CN114977324A - Quantification method for multi-subject benefit change in multi-energy complementary operation of energy base - Google Patents

Abstract

The invention discloses a method for quantifying multi-subject benefit change in multi-energy complementary operation of an energy base, which specifically comprises the following steps: step 1, extracting a scene of typical wind and light output; step 2, selecting loss and benefit indexes of cascade hydropower, wind power and photovoltaic; step 3, setting a water wind light operation scene; step 4, selecting output scenes of a hydropower station, a wind power station and a photovoltaic station; step 5, constructing and solving a water-wind light benefit model; and 6, analyzing the damage and benefit conditions of the whole cascade hydropower station, each hydropower station in the cascade hydropower station and the wind-light power station in the implementation of the water-wind-light integration process of the clean energy base according to the results of the four seasons in spring, summer, autumn and winter in the step 5 under the three water-wind-light operation situations and the damage and benefit index standard selected in the step 2, and quantizing the damage and benefit conditions in a chart mode. The invention quantifies the loss and benefit relationship among the cascade hydropower station, the wind power photovoltaic and the hydropower stations in the cascade hydropower station in the water-wind-solar complementary operation, and fully mobilizes the enthusiasm of the hydropower stations participating in the multi-energy complementary operation.

Description

Quantification method for multi-subject benefit change in multi-energy complementary operation of energy base

Technical Field

The invention belongs to the technical field of clean energy water-wind-solar integrated operation, and relates to a method for quantifying multi-subject benefit change in multi-energy complementary operation of an energy base.

Background

With the explosive growth of wind power and photovoltaic installed capacity, the wind power and photovoltaic installed capacity is expected to be developed from 330GW and 310GW in the end of 2021 to 480GW and 570GW in 2030 and 1440GW and 2160GW in 2050. However, wind, photovoltaic and electricity have strong randomness, intermittence and fluctuation, and large-scale new energy grid connection needs to be combined with a corresponding multi-energy complementary operation mode. Therefore, the full exploitation of the 'peak clipping and valley filling' potential of the flexible power supply of the energy base is an important precondition for large-scale grid connection of new energy.

The flexible power supply in the fully clean energy base mainly refers to a hydropower station. The cascade water and electricity has large installed capacity, strong adjusting performance and strong peak clipping and valley filling potential. With the great increase of the installed capacity of the clean power supply of the full clean energy base, the step hydropower needs to provide auxiliary services such as bidirectional peak regulation, rotation standby and the like to match with wind and light absorption, so that the hydropower can increase power generation at a low-price time period and reduce power generation at a high price, and the internal damage relationship of the clean energy base is very complex. If the damage and benefit relation of each power supply in the clean energy base cannot be well quantized, the enthusiasm of the hydropower station participating in the multi-energy complementary operation is difficult to transfer, and the method is very unfavorable for the energy structure transformation in China. Therefore, it is very important to actively search for a technical scheme capable of scientifically quantifying the damage relationship among the whole cascade hydropower station, the wind power photovoltaic station and each hydropower station in the cascade hydropower station in the water-wind-solar complementary operation.

At present, the average compensation and compensated value among different types of power supplies is determined by means of multi-subject benefit distribution in the water-wind-solar complementary operation based on peak regulation auxiliary service cost payment and the like, but a benefit distribution mode of peak regulation contribution of different subjects in a multi-energy complementary system and a step hydropower station in the multi-energy complementary operation process is lacked.

Disclosure of Invention

The invention aims to provide a method for quantizing multi-main-body benefit change in multi-energy complementary operation of an energy base, quantizes the loss and benefit relation among the cascade hydropower station, the wind-electricity photovoltaic power station and the hydropower stations in the cascade hydropower station in the water-wind-light complementary operation, and fully transfers the enthusiasm of the hydropower stations participating in the multi-energy complementary operation.

The technical scheme adopted by the invention is that the method for quantifying the multi-subject benefit change in the multi-energy complementary operation of the energy base is implemented according to the following steps:

step 1, extracting a scene of typical wind and light output;

step 2, selecting loss and benefit indexes of cascade hydropower, wind power and photovoltaic;

step 3, setting a water wind light operation scene;

step 4, selecting inflow scenes of the hydropower station and output scenes of the wind power station and the photoelectric station;

step 5, constructing and solving a water-wind light benefit model;

and 6, analyzing and analyzing the loss and benefit conditions of the whole cascade hydropower station, each hydropower station inside the cascade hydropower station and the wind-solar power station in the implementation of the water-wind-light integration process of the clean energy base according to the results of the four seasons in the spring, the summer, the autumn and the winter in the step 5 under the three water-wind-solar operation situations and the loss and benefit index standard selected in the step 2, and quantifying in a chart mode.

The invention is also characterized in that:

the specific process of the step 1 is as follows:

step 1.1, respectively establishing a wind energy resource virtual monitoring point and a light energy resource virtual monitoring point in a Greenwich platform and Meteonorm software according to the latitude and longitude of a wind power plant and the latitude and longitude of a photovoltaic power plant planned and constructed by clean energy, and acquiring data for wind and light power simulation from the established virtual monitoring points;

the data acquired by the wind energy resource virtual monitoring point comprises the following data: wind speed, wind direction; the data acquired by the optical energy resource virtual monitoring point comprises the following data: annual and intraday horizontal plane total radiation, annual and intraday horizontal plane scattered radiation, ambient temperature;

step 1.2, drawing a wind speed annual intra-day change line graph, a wind speed and wind direction rose graph and a solar irradiation intensity annual intra-day change line graph, and analyzing annual and intra-day changes of wind speed, wind direction and solar irradiance to obtain a clean energy wind and light resource time-varying rule;

step 1.3, calculating similar distances by using wind speed sequences of various wind power stations by adopting a K-means algorithm, dividing the wind power stations of a clean energy base into K _ wind clusters, and dividing the photovoltaic power stations with similar latitudes into the same cluster according to the change rule of the solar irradiance sequences of the photovoltaic power stations within the year and the day, so that the photovoltaic power stations are divided into K _ pv clusters;

the division rule of the same cluster is as follows: the latitude span from the photovoltaic power station at the lowest latitude to the photovoltaic power station at the highest latitude in the clean energy base of the first photovoltaic cluster is not more than 0.5 degrees, if the latitude difference between the photovoltaic power station at the highest latitude and the adjacent photovoltaic power station at the higher latitude in the cluster is not more than 0.2 degrees, the adjacent photovoltaic power station at the higher latitude is also brought into the cluster, and the other photovoltaic clusters are the photovoltaic power stations contained in the divided clusters; dividing other photovoltaic clusters according to a first cluster division principle;

step 1.4, simulating the output process of the wind power station in each cluster of the clean energy base wind power station by using a wind power physical model to obtain an annual power sequence of each cluster of the wind power station, and simulating the output process of the photovoltaic power station in each cluster of the clean energy base photovoltaic power station by using a photovoltaic power physical model to obtain an annual power sequence of each cluster of the photovoltaic power station;

the wind power physical model formula is as follows:

in formula (2), P _w The power generation power of the wind turbine generator is W; c _P The wind energy utilization coefficient of the wind turbine generator is; a is the swept area of the impeller, m ² (ii) a Rho is air density, kg/m ³ (ii) a v is wind speed, m/s; v. of ₁ Cutting into wind speed, m/s; v. of _N The rated wind speed of the wind turbine generator is m/s; p _e Rated power, W, of the wind turbine generator; v. of ₂ In order to cut out the wind speed, m/s;

the photovoltaic power physical model formula is as follows:

in the formula (3), P _PV,t The generated power of the photovoltaic panel at the moment t is W; p _stc The output of a single photovoltaic panel under standard conditions, W; i is _r,t Is the actual radiation intensity at time t, W/m ² ；I _stc Is the intensity of the corresponding solar radiation under standard conditions, W/m ² ；δ _t Is the power temperature coefficient of the photovoltaic panel; t is _t The temperature of the photovoltaic panel at time t is DEG C; t is a unit of _stc Is the temperature T under standard conditions _stc ＝25℃；

Step 1.5, respectively reducing the generated power samples in two periods of winter and spring and summer and autumn in each cluster of the wind power station through a synchronous back-substitution subtraction method, wherein z _ wind output scenes are obtained in the two periods, and reducing the generated power samples in two periods of winter and spring and summer and autumn in each cluster of the photovoltaic power station through the synchronous back-substitution subtraction method, wherein z _ pv output scenes are obtained in the two periods;

respectively combining z _ wind output scenes obtained in winter and spring and z _ wind output scenes obtained in summer and autumn of each cluster of the wind power station, respectively obtaining m _ wind total output scenes in winter and spring and in summer and autumn, respectively combining z _ pv typical output scenes obtained in winter and spring and z _ pv typical output scenes obtained in summer and autumn of each cluster of the photovoltaic power station, respectively obtaining m _ pv total output scenes in winter and spring and in summer and autumn;

the method comprises the steps of respectively reducing m _ wind total output scenes obtained by a wind power station in winter and spring, m _ wind total output scenes obtained by the wind power station in summer and autumn, m _ pv total output scenes obtained by a photovoltaic power station in winter and spring, and m _ pv total output scenes obtained by the photovoltaic power station in summer and autumn by adopting a K-means clustering method to obtain a typical output scene of the wind power station in winter and spring, a typical output scene of the wind power station in summer and autumn, a typical output scene of the photovoltaic power station in winter and spring, and a typical output scene of the photovoltaic power station in summer and autumn.

The specific process of the step 2 is as follows:

step 2.1, collecting characteristic quantities capable of expressing cascade hydroelectric power, wind power and photovoltaic loss, wherein the characteristic quantities comprise power generation quantity and power generation income of each power supply, wind-solar electricity abandonment quantity and electricity abandonment loss, water abandonment quantity of hydroelectric power, upward peak-shaving climbing compulsion degree of hydroelectric power, power shortage probability caused by each power supply and cascade hydroelectric volatility;

and 2.2, selecting the generated energy, the power generation income, the wind-light electricity abandonment quantity and the wind-light electricity abandonment loss of each power supply as indexes of cascade hydropower, wind power and photovoltaic loss in the clean energy base.

The specific process of the step 3 is as follows:

the water-wind-light integrated output process needs to meet the load fluctuation of a power system, and 3 water-wind-light operation scenes are set:

water wind light operation scene 1, step water and electricity do not cooperate with wind and light absorption: the cascade hydroelectric power is generated according to the load process of the power system, and the wind and light are on line on the premise of meeting the residual load process of the power system;

water wind light operation scene 2, step water and electricity are not completely matched with wind and light absorption: the cascade hydroelectric power generates electricity according to equivalent load, and when the integrated water-wind-light output process does not meet the system load requirement, wind-light abandoning is adopted;

water wind light operation scene 3, step water and electricity are completely matched with wind and light absorption: the cascade hydroelectric power is used for generating power according to equivalent load, and when the water-wind-light comprehensive output process does not meet the system load requirement, the water and the hydroelectric power are abandoned.

The specific process of the step 4 is as follows:

step 4.1, selecting a typical day from spring, summer, autumn and winter according to the representativeness of the generated flow of each period of the hydropower as a typical inflow scene of the hydropower;

step 4.2, adopting the wind power station output scene obtained in the step 1.5 to obtain a wind power station typical output scene in winter and spring and a wind power station typical output scene in summer and autumn;

and 4.3, adopting the photovoltaic power station output scene obtained in the step 1.5 to obtain a photovoltaic power station typical output scene in winter and spring and a photovoltaic power station typical output scene in summer and autumn.

The specific process of the step 5 is as follows:

step 5.1, constructing a cascade hydroelectric benefit model with a scheduling cycle of 1 day and a minimum scheduling time interval of 1 hour according to the water, wind and light operation scene 1 set in the step 3;

the specific objective function is as follows:

the cascade water and electricity benefits are the greatest:

minimum residual load of power system

The power system residual load fluctuation is minimum:

in the formulae (11) to (13), R _1,h Representing the profit and element of the cascade hydropower; t represents a certain time period of the schedule; t represents the total scheduling time period number, and T is taken as 24; c (t) represents the price of the water, wind and light bundled on-line electricity in t period, unit/(MW & h); i denotes the ith downstream hydropower station; n represents the total number of the downstream hydropower stations, and n is 5; n is a radical of _h,i (t) represents the average output, MW, of the ith downstream hydropower station over the period of t; Δ t represents the time, h, at each time interval; n is a radical of _retotal Represents the system residual load, MW; n is a radical of hydrogen _s (t) represents the average load, MW, of the system over a period t; v _re Indicating the remaining load fluctuation, MW of the system ² /h；N _re (t) represents the average residual load, MW, of the system over a period of t;

represents the remaining load average, MW, of the system over the T period;

constraint conditions are as follows:

and (3) water balance constraint:

V _i,t+1 ＝V _i,t +(I _i,t -Q _i,t -q _i,t )×Δt (14)

in the formula (14), V _i,t+1 The water storage capacity of the ith reservoir at the end of the t period, m ³ ；V _i,t The initial i-th reservoir storage capacity m in the t-th period ³ ；I _i,t The storage flow of the ith reservoir in the t period, m ³ /s；Q _i,t The generated flow of the ith reservoir in the t period, m ³ /s；q _i,t Is the water discharge of the ith reservoir in the t period ³ /s；

And (3) water and electricity output restraint:

in the formula (15), the reaction mixture is,

representing the output upper limit, MW, of the ith hydropower station in the t period;

representing the output lower limit, MW, of the ith hydropower station in the t period; n is a radical of _h,i,t The output power, MW, of the ith hydropower station in the t period;

and (4) library capacity constraint:

in the formula (16), the compound represented by the formula,

represents the ith hydropower station upper capacity limit, m3, during the t-th period;

represents the lower limit of the storage capacity of the ith hydropower station m in the t-th period ³ ；V _i,t For the ith hydropower station reserve capacity, m, of the t period ³ ；

Water level restraint:

in the formula (17), the compound represented by the formula (I),

representing the upper limit of the water level of the ith reservoir m in the t-th time period;

representing the lower limit of the water level of the ith reservoir m in the t-th time period; z _i,t The ith reservoir water level m in the t time period;

and (3) restricting the downward flow:

in the formula (18), the reaction mixture,

represents the maximum discharge rate m of the ith reservoir in the t-th period ³ /s；

Represents the minimum discharge rate m of the ith reservoir in the t period ³ /s；Q _i，t Represents the discharge rate m of the ith reservoir in the t period ³ /s；

Non-negative constraints: the above variables are all non-negative values;

step 5.2, constructing a wind-solar benefit model with the operation cycle of 1 day and the minimum calculation time period of 1 hour according to the water-wind-light operation scene 1 set in the step 3;

the wind and light benefit expression is as follows:

R _1,wp ＝c(t)×[N _w (t)+N _p (t)]×Δt (19)

in the formula (19), R _1,wp Representing the total profit of water and wind and light complementation; n is a radical of _w (t) represents the average contribution, MW, of the downstream wind farm over a period of t; n is a radical of _p (t) represents the average contribution, MW, of the downstream photovoltaic electric field over a period of t;

constraint conditions are as follows:

wind-solar output constraint:

in the formula (20), N _w,t Representing the average output, MW, of the downstream wind farm over a period of t; n is a radical of _p,t Represents the average contribution, MW, of the downstream photovoltaic electric field over a period of t;

representing the average simulation output of wind power, MW, in the t-th period;

representing the photovoltaic average simulated output, MW, in the t-th period;

in the formula (21), N _s,t Represents the average load of the power system in the t-th period;

grid connection constraint:

N _s,t ≥N _h,i,t +N _w,t +N _p,t (22)

non-negative constraints: the above variables are all non-negative values;

step 5.3, constructing a water-wind light benefit model with a scheduling cycle of 1 day and a minimum scheduling time period of 1 hour according to the water-wind light operation scenes 1 and 3 set in the step 3;

the specific objective function is as follows:

the water-wind-light complementary system has the maximum benefit:

the system residual load is minimum:

in the formulae (23) to (24), R ₃ Representing the total profit of water and wind and light complementation;

constraint conditions are as follows:

and (3) water balance constraint:

V _i,t+1 ＝V _i,t +(I _i,t -Q _i,t -q _i,t )×Δt (26)

in the formula (26), V _i,t+1 The water storage capacity of the ith reservoir at the end of the t period, m ³ ；V _i,t The water storage capacity m of the ith reservoir at the beginning of the t period ³ ；I _i,t The storage flow of the ith reservoir in the t period, m ³ /s；Q _i,t The generated flow of the ith reservoir in the t period, m ³ /s；q _i,t Is the water discharge of the ith reservoir in the t period ³ /s；

And (3) water and electricity output restraint:

in the formula (27), the reaction mixture is,

the output upper limit, MW, of the ith hydropower station in the t period;

the output lower limit, MW, of the ith hydropower station in the t period; n is a radical of _h,i,t The output power, MW, of the ith hydropower station in the t period;

and (4) library capacity constraint:

in the formula (28), the reaction mixture is,

for the ith hydropower in the t periodUpper limit of station capacity, m ³ ；

Is the ith hydropower station reservoir capacity lower limit m in the t period ³ ；V _i,t For the ith hydropower station reserve capacity, m, of the t period ³ ；

Water level restraint:

in the formula (29), the reaction mixture,

the upper limit of the water level of the ith reservoir m in the t-th time period;

the lower limit of the water level of the ith reservoir m in the t period; z _i,t The ith reservoir water level m in the t time period;

and (3) restricting the downward flow:

in the formula (30), the reaction mixture,

the maximum discharge capacity m of the ith reservoir in the t period ³ /s；

The minimum discharge rate m of the ith reservoir in the t period ³ /s；Q _i，t Represents the discharge rate m of the ith reservoir in the t period ³ /s；

Wind power photovoltaic constraint:

for the water and wind operation scenario 2:

N _w,t +N _p,t ＝min[(N _s,t -N _h,i,t ),(N _s,w,t +N _s,p,t )] (31)

in formula (31), N _s,w,t The average simulation output, MW, of the t-th period of wind power is obtained; n is a radical of _s,p,t Average simulation output, MW, of the photovoltaic at the t-th time period;

for water and wind operation scenario 3:

N _w,t +N _p,t ＝N _s,w,t +N _s,p,t (32)

and (3) grid connection constraint:

N _s,t ≥N _h,i,t +N _w,t +N _p,t (33)

non-negative constraints: the above variables are all non-negative values;

and 5.4, solving the model in the step 5.3 by adopting a particle swarm algorithm.

The beneficial effect of the invention is that,

(1) the method for quantifying the multi-subject benefit change in the multi-energy complementary operation of the energy base can systematically quantify the loss and benefit relationship of each benefit subject in the clean energy base, and can quantitatively analyze the index changes such as the generating capacity, the generating income, the volatility and the like of the cascade hydropower station and each member hydropower station under different seasons and different water and wind and light operation scenes, thereby providing quantitative guidance for realizing wind and light consumption for the hydropower science operation;

(2) the method for quantifying the multi-subject benefit change in the multi-energy complementary operation of the energy base extracts the wind-light typical output scene of the clean energy base, reasonably describes the uncertainty of wind-light output in a quantitative calculation mode, and provides solid support for the construction of a clean energy base ground water wind-light benefit model.

Drawings

FIG. 1 is a flow chart of a method for quantifying multi-subject benefit variations in multi-energy complementary operation of an energy base according to the present invention;

FIG. 2 is a typical wind power output scene in winter and spring according to an embodiment of the invention;

FIG. 3 is a typical wind-electricity output scenario in summer and autumn according to an embodiment of the invention;

FIG. 4 is a typical photovoltaic output scenario in winter and spring according to an embodiment of the invention;

FIG. 5 is a typical output scenario of a summer and autumn photovoltaic of an embodiment of the invention;

FIG. 6 is a total power generation of a water and wind power system according to an embodiment of the present invention;

FIG. 7 is the total generation revenue of a hydro-wind power system in accordance with an embodiment of the present invention;

FIG. 8 illustrates wind-solar power on-line in accordance with an embodiment of the present invention;

FIG. 9 illustrates wind and solar energy revenue generation for a wind and solar energy grid system in accordance with an embodiment of the present invention;

FIG. 10 is a wind-solar power dump in accordance with an embodiment of the present invention;

FIG. 11 is a wind-solar power loss according to an embodiment of the present invention;

FIG. 12 shows the overall power generation of the stepped hydropower station according to the embodiment of the invention;

FIG. 13 shows the overall power generation benefit of the cascade hydroelectric power of the embodiment of the invention;

FIG. 14 shows the spring power generation of each hydropower station in the step hydropower station in the embodiment of the invention;

FIG. 15 shows the summer power generation of each hydropower station in the cascade hydropower station according to the embodiment of the invention;

FIG. 16 shows the autumn generated energy of each hydropower station in the cascade hydroelectric power station in the embodiment of the invention;

FIG. 17 shows the winter power generation of each hydropower station in the step hydropower station according to the embodiment of the invention;

FIG. 18 shows the income of each hydropower station in the cascade hydropower station in the embodiment of the invention;

FIG. 19 shows the summer power generation benefit of each hydropower station in the cascade hydropower station according to the embodiment of the invention;

FIG. 20 shows the yield of electricity generation in autumn of a Tung forest hydropower station in the step hydropower station of the embodiment of the invention;

FIG. 21 shows the benefits of winter power generation of a Tung forest hydropower station in the step hydropower station of the embodiment of the invention.

Detailed Description

The invention is described in detail below with reference to the drawings and the detailed description.

The invention provides a method for quantifying multi-subject benefit change in multi-energy complementary operation of an energy base, which is implemented according to the following steps as shown in figure 1:

step 1, extracting a scene of typical wind and light output

Step 1.1, respectively establishing wind energy resource virtual monitoring points and light energy resource virtual monitoring points in a Greenwich mean platform and Meteonorm software according to the longitude and latitude of a wind power station and the longitude and latitude of a photovoltaic electric field which are planned and constructed by clean energy, and acquiring data for wind and light power simulation from the established virtual monitoring points;

wherein, the data that virtual monitoring point of wind energy resource obtained include: wind speed, wind direction; the data acquired by the optical energy resource virtual monitoring point comprises the following data: annual and intraday horizontal plane total radiation, annual and intraday horizontal plane scattered radiation, ambient temperature;

step 1.2, a wind speed intra-year change broken line graph, a wind speed and wind direction rose graph and a solar irradiation intensity intra-year change broken line graph are drawn to analyze the intra-year and intra-day changes of wind speed, wind direction and solar irradiation intensity (for example, the fact that the wind speed of months is high, the wind speed of months is low, the season is prevailing in which wind direction, the wind speed of time periods in the day is high, and the wind speed of time periods in the day is low is analyzed;

step 1.3, calculating similar distances by using wind speed sequences of various wind power stations by adopting a K-means algorithm, dividing the wind power stations of a clean energy base into K _ wind clusters, wherein according to the change rule of solar irradiance sequences of various photovoltaic power stations in the year and in the day, the latitude span from the photovoltaic power station at the lowest latitude to the photovoltaic power station at the highest latitude in the clean energy base of a first photovoltaic cluster is not more than 0.5 degrees, if the latitude difference between the photovoltaic power station at the highest latitude and the photovoltaic power station at the adjacent higher latitude in the cluster is not more than 0.2 degrees, the photovoltaic power station at the adjacent higher latitude is also brought into the cluster, and other photovoltaic clusters remove the photovoltaic power stations contained in the divided clusters, divide the photovoltaic power stations into K _ pv clusters according to the first cluster division principle, and finally divide the photovoltaic power stations into K _ pv clusters;

the K-means algorithm comprises the following specific steps:

inputting a data set D containing n objects, and determining a clustering number k;

secondly, randomly selecting k objects from the data set D as initial clustering centers (c) ₁ ,c ₂ ,…,c _k )；

Thirdly, calculating each data in the data set to each clustering center c _i (i ═ 1: k) and dividing it into the nearest clusters;

calculating the average value of each cluster and updating the cluster center;

and fifthly, repeating the third step and the fourth step until the iteration times are reached or the clustering center is not changed any more.

Similar distances: let X be (X) ₁ ,x ₂ ,…,x _n ),C＝(c ₁ ,c ₂ ,…,c _n ) Then the similar distance of X and C is defined as:

in the formula (1), X represents a wind speed sequence of a certain wind power station; c represents a wind speed sequence of a wind power station in a certain clustering center; wherein,

in the formula,

p 1 line vectors; x is the number of _j Representing the wind speed at a certain wind power plant moment j; c. C _j Representing the wind speed of a certain clustering center wind power station j moment;

step 1.4, simulating the output process of the wind power station in each cluster of the clean energy base wind power station by using a wind power physical model to obtain an annual power sequence of each cluster of the wind power station, and simulating the output process of the photovoltaic power station in each cluster of the clean energy base photovoltaic power station by using a photovoltaic power physical model to obtain an annual power sequence of each cluster of the photovoltaic power station;

the wind power physical model formula is as follows:

in the formula (2), P _w The power generation power of the wind turbine generator is W; c _P The wind energy utilization coefficient of the wind turbine generator is; a is the swept area of the impeller, m ² (ii) a Rho is air density, kg/m ³ (ii) a v is wind speed, m/s; v. of ₁ Cutting into wind speed, m/s; v. of _N The rated wind speed of the wind turbine generator is m/s; p _e Rated power, W, of the wind turbine generator; v. of ₂ Cutting out wind speed m/s;

the photovoltaic power physical model formula is as follows:

in the formula (3), P _PV,t The generated power of the photovoltaic panel at the moment t is W; p _stc The output of a single photovoltaic panel under standard conditions, W; i is _r,t Is the actual radiation intensity at time t, W/m ² ；I _stc Is the intensity of the corresponding solar radiation under standard conditions, W/m ² ；δ _t Is the power temperature coefficient of the photovoltaic panel; t is _t The temperature of the photovoltaic panel at time t is DEG C; t is _stc Is the temperature T under standard conditions _stc ＝25℃；

Step 1.5, respectively reducing the generated power samples in two periods of winter and spring and summer and autumn in each cluster of the wind power station through a synchronous back-substitution subtraction method, wherein z _ wind output scenes are obtained in the two periods, and reducing the generated power samples in two periods of winter and spring and summer and autumn in each cluster of the photovoltaic power station through the synchronous back-substitution subtraction method, wherein z _ pv output scenes are obtained in the two periods;

respectively combining z _ wind output scenes obtained in winter and spring and z _ wind output scenes obtained in summer and autumn of each cluster of the wind power station, respectively obtaining m _ wind total output scenes in winter and spring and in summer and autumn, respectively combining z _ pv typical output scenes obtained in winter and spring and z _ pv typical output scenes obtained in summer and autumn of each cluster of the photovoltaic power station, respectively obtaining m _ pv total output scenes in winter and spring and in summer and autumn;

respectively reducing m _ wind total output scenes obtained from a wind power station in winter and spring, m _ wind total output scenes obtained from the wind power station in summer and autumn, m _ pv total output scenes obtained from a photovoltaic power station in winter and spring, and m _ pv total output scenes obtained from the photovoltaic power station in summer and autumn by adopting a K-means clustering method (the reduction number is self-set, and the value of the optimal clustering number K of the K-means clustering under the general condition is reduced

In the embodiment, the number of samples in the sample set is N, and the number of reductions in the wind power output scene and the photovoltaic output scene is set to 5, because the reduction number is set to optimize the subsequent production scheduling calculation efficiency in order to obtain fewer wind power output scenes and photovoltaic output scenes with strong representativeness, the reduction number may be set to 5 respectively

Each integer of the two is represented by wind power and photoelectric typical output scenes with different reduced numbers), and a wind power station winter and spring typical output scene, a wind power station summer and autumn typical output scene, a photovoltaic power station winter and spring typical output scene and a photovoltaic power station summer and autumn typical output scene are obtained;

the synchronous back-substitution reduction technology comprises the following operation steps:

firstly, recording an initial scene sample set S, recording a random variable as xi ∈ S, sequentially deleting a scene in S in the operation process, recording a deleted scene set as J, finally obtaining a required scene set S-J, and utilizing a kantorovich distance D in the scene reduction process _h Evaluating the distance between the initial scene and the subtracted scene, wherein the expression is as follows:

p in formula (4) _i A probability value for a selected trial scenario i; c. C _T Is the distance between scenes i and j within the set; xi ⁱ Representing a sample sequence corresponding to scene i; xi ^j Representing a sample sequence corresponding to scene j;

② reduced scene j

The probability of (d) is updated as:

q in the formula (6) _j The probability of the scene obtained after the scene reduction is represented, is equal to the sum of the initial probability of the scene and the probabilities of other scenes deleted due to high similarity, and the sum of the probabilities of the scene sets before and after deletion is invariable and is always 1; p is a radical of _j A probability value representing the selected trial scenario j;

thirdly, in the selection of the initial scene set in the scene reduction, D between the scene sets before and after deletion needs to be met _h Minimum:

fourthly, under the condition that the deleted scene number J is given, in order to minimize the equation (8), the specific calculation steps are as follows:

step 0: calculating the distance between every two combined scenes in the initial scene set:

c _ij ＝c _T (ξ ⁱ ,ξ ^j ),(i,j∈S),J ⁽⁰⁾ ＝Φ (9)

in formula (9), Φ represents an empty set, which refers to a set that does not contain any element and is used for creating an initial scene set;

step J: calculated one by one according to the formula (10) to obtain:

in the formula (10), c _lj Representing the set S-J after J-1 scenes have been deleted ^(J-1) The distance between any two different scenes;

representing the set S-J after J-1 scenes have been deleted ^(J-1) The minimum distance between any two different scenes; p is a radical of _h Representing the set S-J after J-1 scenes have been deleted ^(J-1) The probability of the scene corresponding to the minimum distance value between any two different scenes;

represents the distance between scene sets before and after the J-th deletion, will

Scene l with minimum correspondence of value is selected from scene set S-J ^(J-1) Deleting, and merging the deleted scenes into a deleted scene set;

step J + 1: finally, S-J scene sets and probabilities thereof are obtained;

step 2, selecting the loss and benefit indexes of the cascade hydropower, wind power and photovoltaic

Step 2.1, collecting characteristic quantities capable of expressing cascade hydroelectric power, wind power and photovoltaic loss by searching documents, wherein the characteristic quantities comprise the generated energy and the power generation income of each power supply, the wind-solar power waste and the power waste loss, the water waste amount of the hydroelectric power, the upward peak-shaving climbing compulsion degree of the hydroelectric power, the power shortage probability caused by each power supply and the fluctuation of the cascade hydroelectric power;

step 2.2, selecting the generated energy, the power generation income, the wind-light electricity abandonment quantity and the wind-light electricity abandonment loss of each power supply as indexes of cascade hydropower, wind power and photovoltaic loss in the clean energy base;

step 3, setting of water, wind and light operation scene

Considering that the wind power and the photovoltaic with strong fluctuation are connected into a power grid in a large scale, huge potential safety hazards are certainly brought to a power system, therefore, the invention ensures the safe and stable operation of the power system on the basis of ensuring that the water and wind light comprehensive output process meets the load fluctuation of the power system, and 3 water and wind light operation scenes are set:

water wind light operation scene 1, step water and electricity do not cooperate with wind and light absorption: the cascade hydroelectric power is generated according to the load process of the power system, and the wind and light are on line on the premise of meeting the residual load process of the power system;

water wind light operation scene 2, step water and electricity are not completely matched with wind and light absorption: generating power by cascade hydroelectric power according to equivalent load (power system load minus wind-solar output), and adopting abandoned wind and light when the integrated water-wind-solar output process does not meet the system load requirement;

water wind light operation scene 3, step water and electricity are completely matched with wind and light absorption: the cascade hydroelectric power is used for generating power according to equivalent load, and when the water-wind-light comprehensive output process does not meet the system load requirement, the water and the wind-light comprehensive output process adopts abandoned hydroelectric power;

step 4, selecting inflow scenes of the hydropower station, and selecting output scenes of the wind power station and the photovoltaic station

Step 4.1, selecting a typical day from spring, summer, autumn and winter according to the representativeness of the generated flow of each period of the hydropower as a typical inflow scene of the hydropower;

step 4.2, adopting the wind power station output scene obtained in the step 1.5 to obtain a wind power station typical output scene in winter and spring and a wind power station typical output scene in summer and autumn;

step 4.3, adopting the photovoltaic power station output scene obtained in the step 1.5 to obtain a photovoltaic power station typical output scene in winter and spring and a photovoltaic power station typical output scene in summer and autumn;

step 5, constructing and solving a water-wind-light effect model

Step 5.1, constructing a cascade hydroelectric benefit model with a scheduling cycle of 1 day and a minimum scheduling time interval of 1 hour according to the water, wind and light operation scene 1 set in the step 3;

the specific objective function is as follows:

the step hydroelectric benefit is the biggest:

the remaining load of the power system is minimum:

the power system residual load fluctuation is minimum:

in the formulae (11) to (13), R _1,h Representing the profit and element of the cascade hydropower; t represents a certain time period of the schedule; t represents the total scheduling time period number, and T is taken as 24; c (t) represents the price of the water, wind and light bundled on-line electricity in t period, unit/(MW & h); i represents the ith hydropower station downstream of Yazhejiang; n represents the total number of hydropower stations at the downstream of Yazhenjiang, and n is 5; n is a radical of _h,i (t) represents the average output, MW, of the ith hydropower station downstream of yamojiang over a period of t; Δ t represents the time, h, at each time interval; n is a radical of _retotal Represents the system residual load, MW; n is a radical of _s (t) represents the average load, MW, of the system over a period t; v _re Indicating the remaining load fluctuation, MW of the system ² /h；N _re (t) represents the average residual load, MW, of the system over a period of t;

represents the remaining load average, MW, of the system over the T period;

constraint conditions are as follows:

and (3) water balance constraint:

V _i,t+1 ＝V _i,t +(I _i,t -Q _i,t -q _i,t )×Δt (14)

in the formula (14), V _i,t+1 The water storage capacity of the ith reservoir at the end of the t period, m ³ ；V _i,t The water storage capacity m of the ith reservoir at the beginning of the t period ³ ；I _i,t The storage flow of the ith reservoir in the t period, m ³ /s；Q _i,t The generated flow of the ith reservoir in the t period, m ³ /s；q _i,t Is the water discharge of the ith reservoir in the t period ³ /s；

And (3) water and electricity output restraint:

in the formula (15), the reaction mixture is,

representing the output upper limit, MW, of the ith hydropower station in the t period;

representing the output lower limit, MW, of the ith hydropower station in the t period; n is a radical of _h,i,t The output power, MW, of the ith hydropower station in the t period;

and (4) library capacity constraint:

in the formula (16), the compound represented by the formula,

represents the ith hydropower station upper capacity limit, m3, during the t-th period;

represents the lower limit of the storage capacity of the ith hydropower station m in the t-th period ³ ；V _i,t For the ith hydropower station storage capacity in the t period，m ³ ；

Water level restraint:

in the formula (17), the compound represented by the formula (I),

representing the upper limit of the water level of the ith reservoir m in the t-th time period;

representing the lower limit of the water level of the ith reservoir m in the t-th time period; z _i,t The ith reservoir water level m in the t time period;

and (4) lower leakage flow rate constraint:

in the formula (18), the reaction mixture,

represents the maximum discharge rate m of the ith reservoir in the t-th period ³ /s；

Represents the minimum discharge rate m of the ith reservoir in the t period ³ /s；Q _i，t Represents the discharge rate m of the ith reservoir in the t period ³ /s；

Non-negative constraints: the above variables are all non-negative values;

step 5.2, constructing a wind-solar benefit model with the operation cycle of 1 day and the minimum calculation time period of 1 hour according to the water-wind-light operation scene 1 set in the step 3;

the wind and light benefit expression is as follows:

R _1,wp ＝c(t)×[N _w (t)+N _p (t)]×Δt (19)

in the formula (19), R _1,wp Representing the total profit of water and wind and solar complementation; n is a radical of _w (t) represents the average output, MW, of the wind power plant at the downstream of Yazhejiang in the period t; n is a radical of _p (t) represents the average output of the photovoltaic electric field at the downstream of the Yajianjiang in a period t, MW;

constraint conditions are as follows:

wind-solar output constraint:

in the formula (20), N _w,t Representing the average output, MW, of the downstream wind farm over a period of t; n is a radical of _p,t Represents the average contribution, MW, of the downstream photovoltaic electric field over a period of t;

representing the average simulation output of wind power, MW, in the t-th period;

representing the photovoltaic average simulated output, MW, in the t-th period;

in the formula (21), N _s,t Represents the average load of the power system during the t-th period;

grid connection constraint:

N _s,t ≥N _h,i,t +N _w,t +N _p,t (22)

non-negative constraints: the above variables are all non-negative values;

step 5.3, constructing a water-wind light benefit model with a scheduling cycle of 1 day and a minimum scheduling time period of 1 hour according to the water-wind light operation scenes 1 and 3 set in the step 3;

the specific objective function is as follows:

the water-wind-light complementary system has the maximum benefit:

the system residual load is minimum:

in the formulae (23) to (24), R ₃ Representing the total profit of water and wind and light complementation;

constraint conditions are as follows:

and (3) water balance constraint:

V _i,t+1 ＝V _i,t +(I _i,t -Q _i,t -q _i,t )×Δt (26)

in the formula (26), V _i,t+1 The water storage capacity of the ith reservoir at the end of the t period, m ³ ；V _i,t The water storage capacity m of the ith reservoir at the beginning of the t period ³ ；I _i,t The storage flow of the ith reservoir in the t period, m ³ /s；Q _i,t The generated flow of the ith reservoir in the t period, m ³ /s；q _i,t Is the water discharge of the ith reservoir in the t period ³ /s；

And (3) water and electricity output restraint:

in the formula (27), the reaction mixture is,

the output upper limit, MW, of the ith hydropower station in the t period;

the output lower limit, MW, of the ith hydropower station in the t period; n is a radical of _h,i,t The output power, MW, of the ith hydropower station in the t period;

and (4) library capacity constraint:

in the formula (28), the reaction mixture is,

is the ith hydropower station capacity upper limit m in the t period ³ ；

Is the ith hydropower station reservoir capacity lower limit m in the t period ³ ；V _i,t For the ith hydropower station reserve capacity, m, of the t period ³ ；

Water level restraint:

in the formula (29), the reaction mixture,

the upper limit of the water level of the ith reservoir m in the t-th time period;

the lower limit of the water level of the ith reservoir m in the t period; z _i,t The ith reservoir water level m in the t time period;

and (3) restricting the downward flow:

in the formula (30), the reaction mixture,

the maximum discharge capacity m of the ith reservoir in the t period ³ /s；

The minimum discharge rate m of the ith reservoir in the t period ³ /s；Q _i，t Represents the discharge rate m of the ith reservoir in the t period ³ /s；

Wind power photovoltaic constraint:

for the water and wind operation scenario 2:

N _w,t +N _p,t ＝min[(N _s,t -N _h,i,t ),(N _s,w,t +N _s,p,t )] (31)

in formula (31), N _s,w,t Average simulation output, MW, of the t period of wind power; n is a radical of _s,p,t The average simulated output, MW, of the photovoltaic at the t-th time period;

for water and wind operation scenario 3:

N _w,t +N _p,t ＝N _s,w,t +N _s,p,t (32)

grid connection constraint:

N _s,t ≥N _h,i,t +N _w,t +N _p,t (33)

non-negative constraints: the above variables are all non-negative values;

step 5.4, solving the model in the step 5.3 by adopting a particle swarm algorithm;

the particle swarm algorithm comprises the following operation steps:

firstly, initializing parameters: setting parameters such as particle swarm size, iteration number upper limit, independent variable number and the like, and randomly endowing initial speed and initial position of particles in a specified speed range and a specified search space;

solving the individual optimal solution and the current optimal solution: defining a fitness function, obtaining an individual optimal solution by comparing the fitness of all particles in a generation, and obtaining a current optimal solution by comparing the individual optimums in all current iterations;

and thirdly, updating the speed and the position through the following formulas, and calculating a new fitness function value according to the speed and the position:

V _i+1 ＝ω×V _i +c ₁ ×r ₁ ×(P _best -P _i )+c ₂ ×r ₂ ×(G _best -P _i ) (34)

P _i+1 ＝P _i +V _i+1 (35)

in formulae (34) to (35), V _i+1 Is the next generation particle velocity; omega is the cause of inertiaA seed; v _i Is the current particle velocity; c. C ₁ And c ₂ Is a learning factor; r is a radical of hydrogen ₁ 、r ₂ Is [0,1 ]]A random number generated above; p _best For individual optimization, P _i Is the position of the particle, G _best Is the current optimal solution; p _i+1 Is the next generation particle location;

fourthly, iterative updating: calculating the fitness of each generation of individuals to update the optimal solution of the individuals and the current optimal solution:

and (5) termination condition: when the iteration times reach the preset upper time limit of times, terminating the iteration, wherein the current optimal solution is the global optimal solution;

and 6, analyzing the damage and benefit conditions of the whole cascade hydropower station, each hydropower station in the cascade hydropower station and the wind-light power station in the implementation of the water-wind-light integration process of the clean energy base according to the results of the step 5 in the three water-wind-light operation scenes in four seasons, namely spring, summer, autumn and winter, and according to the damage and benefit index standard selected in the step 2, and quantizing the damage and benefit conditions in a chart mode.

Examples

A clean energy base at the downstream of a Yashujiang river, in which 1470 ten thousand kW hydropower stations, 701.4 ten thousand kW wind power stations and 567.5 ten thousand kW photovoltaic power station installation are planned, is taken as a practical object, and hydropower related data are provided by Yashujiang river basin hydropower development limited;

65 wind power plants and 19 photovoltaic electric fields which are constructed according to the Yazhenjiang downstream clean energy planning;

calculating similar distances by using wind speed sequences of all wind power stations by adopting a K-means algorithm, and dividing the wind power stations planned by the clean energy base at the downstream of Yazhenjiang into 6 clusters; according to the change characteristics of solar irradiance sequences of all photovoltaic power stations in the day, the photovoltaic power stations with similar latitude are divided into the same cluster, and finally the photovoltaic power stations are divided into 3 clusters;

respectively reducing the power samples of each wind power and photovoltaic cluster in winter and spring and summer and autumn through a synchronous back-substitution reduction method to obtain 5 output scenes of each wind power cluster in winter and spring, 5 output scenes of each photovoltaic cluster in winter and spring and 5 output scenes of each photovoltaic cluster in summer and autumn, and combining each wind power station with each clusterThe 5 output scenes obtained in winter and spring are combined, the 5 output scenes obtained in summer and autumn are combined, the wind power clusters of the Yajiangjiang clean energy base in winter and spring (summer and autumn) are 6, and the number of the total wind power output scenes of the Yajiangjiang clean energy base in winter and spring and summer and autumn is 5 respectively ⁶ 15625 pieces; the photoelectric scene combination method is similar: the number of total photoelectric output scenes of the Yajiajiang clean energy base in winter and spring (summer and autumn) is 5 ³ 125, i.e.: 15625 wind power total output scenes and 125 photovoltaic total output scenes are obtained in two periods of winter spring and autumn and summer respectively, and a K-means clustering method is adopted to reduce a plurality of wind and light output scenes to obtain 5 wind power typical output scenes (shown in figures 2 and 3) and 5 photoelectric typical output scenes (shown in figures 4 and 5) with strong representativeness in winter spring, summer and autumn respectively;

according to announcements issued by the development and reform committee of Sichuan province, 12 months-4 months of the next year are low water periods of the downstream river segment of the Yazhenjiang, 5 months and 11 months are low water periods of the downstream river segment of the Yazhenjiang, and 6 months-10 months are high water periods of the downstream river segment of the Yazhenjiang, the method aims to scientifically measure the losses and benefits of the downstream clean energy base of the Yazhenjiang before and after water-saving wind-solar complementary operation in different seasons, and respectively selects 2 months 5 days, 5 months 19 days, 8 months 13 days and 11 months 30 days as incoming water typical days of spring, summer, autumn and winter according to the corresponding months of the low water periods, the low water periods and the low water periods of the Yazhenjiang as typical inflow scenes of water and electricity;

each hydropower station in step water and electricity inside among the water and wind integrated clean energy base of the river of elegant rice huller includes: brocade I grade, brocade II grade, official place, second beach, and Tongzhai forest;

as can be derived from fig. 6 to 21:

(1) the total benefits of the water and wind power system of the scene 2 and the scene 3 are obviously improved compared with the scene 1, wherein the benefits of the wind power photovoltaic are obviously increased, and the benefits of the cascade hydropower have losses of different degrees, which shows that the benefits of the cascade hydropower, namely peak clipping and valley filling, are sacrificed in the water and wind power complementation process, and a great contribution is made to wind power photovoltaic absorption; the total benefits of the water and wind system of the scene 2 and the scene 3 are basically equal, but compared with the scene 2, the wind and light benefit increment of the scene 3 is more remarkable, and the wind and light electricity abandon amount is less.

(2) The benefits of each hydropower station in the cascade hydropower stations of the scenes 2 and 3 are different from those of the scene 1 in increase and decrease, but the hydropower stations with impaired benefits are most. Compared with scenario 2, the benefit reduction amount of each hydropower station of scenario 3 is obviously larger than the benefit increase amount thereof.

Claims (6)

1. The method for quantifying the multi-subject benefit change in the multi-energy complementary operation of the energy base is characterized by comprising the following steps of:

step 1, extracting a wind and light typical output scene;

step 2, selecting the loss indexes of cascade hydropower, wind power and photovoltaic;

step 3, setting a water wind light operation scene;

step 4, selecting inflow scenes of the hydropower station, and selecting output scenes of the wind power station and the photovoltaic station;

step 5, constructing and solving a water-wind light benefit model;

and 6, analyzing the damage and benefit conditions of the whole cascade hydropower station, each hydropower station in the cascade hydropower station and the wind-light power station in the implementation of the water-wind-light integration process of the clean energy base according to the results of the four seasons in spring, summer, autumn and winter in the step 5 under the three water-wind-light operation situations and the damage and benefit index standard selected in the step 2, and quantizing the damage and benefit conditions in a chart mode.

2. The method for quantifying the multi-subject benefit variation in the multi-energy complementary operation of an energy base according to claim 1, wherein the specific process of step 1 is as follows:

step 1.1, respectively establishing a wind energy resource virtual monitoring point and a light energy resource virtual monitoring point in a Greenwich platform and Meteonorm software according to the latitude and longitude of a wind power plant and the latitude and longitude of a photovoltaic power plant planned and constructed by clean energy, and acquiring data for wind and light power simulation from the established virtual monitoring points;

the data acquired by the wind energy resource virtual monitoring point comprises the following data: wind speed, wind direction; the data acquired by the optical energy resource virtual monitoring point comprises the following data: annual and intraday horizontal plane total radiation, annual and intraday horizontal plane scattered radiation, ambient temperature;

step 1.2, drawing a wind speed annual intra-day change broken line graph, a wind speed and wind direction rose graph and a solar irradiation intensity annual intra-day change broken line graph, and analyzing annual and intra-day changes of wind speed, wind direction and solar irradiance to obtain a clean energy wind and light resource time-varying rule;

step 1.3, calculating similar distances by using wind speed sequences of various wind power stations by adopting a K-means algorithm, dividing the wind power stations of a clean energy base into K _ wind clusters, and dividing the photovoltaic power stations with similar latitudes into the same cluster according to the change rule of the solar irradiance sequences of the photovoltaic power stations within the year and the day, so that the photovoltaic power stations are divided into K _ pv clusters;

the division rule of the same cluster is as follows: the latitude span from the photovoltaic power station at the lowest latitude to the photovoltaic power station at the highest latitude in the clean energy base of the first photovoltaic cluster is not more than 0.5 degrees, if the latitude difference between the photovoltaic power station at the highest latitude and the adjacent photovoltaic power station at the higher latitude in the cluster is not more than 0.2 degrees, the adjacent photovoltaic power station at the higher latitude is also brought into the cluster, and the other photovoltaic clusters are the photovoltaic power stations contained in the divided clusters; dividing other photovoltaic clusters according to a first cluster division principle;

step 1.4, simulating the output process of the wind power station in each cluster of the clean energy base wind power station by using a wind power physical model to obtain an annual power sequence of each cluster of the wind power station, and simulating the output process of the photovoltaic power station in each cluster of the clean energy base photovoltaic power station by using a photovoltaic power physical model to obtain an annual power sequence of each cluster of the photovoltaic power station;

the wind power physical model formula is as follows:

in the formula (2), P _w The power generation power of the wind turbine generator is W; c _P The wind energy utilization coefficient of the wind turbine generator is; a is the swept area of the impeller, m ² (ii) a ρ isAir Density, kg/m ³ (ii) a v is wind speed, m/s; v. of ₁ Cutting into wind speed, m/s; v. of _N The rated wind speed of the wind turbine generator is m/s; p _e Rated power, W, of the wind turbine generator; v. of ₂ Cutting out wind speed m/s;

the photovoltaic power physical model formula is as follows:

in the formula (3), P _PV,t The generated power of the photovoltaic panel at the moment t is W; p _stc The output of a single photovoltaic panel under standard conditions, W; i is _r,t Is the actual radiation intensity at time t, W/m ² ；I _stc Is the intensity of the corresponding solar radiation under standard conditions, W/m ² ；δ _t Is the power temperature coefficient of the photovoltaic panel; t is _t The temperature of the photovoltaic panel at time t is DEG C; t is _stc Is the temperature T under standard conditions _stc ＝25℃；

Step 1.5, respectively reducing the generated power samples in two periods of winter and spring and summer and autumn in each cluster of the wind power station through a synchronous back-substitution subtraction method, wherein z _ wind output scenes are obtained in the two periods, and reducing the generated power samples in two periods of winter and spring and summer and autumn in each cluster of the photovoltaic power station through the synchronous back-substitution subtraction method, wherein z _ pv output scenes are obtained in the two periods;

respectively combining z _ wind output scenes obtained in winter and spring and z _ wind output scenes obtained in summer and autumn of each cluster of the wind power station, respectively obtaining m _ wind total output scenes in winter and spring and in summer and autumn, respectively combining z _ pv typical output scenes obtained in winter and spring and z _ pv typical output scenes obtained in summer and autumn of each cluster of the photovoltaic power station, respectively obtaining m _ pv total output scenes in winter and spring and in summer and autumn;

the method comprises the steps of respectively reducing m _ wind total output scenes obtained by a wind power station in winter and spring, m _ wind total output scenes obtained by the wind power station in summer and autumn, m _ pv total output scenes obtained by the photovoltaic power station in winter and spring, and m _ pv total output scenes obtained by the photovoltaic power station in summer and autumn by adopting a K-means clustering method to obtain a wind power station typical output scene in winter and spring, a wind power station typical output scene in summer and autumn, a photovoltaic power station typical output scene in winter and spring, and a photovoltaic power station typical output scene in summer and autumn.

3. The method for quantifying the multi-subject benefit variation in the multi-energy complementary operation of an energy base according to claim 1, wherein the specific process of step 2 is as follows:

step 2.1, collecting characteristic quantities capable of expressing cascade hydroelectric power, wind power and photovoltaic loss, wherein the characteristic quantities comprise power generation quantity and power generation income of each power supply, wind-solar electricity abandonment quantity and electricity abandonment loss, water abandonment quantity of hydroelectric power, upward peak-shaving climbing compulsion degree of hydroelectric power, power shortage probability caused by each power supply and cascade hydroelectric volatility;

and 2.2, selecting the generated energy, the power generation income, the wind-light electricity abandonment quantity and the wind-light electricity abandonment loss of each power supply as indexes of cascade hydropower, wind power and photovoltaic loss in the clean energy base.

4. The method for quantifying the multi-subject benefit variation in the multi-energy complementary operation of an energy base according to claim 1, wherein the specific process of step 3 is as follows:

the water-wind-light integrated output process needs to meet the load fluctuation of a power system, and 3 water-wind-light operation scenes are set:

water wind light operation scene 1, step water and electricity are not matched with wind and light absorption: the cascade hydroelectric power is generated according to the load process of the power system, and the wind and light are on line on the premise of meeting the residual load process of the power system;

water wind light operation scene 2, step water and electricity are not completely matched with wind and light absorption: the cascade hydroelectric power generates electricity according to equivalent load, and when the integrated water-wind-light output process does not meet the system load requirement, wind-light abandoning is adopted;

water wind light operation scene 3, step water and electricity are completely matched with wind and light absorption: the cascade hydroelectric power is used for generating power according to equivalent load, and when the water-wind-light comprehensive output process does not meet the system load requirement, the water and the hydroelectric power are abandoned.

5. The method for quantifying the multi-subject benefit variation in the multi-energy complementary operation of an energy base according to claim 2, wherein the specific process of step 4 is as follows:

step 4.1, selecting a typical day from spring, summer, autumn and winter according to the representativeness of the generated flow of each period of the hydropower as a typical inflow scene of the hydropower;

step 4.2, adopting the wind power station output scene obtained in the step 1.5 to obtain a wind power station typical output scene in winter and spring and a wind power station typical output scene in summer and autumn;

and 4.3, adopting the photovoltaic power station output scene obtained in the step 1.5 to obtain a photovoltaic power station typical output scene in winter and spring and a photovoltaic power station typical output scene in summer and autumn.

6. The method for quantifying the multi-subject benefit variation in the multi-energy complementary operation of an energy base according to claim 1, wherein the specific process of step 5 is as follows:

step 5.1, constructing a cascade hydroelectric benefit model with a scheduling cycle of 1 day and a minimum scheduling time interval of 1 hour according to the water, wind and light operation scene 1 set in the step 3;

the specific objective function is as follows:

the step hydroelectric benefit is the biggest:

the remaining load of the power system is minimum:

the power system residual load fluctuation is minimum:

in the formulae (11) to (13), R _1,h Representing the profit and element of the cascade hydropower; t represents a certain time period of the schedule; t represents the total scheduling time period number, and T is taken as 24; c (t) represents the price of the water, wind and light bundled on-line electricity in t period, unit/(MW & h); i denotes the ith downstream hydropower station; n represents the total number of the downstream hydropower stations, and n is 5; n is a radical of _h,i (t) represents the average output, MW, of the ith downstream hydropower station over the period of t; Δ t represents the time, h, at each time interval; n is a radical of _retotal Represents the system residual load, MW; n is a radical of _s (t) represents the average load, MW, of the system over a period t; v _re Indicating the remaining load fluctuation, MW of the system ² /h；N _re (t) represents the average residual load, MW, of the system over a period of t;

represents the remaining load average, MW, of the system over the T period;

constraint conditions are as follows:

and (3) water balance constraint:

V _i,t+1 ＝V _i,t +(I _i,t -Q _i,t -q _i,t )×Δt (14)

in the formula (14), V _i,t+1 The water storage capacity of the ith reservoir at the end of the t period, m ³ ；V _i,t The water storage capacity m of the ith reservoir at the beginning of the t period ³ ；I _i,t The storage flow of the ith reservoir in the t period, m ³ /s；Q _i,t The generated flow of the ith reservoir in the t period, m ³ /s；q _i,t Is the water discharge of the ith reservoir in the t period ³ /s；

And (3) water and electricity output restraint:

in the formula (15), the reaction mixture is,

representing the output upper limit, MW, of the ith hydropower station in the t period;

representing the output lower limit, MW, of the ith hydropower station in the t period; n is a radical of _h,i,t Output power, MW, of the ith hydropower station in the t period;

and (4) library capacity constraint:

in the formula (16), the compound represented by the formula,

represents the ith hydropower station upper capacity limit, m3, during the t-th period;

represents the lower limit of the storage capacity of the ith hydropower station m in the t-th period ³ ；V _i,t For the ith hydropower station storage capacity, m, of the t-th time period ³ ；

Water level restraint:

in the formula (17), the compound represented by the formula (I),

representing the upper limit of the water level of the ith reservoir m in the t-th time period;

representing the lower limit of the water level of the ith reservoir m in the t-th time period; z _i,t The ith reservoir water level m in the t time period;

and (3) restricting the downward flow:

in the formula (18), the reaction mixture,

represents the maximum discharge rate m of the ith reservoir in the t-th time period ³ /s；

Represents the minimum discharge rate m of the ith reservoir in the t period ³ /s；Q _i，t Represents the discharge rate of the ith reservoir in the t period, m ³ /s；

Non-negative constraints: the above variables are all non-negative values;

step 5.2, constructing a wind-solar benefit model with the operation cycle of 1 day and the minimum calculation time period of 1 hour according to the water-wind-light operation scene 1 set in the step 3;

the wind and light benefit expression is as follows:

R _1,wp ＝c(t)×[N _w (t)+N _p (t)]×Δt (19)

in the formula (19), R _1,wp Representing the total profit of water and wind and light complementation; n is a radical of _w (t) represents the average contribution, MW, of the downstream wind farm over a period of t; n is a radical of _p (t) represents the average contribution, MW, of the downstream photovoltaic electric field over a period of t;

constraint conditions are as follows:

wind-solar output constraint:

in the formula (20), N _w,t Representing the average output, MW, of the downstream wind power plant in a period t; n is a radical of _p,t Represents the average contribution, MW, of the downstream photovoltaic electric field over a period of t;

the average simulation output of wind power in the t-th time period MW is represented;

representing the photovoltaic average simulated output, MW, in the t-th period;

in the formula (21), N _s,t Represents the average load of the power system during the t-th period;

grid connection constraint:

N _s,t ≥N _h,i,t +N _w,t +N _p,t (22)

non-negative constraints: the above variables are all non-negative values;

step 5.3, constructing a water-wind light benefit model with a scheduling cycle of 1 day and a minimum scheduling time period of 1 hour according to the water-wind light operation scenes 1 and 3 set in the step 3;

the specific objective function is as follows:

the water-wind-light complementary system has the maximum benefit:

the system residual load is minimum:

in the formulae (23) to (24), R ₃ Representing the total profit of water and wind and light complementation;

constraint conditions are as follows:

and (3) water balance constraint:

V _i,t+1 ＝V _i,t +(I _i,t -Q _i,t -q _i,t )×Δt (26)

in the formula (26), V _i,t+1 The water storage capacity of the ith reservoir at the end of the t period, m ³ ；V _i,t The initial i-th reservoir storage capacity m in the t-th period ³ ；I _i,t The storage flow of the ith reservoir in the t period, m ³ /s；Q _i,t For the ith water in the t periodGenerated flow of reservoir, m ³ /s；q _i,t Is the water discharge of the ith reservoir in the t period ³ /s；

And (3) water and electricity output restraint:

in the formula (27), the reaction mixture is,

the output upper limit, MW, of the ith hydropower station in the t period;

the output lower limit, MW, of the ith hydropower station in the t period; n is a radical of _h,i,t The output power, MW, of the ith hydropower station in the t period;

and (4) library capacity constraint:

in the formula (28), the reaction mixture is,

the upper limit of the storage capacity of the ith hydropower station m in the t period ³ ；

Is the ith hydropower station reservoir capacity lower limit m in the t period ³ ；V _i,t For the ith hydropower station storage capacity, m, of the t-th time period ³ ；

Water level restraint:

in the formula (29), the reaction mixture,

the upper limit of the water level of the ith reservoir m in the t-th time period;

the lower limit of the water level of the ith reservoir m in the t period; z _i,t The ith reservoir water level m in the t time period;

and (3) restricting the downward flow:

in the formula (30), the reaction mixture,

the maximum discharge capacity m of the ith reservoir in the t period ³ /s；

The minimum discharge rate m of the ith reservoir in the t period ³ /s；Q _i，t Represents the discharge rate m of the ith reservoir in the t period ³ /s；

Wind power photovoltaic constraint:

for the water and wind operation scenario 2:

N _w,t +N _p,t ＝min[(N _s,t -N _h,i,t ),(N _s,w,t +N _s,p,t )] (31)

in formula (31), N _s,w,t The average simulation output, MW, of the t-th period of wind power is obtained; n is a radical of _s,p,t The average simulated output, MW, of the photovoltaic at the t-th time period;

for the water and wind power operation scenario 3:

N _w,t +N _p,t ＝N _s,w,t +N _s,p,t (32)

grid connection constraint:

N _s,t ≥N _h,i,t +N _w,t +N _p,t (33)

non-negative constraints: the above variables are all non-negative values;

and 5.4, solving the model in the step 5.3 by adopting a particle swarm algorithm.

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