Drainage basin water, wind and light medium and long term scheduling decision-making method

Abstract

The invention provides a basin water-wind-solar medium-and-long-term scheduling decision-making method. The method comprises the following steps: obtaining a water-electricity historical resource long sequence and a wind-solar historical resource long sequence of a research basin; the method comprises the following steps: establishing a water-wind-light medium-and-long-term long series optimization scheduling model and solving to obtain a hydropower station reservoir water storage process long sequence, further establishing a cascade total water storage decision model and a reservoir water storage decision model to obtain time period end total water storage of cascade hydropower stations and time period end water storage of each stage of hydropower station, and correcting by adopting a water quantity correction model to obtain a water quantity correction result. And forming a scheduling decision. The invention provides a basin water-wind-light medium-and-long-term scheduling decision-making method, which is based on long-series water-wind-light medium-and-long-term optimization scheduling results, adopts a gradient lifting regression method to train to obtain a reservoir water storage decision-making model and a cascade total water storage decision-making model, and generates time period end water storage decisions of a single-stage hydropower station and a cascade hydropower station according to the time period end water storage decisions. And finally, a scheduling decision is obtained by adopting a water volume correction model, and technical support is provided for medium-and-long-term reliable operation of cascade reservoirs and water, wind and light.

Description

Technical Field

[0001] The present invention belongs to the field of optimized operation of hydropower energy systems, and specifically relates to a medium- and long-term scheduling decision method for water, wind and solar power in a river basin. Background Art

[0002] In the medium and long term, the inflow of hydropower station reservoirs and the output of wind power and photovoltaic power are uncertain. The existing medium and long term forecasting methods are not enough to meet the requirements of accurate scheduling. In addition, if the scheduling plan is not accurately formulated in the medium and long term, if special meteorological conditions occur in the short term, there may not be enough hydropower capacity and electricity for new energy compensation scheduling. How to generate scheduling decisions based on the water, wind and solar conditions faced, and guide the medium and long term random scheduling of cascade hydropower consumption, wind power and photovoltaic power, is an urgent problem to be solved. Summary of the invention

[0003] In view of the defects of the prior art, the present invention provides a medium- and long-term scheduling decision method for water, wind and solar power in a river basin, which can effectively solve the above problems.

[0004] The technical solution adopted by the present invention is as follows:

[0005] The present invention provides a medium- and long-term scheduling decision method for water, wind and solar power in a river basin, comprising the following steps:

[0006] Step S1, obtaining the water-wind-solar topological structure of the study basin: the cascade hydropower stations in the study basin have multiple levels of hydropower stations, and each level of hydropower station transmits electric energy with the corresponding wind power and photovoltaic power through the same transmission channel;

[0007] Step S2, obtaining a long sequence of historical hydropower resources and a long sequence of historical wind and solar resources in the study basin;

[0008] Step S3, with the goal of maximizing the sum of the minimum outputs of hydropower stations at all levels in the basin, maximizing the total power generation of hydropower, wind and solar power, and minimizing water abandonment, establish a medium- and long-term series optimization scheduling model for hydropower, wind and solar power;

[0009] Step S4, inputting the long sequence of hydropower historical resources and the long sequence of wind and solar historical resources obtained in step S2 into the medium- and long-term long sequence optimization scheduling model of water, wind and solar power, and solving to obtain the long sequence of water storage process of the hydropower station reservoir that best matches the long sequence of wind and solar historical resources;

[0010] Step S5, analyzing the relationship between the long sequence of the reservoir water storage process of the hydropower station, the long sequence of the hydropower historical resources and the long sequence of the wind and solar historical resources, and constructing a cascade total water storage decision model and a reservoir water storage decision model;

[0011] Step S6, obtaining the hydropower resources and wind and solar resources of the study basin in the decision cycle, and inputting them into the cascade total water storage decision model and the reservoir water storage decision model respectively, to obtain the total water storage at the end of the period of the cascade hydropower station and the water storage at the end of the period of each hydropower station;

[0012] Step S7, using the water volume correction model, correcting the end-of-period water storage of each level of hydropower station, obtaining the corrected end-of-period water storage of each level of hydropower station, and forming a dispatching decision.

[0013] Preferably, the cascade hydropower stations in the study basin have For each hydropower station , , and wind power and Photovoltaic Transmitting electrical energy through the same transmission channel;

[0014] The method for obtaining the long sequence of historical hydropower resources and the long sequence of historical wind and solar resources is: determine the range of historical years, and obtain historical hydropower resources and historical wind and solar resources in each historical year; the historical hydropower resources of multiple historical years form the long sequence of historical hydropower resources; the historical wind and solar resources of multiple historical years form the long sequence of historical wind and solar resources; wherein the historical wind and solar resources include historical wind power resources and historical photovoltaic resources.

[0015] Preferably, in each historical year, the hydropower historical resources obtained are In each period of the historical year Inbound flow The wind power historical resources obtained are wind power In each period of the historical year Available power The photovoltaic historical resources obtained are photovoltaic In each period of the historical year Available power .

[0016] Preferably, the wind power In each period of the historical year Available power , obtained by:

[0017] for Historical years, collect information for each historical year Wind speed and wind power Output curve, , calculated in each historical year Wind speed and wind power The correlation coefficient of output is obtained Correlation coefficient The correlation coefficients are compared, and the historical year with the greatest correlation is selected, and the wind speed and wind power of the historical year with the greatest correlation are calculated. Output is analyzed to establish wind speed and wind power Statistical model of output;

[0018] Each historical year The wind speed is input into the wind speed and wind power The statistical model of output is used to obtain the historical year Wind power Time period Available power ;

[0019] The photovoltaic In each period of the historical year Available power Obtained through the following methods:

[0020] for Historical years, collect information for each historical year sunshine hours, temperature and photovoltaic Output curve, , calculated in each historical year sunshine hours, temperature and photovoltaic The correlation coefficient of output is obtained Correlation coefficient The correlation coefficients were compared, and the historical year with the greatest correlation was selected, and the sunshine hours, temperature and photovoltaic Output is analyzed to establish the relationship between sunshine hours, temperature and photovoltaic Statistical model of output;

[0021] Each historical year The sunshine hours and temperature are input into the sunshine hours, temperature and photovoltaic The statistical model of output is used to obtain the historical year Photovoltaic Time period Available power ;

[0022] Preferably, the medium- and long-term series optimization scheduling model for water, wind and solar power includes an objective function and constraints:

[0023] The objective function is:

[0024] (1)

[0025] Among them: the scheduling cycle is one year; The time period included in the scheduling cycle the number of For hydropower station The minimum output of Minimum output for water and scenery; is the output of hydropower station i in time period t; is the power delivered by wind power i in time period t; is the power delivered by photovoltaic i in time period t; For hydropower station The comprehensive output coefficient; is the outflow of hydropower station i in period t; is the power generation flow of hydropower station i in time period t; is the generating head of hydropower station i in period t; For the period Number of hours possessed;

[0026] The constraints are:

[0027] ① Restrictions on hydropower output:

[0028] (2)

[0029] Where: The generating head of hydropower station i in time period t The following relations are satisfied:

[0030] (3)

[0031] in: For hydropower station The reservoir water level at time period t is determined by the water storage capacity and satisfies the relationship: ,represent Depend on Decide; and , respectively hydropower station The amount of water stored in the reservoir at the beginning of time period t and the amount of water stored in the reservoir at the end of time period t;

[0032] For hydropower station The head loss in period t is related to the hydropower station The power generation flow in time period t is proportional to the square of , satisfying equation (4):

[0033] (4)

[0034] in: For hydropower station Head loss coefficient;

[0035] For hydropower station The tailwater level in time period t is determined by the hydropower station Outbound flow in period t Decision; Due to the hydropower station Outbound flow in period t , including hydroelectric power stations The power generation flow in time period t and abandoned water, therefore, hydropower stations The power generation flow in time period t and hydropower station Outbound flow in period t Satisfies the relationship (5):

[0036] (5)

[0037] ② Output limit:

[0038] Hydropower Station Minimum output Satisfies the relationship (6):

[0039] (6)

[0040] Minimum output of water, wind and solar Satisfies the relationship (7):

[0041] (7)

[0042] ③ Expected output limit of hydropower station and output limit of hydropower station:

[0043] Hydropower Station Output in period t The expected output limit of equation (8) and the hydropower output limit of equation (9) are:

[0044] (8)

[0045] in: is the expected output of hydropower station i in period t, and the generating head of hydropower station i in period t Related;

[0046] (9)

[0047] in: and are the lower and upper limits of the output of hydropower station i in time period t, respectively;

[0048] ④Channel capacity limitation:

[0049] (10)

[0050] in: For hydropower station Channel capacity limitations;

[0051] Wind power The power delivered in time period t , which is less than the wind power in the historical years adopted this time. In the period Available power Photovoltaic The power delivered in time period t , which is less than the photovoltaic In the period Available power , that is, satisfying equations (11) and (12):

[0052] (11)

[0053] (12)

[0054] ⑤Reservoir water balance:

[0055] For medium- and long-term dispatching, the water lag time of the directly connected upstream reservoir is negligible, and the reservoir water balance relationship is expressed as:

[0056] (13)

[0057] in: The hydropower stations in the historical years used this time In the period Inbound flow ; For hydropower station A collection of directly connected upstream hydroelectric power stations, For hydropower station In the period The outflow of the hydropower station belong ; For the period The number of seconds it has;

[0058] ⑥Reservoir operating water level limit:

[0059] (14)

[0060] in: and Hydropower Station In the period The lower and upper limits of the water level.

[0061] Preferably, step S4 is specifically:

[0062] The hydropower stations in each historical year Time period Inbound flow 、Wind power Time period Available power and photovoltaic Time period Available power Input into the medium- and long-term series optimization scheduling model of water, wind and solar power, call ipopt to solve and obtain the water storage process of the hydropower station reservoir that best matches the historical wind and solar resources of each historical year, and the water storage process of the hydropower station reservoir in multiple historical years forms a long sequence of the water storage process of the hydropower station reservoir; wherein, the water storage process of the hydropower station reservoir includes the hydropower station The amount of water stored in the reservoir at the beginning of time period t , Hydropower Station Outbound flow in period t , Hydropower Station Output in period t , and hydropower stations at all levels The amount of water stored in the reservoir at the beginning of time period t Superposition, to obtain the total water storage capacity of the cascade at the beginning of time period t , the formula is: .

[0063] Preferably, step S5 is specifically:

[0064] Study the total water storage capacity of the cascade at the beginning of time period t in each historical year , the total inflow flow in period t , the total available power of wind power and photovoltaic power in period t , and the total water storage capacity of the cascade at the end of period t The relationship between , , For input, The output of the cascade total water storage decision model ;

[0065] Study the hydropower station i of each historical year in the period Initial reservoir water storage , Time period Inbound flow 、Wind power and Photovoltaic In the period Total available power , and the hydropower station i in the period The reservoir water storage capacity at the end The relationship between , and For input, Hydropower station for export Time period Reservoir storage decision model .

[0066] Preferably, the water volume correction model includes an objective function and constraint conditions;

[0067] The objective function is:

[0068] (15)

[0069] in: , , , , respectively, in the period t of the decision cycle, the hydropower station Output, outflow flow, power generation flow, power generation head;

[0070] and , respectively, in the period t of the decision cycle, wind power Power delivered and photovoltaic Power delivered;

[0071] and , respectively, the time period of hydropower station i in the decision cycle The positive and negative adjustments of the reservoir water storage at the end of the experiment are both positive values;

[0072] For hydropower station The comprehensive output coefficient; For the period The number of seconds it has;

[0073] The constraints include the reservoir water balance constraint during the period of water storage adjustment, the cascade water storage adjustment constraint, the reservoir water storage limit, and the hydropower output relationship constraint, the hydropower station expected output constraint, the hydropower station output constraint, the channel capacity constraint, and the reservoir operating water level constraint; among them, the hydropower output relationship constraint, the hydropower station expected output constraint, the hydropower station output constraint, the channel capacity constraint, and the reservoir operating water level constraint are the same as the corresponding constraints in the medium- and long-term series optimization scheduling model of water, wind, and solar power;

[0074] The water balance constraint of the reservoir during the period considering water storage adjustment is:

[0075] (16)

[0076] in: is the decision cycle of each hydropower station i in the time period Initial reservoir water storage capacity; For hydropower station During the decision cycle Inbound flow of Hydropower Station During the decision cycle The outflow of the hydropower station belong , For hydropower station A collection of directly connected upstream hydroelectric power stations;

[0077] The cascade water storage capacity adjustment constraint is:

[0078] (17)

[0079] The reservoir storage limits are:

[0080] (18)

[0081] and , respectively, hydropower station i in time period The lower and upper limits of reservoir water storage at the end of the year.

[0082] Preferably, step S7 specifically includes:

[0083] Using the water quantity correction model, we can obtain the water quantity correction model for each hydropower station i in the decision cycle. The reservoir water storage capacity at the end Positive adjustment and negative adjustment , and each hydropower station i in the time period Hydropower output , wind power i in the period Output power , photovoltaic i in the period Output power ;

[0084] Each hydropower station i in the period Corrected value of reservoir water storage at the end of ;

[0085] This results in the period of the decision cycle The scheduling decision includes: each hydropower station i in the period Corrected value of reservoir water storage at the end of , each hydropower station i in the period Hydropower output , wind power i in the period Output power , photovoltaic i in the period Output power .

[0086] Preferably, the method further comprises: according to the reservoir water level capacity curve of the hydropower station i, Corrected value of reservoir water storage at the end of Converted to water level for scheduling decision.

[0087] The medium- and long-term scheduling decision method for water, wind and solar power in a river basin provided by the present invention has the following advantages:

[0088] The present invention provides a method for medium- and long-term scheduling decision-making of water, wind and solar power in a river basin. Based on a long series of medium- and long-term optimization scheduling results of water, wind and solar power, a gradient boosting regression method is used to train a reservoir water storage decision model and a cascade total water storage decision model, and end-of-period water storage decisions for single-stage hydropower stations and cascade hydropower stations are generated accordingly. Finally, a water volume correction model is used to obtain a scheduling decision, thereby providing technical support for the medium- and long-term reliable operation of cascade reservoirs and water, wind and solar power stations. BRIEF DESCRIPTION OF THE DRAWINGS

[0089] Figure 1 A flow chart of a medium- and long-term scheduling decision method for water, wind and solar power in a river basin provided by the present invention;

[0090] Figure 2 A diagram showing the water, wind and solar power output decision results provided by an embodiment of the present invention;

[0091] Figure 3 A diagram of the decision results of the water level and power generation flow of a hydropower station provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0092] In order to make the technical problems, technical solutions and beneficial effects solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0093] like Figure 1 As shown, the present invention provides a medium- and long-term scheduling decision method for water, wind and solar power in a river basin, comprising the following steps:

[0094] Step S1, obtaining the water-wind-solar topological structure of the study basin: the cascade hydropower stations in the study basin have multiple levels of hydropower stations, and each level of hydropower station transmits electric energy with the corresponding wind power and photovoltaic power through the same transmission channel;

[0095] Step S2, obtaining a long sequence of historical hydropower resources and a long sequence of historical wind and solar resources in the study basin;

[0096] Step S3, with the goal of maximizing the sum of the minimum outputs of hydropower stations at all levels in the basin, maximizing the total power generation of hydropower, wind and solar power, and minimizing water abandonment, establish a medium- and long-term series optimization scheduling model for hydropower, wind and solar power;

[0097] Step S4, inputting the long sequence of hydropower historical resources and the long sequence of wind and solar historical resources obtained in step S2 into the medium- and long-term long sequence optimization scheduling model of water, wind and solar power, and solving to obtain the long sequence of water storage process of the hydropower station reservoir that best matches the long sequence of wind and solar historical resources;

[0098] Step S5, analyzing the relationship between the long sequence of the reservoir water storage process of the hydropower station, the long sequence of the hydropower historical resources and the long sequence of the wind and solar historical resources, and constructing a cascade total water storage decision model and a reservoir water storage decision model;

[0099] Step S6, obtaining the hydropower resources and wind and solar resources of the study basin in the decision cycle, and inputting them into the cascade total water storage decision model and the reservoir water storage decision model respectively, to obtain the total water storage at the end of the period of the cascade hydropower station and the water storage at the end of the period of each hydropower station;

[0100] Step S7, using the water volume correction model, correcting the end-of-period water storage of each level of hydropower station, obtaining the corrected end-of-period water storage of each level of hydropower station, and forming a dispatching decision.

[0101] The following describes each step of the present invention in detail:

[0102] Step S1, obtaining the water-wind-solar topological structure of the study basin: the cascade hydropower stations in the study basin have multiple levels of hydropower stations, and each level of hydropower station transmits electric energy with the corresponding wind power and photovoltaic power through the same transmission channel;

[0103] Specifically, the present invention is mainly used to study cascade hydropower stations and their supporting wind power stations and photovoltaic stations. For each hydropower station , , and wind power and Photovoltaic Transmit electrical energy through the same transmission channel.

[0104] Step S2, obtaining a long sequence of historical hydropower resources and a long sequence of historical wind and solar resources in the study basin;

[0105] The acquisition method of the long sequence of hydropower historical resources and the long sequence of wind and solar historical resources is as follows: determine the historical year interval range, and obtain the hydropower historical resources and wind and solar historical resources in each historical year; the hydropower historical resources of multiple historical years form the long sequence of hydropower historical resources; the wind and solar historical resources of multiple historical years form the long sequence of wind and solar historical resources; wherein the wind and solar historical resources include wind power historical resources and photovoltaic historical resources. For example, if the historical year interval range is the last 50 years, then the long sequence of hydropower historical resources and the long sequence of wind and solar historical resources formed by 50 years of data are finally obtained; wherein the long sequence of hydropower historical resources includes 50 elements, each of which represents the hydropower historical resources corresponding to one year. Similarly, the long sequence of wind power historical resources includes 50 elements, each of which represents the wind power historical resources corresponding to one year; the long sequence of photovoltaic historical resources includes 50 elements, each of which represents the photovoltaic historical resources corresponding to one year. Each year represents a scheduling cycle or research cycle.

[0106] Furthermore, in each historical year, the hydropower historical resources obtained are In each period of the historical year Inbound flow The wind power historical resources obtained are wind power In each period of the historical year Available power The photovoltaic historical resources obtained are photovoltaic In each period of the historical year Available power .

[0107] In the present invention, the hydropower historical resources are directly acquired hydropower resources; the photovoltaic historical resources are realized by the algorithm of the present invention, specifically, wind power In each period of the historical year Available power , obtained by:

[0108] for Historical years, collect information for each historical year Wind speed and wind power Output curve, , calculated in each historical year Wind speed and wind power The correlation coefficient of output is obtained Correlation coefficient The correlation coefficients are compared, and the historical year with the greatest correlation is selected, and the wind speed and wind power of the historical year with the greatest correlation are calculated. Output is analyzed to establish wind speed and wind power Statistical model of output;

[0109] Each historical year The wind speed is input into the wind speed and wind power The statistical model of output is used to obtain the historical year Wind power Time period Available power ;

[0110] The photovoltaic In each period of the historical year Available power Obtained through the following methods:

[0111] for Historical years, collect information for each historical year sunshine hours, temperature and photovoltaic Output curve, , calculated in each historical year sunshine hours, temperature and photovoltaic The correlation coefficient of output is obtained Correlation coefficient The correlation coefficients were compared, and the historical year with the greatest correlation was selected, and the sunshine hours, temperature and photovoltaic Output is analyzed to establish the relationship between sunshine hours, temperature and photovoltaic Statistical model of output;

[0112] Each historical year The sunshine hours and temperature are input into the sunshine hours, temperature and photovoltaic The statistical model of output is used to obtain the historical year Photovoltaic Time period Available power ;

[0113] Step S3, with the goal of maximizing the sum of the minimum outputs of hydropower stations at all levels in the basin, maximizing the total power generation of hydropower, wind and solar power, and minimizing water abandonment, establish a medium- and long-term series optimization scheduling model for hydropower, wind and solar power;

[0114] The medium- and long-term series optimization scheduling model for water, wind and solar power includes objective functions and constraints:

[0115] The objective function is:

[0116] (1)

[0117] Among them: the scheduling cycle is one year; The time period included in the scheduling cycle the number of For hydropower station The minimum output of Minimum output for water and scenery; is the output of hydropower station i in time period t; is the power delivered by wind power i in time period t; is the power delivered by photovoltaic i in time period t; For hydropower station The comprehensive output coefficient; is the outflow of hydropower station i in period t; is the power generation flow of hydropower station i in time period t; is the generating head of hydropower station i in period t; For the period Number of hours possessed;

[0118] The constraints are:

[0119] ① Restrictions on hydropower output:

[0120] (2)

[0121] Where: The generating head of hydropower station i in time period t The following relations are satisfied:

[0122] (3)

[0123] in: For hydropower station The reservoir water level at time period t is determined by the water storage capacity and satisfies the relationship: ,represent Depend on Decide; and , respectively hydropower station The amount of water stored in the reservoir at the beginning of time period t and the amount of water stored in the reservoir at the end of time period t;

[0124] For hydropower station The head loss in period t is related to the hydropower station The power generation flow in time period t is proportional to the square of , satisfying equation (4):

[0125] (4)

[0126] in: For hydropower station Head loss coefficient;

[0127] For hydropower station The tailwater level in time period t is determined by the hydropower station Outbound flow in period t Decision; Due to the hydropower station Outbound flow in period t , including hydroelectric power stations The power generation flow in time period t and abandoned water, therefore, hydropower stations The power generation flow in time period t and hydropower station Outbound flow in period t Satisfies the relationship (5):

[0128] (5)

[0129] ② Output limit:

[0130] Hydropower Station Minimum output Satisfies the relationship (6):

[0131] (6)

[0132] Minimum output of water, wind and solar Satisfies the relationship (7):

[0133] (7)

[0134] ③ Expected output limit of hydropower station and output limit of hydropower station:

[0135] Hydropower Station Output in period t The expected output limit of equation (8) and the hydropower output limit of equation (9) are:

[0136] (8)

[0137] in: is the expected output of hydropower station i in period t, and the generating head of hydropower station i in period t Related;

[0138] (9)

[0139] in: and are the lower and upper limits of the output of hydropower station i in time period t, respectively;

[0140] ④Channel capacity limitation:

[0141] (10)

[0142] in: For hydropower station Channel capacity limitations;

[0143] Wind power The power delivered in time period t , which is less than the wind power in the historical years adopted this time. In the period Available power Photovoltaic The power delivered in time period t , which is less than the photovoltaic In the period Available power , that is, satisfying equations (11) and (12):

[0144] (11)

[0145] (12)

[0146] ⑤Reservoir water balance:

[0147] For medium- and long-term dispatching, the water lag time of the directly connected upstream reservoir is negligible, and the reservoir water balance relationship is expressed as:

[0148] (13)

[0149] in: The hydropower stations in the historical years used this time In the period Inbound flow ; For hydropower station A collection of directly connected upstream hydroelectric power stations, For hydropower station In the period The outflow of the hydropower station belong ; For the period The number of seconds it has;

[0150] ⑥Reservoir operating water level limit:

[0151] (14)

[0152] in: and Hydropower Station In the period The lower and upper limits of the water level.

[0153] Step S4, inputting the long sequence of hydropower historical resources and the long sequence of wind and solar historical resources obtained in step S2 into the medium- and long-term long sequence optimization scheduling model of water, wind and solar power, and solving to obtain the long sequence of water storage process of the hydropower station reservoir that best matches the long sequence of wind and solar historical resources;

[0154] Step S4 is specifically as follows:

[0155] The hydropower stations in each historical year Time period Inbound flow 、Wind power Time period Available power and photovoltaic Time period Available power Input into the medium- and long-term series optimization scheduling model of water, wind and solar power, call ipopt to solve and obtain the water storage process of the hydropower station reservoir that best matches the historical wind and solar resources of each historical year, and the water storage process of the hydropower station reservoir in multiple historical years forms a long sequence of the water storage process of the hydropower station reservoir; wherein, the water storage process of the hydropower station reservoir includes the hydropower station The amount of water stored in the reservoir at the beginning of time period t , Hydropower Station Outbound flow in period t , Hydropower Station Output in period t , and hydropower stations at all levels The amount of water stored in the reservoir at the beginning of time period t Superposition, to obtain the total water storage capacity of the cascade at the beginning of time period t , the formula is: .

[0156] Step S5, analyzing the relationship between the long sequence of the reservoir water storage process of the hydropower station, the long sequence of the hydropower historical resources and the long sequence of the wind and solar historical resources, and constructing a cascade total water storage decision model and a reservoir water storage decision model;

[0157] Step S5 is specifically as follows:

[0158] Study the total water storage capacity of the cascade at the beginning of time period t in each historical year , the total inflow flow in period t , the total available power of wind power and photovoltaic power in period t , and the total water storage capacity of the cascade at the end of period t The relationship between , , For input, The output of the cascade total water storage decision model ;

[0159] Study the hydropower station i of each historical year in the period Initial reservoir water storage , Time period Inbound flow 、Wind power and Photovoltaic In the period Total available power , and the hydropower station i in the period The reservoir water storage capacity at the end The relationship between , and For input, Hydropower station for export Time period Reservoir storage decision model .

[0160] Step S6, obtaining the hydropower resources and wind and solar resources of the study basin in the decision cycle, and inputting them into the cascade total water storage decision model and the reservoir water storage decision model respectively, to obtain the total water storage at the end of the period of the cascade hydropower station and the water storage at the end of the period of each hydropower station;

[0161] Specifically, the hydropower resources and wind and solar resources of the research basin in the scheduling decision cycle are obtained and input into the cascade total water storage decision model. , get the time period in the scheduling decision cycle Total water storage capacity of the last cascade ; Input the hydropower resources and wind and solar resources of the study basin during the scheduling decision cycle into the reservoir water storage decision model , we can get the number of hydropower stations i in each period in the scheduling decision cycle. The reservoir water storage capacity at the end

[0162] Step S7, using the water volume correction model, correcting the end-of-period water storage of each level of hydropower station, obtaining the corrected end-of-period water storage of each level of hydropower station, and forming a dispatching decision.

[0163] The water storage capacity of each hydropower station at the end of the time period is obtained by step S6, and the total water storage capacity of the cascade at the end of the time period is obtained by adding them together, which may be different from the total water storage capacity of the cascade obtained by step S6. Imbalance, therefore, it is necessary to use the water volume correction model in this step for correction processing.

[0164] The water quantity correction model includes an objective function and constraint conditions;

[0165] The objective function is:

[0166] (15)

[0167] in: , , , , respectively, in the period t of the decision cycle, the hydropower station Output, outflow flow, power generation flow, power generation head;

[0168] and , respectively, in the period t of the decision cycle, wind power Power delivered and photovoltaic Power delivered;

[0169] and , respectively, the time period of hydropower station i in the decision cycle The positive and negative adjustments of the reservoir water storage at the end of the experiment are both positive values;

[0170] For hydropower station The comprehensive output coefficient; For the period The number of seconds it has;

[0171] The constraints include the reservoir water balance constraint during the period of water storage adjustment, the cascade water storage adjustment constraint, the reservoir water storage limit, and the hydropower output relationship constraint, the hydropower station expected output constraint, the hydropower station output constraint, the channel capacity constraint, and the reservoir operating water level constraint; among them, the hydropower output relationship constraint, the hydropower station expected output constraint, the hydropower station output constraint, the channel capacity constraint, and the reservoir operating water level constraint are the same as the corresponding constraints in the medium- and long-term series optimization scheduling model of water, wind, and solar power;

[0172] The water balance constraint of the reservoir during the period considering water storage adjustment is:

[0173] (16)

[0174] in: is the decision cycle of each hydropower station i in the time period Initial reservoir water storage capacity; For hydropower station During the decision cycle Inbound flow of Hydropower Station During the decision cycle The outflow of the hydropower station belong , For hydropower station A collection of directly connected upstream hydroelectric power stations;

[0175] The cascade water storage capacity adjustment constraint is:

[0176] (17)

[0177] The reservoir storage limits are:

[0178] (18)

[0179] and , respectively, hydropower station i in time period The lower and upper limits of reservoir water storage at the end of the year.

[0180] Using the water quantity correction model, we can obtain the water quantity correction model for each hydropower station i in the decision cycle. The reservoir water storage capacity at the end Positive adjustment and negative adjustment , and each hydropower station i in the time period Hydropower output , wind power i in the period Output power , photovoltaic i in the period Output power ;

[0181] Each hydropower station i in the period Corrected value of reservoir water storage at the end of ;

[0182] This results in the period of the decision cycle The scheduling decision includes: each hydropower station i in the period Corrected value of reservoir water storage at the end of , each hydropower station i in the period Hydropower output , wind power i in the period Output power , photovoltaic i in the period Output power .

[0183] In practical applications, according to the reservoir water level capacity curve of hydropower station i, the hydropower station i in the time period Corrected value of reservoir water storage at the end of Converted to water level for scheduling decision.

[0184] An embodiment is described below:

[0185] Tianshengqiao I, Longtan and Yantan hydropower stations are cascade hydropower stations formed from upstream to downstream. The basic parameters and characteristic curves of the reservoirs of Tianshengqiao I, Longtan and Yantan hydropower stations were collected; the long sequence of hydropower historical resources and the long sequence of wind and solar historical resources formed in the 50 years from 1961 to 2010 were obtained; among them, the long sequence of wind and solar historical resources is the long sequence of wind power and photovoltaic output process of each hydropower station generated based on the historical data of the meteorological station.

[0186] Using the basin water, wind and solar mid- and long-term scheduling decision method provided by the present invention, the decision is made as follows: Figure 2 The water, wind and solar output decisions shown, and Figure 3 The water level and power generation flow decision results of the hydropower station are shown. Figure 2 and Figure 3 It can be seen that the medium- and long-term scheduling decision-making method for water, wind and solar power in the river basin provided by the present invention can achieve a smooth water, wind and solar power output process, improve the reliable capacity of the medium- and long-term operation of water, wind and solar power in the river basin, and effectively reduce the abandoned energy of wind and solar power.

[0187] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be considered as the scope of protection of the present invention.

Claims

1. A medium- and long-term scheduling decision method for water, wind and solar power in a river basin, characterized in that: The following steps are involved: Step S1, obtaining the water-wind-solar topological structure of the study basin: the cascade hydropower stations in the study basin have multiple levels of hydropower stations, and each level of hydropower station transmits electric energy with the corresponding wind power and photovoltaic power through the same transmission channel; Step S2, obtaining a long sequence of historical hydropower resources and a long sequence of historical wind and solar resources in the study basin; Step S3, with the goal of maximizing the sum of the minimum outputs of hydropower stations at all levels in the basin, maximizing the total power generation of hydropower, wind and solar power, and minimizing water abandonment, establish a medium- and long-term series optimization scheduling model for hydropower, wind and solar power; Step S4, inputting the long sequence of hydropower historical resources and the long sequence of wind and solar historical resources obtained in step S2 into the medium- and long-term long sequence optimization scheduling model of water, wind and solar power, and solving to obtain the long sequence of water storage process of the hydropower station reservoir that best matches the long sequence of wind and solar historical resources; Step S5, analyzing the relationship between the long sequence of the reservoir water storage process of the hydropower station, the long sequence of the hydropower historical resources and the long sequence of the wind and solar historical resources, and constructing a cascade total water storage decision model and a reservoir water storage decision model; Step S6, obtaining the hydropower resources and wind and solar resources of the study basin in the decision cycle, and inputting them into the cascade total water storage decision model and the reservoir water storage decision model respectively, to obtain the total water storage at the end of the period of the cascade hydropower station and the water storage at the end of the period of each hydropower station; Step S7, using the water volume correction model, correcting the end-of-period water storage of each level of hydropower station, obtaining the corrected end-of-period water storage of each level of hydropower station, and forming a dispatching decision.

2. A basin water, wind and solar mid- and long-term scheduling decision-making method according to claim 1, characterized in that: The cascade hydropower stations in the study basin have For each hydropower station , , and wind power and Photovoltaic Transmitting electrical energy through the same transmission channel; The method for obtaining the long sequence of historical hydropower resources and the long sequence of historical wind and solar resources is: determine the range of historical years, and obtain historical hydropower resources and historical wind and solar resources in each historical year; the historical hydropower resources of multiple historical years form the long sequence of historical hydropower resources; the historical wind and solar resources of multiple historical years form the long sequence of historical wind and solar resources; wherein the historical wind and solar resources include historical wind power resources and historical photovoltaic resources.

3. A basin water, wind and solar mid- and long-term scheduling decision-making method according to claim 2, characterized in that: In each historical year, the hydropower historical resources obtained are In each period of the historical year Inbound flow The wind power historical resources obtained are wind power In each period of the historical year Available power The photovoltaic historical resources obtained are photovoltaic In each period of the historical year Available power .

4. The method for medium- and long-term scheduling decision-making of water, wind and solar power in a river basin according to claim 3 is characterized in that: The wind power In each period of the historical year Available power , obtained by: for Historical years, collect information for each historical year Wind speed and wind power Output curve, , calculated in each historical year Wind speed and wind power The correlation coefficient of output is obtained Correlation coefficient The correlation coefficients are compared, and the historical year with the greatest correlation is selected, and the wind speed and wind power of the historical year with the greatest correlation are calculated. Output is analyzed to establish wind speed and wind power Statistical model of output; Each historical year The wind speed is input into the wind speed and wind power The statistical model of output is used to obtain the historical year Wind power Time period Available power ; The photovoltaic In each period of the historical year Available power Obtained through the following methods: for Historical years, collect information for each historical year sunshine hours, temperature and photovoltaic Output curve, , calculated in each historical year sunshine hours, temperature and photovoltaic The correlation coefficient of output is obtained Correlation coefficient The correlation coefficients were compared, and the historical year with the greatest correlation was selected, and the sunshine hours, temperature and photovoltaic Output is analyzed to establish the relationship between sunshine hours, temperature and photovoltaic Statistical model of output; Each historical year The sunshine hours and temperature are input into the sunshine hours, temperature and photovoltaic The statistical model of output is used to obtain the historical year Photovoltaic Time period Available power .

5. The method for medium- and long-term scheduling decision-making of water, wind and solar power in a river basin according to claim 3 is characterized in that: The medium- and long-term series optimization scheduling model for water, wind and solar power includes objective functions and constraints: The objective function is: （1） Among them: the scheduling cycle is one year; The time period included in the scheduling cycle the number of For hydropower station The minimum output of Minimum output for water and scenery; is the output of hydropower station i in time period t; is the power delivered by wind power i in time period t; is the power delivered by photovoltaic i in time period t; For hydropower station The comprehensive output coefficient; is the outflow of hydropower station i in period t; is the power generation flow of hydropower station i in time period t; is the generating head of hydropower station i in period t; For the period Number of hours possessed; The constraints are: ① Restrictions on hydropower output: （2） Where: The generating head of hydropower station i in time period t The following relations are satisfied: （3） in: For hydropower station The reservoir water level at time period t is determined by the water storage capacity and satisfies the relationship: ,represent Depend on Decide; and , respectively hydropower station The amount of water stored in the reservoir at the beginning of time period t and the amount of water stored in the reservoir at the end of time period t; For hydropower station The head loss in period t is related to the hydropower station The power generation flow in time period t is proportional to the square of , satisfying equation (4): （4） in: For hydropower station Head loss coefficient; For hydropower station The tailwater level at time period t is determined by the hydropower station Outbound flow in period t Decision; Due to the hydropower station Outbound flow in period t , including hydroelectric power stations The power generation flow in time period t and abandoned water, so the hydropower station The power generation flow in time period t and hydropower station Outbound flow in period t Satisfies the relationship (5): （5） ②Output limit: Hydropower Station Minimum output Satisfies the relationship (6): （6） Minimum output of water, wind and solar Satisfies the relationship (7): （7） ③ Expected output limit of hydropower station and output limit of hydropower station: Hydropower Station Output in period t The expected output limit of equation (8) and the hydropower output limit of equation (9) are: （8） in: is the expected output of hydropower station i in period t, and the generating head of hydropower station i in period t Related; （9） in: and are the lower and upper limits of the output of hydropower station i in time period t, respectively; ④Channel capacity limitation: （10） in: For hydropower station Channel capacity limitations; Wind power The power delivered in time period t , which is less than the wind power in the historical years adopted this time. In the period Available power Photovoltaic The power delivered in time period t , which is less than the photovoltaic In the period Available power , that is, satisfying equations (11) and (12): （11） （12） ⑤Reservoir water balance: For medium- and long-term dispatching, the water lag time of the directly connected upstream reservoir is negligible, and the reservoir water balance relationship is expressed as: （13） in: The hydropower stations in the historical years used this time In the period Inbound flow ; For hydropower station A collection of directly connected upstream hydroelectric power stations, For hydropower station In the period The outflow of the hydropower station belong ; For the period The number of seconds it has; ⑥Reservoir operating water level limit: （14） in: and Hydropower Station In the period The lower and upper limits of the water level.

6. A method for medium- and long-term scheduling decision-making of water, wind and solar power in a river basin according to claim 5, characterized in that: Step S4 is specifically as follows: The hydropower stations in each historical year Time period Inbound flow 、Wind power Time period Available power and photovoltaic Time period Available power Input into the medium- and long-term series optimization scheduling model of water, wind and solar power, call ipopt to solve and obtain the water storage process of the hydropower station reservoir that best matches the historical wind and solar resources of each historical year, and the water storage process of the hydropower station reservoir in multiple historical years forms a long sequence of the water storage process of the hydropower station reservoir; wherein, the water storage process of the hydropower station reservoir includes the hydropower station The amount of water stored in the reservoir at the beginning of time period t , Hydropower Station Outbound flow in period t , Hydropower Station Output in period t , and hydropower stations at all levels The amount of water stored in the reservoir at the beginning of time period t Superposition, to obtain the total water storage capacity of the cascade at the beginning of time period t , the formula is: .

7. A basin water, wind and solar mid- and long-term scheduling decision-making method according to claim 6, characterized in that: Step S5 is specifically as follows: Study the total water storage capacity of the cascade at the beginning of time period t in each historical year , the total inflow flow in period t , the total available power of wind power and photovoltaic power in period t , and the total water storage capacity of the cascade at the end of period t The relationship between , , For input, The output of the cascade total water storage decision model ; Study the hydropower station i of each historical year in the period Initial reservoir water storage , Time period Inbound flow 、Wind power and Photovoltaic In the period Total available power , and the hydropower station i in the period The reservoir water storage capacity at the end The relationship between , and For input, Hydropower station for export Time period Reservoir storage decision model .

8. The method for medium- and long-term scheduling decision-making of water, wind and solar power in a river basin according to claim 7 is characterized in that: The water quantity correction model includes an objective function and constraint conditions; The objective function is: （15） in: , , , , respectively, in the period t of the decision cycle, the hydropower station Output, outflow flow, power generation flow, power generation head; and , respectively, in the period t of the decision cycle, wind power Power delivered and photovoltaic Power delivered; and , respectively, the time period of hydropower station i in the decision cycle The positive and negative adjustments of the reservoir water storage at the end of the experiment are both positive values; For hydropower station The comprehensive output coefficient; For the period The number of seconds it has; The constraints include the reservoir water balance constraint during the period of water storage adjustment, the cascade water storage adjustment constraint, the reservoir water storage limit, and the hydropower output relationship constraint, the hydropower station expected output constraint, the hydropower station output constraint, the channel capacity constraint, and the reservoir operating water level constraint; among them, the hydropower output relationship constraint, the hydropower station expected output constraint, the hydropower station output constraint, the channel capacity constraint, and the reservoir operating water level constraint are the same as the corresponding constraints in the medium- and long-term series optimization scheduling model of water, wind, and solar power; The water balance constraint of the reservoir during the period considering water storage adjustment is: （16） in: is the decision cycle of each hydropower station i in the time period Initial reservoir water storage capacity; For hydropower station During the decision cycle Inbound flow; Hydropower Station During the decision cycle The outflow of the hydropower station belong , For hydropower station A collection of directly connected upstream hydroelectric power stations; The cascade water storage capacity adjustment constraint is: （17） The reservoir storage limits are: （18） and , respectively, hydropower station i in time period The lower and upper limits of reservoir water storage at the end of the year.

9. A method for medium- and long-term scheduling decision-making of water, wind and solar power in a river basin according to claim 8, characterized in that: Step S7 is specifically as follows: Using the water quantity correction model, we can obtain the water quantity correction model for each hydropower station i in the decision cycle. The reservoir water storage capacity at the end Positive adjustment and negative adjustment , and each hydropower station i in the time period Hydropower output , wind power i in the period Output power , photovoltaic i in the period Output power ; Each hydropower station i in the period Corrected value of reservoir water storage at the end of ; This results in the period of the decision cycle The scheduling decision includes: each hydropower station i in the period Corrected value of reservoir water storage at the end of , each hydropower station i in the period Hydropower output , wind power i in the period Output power , photovoltaic i in the period Output power .

10. A method for medium- and long-term scheduling decision-making of water, wind and solar power in a river basin according to claim 9, characterized in that: Also includes: According to the reservoir water level capacity curve of hydropower station i, the hydropower station i in the period Corrected value of reservoir water storage at the end of Converted to water level for scheduling decision.

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