the occurrence frequency, the duration, the estimated long-series grid load, and the future water-wind-light output, wherein a calculation formula of the occurrence frequency is as follows:, In the formula (I), in the formula (II),The occurrence times of the extreme weather event meeting the corresponding threshold value in the corresponding time period; Counting total time length; The actual value of the target wind speed, the target air temperature, the target solar radiation or the target precipitation amount at the moment a; judging whether the current climate at the time a is the upper limit value of an extreme climate event or not; In order to judge whether the current climate at the time a is the lower limit value of the extreme climate event, F is the occurrence frequency of the corresponding extreme climate event, and the calculation formula of the duration time is as follows: In the formula (I), in the formula (II),Is the duration of the kth extreme weather event; the starting time of the kth extreme climate event; is the termination time of the kth extreme weather event.
In some embodiments, the apparatus further comprises a building module for building constraints of the first objective function and the second objective function;
the constraint conditions comprise wind power output constraint: In the formula (I), in the formula (II),Wind power generation is carried out for the ith period; The upper limit of wind power output is set;
photoelectric output constraint: In the formula (I), in the formula (II),The photoelectric output is the ith period; Is the upper limit of photoelectric output;
Discarding the constraint of the electricity rate: In the formula (I), in the formula (II),The total generating capacity of wind and light in a period; the total wind and light discarding electric quantity in the period is used; the wind and light rejection rate is allowed for the system;
hydropower station output constraint: In the process of the formula (I),Water power for the i-th period;、 the lower output limit and the upper output limit of the hydropower station are respectively;
Reservoir level constraint: In the process of the formula (I),Reservoir level for the ith period;、 The minimum limit water level and the maximum limit water level of the reservoir in the ith period are respectively;
force balance constraint: In the process of the formula (I),Load demand for the power system;
and (3) binding installed capacity: In the process of the formula (I),、The maximum capacity values of the wind turbine generator and the photovoltaic power generation are respectively;、 The installed capacities of the wind turbine generator and the photovoltaic power generation are respectively set;
Electric quantity stability constraint: In the process of the formula (I),The generated power is the generated power at the time a; The generated power is the generated power at the time a-1; the maximum amount of variation allowed for the power system.
In a third aspect, an embodiment of the present invention provides an electronic device, including a memory configured to store executable instructions, and a processor configured to implement the above-mentioned method for optimizing a capacity of a water-wind-solar integrated base, in consideration of a risk of an extreme weather event, when executing the executable instructions stored in the memory.
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 capacity of a water-wind-solar integrated base considering the risk of an extreme weather event.
The capacity optimization method of the water-wind-solar integrated base considering the risk of the extreme climate event comprises the steps of firstly obtaining a plurality of pieces of CMIP6 global climate mode data and meteorological hydrology historical data, carrying out deviation correction on future climate mode data based on the historical climate mode data and the meteorological hydrology historical data to obtain corrected climate mode data, estimating future water-wind-solar output based on the corrected climate mode data, determining the extreme climate event based on the corrected climate mode data, determining the risk of the extreme climate event based on the estimated long-series grid load and the future water-wind-solar output during the occurrence period of the extreme climate event, constructing a multi-target capacity optimization configuration model of the water-wind-solar integrated base considering the risk of the extreme climate event based on the capacity optimization configuration model, and determining a multi-target capacity optimization configuration strategy of the water-wind-solar integrated base based on the capacity optimization configuration model. The water-wind-solar integrated base capacity optimization configuration method is oriented to different benefit subjects such as a power grid and a power generation enterprise, can ensure safe and stable operation of the power system and maximize benefit subjects, and in the fourth aspect, a multi-objective optimization model is adopted, influences of two aspects of future extreme weather event risks and full life cycle benefits are comprehensively considered, and optimization of the water-wind-solar integrated base capacity configuration is achieved. In addition, the scheme of the invention is suitable for the capacity configuration problem of the water-wind-light integrated base under different geographic positions and different climatic conditions, and has stronger universality.
Detailed Description
The present invention will be further described in detail with reference to the accompanying drawings, for the purpose of making the objects, technical solutions and advantages of the present invention more apparent, and the described embodiments should not be construed as limiting the present invention, and all other embodiments obtained by those skilled in the art without making any inventive effort are within the scope of the present invention.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is to be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with one another without conflict. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which embodiments of this invention belong. The terminology used in the embodiments of the invention is for the purpose of describing embodiments of the invention only and is not intended to be limiting of the invention.
The following describes an exemplary application of the water-wind-solar integrated base capacity optimization device considering the risk of an extreme weather event according to the embodiment of the present invention, where the water-wind-solar integrated base capacity optimization device considering the risk of an extreme weather event provided by the embodiment of the present invention may be implemented as a terminal or as a server. In one implementation manner, the water-wind-solar integrated base capacity optimization device considering the risk of the extreme weather event provided by the embodiment of the invention can be implemented as various types of terminals such as a notebook computer, a tablet computer, a desktop computer, a mobile device and the like, and in another implementation manner, the water-wind-solar integrated base capacity optimization device considering the risk of the extreme weather event provided by the embodiment of the invention can also be implemented as a server, wherein the server can be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and can also be a cloud server for providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content distribution networks (CDNs, content Delivery Network), basic cloud computing services such as big data and artificial intelligent platforms and the like. The terminal and the server may be directly or indirectly connected through wired or wireless communication, which is not limited in the embodiment of the present invention. In the following, an exemplary application when the water-wind integrated base capacity optimization device taking into account the risk of extreme climate events is a server will be described.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a water-wind-solar integrated base capacity optimization system 10 that considers the risk of extreme climate events according to an embodiment of the present invention. In order to optimize the capacity of the water-wind-light integrated base taking the risk of the extreme climate event into consideration, the embodiment of the invention can provide a water-wind-light integrated base capacity optimization platform taking the risk of the extreme climate event into consideration, and the water-wind-light integrated base capacity optimization platform taking the risk of the extreme climate event into consideration can be applied to the capacity optimization of the water-wind-light integrated base taking the risk of the extreme climate event into consideration. The system 10 for optimizing the capacity of the water-wind-solar integrated base taking the risk of the extreme weather event into consideration comprises a terminal 110, a network 120 and a server 130, wherein the server 130 is a server for optimizing the capacity of the water-wind-solar integrated base taking the risk of the extreme weather event into consideration. The server 130 may constitute a water and wind integrated base capacity optimization device that accounts for the risk of extreme climate events in accordance with an embodiment of the present invention. Terminal 110 is connected to server 130 via network 120. Network 120 may be a wide area network or a local area network, or a combination of both.
In some embodiments, referring to fig. 1, when performing capacity optimization configuration on a water-wind-solar integrated base, a terminal 110 sends a capacity optimization task of the water-wind-solar integrated base to a server 130 through a network 120, the server 130 responds to the capacity optimization task of the water-wind-solar integrated base initiated by the terminal 110 to obtain a plurality of pieces of CMIP6 global climate mode data and meteorological hydrologic historical data, performs bias correction on future climate mode data based on the historical climate mode data and the meteorological hydrologic historical data to obtain corrected climate mode data, predicts future water-wind-solar power based on the corrected climate mode, calculates extreme climate events based on the corrected climate mode data, determines a risk of the extreme climate events based on predicted long-series grid load and future water output during the occurrence of the extreme climate events, and constructs a multi-objective capacity optimization configuration model of the water-wind-solar integrated base taking the risk of the extreme climate events into consideration by taking the extreme climate events to minimize and maximizing full life cycle benefits as targets. After obtaining the multi-objective capacity optimization configuration strategy of the water-wind-solar integrated base, the server 130 sends the multi-objective capacity optimization configuration strategy of the water-wind-solar integrated base to the terminal 110 through the network 120.
The embodiment of the invention provides a water-wind-solar integrated base capacity optimization method considering extreme weather event risks, and referring to fig. 2, fig. 2 is a schematic flow chart of the water-wind-solar integrated base capacity optimization method considering extreme weather event risks, and the method is described with reference to steps shown in fig. 2.
Step S210, acquiring a plurality of CMIP6 global climate pattern data and meteorological hydrologic historical data, wherein the CMIP6 global climate pattern data comprises historical climate pattern data and future climate pattern data.
Note that CMIP6 (Coupled Model Intercomparison Project Phase) is the sixth phase of the global climate pattern comparison program, organized by the world climate research program. CMIP6 provides historical and future climate simulation data for a number of global climate patterns for studying climate change and its effects. The data comprise meteorological factors such as air temperature, precipitation, wind speed, solar radiation and the like, and cover the whole world, and the time span is from a historical period to different weather scenes in the future.
In the present invention, the plurality of CMIP6 global climate pattern data may be a plurality of sets of institutional data from different global research institutions (e.g., beijing climate center (beijin CLIMATE CENTER, BCC), french National DE RECHERCHES M, orologiques, CNRM), european Earth system Model alliance (European Consortium-EARTH SYSTEM Model, EC-Earth), goldade space institute (Goddard Institute for Space Studies, GISS), numerical math institute (Institute of Numerical Mathematics, INM), climate interdisciplinary research Model (Model for INTERDISCIPLINARY RESEARCH on Climate, MIROC)), and the like.
In the embodiment of the invention, the CMIP6 data can provide a reliable climate change background for capacity optimization of the water-wind-solar integrated base. By using multiple CMIP6 mode data, single mode uncertainty can be reduced, improving prediction accuracy.
In some embodiments, weather-hydrologic history refers to weather and hydrologic observations recorded over a period of time, including precipitation, air temperature, wind speed, runoff, reservoir level, and the like. Such data is typically collected by observers of weather stations, hydrologic stations, etc., and subjected to quality control and processing.
In the embodiment of the invention, the weather hydrologic historical data is the basis for correcting the future climate mode data, so that systematic deviation in the climate mode can be eliminated, and the accuracy of future climate prediction is ensured.
And step S220, performing deviation correction on the future climate pattern data based on the historical climate pattern data and the meteorological hydrologic historical data to obtain corrected climate pattern data.
In some embodiments, bias correction refers to comparing future climate pattern data with historical observations, eliminating systematic bias in climate patterns, making future climate predictions closer to reality. Here, the offset correction method includes at least linear scaling, quantile mapping, and the like.
And step S230, estimating the future water-wind-light output based on the corrected climate mode data.
In some embodiments, the future hydroelectric power output, the future wind power output and the future photovoltaic power output are estimated based on a corresponding hydroelectric power output estimation model, a wind power output estimation model and a photovoltaic power output estimation model respectively.
Step S240, determining an extreme climate event based on the corrected climate pattern data.
In some embodiments, different extreme climate event types refer to extreme climates such as high temperature, drought, and cold.
Step S250, determining an extreme climate event risk based on the estimated long-series grid load and the future water-wind-light output during the occurrence of the extreme climate event.
In some embodiments, estimating a long-series grid load refers to estimating the grid load by a linear trend approach.
In some embodiments, the extreme climate event risk refers to a ratio of a grid dead-time period during which the extreme climate event occurs to a total duration of the extreme climate event.
In some embodiments, the extreme climate event may be determined by an extreme climate event risk assessment model, where the extreme climate event risk assessment model is a mathematical model established to assess the risk of the extreme climate event based on historical climate pattern data, weather hydrologic historical data, and different extreme climate event types. The model typically includes parameters such as probability of occurrence, degree of influence, etc. of the extreme climate event.
And step S260, constructing a multi-target capacity optimization configuration model of the water-wind-solar integrated base taking the extreme climate event risk into consideration with the aim of minimizing the extreme climate event risk and maximizing the full life cycle benefit, and determining a multi-target capacity optimization configuration strategy of the water-wind-solar integrated base based on the capacity optimization configuration model.
Here, the first objective function of the extreme climate event risk minimization is: wherein I is the number of contextual models, I is the period; The occurrence frequency of extreme climate events which lead to reduced output in the period i is the same;、、...、 the probability of the water, wind, light and electricity low-output operation under the weather event conditions of different polar ends is respectively shown.
Here, the second objective function of full life cycle benefit maximization is:
Wherein, T is time period, T is project period life, r is discount rate; the method is an electricity selling benefit; Benefit subsidized for government; Revenue for carbon trade; The running cost of the power station; Maintenance costs for the power plant facility; Is the cost of the energy storage system; is the construction cost of the wind-light water storage facility.
In some embodiments, the water-wind-solar integrated base multi-objective capacity optimization configuration model is a mathematical model established with the aim of minimizing the risk of extreme climate events and maximizing the full life cycle benefit. The model realizes the economy and reliability of the power system by optimizing the capacity configuration of water, wind, light and energy storage.
In some embodiments, the first objective function is intended to minimize the risk of extreme weather events, ensuring the stability of the power system. The function quantifies the influence of power generation fluctuation by considering power generation output fluctuation under different polar climate event scenes.
In the invention, the first objective function can effectively reduce the influence of the extreme climate event on the power system and improve the risk resistance of the system.
In some embodiments, the second objective function is directed to maximizing the full life cycle benefits of the water and wind integrated base, including electricity sales benefits, government subsidy benefits, carbon trade benefits, and the like, while taking into account plant operating costs, maintenance costs, energy storage system costs, and facility construction costs.
In the invention, the second objective function can ensure the economic feasibility of the water-wind-solar integrated base, and simultaneously consider the environmental effect and the social benefit.
The capacity optimization method of the water-wind-solar integrated base considering the risk of the extreme climate event comprises the steps of firstly obtaining a plurality of pieces of CMIP6 global climate mode data and meteorological hydrology historical data, carrying out deviation correction on future climate mode data based on the historical climate mode data and the meteorological hydrology historical data to obtain corrected climate mode data, estimating future water-wind-solar output based on the corrected climate mode data, determining the extreme climate event based on the corrected climate mode data, determining the risk of the extreme climate event based on the estimated long-series grid load and the future water-wind-solar output during the occurrence period of the extreme climate event, constructing a multi-target capacity optimization configuration model of the water-wind-solar integrated base considering the risk of the extreme climate event based on the capacity optimization configuration model, and determining a multi-target capacity optimization configuration strategy of the water-wind-solar integrated base based on the capacity optimization configuration model. The water-wind-solar integrated base capacity optimization configuration method is oriented to different benefit subjects such as a power grid and a power generation enterprise, can ensure safe and stable operation of the power system and maximize benefit subjects, and in the fourth aspect, a multi-objective optimization model is adopted, influences of two aspects of future extreme weather event risks and full life cycle benefits are comprehensively considered, and optimization of the water-wind-solar integrated base capacity configuration is achieved. In addition, the scheme of the invention is suitable for the capacity configuration problem of the water-wind-light integrated base under different geographic positions and different climatic conditions, and has stronger universality.
In some embodiments, the step S220 may be implemented by the following steps S221 to S222:
Step S221, comparing the historical climate model data with the meteorological hydrologic historical data to obtain meteorological data difference values.
Step S222, based on the meteorological data difference value, performing deviation correction on the future climate pattern data by adopting a quantile mapping method to obtain corrected climate pattern data.
In some embodiments, the corrected climate pattern data includes a target wind speed, a target air temperature, a target solar radiation, and a target precipitation amount, and the above step S230 may be implemented by the following steps S231 to S233:
Step S231, based on the target wind speed, a preset wind power output estimation model is adopted to estimate future wind power output.
Step S232, based on the target air temperature and the target solar radiation, a preset photovoltaic output estimation model is adopted to estimate future photoelectric output.
And S233, estimating the river basin runoff based on the target precipitation and a preset monthly water balance model, and estimating the future hydropower output by adopting a preset hydropower estimation model based on the river basin runoff.
Here, the preset wind power output estimation model is as follows:
In the formula (I), in the formula (II),Wind power generation is carried out for the ith period; is the installed capacity of the wind power plant; Wind speed at the wind turbine for the i-th period;、 AndThe cut-in wind speed, cut-out wind speed and full wind speed of the wind turbine are respectively.
In the embodiment of the invention, the preset wind power output estimation model considers the cut-in wind speed, the cut-out wind speed and the full-wind speed of the wind turbine, and can more accurately reflect the output condition of the wind power plant under different wind speed conditions. Particularly, under the condition of extreme wind speed, the model can effectively predict the output fluctuation of the wind power plant and provide a reliable basis for capacity optimization.
Here, the preset photovoltaic output estimation model is: Wherein: the actual average output force of the photovoltaic in the ith period; the installed capacity of the photovoltaic power station; Solar radiation intensity for the i-th period; is the solar radiation intensity under standard test conditions; the temperature of the solar panel; Air temperature under standard test conditions; Is the air temperature power conversion coefficient.
In the embodiment of the invention, the preset photovoltaic output estimation model not only considers the solar radiation intensity, but also introduces the temperature and the air temperature power conversion coefficient of the solar panel, and can more accurately reflect the actual output condition of the photovoltaic power station. Particularly, under the extreme climate conditions of high temperature or low temperature, the model can effectively predict the change of the photovoltaic output.
Here, the preset lunar water balance model is: In the formula (I), in the formula (II),Is the runoff of the nth month; the water content of the soil at the end of the last month; precipitation for the nth month; is the actual evaporation capacity of the nth month, and SC is the maximum water storage capacity of the basin.
In the embodiment of the invention, the preset lunar water balance model can more accurately estimate the river basin runoff by considering the soil water content, the precipitation and the actual evaporation at the last month. Particularly, under drought or flood extreme climate conditions, the model can effectively reflect the change of runoff, and further the estimation accuracy of hydroelectric power output is improved.
Here, the preset water energy prediction model is as follows: In the formula (I), in the formula (II),Is the efficiency coefficient of the hydropower station; H is the water purifying head of the hydropower station.
In some embodiments, the above step S240 may be implemented by the following steps S241 to S242:
Step S241, determining a wind speed threshold value, an air temperature threshold value, a solar radiation threshold value, and a precipitation amount threshold value based on the target wind speed, the target air temperature, the target solar radiation, and the target precipitation amount.
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.
In some embodiments, the above method further comprises calculating the frequency and duration of occurrence of the extreme weather event. Based on the foregoing embodiment, the above step S250 may also be implemented by:
and determining the extreme weather event risk based on the occurrence frequency, the duration, the estimated long-series grid load and the future water-wind-light output.
In the present invention, the occurrence frequency and duration of extreme climate events refer to the occurrence frequency and duration of extreme climate events such as drought, high temperature, cold and the like under future climate conditions. These parameters are important indicators for assessing the risk of extreme weather events.
In the invention, data support can be provided for extreme climate event risk assessment by counting the occurrence frequency and duration of the extreme climate event.
Here, the calculation formula of the occurrence frequency is:, In the formula (I), in the formula (II),The occurrence times of the extreme weather event meeting the corresponding threshold value in the corresponding time period; Counting total time length; The actual value of the target wind speed, the target air temperature, the target solar radiation or the target precipitation amount at the moment a; judging whether the current climate at the time a is the upper limit value of an extreme climate event or not; in order to judge whether the current climate at the time a is the lower limit value of the extreme climate event, F is the occurrence frequency of the corresponding extreme climate event.
Here, the calculation formula of the duration is: In the formula (I), in the formula (II),Is the duration of the kth extreme weather event; the starting time of the kth extreme climate event; is the termination time of the kth extreme weather event.
In some embodiments, the above method further comprises constructing constraints for the first objective function and the second objective function. Wherein the constraint conditions include:
wind power output constraint: In the formula (I), in the formula (II),Wind power generation is carried out for the ith period; The upper limit of wind power output is set;
photoelectric output constraint: In the formula (I), in the formula (II),The photoelectric output is the ith period; Is the upper limit of photoelectric output;
Discarding the constraint of the electricity rate: In the formula (I), in the formula (II),The total generating capacity of wind and light in a period; the total wind and light discarding electric quantity in the period is used; the wind and light rejection rate is allowed for the system;
hydropower station output constraint: In the process of the formula (I),Water power for the i-th period;、 the lower output limit and the upper output limit of the hydropower station are respectively;
Reservoir level constraint: In the process of the formula (I),Reservoir level for the ith period;、 The minimum limit water level and the maximum limit water level of the reservoir in the ith period are respectively;
force balance constraint: In the process of the formula (I),Load demand for the power system;
and (3) binding installed capacity: In the process of the formula (I),、The maximum capacity values of the wind turbine generator and the photovoltaic power generation are respectively;、 The installed capacities of the wind turbine generator and the photovoltaic power generation are respectively set;
Electric quantity stability constraint: In the process of the formula (I),The generated power is the generated power at the time a; The generated power is the generated power at the time a-1; the maximum amount of variation allowed for the power system.
In the following, an exemplary application of the embodiment of the present application in a practical application scenario will be described.
In order to improve the resistance of the water-wind-solar integrated base under the climate risk and promote the large-scale development of renewable energy sources, the invention provides a flow diagram of a capacity optimization configuration method of the water-wind-solar integrated base considering the extreme climate event risk, as shown in fig. 3, the method specifically comprises the following implementation steps:
and S31, estimating the water and wind power output under the CMIP6 climate mode.
CMIP6 climate pattern data is the most up-to-date global climate pattern simulation result. Firstly, by selecting historical and future data of sun-by-sun wind speed, air temperature, solar radiation and precipitation elements in 5 CMIP6 global climate modes, according to meteorological hydrologic historical data of a meteorological site, deviation correction is carried out on the CMIP6 climate mode data by adopting a fractional mapping method (Quantile Mapping, QM), and differences between model simulation and observation data are eliminated, so that the model simulation and observation data are more in line with local historical meteorological observation data, and high-precision meteorological element prediction is ensured. Wherein 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 invention, the formula of the fractional number mapping method is as follows:
;
wherein: For a corrected predicted sequence; for correcting the pre-prediction sequence; Is a specific value in the predicted sequence; The inverse function of the cumulative distribution function measured periodically is used for converting the cumulative probability back to the original value; A cumulative distribution function measured periodically for the rate; a cumulative distribution function of the series is predicted periodically for the rate.
Based on the corrected data, estimating the wind power output process in 2035-2065 years by adopting a wind power output model and a photovoltaic output calculation model, wherein a wind power output calculation formula (namely a preset wind power output estimation model in the embodiment) is as follows:
;
wherein: wind power generation is carried out for the ith period; is the installed capacity of the wind power plant; Wind speed at the wind turbine for the i-th period;、 AndThe cut-in wind speed, cut-out wind speed and full wind speed of the wind turbine are respectively.
The calculation formula of the photovoltaic output (i.e. the preset photovoltaic output estimation model in the above embodiment) is as follows:
;
wherein: the actual average output force of the photovoltaic in the ith period; the installed capacity of the photovoltaic power station; Solar radiation intensity for the i-th period; is the solar radiation intensity under standard test conditions; the temperature of the solar panel; Air temperature under standard test conditions; Is the air temperature power conversion coefficient.
The calculation of the water power output can be realized by using a two-parameter lunar water balance model to estimate the river basin runoff, and then adopting a water power formula to estimate the water power output. The core idea of the lunar water balance model is to simulate the water balance process of a river basin by considering factors such as precipitation, evaporation, water storage capacity of the river basin and the like. Two main parameters of the lunar water balance model include parameters C and SC. C represents the water storage capacity or regulation capacity of the river basin, is related to the soil humidity and vegetation coverage of the river basin, and SC represents the maximum water storage capacity of the river basin, and is closely related to the characteristics of the topography, geology, land utilization and the like of the river basin. The basic form of the lunar water balance model (i.e., the preset lunar water balance model in the above embodiment) is:
;
In the formula,Is the runoff of the nth month; the water content of the soil at the end of the last month; precipitation for the nth month; is the actual evaporation capacity of the nth month, and SC is the maximum water storage capacity of the basin.
The water energy formula (i.e. the preset water energy estimation model in the above embodiment) is as follows:
;
In the formula,Is the efficiency coefficient of the hydropower station; H is the water purifying head of the hydropower station.
And S32, calculating the risk of an extreme climate event of the water-wind-solar integrated base.
Based on the data of the deviation corrected 2035-2065 year CMIP6 climate mode wind speed, air temperature, solar radiation and precipitation, determining the threshold value of the data such as wind speed, solar radiation and the like, and used for identifying the events of no wind, no light and no wind and calculating the occurrence frequency and duration of the climate events with different polar ends. The frequency and duration of occurrence of different terminal climate events are calculated as follows:
The calculation formula of the occurrence frequency is as follows:, In the formula (I), in the formula (II),The occurrence times of the extreme weather event meeting the corresponding threshold value in the corresponding time period; Counting total time length; The actual value of the target wind speed, the target air temperature, the target solar radiation or the target precipitation amount at the moment a; judging whether the current climate at the time a is the upper limit value of an extreme climate event or not; in order to judge whether the current climate at the time a is the lower limit value of the extreme climate event, F is the occurrence frequency of the corresponding extreme climate event.
The calculation formula of the duration is as follows: In the formula (I), in the formula (II),Is the duration of the kth extreme weather event; the starting time of the kth extreme climate event; is the termination time of the kth extreme weather event.
And then, predicting long-series power grid demands (power grid load) by a linear trend method, calculating the loss load risk by comparing the output (namely the future hydroelectric output, the future wind power output and the future photoelectric output in the embodiment) and the load under corresponding time, and combining the occurrence frequency and the duration of the extreme climate event, thereby identifying different extreme climate event types and calculating the occurrence probability thereof, and achieving the purpose of quantifying the extreme climate event risk.
And finally, establishing a risk assessment model of the extreme climate event by combining the climate data, the historical data and the identification of the extreme climate event. The occurrence frequency of different extreme climate events (such as high temperature, drought, cold wave, etc.) is analyzed, and the risk of the extreme climate events is estimated by the Extreme Value Theory (EVT).
It should be noted that the extreme value theory is an important branch of statistics, mainly focuses on probability distribution and properties of extreme climate events, and the EVT can accurately measure risk value of extreme climate events distributed at the tail, so that the method is widely applied to the field of natural disaster prediction.
In some embodiments, the risk of load loss refers to the risk that the power system fails to meet the user demand due to insufficient power generation output or excessive grid load.
In the invention, the load loss risk is calculated by comparing the future hydroelectric power output, the future wind power output, the future photoelectric power output and the load, and the fluctuation of the generated power and the change of the load of the power grid are considered.
And step S33, carrying out multi-objective capacity optimization configuration on the water-wind-solar integrated base in consideration of the risk of extreme climate events.
Firstly, establishing capacity optimization configuration principles, including feasibility rules, can be understood as ensuring that the designed system is technically and environmentally practicable, including full utilization of resources, evaluation of environmental impact and verification of technical feasibility, economy rules can be understood as ensuring that the design and operation of the system have good economic benefits, minimizing cost and maximizing benefit can be realized, volatility rules can be understood as considering that the output of renewable energy sources has uncertainty and volatility, supply and demand needs to be balanced through proper design, and load matching rules can be understood as ensuring that the output of a power generation system can effectively meet the power requirements of users, supply and demand unbalance is avoided, and the like. And then, establishing a water-wind-solar integrated base multi-target capacity optimal configuration model considering the risk of the climate event, wherein the first objective function, namely minimizing the risk of the extreme climate event, quantifies the operation risk caused by the extreme climate event through the comprehensive risk rate.
Here the number of the elements is the number,;
Wherein I is the number of contextual models, I is the period; The occurrence frequency of extreme climate events which lead to reduced output in the period i is the same;、、...、 the probability of the water, wind, light and electricity low-output operation under the weather event conditions of different polar ends is respectively shown.
Maximizing the full life cycle benefit objective function, namely the second objective function is:
;
wherein T is a time period, T is the project period service life, and r is the discount rate; the method is an electricity selling benefit; Benefit subsidized for government; the method is an electricity selling benefit; Benefit subsidized for government; Revenue for carbon trade; The running cost of the power station; Maintenance costs for the power plant facility; Is the cost of the energy storage system; is the construction cost of the wind-light water storage facility.
The constraint conditions include:
wind power output constraint: In the formula (I), in the formula (II),Wind power generation is carried out for the ith period; the upper limit of wind power output is set.
Photoelectric output constraint: In the formula (I), in the formula (II),The photoelectric output is the ith period; Is the upper limit of photoelectric output.
Discarding the constraint of the electricity rate: In the formula (I), in the formula (II),The total generating capacity of wind and light in a period; the total wind and light discarding electric quantity in the period is used; And (5) the wind and light rejection rate is allowed for the system.
Hydropower station output constraint: In the process of the formula (I),Water power for the i-th period;、 the lower output limit and the upper output limit of the hydropower station are adopted.
Reservoir level constraint: In the process of the formula (I),Reservoir level for the ith period;、 The lowest limit water level and the highest limit water level of the reservoir in the ith period. Force balance constraint: In the process of the formula (I),Is a power system load demand.
And (3) binding installed capacity: In the process of the formula (I),、The maximum capacity values of the wind turbine generator and the photovoltaic power generation are respectively;、 the installed capacities of the wind turbine generator and the photovoltaic power generation are respectively.
Electric quantity stability constraint: In the process of the formula (I),The generated power is the generated power at the time a; The generated power is the generated power at the time a-1; the maximum amount of variation allowed for the power system.
In the embodiment of the invention, based on typical water-wind-solar power output data, a second generation non-dominant sorting genetic algorithm (NSGA-II) is adopted to carry out Pareto solution set solution on a mathematical model of the water-wind-solar integrated operation system. And further, according to the model output scheme, adopting an Analytic Hierarchy Process (AHP) to consider the preferences of different decision makers and formulating different capacity optimization configuration schemes. The analytic hierarchy process divides each complex index into orderly and correlative indexes by establishing a multi-level structure, and optimizes the weight assignment process among the indexes by comparing the importance among index factors in pairs, thereby increasing the objectivity and logic of decision judgment among multiple indexes. Finally, by comparing the capacity proportion of other water-wind-solar integrated projects, the risk reduction and income improvement degree of the capacity optimization configuration of the research are calculated, and the purpose of the optimization configuration is achieved.
NSGA-II algorithms are known for their ability to handle the efficiency of a large population and to maintain solution diversity, and employ fast non-dominant ordering methods, elite and crowding distance mechanisms to ensure that Pareto fronts are well distributed. The basic steps of NSGA-II algorithm implementation include initializing population, fitness function evaluation, non-dominant ranking, calculating crowding distances, selecting parents for crossover, crossover/mutation operations, and iteration.
Fig. 4 is a schematic structural diagram of a water-wind integrated base capacity optimization device considering the risk of an extreme weather event, as shown in fig. 4, where the water-wind integrated base capacity optimization device 400 considering the risk of an extreme weather event includes an acquisition module 401 for acquiring a plurality of CMIP6 global weather mode data and meteorological hydrologic historical data, the CMIP6 global weather mode data includes historical weather mode data and future weather mode data, a correction module 402 for performing bias correction on the future weather mode data based on the historical weather mode data and the meteorological hydrologic historical data to obtain corrected weather mode data, an estimation module 403 for estimating future water-wind-photovoltaic output based on the corrected weather mode data, a determination module 404 for determining an extreme weather event based on the estimated long-series grid load and the future water-wind-photovoltaic output during the occurrence of the extreme weather event, a determination module 404 for constructing a model of an optimal base capacity configuration for the extreme weather event based on the maximum risk of the extreme weather event, and an optimal base capacity configuration for the extreme weather event based on the maximum life cycle, and a target configuration of the maximum water-wind integrated base capacity optimization model based on the corrected weather event, and a determination module 404 for determining an extreme weather event based on the estimated weather event, wherein: wherein I is the number of contextual models, I is the period; The occurrence frequency of extreme climate events which lead to reduced output in the period i is the same;、、...、 The probability of the low-output operation of the water, wind, light and electricity under the weather event conditions of different terminals is respectively determined, and the second objective function of the full life cycle benefit maximization is as follows: Wherein, T is time period, T is project period life, r is discount rate; the method is an electricity selling benefit; Benefit subsidized for government; Revenue for carbon trade; The running cost of the power station; Maintenance costs for the power plant facility; Is the cost of the energy storage system; is the construction cost of the wind-light water storage facility.
It should be noted that, the description of the apparatus according to the embodiment of the present invention is similar to the description of the embodiment of the method described above, and has similar beneficial effects as the embodiment of the method described above, so that a detailed description is omitted. For technical details not disclosed in the present apparatus embodiment, please refer to the description of the method embodiment of the present invention for understanding.
It should be noted that, in the embodiment of the present invention, if the above-mentioned water-wind-solar integrated base capacity optimization method considering the risk of the extreme climate event is implemented in the form of a software functional module, the method may also be stored in a computer readable storage medium when sold or used as an independent product. Based on such understanding, the technical solution of the embodiments of the present invention may be essentially or part contributing to the related art, embodied in the form of a software product stored in a storage medium, including several instructions for causing a terminal to execute all or part of the methods described in the embodiments of the present invention. The storage medium includes various media capable of storing program codes, such as a usb (universal serial bus), a removable hard disk, a Read Only Memory (ROM), a magnetic disk, or an optical disk. Thus, embodiments of the invention are not limited to any specific combination of hardware and software.
Correspondingly, an embodiment of the present invention provides an electronic device, and fig. 5 is a schematic diagram of a composition structure of the electronic device provided by the embodiment of the present invention, as shown in fig. 5, the electronic device 500 at least includes a processor 501 and a computer readable storage medium 502 configured to store executable instructions, where 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 may also cache data to be processed or processed by various modules in the processor 501 and the electronic device 500, and may be implemented by FLASH memory (FLASH) or random access memory (RAM, random Access Memory).
Embodiments of the present invention provide a storage medium having stored therein executable instructions which, when executed by a processor, cause the processor to perform a method provided by embodiments of the present invention, for example, as shown in fig. 2.
In some embodiments, the storage medium may be a computer readable storage medium, such as ferroelectric Memory (FRAM, ferromagnetic Random Access Memory), read Only Memory (ROM), programmable Read Only Memory (PROM, programmable Read Only Memory), erasable programmable Read Only Memory (EPROM, erasable Programmable Read Only Memory), electrically erasable programmable Read Only Memory (EEPROM, ELECTRICALLY ERASABLE PROGRAMMABLE READ ONLY MEMORY), flash Memory, magnetic surface Memory, optical Disk, or Compact Disk-Read Only Memory (CD-ROM), or various devices including one or any combination of the above.
In some embodiments, the executable instructions may be in the form of programs, software modules, scripts, or code, written in any form of programming language (including compiled or interpreted languages, or declarative or procedural languages), and they may be deployed in any form, including as stand-alone programs or as modules, components, subroutines, or other units suitable for use in a computing environment.
As an example, executable instructions may, but need not, correspond to files in a file system, may be stored as part of a file that holds other programs or data, such as in one or more scripts in a hypertext markup language (HTML, hyper Text Markup Language) document, in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub-programs, or portions of code). As an example, executable instructions may be deployed to be executed on one electronic device or on multiple electronic devices located at one site or distributed across multiple sites and interconnected by a communication network.
The foregoing is merely exemplary embodiments of the present invention and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and scope of the present invention are included in the protection scope of the present invention.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in various embodiments of the present invention, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present invention. The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, or apparatus. Without further limitation, an element defined by the phrase "comprising one does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element. In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is merely a logical function division, and there may be additional divisions of actual implementation, such as multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed.
The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.