Medium and long term optimization scheduling modeling and solving method for cooperative operation of cascade hydropower stations and photovoltaic power stations

Abstract

The invention provides a medium and long term optimization scheduling modeling and solving method for cooperative operation of cascade hydropower stations and photovoltaic power stations. The method comprises the following steps: acquiring basic data of a scheduling model; establishing a photovoltaic output scene construction model; establishing a medium-and-long-term optimization scheduling model for cooperative operation of the cascade hydropower stations and the photovoltaic power stations; and adopting a two-stage nested coupling solving method to solve the medium and long term optimization scheduling model of the cascade hydropower station and photovoltaic power station cooperative operation, and obtaining the hydropower station water level, the hydropower output and the photovoltaic power station actual output of the cooperative operation in the operation simulation year. According to the medium-and-long-term optimization scheduling modeling and solving method for cooperative operation of the cascade hydropower station and the photovoltaic power station provided by the invention, multiple factors such as power generation capacity, power abandoning, water abandoning and multi-time-scale coupling of the hydropower station are comprehensively considered, so that efficient operation and sustainable development of a watershed hydropower-photovoltaic power generation system are realized; and powerful support is provided for optimizing resource configuration and improving system flexibility.

Description

Technical Field

[0001] The present invention belongs to the technical field of research on integrated water, wind and solar energy systems, and particularly relates to a medium- and long-term optimal scheduling modeling and solution method for coordinated operation of cascade hydropower stations and photovoltaic power stations. Background Art

[0002] Randomness and volatility are the most prominent characteristics of the power output of wind power and photovoltaic power generation systems. If the wind and solar power generation systems are directly connected to the grid, it will seriously affect the stability and security of the power system. Therefore, wind and solar power generation must be combined with other adjustable power sources to provide stable and high-quality power output. Hydropower, which has renewable characteristics and stable output capacity, is one of the optimal choices.

[0003] Currently, the research on the joint scheduling of basin hydropower-photovoltaic power generation systems mainly focuses on short-term operation within a day and for 1 - 3 days. The scheduling objectives usually focus on system peak shaving, enhancing output stability, and reducing residual load. However, from the perspective of annual scheduling, during the peak photovoltaic output period in spring and autumn and the peak hydropower output period in the flood season, how to reasonably arrange the water levels and outputs of hydropower stations and effectively connect the multi-scale couplings of years, months, and weeks to achieve the efficient operation and sustainable development of hydropower-photovoltaic power generation systems still lacks effective models and algorithms to solve this problem. Summary of the Invention

[0004] Aiming at the defects existing in the prior art, the present invention provides a medium- and long-term optimal scheduling modeling and solution method for coordinated operation of cascade hydropower stations and photovoltaic power stations, which can effectively solve the above problems.

[0005] The technical solution adopted by the present invention is as follows:

[0006] The present invention provides a medium- and long-term optimal scheduling modeling and solution method for coordinated operation of cascade hydropower stations and photovoltaic power stations, including the following steps:

[0007] Step S1, obtaining the basic data of the scheduling model; the basic data of the scheduling model includes the basic data of basin cascade hydropower stations and the basic data of photovoltaic power stations;

[0008] The basic data of the basin cascade hydropower stations includes the basic information of each hydropower station, the inflow runoff of each hydropower station in the simulated annual operation, the operation constraint information of each hydropower station, and the topological relationship of each hydropower station;

[0009] The basic information of the hydropower station includes the installed capacity of the hydropower station, the characteristic water level of the hydropower station, and the characteristic curve of the hydropower station;

[0010] The basic data of the photovoltaic power station includes the installed information of the photovoltaic power station and the photovoltaic output data of the photovoltaic power station in multiple historical years;

[0011] Step S2, establish a photovoltaic output scenario construction model:

[0012] The photovoltaic output scenario construction model is used to input the photovoltaic output data of a photovoltaic power station in multiple historical years at a given scheduling period and scheduling time scale, and perform photovoltaic output scenario analysis to obtain a set of typical photovoltaic output scenarios; each typical photovoltaic output scenario in the set of typical photovoltaic output scenarios has a scenario photovoltaic output sequence and a scenario probability.

[0013] Step S3, establish a medium- and long-term optimal scheduling model for the coordinated operation of cascade hydropower stations and photovoltaic power stations:

[0014] Taking the basic information of each hydropower station, the inflow runoff in the operation simulation year, the topological relationship of each hydropower station, the scenario photovoltaic output sequence and scenario probability of the typical photovoltaic output scenarios constructed by the photovoltaic output scenario construction model as inputs, with the maximum total power generation of the cascade hydropower stations and photovoltaic power stations as the objective function, comprehensively considering the operation constraint information of each hydropower station, the characteristic curves of each hydropower station processed by linearization technology, the converted water abandonment constraint and light abandonment constraint, and taking the water levels, hydropower outputs, and actual outputs of the photovoltaic power station at each scheduling time in the operation simulation year as outputs, establish a medium- and long-term optimal scheduling model for the coordinated operation of cascade hydropower stations and photovoltaic power stations.

[0015] Step S4, adopt a two-stage nested coupling solution method to solve the medium- and long-term optimal scheduling model for the coordinated operation of cascade hydropower stations and photovoltaic power stations, and obtain the water levels, hydropower outputs of the hydropower stations and the actual outputs of the photovoltaic power station during the coordinated operation in the operation simulation year.

[0016] Preferably, the characteristic curves of the hydropower station include the water level-storage capacity curve, the discharge-tail water level curve, the output-head-discharge curve, and the discharge-head loss curve of each hydropower station;

[0017] The operation constraint information of the hydropower station includes the following operation constraint information of each hydropower station at each scheduling time: water balance constraint, water level constraint, water level variation constraint, discharge constraint, net head constraint, and output constraint, as well as the capacity limit of the transmission line channel when the hydropower station and the photovoltaic power station operate in coordination;

[0018] The topological relationship of the hydropower stations is: determine the topological relationship of the hydropower stations according to the hydraulic connection of each hydropower station in the cascade hydropower stations in the basin.

[0019] Preferably, in step S2, the operation mode of the photovoltaic output scenario construction model is:

[0020] Step S21, obtain the photovoltaic output hourly data of the photovoltaic power station in multiple historical years;

[0021] Step S22: According to the currently given scheduling period scale, divide the photovoltaic output hour data of all historical years into scheduling periods;

[0022] Based on the scale of the scheduling time period , analyze and process the photovoltaic output hour data of the photovoltaic power station in each scheduling period to obtain the photovoltaic output of the photovoltaic power station in each scheduling time period of each scheduling period . Thus, obtain the photovoltaic output sequence of the photovoltaic power station in each scheduling period , where , is the number of scheduling time periods included in each scheduling period;

[0023] Step S23: Use a clustering algorithm to perform clustering analysis and extraction on the photovoltaic output sequences of scheduling periods to obtain clusters. Each cluster represents a typical photovoltaic output scenario, and use the photovoltaic output sequence of the cluster center of each cluster as the photovoltaic output sequence of the corresponding typical photovoltaic output scenario. Thus, obtain a set of typical photovoltaic output scenarios with types of typical photovoltaic output scenarios;

[0024] Determine the scenario probability of each typical photovoltaic output scenario according to the ratio of the number of photovoltaic output sequences included in the cluster corresponding to each typical photovoltaic output scenario to the total number of photovoltaic output sequences .

[0025] Preferably, the photovoltaic output of the photovoltaic power station in each scheduling time period of each scheduling period is specifically the average photovoltaic output of the photovoltaic power station in the scheduling time period , which is calculated by formula (1):

[0026] (1)

[0027] Where: represents the output of the photovoltaic power station at the th hour of the scheduling time period in the scheduling period ; is the number of hours included in each scheduling time period .

[0028] Preferably, step S23 is specifically:

[0029] Step S231, determine the initial clustering centers:

[0030] In the photovoltaic output sequence randomly select photovoltaic output sequences as the initial clustering centers , ; where , represents the photovoltaic output sequence of the clustering center at each scheduling period during the scheduling period ; represents the randomly selected photovoltaic output sequence at each scheduling period ; ;

[0031] Step S232, use formula (2) to calculate the Euclidean distance from each photovoltaic output sequence to the clustering center :

[0032] (2)

[0033] Step S233, update the clustering centers:

[0034] According to the Euclidean distance from each photovoltaic output sequence to the clustering center , assign each photovoltaic output sequence to the nearest clustering center , complete one clustering, and divide the photovoltaic output sequence into clusters;

[0035] For each newly obtained cluster, use formula (3) to calculate the new clustering center :

[0036] (3)

[0037] Where: represents the number of photovoltaic output sequences included in the cluster ; represents all the combinations of photovoltaic output sequences assigned to the cluster ;

[0038] Step S234, determine the clustering centers of each final cluster:

[0039] If , , then the clustering center As the clustering of the final clustering center; if it is, use the clustering center as the initial clustering center , and return to step S232;

[0040] Step S235, cluster the photovoltaic output sequences into clusters, and each cluster represents a typical photovoltaic output scenario , and the photovoltaic output sequence of its clustering center is used as the scenario photovoltaic output sequence of the corresponding typical photovoltaic output scenario ;

[0041] Step S236, use formula (4) to obtain the scenario probability of each typical photovoltaic output scenario :

[0042] (4)

[0043] This step ends.

[0044] Preferably, in step S3, the objective function of the medium and long-term optimal scheduling model for the coordinated operation of the cascade hydropower station and the photovoltaic power station is:

[0045] (5)

[0046] Wherein: represents the total power generation of the cascade hydropower station and the photovoltaic power station;

[0047] represents the average output of the hydropower station during the scheduling period of the scheduling period ; represents the time length of the scheduling period ; represents the number of hydropower stations in the basin; represents the output of the photovoltaic power station for the typical photovoltaic output scenario during the scheduling period ; represents the curtailment of the photovoltaic power station for the typical photovoltaic output scenario during the scheduling period ; , represents the number of typical photovoltaic output scenarios in the set of the typical photovoltaic output scenarios; represents the scenario probability of the typical photovoltaic output scenario ;

[0048] The medium- and long-term optimal scheduling model for the coordinated operation of the cascade hydropower station and the photovoltaic power station includes the following constraint conditions:

[0049] (a) Water balance constraint of the hydropower station:

[0050] (6)

[0051] Where: , are the initial and final reservoir water volumes of the hydropower station at the beginning and end of the scheduling period respectively; is the inflow of the hydropower station at the scheduling period ; is the outflow of the hydropower station at the scheduling period ;

[0052] (b) Water level constraint of the hydropower station:

[0053] (7)

[0054] Where: is the initial water level of the hydropower station at the scheduling period ; is the lower limit of the allowable water level at the initial moment of the hydropower station at the scheduling period ; is the upper limit of the allowable water level at the end moment of the hydropower station at the scheduling period ;

[0055] (c) Outflow constraint of the hydropower station:

[0056] (8)

[0057] Where: is the discharge of the hydropower station at the scheduling period ; and are the lower and upper limits of the discharge of the hydropower station at the scheduling period respectively; and are the power generation flow and the spillage flow of the hydropower station at the scheduling period respectively;

[0058] (d) Initial and final water level constraints of the hydropower station scheduling:

[0059] (9)

[0060] Wherein: is the initial water level of the hydropower station at the initial moment of the scheduling period; is the initial water level of the hydropower station at the initial stage of scheduling period 1 of the scheduling period; is the hydropower station is the final water level of the hydropower station at the end of scheduling period is the hydropower station in the scheduling period of the final water level; and are, respectively, the lower limit value and the upper limit value of the final water level of the hydropower station in the scheduling period ;

[0061] (e) Hydropower station output constraint:

[0062] (10)

[0063] Wherein: and are, respectively, the lower limit value and the upper limit value of the average output of the hydropower station in the scheduling period ;

[0064] (f) Hydropower station water level amplitude constraint:

[0065] (11)

[0066] Wherein: is the initial water level of the hydropower station in the scheduling period ; is the upper limit of the water level amplitude;

[0067] (g) Hydropower station head constraint:

[0068] (12)

[0069] Wherein: is the upstream and downstream water level difference of the hydropower station in the scheduling period ; and are, respectively, the minimum allowable value and the maximum allowable value of the upstream and downstream water level difference of the hydropower station in the scheduling period ;

[0070] Calculated by formula (13):

[0071] (13)

[0072] Wherein: For a hydropower station At the scheduling period The initial water level; For a hydropower station At the scheduling period The downstream water level; For a hydropower station At the scheduling period The head loss;

[0073] (h) Transmission line corridor capacity constraint:

[0074] (14)

[0075] Wherein: Is the maximum output that the transmission line corridor can send out; Is the transmitted power of the transmission line corridor at the scheduling period ;

[0076] (i) Photovoltaic curtailment constraint:

[0077] When the cascade hydropower station and the photovoltaic power station operate in coordination and send out power through the transmission line corridor, and the power exceeds the capacity of the transmission line corridor, photovoltaic power is preferentially curtailed to meet the transmission line corridor capacity constraint. Formula (15) is used to determine the photovoltaic curtailment of the photovoltaic power station at the scheduling period under the typical photovoltaic output scenario : :

[0078] (15)

[0079] Wherein: Is the maximum output that the transmission line corridor can send out.

[0080] Preferably, in step S3, in the medium- and long-term optimal scheduling model for the coordinated operation of the cascade hydropower station and the photovoltaic power station, the characteristic curves of each hydropower station processed by the linearization technology refer to the linearization of the characteristic curves of the hydropower station. Among them, the linearization principles of the water level-storage capacity curve, the discharge-downstream water level curve, and the discharge-head loss curve of the hydropower station are the same;

[0081] For the water level-storage capacity curve of the hydropower station, formula (16) is used for linearization:

[0082] (16)

[0083] Wherein: Is the total number of storage capacity intervals;

[0084] Is a 0-1 integer variable representing the hydropower station During the scheduling period storage capacity An indicator variable indicating whether it is in the c-th storage capacity interval, indicating the storage capacity is in the c-th storage capacity interval;

[0085] is the hydropower station During the scheduling period The storage capacity value located within the c-th storage capacity interval; is the right endpoint value of the c-th storage capacity interval for hydropower station i; is the hydropower station At the right endpoint value of the (c - 1)-th storage capacity interval, that is, the hydropower station At the left endpoint value of the c-th storage capacity interval;

[0086] is the hydropower station During the scheduling period initial water level; is the hydropower station At the right endpoint value of the (c - 1)-th water level interval; is the right endpoint value of the c-th water level interval for hydropower station i;

[0087] The output-head-discharge curve is linearized using formula (17):

[0088] (17)

[0089] Where: is the total number of discrete values of the head; is the total number of discrete values of the power generation discharge; is the hydropower station In the output function of the hydropower station, the th discrete value of the power generation discharge; ; is the hydropower station In the output function of the hydropower station, the th discrete value of the head; ; is the hydropower station In the output-head-discharge curve of the hydropower station, corresponding to ( ) the corresponding output value; is the hydropower station During the scheduling period The weight value of the head and power generation discharge corresponding to ( ) forming the vertex of the dissected triangular interval.

[0090] Preferably, in step S3, in the medium- and long-term optimal scheduling model for the coordinated operation of the cascade hydropower station and the photovoltaic power station, the converted water abandonment constraint and light abandonment constraint include: converting the water abandonment constraint of the hydropower station into a water abandonment inequality constraint group and converting the photovoltaic power abandonment constraint into a photovoltaic power abandonment inequality constraint group;

[0091] The conversion of the water abandonment constraint of the hydropower station into a water abandonment inequality constraint group is specifically as follows:

[0092] (18)

[0093] Where: is a 0-1 integer variable representing the water abandonment status indicator variable of the hydropower station in the scheduling period ; , indicating that the hydropower station has no water abandonment in the scheduling period ; , indicating that the hydropower station has water abandonment in the scheduling period ;

[0094] is essentially a sufficiently large constant; is essentially a very small constant; is the set of positive real numbers;

[0095] is the water abandonment flow of the hydropower station in the scheduling period ; is the initial water level of the hydropower station in the scheduling period ; is the lower limit of the allowable water level at the initial moment of the hydropower station in the scheduling period ; is the upper limit of the allowable water level at the end moment of the hydropower station in the scheduling period ;

[0096] The conversion of the photovoltaic power abandonment constraint into a photovoltaic power abandonment inequality constraint group is specifically as follows:

[0097] ;(19)

[0098] Where: is the photovoltaic power abandonment of the photovoltaic power station in the scheduling period under the typical photovoltaic output scenario ;

[0099] Preferably, step S4 specifically includes:

[0100] Step S41, the first-stage solution:

[0101] In step S411, set the scheduling period as the operation simulation year, and the scheduling time period is monthly;

[0102] In step S412, analyze and process the incoming runoff of the hydropower station in the operation simulation year to obtain the monthly runoff data of the hydropower station in the operation simulation year;

[0103] In step S413, analyze and process the photovoltaic output data of the photovoltaic power station in each historical year to obtain the monthly photovoltaic output data of the photovoltaic power station in each historical year, and input it into the photovoltaic output scenario construction model. The photovoltaic output scenario construction model outputs a set of typical photovoltaic output scenarios; each typical photovoltaic output scenario in the set of typical photovoltaic output scenarios is a monthly sequence of scenario photovoltaic output and scenario probability;

[0104] In step S414, set the control water levels of each hydropower station at the beginning and end of the scheduling period, and input the monthly runoff data of the hydropower station in the operation simulation year, the monthly sequence of scenario photovoltaic output and scenario probability of each typical photovoltaic output scenario into the medium- and long-term optimal scheduling model for the coordinated operation of cascade hydropower stations and photovoltaic power stations, and use the mixed integer linear programming solution algorithm to solve and obtain the monthly-scale water level process of each hydropower station in the operation simulation year;

[0105] Step S42, the second-stage solution:

[0106] In step S421, set the scheduling period as monthly, and the scheduling time period is the time scale ; where the time length of the time scale is less than the monthly scale;

[0107] In step S422, analyze and process the incoming runoff of the hydropower station in the operation simulation year to obtain the runoff data of the hydropower station in the operation simulation year at each time scale ;

[0108] In step S423, analyze and process the photovoltaic output data of the photovoltaic power station in each historical year to obtain the photovoltaic output data of the photovoltaic power station in each historical year at each time scale and input it into the photovoltaic output scenario construction model. The photovoltaic output scenario construction model outputs a set of typical photovoltaic output scenarios; each typical photovoltaic output scenario in the set of typical photovoltaic output scenarios is a sequence of scenario photovoltaic output at each time scale and scenario probability;

[0109] Step S424, recursively solve the problem month by month, and for the first moon, , the solution is:

[0110] Set each hydropower station in The controlled water levels at the beginning and end of the monthly dispatch period are the monthly scale water levels of the hydropower station in the corresponding month obtained by solving step S414;

[0111] The hydropower station is simulated in the operation of the time scale of the year Runoff data, scenario PV output per typical PV output scenario, time scale The sequence and scenario probability are input into the medium- and long-term optimization scheduling model for the coordinated operation of the cascade hydropower stations and photovoltaic power stations, and the mixed integer linear programming algorithm is used to solve the first Monthly time scale Water level process;

[0112] After obtaining the first Monthly time scale After the water level process, recurse backward to the +1 month, and repeat this process repeatedly until all the months of the simulation year are solved;

[0113] Step S425, the time scale of the hydropower station in the simulated year is calculated. Water level process, forming the time scale of the hydropower station Water level sequence;

[0114] Based on the hydropower station time scale Water level sequence, calculate the time scale of the hydropower station Hydropower output and actual output of photovoltaic power stations.

[0115] Preferably, the time scale The time scale is ten days, weeks or days.

[0116] The medium- and long-term optimization scheduling modeling and solution method for the coordinated operation of cascade hydropower stations and photovoltaic power stations provided by the present invention has the following advantages:

[0117] The present invention provides a medium- and long-term optimal scheduling modeling and solution method for the coordinated operation of cascade hydropower stations and photovoltaic power stations, which comprehensively considers multiple factors such as the power generation capacity, power abandonment, water abandonment and coupling of multiple time scales of the hydropower station to achieve efficient operation and sustainable development of the hydropower-photovoltaic power generation system in the basin, and provides strong support for optimizing resource allocation and improving system flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0118] Figure 1 The overall flowchart of a medium- and long-term optimal scheduling modeling and solving method for the coordinated operation of cascade hydropower stations and photovoltaic power stations provided by the present invention;

[0119] Figure 2 The schematic diagram of the steps of the two-stage nested coupling solving method provided by the present invention;

[0120] Figure 3 The scheduling plan diagram of a certain No. 1 hydropower station in a certain year using the improved and conventional solving algorithms provided by the embodiment of the present invention;

[0121] Figure 4 The scheduling plan diagram of a certain No. 2 hydropower station in a certain year using the improved and conventional solving algorithms provided by the embodiment of the present invention. Detailed implementation manners

[0122] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention will be described in more detail below in combination with actual cases, drawings. It should be understood that the actual cases described here are only used to illustrate the present invention and are not used to limit its scope. In addition, the technical features in the following various embodiments can be freely combined as long as there is no conflict between them.

[0123] The present invention provides a medium- and long-term optimal scheduling modeling and solving method for the coordinated operation of cascade hydropower stations and photovoltaic power stations, comprehensively considering multiple factors such as the power generation capacity, abandoned electricity, abandoned water of hydropower stations, and the coupling of multiple time scales, so as to realize the efficient operation and sustainable development of the basin hydropower-photovoltaic power generation system, and provide strong support for optimizing resource allocation and improving system flexibility.

[0124] The method proposed by the present invention can increase the total power generation of the coordinated operation of hydropower and photovoltaic, reduce unnecessary water abandonment, reduce the calculation burden, and improve the success rate of model solving.

[0125] Refer to Figure 1 , the present invention provides a medium- and long-term optimal scheduling modeling and solving method for the coordinated operation of cascade hydropower stations and photovoltaic power stations, including the following steps:

[0126] Step S1, obtaining the basic data of the scheduling model; the basic data of the scheduling model includes the basic data of the basin cascade hydropower stations and the basic data of the photovoltaic power stations;

[0127] The basic data of the basin cascade hydropower stations includes the basic information of each hydropower station, the inflow runoff of each hydropower station in the operation simulation year, the operation constraint information of each hydropower station, and the topological relationship of each hydropower station;

[0128] The basic information of the hydropower station includes the installed capacity of the hydropower station, the characteristic water level of the hydropower station, and the characteristic curve of the hydropower station;

[0129] The basic data of the photovoltaic power station includes the installed capacity information of the photovoltaic power station and the photovoltaic output data of the photovoltaic power station in multiple historical years;

[0130] Step S2, establish a photovoltaic output scenario construction model:

[0131] The photovoltaic output scenario construction model is used to input the photovoltaic output data of the photovoltaic power station in multiple historical years when a given scheduling period and scheduling time scale are given, and perform photovoltaic output scenario analysis to obtain a set of typical photovoltaic output scenarios; each typical photovoltaic output scenario in the set of typical photovoltaic output scenarios has a scenario photovoltaic output sequence and a scenario probability;

[0132] Step S3, establish a medium and long-term optimal scheduling model for the coordinated operation of cascade hydropower stations and photovoltaic power stations:

[0133] Taking the basic information of each hydropower station, the inflow runoff in the operation simulation year, the topological relationship of each hydropower station, the scenario photovoltaic output sequence and scenario probability of the typical photovoltaic output scenarios constructed by the photovoltaic output scenario construction model as inputs, with the maximum total power generation of the cascade hydropower stations and the photovoltaic power station as the objective function, comprehensively considering the operation constraint information of each hydropower station, the characteristic curves of each hydropower station processed by linearization technology, the converted water discharge constraint and light abandonment constraint, and taking the water levels, hydropower outputs, and actual outputs of the photovoltaic power station of each hydropower station in each scheduling period of the operation simulation year as outputs, establish a medium and long-term optimal scheduling model for the coordinated operation of cascade hydropower stations and photovoltaic power stations;

[0134] Step S4, adopt a two-stage nested coupling solution method to solve the medium and long-term optimal scheduling model for the coordinated operation of the cascade hydropower stations and the photovoltaic power station, and obtain the water levels, hydropower outputs, and actual outputs of the photovoltaic power station of the hydropower stations in coordinated operation in the operation simulation year.

[0135] The following is a detailed introduction to Steps S1 to S4:

[0136] Step S1, obtain the basic data of the scheduling model; the basic data of the scheduling model includes the basic data of cascade hydropower stations in the basin and the basic data of the photovoltaic power station;

[0137] The basic data of the cascade hydropower stations in the basin includes the basic information of each hydropower station, the inflow runoff in the operation simulation year of each hydropower station, the operation constraint information of each hydropower station, and the topological relationship of each hydropower station; the operation constraint information of the hydropower station includes the following operation constraint information of each hydropower station in each scheduling period: water balance constraint, water level constraint, water level amplitude constraint, downstream discharge constraint, net head constraint, and output constraint, as well as the capacity limit of the transmission line channel when the hydropower station and the photovoltaic power station are in coordinated operation; the topological relationship of the hydropower station is: according to the hydraulic connection of each hydropower station in the cascade hydropower stations in the basin, determine the topological relationship of the hydropower station.

[0138] The basic information of the hydropower station, including the installed capacity of the hydropower station, the characteristic water levels of the hydropower station, and the characteristic curves of the hydropower station; among them, the characteristic curves of the hydropower station include the water level-storage capacity curve, the discharge-tail water level curve, the output-head-discharge curve, and the discharge-head loss curve of each hydropower station;

[0139] The basic data of the photovoltaic power station includes the installed capacity information of the photovoltaic power station and the photovoltaic output data of the photovoltaic power station in multiple historical years;

[0140] Step S2, establish a photovoltaic output scenario construction model:

[0141] The photovoltaic output scenario construction model is used to input the photovoltaic output data of the photovoltaic power station in multiple historical years when a given scheduling period and scheduling time scale are provided, and perform photovoltaic output scenario analysis to obtain a set of typical photovoltaic output scenarios; each typical photovoltaic output scenario in the set of typical photovoltaic output scenarios has a scenario photovoltaic output sequence and a scenario probability;

[0142] The operation mode of the photovoltaic output scenario construction model is as follows:

[0143] Step S21, obtain the hourly photovoltaic output data of the photovoltaic power station in multiple historical years;

[0144] Step S22, according to the currently given scheduling period scale, divide the hourly photovoltaic output data of all historical years into scheduling periods;

[0145] According to the scale of the scheduling time period , analyze and process the hourly photovoltaic output data of the photovoltaic power station in each scheduling period to obtain the photovoltaic output of the photovoltaic power station in each scheduling time period of each scheduling period , thereby obtaining the photovoltaic output sequence of the photovoltaic power station in each scheduling period , where , is the number of scheduling time periods included in each scheduling period;

[0146] In this step, the photovoltaic output of the photovoltaic power station in each scheduling time period of each scheduling period is specifically the average photovoltaic output of the photovoltaic power station in the scheduling time period , which is calculated by formula (1):

[0147] (1)

[0148] Wherein: represents the power output of the PV power station in the scheduling period at the scheduling time interval of the th hour; is the number of hours in each scheduling time interval .

[0149] Step S23: Use the clustering algorithm to cluster and extract the PV power output sequences of each scheduling period, to obtain clusters, each cluster representing a typical PV power output scenario, and use the PV power output sequence of the cluster center of each cluster as the PV power output sequence of the corresponding typical PV power output scenario, thereby obtaining a set of typical PV power output scenarios with types of typical PV power output scenarios;

[0150] Determine the scenario probability of each typical PV power output scenario according to the ratio of the number of PV power output sequences included in the cluster corresponding to each typical PV power output scenario to the total number of PV power output sequences .

[0151] Step S23 is specifically as follows:

[0152] Step S231: Determine the initial cluster center:

[0153] Randomly select PV power output sequences in the PV power output sequence as the initial cluster centers , wherein, , represents the cluster center at each scheduling time interval in the scheduling period of the PV power output sequence; represents the randomly selected PV power output sequence at each scheduling time interval of the PV power output sequence; ;

[0154] Step S232: Use formula (2) to calculate the Euclidean distance from each PV power output sequence to the cluster center :

[0155] (2)

[0156] Step S233: Update the cluster center:

[0157] According to each PV power output sequence The Euclidean distance to the cluster center is calculated, and each photovoltaic output sequence is assigned to the nearest cluster center to complete one clustering. The photovoltaic output sequences are divided into clusters;

[0158] For each of the newly obtained clusters, formula (3) is used to calculate the new cluster center as follows:

[0159] (3)

[0160] where: represents the number of photovoltaic output sequences included in cluster ; represents all combinations of photovoltaic output sequences assigned to cluster ;

[0161] Step S234, determine the final cluster centers of each cluster:

[0162] If , , then the cluster center is used as the final cluster center of cluster ; if , the cluster center is used as the initial cluster center , and step S232 is returned;

[0163] Step S235, the photovoltaic output sequences are clustered into clusters, and each cluster represents a typical photovoltaic output scenario , and the photovoltaic output sequence of its cluster center is used as the scenario photovoltaic output sequence of the corresponding typical photovoltaic output scenario ;

[0164] Step S236, formula (4) is used to obtain the scenario probability of each typical photovoltaic output scenario as follows:

[0165] (4)

[0166] This step ends.

[0167] Step S3, establish a medium- and long-term optimal scheduling model for the coordinated operation of cascade hydropower stations and photovoltaic power stations:

[0168] ​Taking the basic information of each hydropower station, the inflow runoff in the operation simulation year, the topological relationship of each hydropower station, the scenario photovoltaic output sequence and the scenario probability of the typical photovoltaic output scenarios constructed by the model constructed with the photovoltaic output scenarios as the input, with the maximum total power generation of the cascade hydropower stations and the photovoltaic power station as the objective function, comprehensively considering the operation constraint information of each hydropower station, the characteristic curves of each hydropower station processed by linearization technology, the converted water abandonment constraint and the light abandonment constraint, and taking the water levels, hydropower outputs, and actual outputs of the photovoltaic power station at each scheduling period in the operation simulation year as the output, a medium- and long-term optimal scheduling model for the coordinated operation of the cascade hydropower stations and the photovoltaic power station is established;

[0169] In step S3, the objective function of the medium- and long-term optimal scheduling model for the coordinated operation of the cascade hydropower stations and the photovoltaic power station is:

[0170] (5)

[0171] Where: represents the total power generation of the cascade hydropower stations and the photovoltaic power station;

[0172] represents the hydropower station at the scheduling period of the scheduling period; represents the scheduling period of the time length; represents the number of hydropower stations in the basin; represents the typical photovoltaic output scenario at the scheduling period of the photovoltaic power station output; represents the typical photovoltaic output scenario at the scheduling period of the photovoltaic power station curtailment; , represents the number of typical photovoltaic output scenarios in the set of the typical photovoltaic output scenarios; represents the typical photovoltaic output scenario of the scenario probability;

[0173] The medium- and long-term optimal scheduling model for the coordinated operation of the cascade hydropower stations and the photovoltaic power station includes the following constraint conditions:

[0174] (a)Hydropower station water balance constraint:

[0175] (6)

[0176] Where: 、 are respectively the initial and final water storage volumes of the hydropower station at the scheduling period ; For a hydropower station During the scheduling period of the inflow discharge; For a hydropower station During the scheduling period of the outflow discharge;

[0177] (b) Hydropower station water level constraint:

[0178] (7)

[0179] Where: For a hydropower station During the scheduling period of the initial water level; For a hydropower station During the scheduling period of the lower limit of the allowable water level at the initial moment; For a hydropower station During the scheduling period of the upper limit of the allowable water level at the end moment;

[0180] (c) Hydropower station outflow discharge constraint:

[0181] (8)

[0182] Where: For a hydropower station During the scheduling period of the discharge flow; and For a hydropower station During the scheduling period of the lower and upper limits of the discharge flow; and For a hydropower station During the scheduling period of the power generation flow and the water abandonment flow;

[0183] (d) Hydropower station initial and final water level constraints during scheduling:

[0184] (9)

[0185] Where: For a hydropower station at the starting water level at the initial moment of scheduling; For a hydropower station at the initial water level during the first scheduling period of the scheduling period; For a hydropower station During the scheduling period of the scheduling period of the final water level; and and, respectively, for a hydropower station At the end of the scheduling period Lower and upper limit values of the water level

[0186] (e)Hydropower station output constraint:

[0187] (10)

[0188] Where: and are the lower and upper limit values of the average output of the hydropower station during the scheduling period respectively;

[0189] (f)Hydropower station water level variation range constraint:

[0190] (11)

[0191] Where: is the initial water level of the hydropower station during the scheduling period ; is the upper limit of the water level variation range;

[0192] (g)Hydropower station head constraint:

[0193] (12)

[0194] Where: is the upstream and downstream water level difference of the hydropower station during the scheduling period ; and are the minimum allowable value and the maximum allowable value of the upstream and downstream water level difference of the hydropower station during the scheduling period respectively;

[0195] Calculated by formula (13):

[0196] (13)

[0197] Where: is the initial water level of the hydropower station during the scheduling period ; is the downstream water level of the hydropower station during the scheduling period ; is the head loss of the hydropower station during the scheduling period ;

[0198] (h)Outgoing line channel capacity constraint:

[0199] (14)

[0200] Wherein: is the maximum output that can be sent out by the external transmission line channel; is the power output of the external transmission line channel during the scheduling period ;

[0201] (i) Photovoltaic curtailment constraint:

[0202] When the cascade hydropower station and the photovoltaic power station operate in coordination and send out power through the external transmission line channel, and the power exceeds the capacity of the external transmission line channel, photovoltaic power is preferentially curtailed to meet the capacity constraint of the external transmission line channel. Formula (15) is used to determine the photovoltaic power output scenario during the scheduling period of the photovoltaic power station curtailment :

[0203] (15)

[0204] Wherein: is the maximum output that can be sent out by the external transmission line channel.

[0205] In step S3, in the medium- and long-term optimal scheduling model for the coordinated operation of the cascade hydropower station and the photovoltaic power station, the characteristic curves of each hydropower station processed by the linearization technology refer to the linearization of the characteristic curves of the hydropower station. Among them, the linearization principles of the water level-storage capacity curve, the discharge-tail water level curve, and the discharge-head loss curve of the hydropower station are the same;

[0206] Taking the water level-storage capacity curve of the hydropower station as an example, the linearization is carried out using formula (16):

[0207] (16)

[0208] Wherein: is the total number of storage capacity intervals;

[0209] is a 0-1 integer variable representing the hydropower station during the scheduling period of the storage capacity is an indicator variable indicating whether it is in the c-th storage capacity interval, indicating that the storage capacity is in the c-th storage capacity interval;

[0210] is the storage capacity value of the hydropower station during the scheduling period within the c-th storage capacity interval; is the right endpoint value of the c-th storage capacity interval of hydropower station i For a hydropower station At the right endpoint value of the (c - 1)-th reservoir capacity interval, i.e., for the hydropower station At the left endpoint value of the c-th reservoir capacity interval;

[0211] For a hydropower station At the initial water level during the scheduling period ; For a hydropower station At the right endpoint value of the (c - 1)-th water level interval; Is the right endpoint value of the c-th water level interval for hydropower station i;

[0212] The output-head-discharge curve is linearized using formula (17):

[0213] (17)

[0214] Where: Is the total number of discrete values of the head; Is the total number of discrete values of the power generation discharge; For a hydropower station In the output function of the hydropower station, the -th discrete value of the power generation discharge; ; For a hydropower station In the output function of the hydropower station, the -th discrete value of the head; ; For a hydropower station In the output-head-discharge curve of the hydropower station, the output value corresponding to ( ); For a hydropower station At the head and power generation discharge during the scheduling period The weight value of the vertex of the dissection triangle interval formed corresponding to ( ).

[0215] In step S3, in the medium- and long-term optimal scheduling model for the coordinated operation of cascade hydropower stations and photovoltaic power stations, the converted water abandonment constraint and light abandonment constraint include: converting the water abandonment constraint of the hydropower station into a water abandonment inequality constraint group and converting the photovoltaic power abandonment constraint into a photovoltaic power abandonment inequality constraint group;

[0216] The conversion of the water abandonment constraint of the hydropower station into a water abandonment inequality constraint group is specifically:

[0217] (18)

[0218] Where: Is a 0 - 1 integer variable, representing the hydropower station During the scheduling period Indicator variable for the water abandonment status; indicating that the hydropower station has no water abandonment during the scheduling period ; indicating that the hydropower station has water abandonment during the scheduling period ;

[0219] is essentially a sufficiently large constant; is essentially a very small constant; is the set of positive real numbers;

[0220] is the water abandonment flow of the hydropower station during the scheduling period ; is the initial water level of the hydropower station during the scheduling period ; is the lower limit of the allowable water level at the initial time of the hydropower station during the scheduling period ; is the upper limit of the allowable water level at the end time of the hydropower station during the scheduling period ;

[0221] The conversion of the PV curtailment constraint into a set of PV curtailment inequality constraints is specifically as follows:

[0222] ; (19)

[0223] where: is the PV curtailment of the PV power station during the scheduling period under the typical PV output scenario ;

[0224] Step S4. A two-stage nested coupling solution method is adopted to solve the medium- and long-term optimal scheduling model for the coordinated operation of the cascade hydropower stations and PV power stations, and the water levels, hydropower outputs of the hydropower stations and the actual outputs of the PV power stations during the coordinated operation in the operation simulation year are obtained.

[0225] Refer to Figure 2 , and step S4 specifically includes:

[0226] Step S41. First-stage solution:

[0227] Step S411. Set the scheduling period as the operation simulation year, and the scheduling period is month;

[0228] Step S412, analyzing and processing the inflow runoff of the hydropower station in the operation simulation year to obtain the monthly runoff data of the hydropower station in the operation simulation year;

[0229] Step S413, analyzing and processing the photovoltaic output data of the photovoltaic power station in each historical year, obtaining the monthly photovoltaic output data of the photovoltaic power station in each historical year, and inputting the data into the photovoltaic output scenario construction model, the photovoltaic output scenario construction model outputs a typical photovoltaic output scenario set; each typical photovoltaic output scenario in the typical photovoltaic output scenario set is a monthly sequence of scene photovoltaic output and a scene probability;

[0230] Step S414, setting the control water level of each hydropower station at the beginning and end of the scheduling period, inputting the monthly runoff data of the hydropower station in the operation simulation year, the monthly sequence of scene photovoltaic output of each typical photovoltaic output scene and the scene probability into the medium- and long-term optimization scheduling model of the coordinated operation of the cascade hydropower station and the photovoltaic power station, and using a mixed integer linear programming solution algorithm to solve the monthly scale water level process of each hydropower station in the operation simulation year;

[0231] Step S42, second stage solution:

[0232] Step S421, set the scheduling period to be monthly, and the scheduling period For time scale ; Among them, the time scale The length of time is smaller than the month scale, for example, the time scale The time scale is ten days, weeks or days.

[0233] Step S422, analyzing and processing the inflow runoff of the hydropower station in the operation simulation year to obtain the time scale of the hydropower station in the operation simulation year Runoff data;

[0234] Step S423: Analyze and process the photovoltaic output data of the photovoltaic power station in each historical year to obtain the time scale of the photovoltaic power station in each historical year. The photovoltaic output data is input into the photovoltaic output scenario construction model, and the photovoltaic output scenario construction model outputs a typical photovoltaic output scenario set; each typical photovoltaic output scenario in the typical photovoltaic output scenario set is a scenario photovoltaic output time scale Sequence and scenario probabilities;

[0235] Step S424, recursively solve the problem month by month, and for the first moon, , the solution is:

[0236] Set each hydropower station in The control water levels at the beginning and end of the monthly scheduling period are the monthly-scale water levels of the hydropower station obtained by solving in step S414.

[0237] For each time scale of the hydropower station during the operation simulation year runoff data, the scene photovoltaic output sequence at each time scale for each typical photovoltaic output scenario, and the scenario probability are input into the medium- and long-term optimal scheduling model for the coordinated operation of the cascade hydropower stations and photovoltaic power stations. Using the mixed integer linear programming solution algorithm, the water levels of each hydropower station at the monthly time scale during the operation simulation year are solved and obtained. monthly time scale water level process;

[0238] After obtaining the water level process of each hydropower station at the monthly time scale during the operation simulation year, it is recursively pushed back to the +(1)th month, and so on in continuous circular recursion until the solution for all months of the operation simulation year is completed;

[0239] In step S425, the water level process of the hydropower station at the monthly time scale during the operation simulation year is solved and obtained, and a water level sequence of the hydropower station at the time scale is formed. water level process of the hydropower station at the time scale water level sequence;

[0240] Based on the water level sequence of the hydropower station at the time scale the hydropower output and the actual output of the photovoltaic power station at the time scale of the hydropower station are calculated.

[0241] Compared with the prior art, the advantages of the present invention are as follows:

[0242] (1) The objective function takes into account the photovoltaic output scenario combination and the actual light curtailment problem, thereby promoting photovoltaic power consumption and increasing the total system power generation;

[0243] (2) The water discharge constraint is transformed, effectively avoiding the problem of unfulfilled water discharge and improving water resource efficiency;

[0244] (3) Based on the large system decomposition and coordination theory, a complex problem with multiple stages and multiple variables is transformed into multiple feasible sub-problems for solution one by one, effectively reducing the computational burden and improving the success rate of model solution.

[0245] The following introduces an embodiment:

[0246] (1) Taking the data of a certain water-light clean energy base collected as an example, a medium- and long-term optimal scheduling model for the coordinated operation of the cascade hydropower stations and photovoltaic power stations is established;

[0247] ​To further elaborate on the specific embodiments of the present invention, basic information and operation data of a certain integrated water and light clean energy base were collected. This clean energy base has a total of 3 cascade hydropower stations and 2 photovoltaic cluster stations. The power outputs of the cascade hydropower stations and the photovoltaic clusters are all transmitted externally in a bundled manner, and the capacity of the external transmission channel is 8 million kW. Among them, the cascade hydropower stations are in the order of the First Hydropower Station, the Second Hydropower Station, and the Third Hydropower Station, with installed capacities of 1.5 million kW, 2.6 million kW, and 0.72 million kW respectively; the installed capacities of the 2 photovoltaic cluster stations are 8.4 million kW and 9.38 million kW respectively. The time scale of the inflow runoff data of the cascade hydropower stations is weekly, and the time range is from January 1, 2000 to December 31, 2020. The time scale of the power output data of the photovoltaic stations is hourly, and the time range is the same as that of the cascade hydropower stations, which is the photovoltaic power output data of the photovoltaic power station in multiple historical years. In the power output function of the hydropower station, the power output coefficient is taken as 8.5. Each year from January 1, 2000 to December 31, 2020 is an operation simulation year. In the algorithm of the present invention, the 2 photovoltaic cluster stations are considered as a whole.

[0248] Using the basic information and operation data of this integrated water and light clean energy base, based on Python 3.11 and the open-source libraries of Pandas, Numpy, Scikit-learn, and Pyomo, a program for the medium- and long-term optimal scheduling model of the coordinated operation of cascade hydropower stations and photovoltaic power stations established by the present invention was written.

[0249] (2) Combining the two-stage nested coupling solution method and the open-source solver SCIP, the medium- and long-term optimal scheduling model of the coordinated operation of cascade hydropower stations and photovoltaic power stations was solved;

[0250] The medium- and long-term optimal scheduling model of the coordinated operation of cascade hydropower stations and photovoltaic power stations was solved using the constraint handling method (the content from Formula 16 to Formula 19), the two-stage nested coupling solution method, and the open-source solver SCIP. Subsequently, it is called the improved solution algorithm.

[0251] The solution algorithm that does not use the constraint handling method (the content from Formula 16 to Formula 19) and does not use the two-stage nested coupling solution framework in the improved solution algorithm is regarded as the conventional solution algorithm, and the medium- and long-term optimal scheduling model of the coordinated operation of cascade hydropower stations and photovoltaic power stations was solved. Considering the actual calculation time requirements and costs, the maximum solution duration was set to 600 seconds. If it exceeds 600 seconds, it is regarded as a solution failure. Among them, the solver uniformly uses SCIP, and the calculation results are shown in Table 1. The CPU of the computing platform is Intel(R)- Core(TM)- i7-9750H -2.6GHz, and the calculation period is a total of 20 years (from 2000 to 2020).

[0252] Table 1. Calculation Results of Different Solution Algorithms

[0253] Average solution time (seconds) Solution success rate Average total power generation (100 million kW·h) <![CDATA[Average total water abandonment (100 million m 3 ).]]> Total number of times of discharging water due to un-full storage Improved solution algorithm 89 100% 428.81 7.18 0 Conventional solution algorithm 116 85% 427.65 7.96 23

[0254] The solution method of the present invention not only greatly improves the solution success rate, but also reduces the average solution time from 116 s to 89 s. Thanks to the reasonable division of the whole stage, the solution in stages reduces the number of decision variables that need to participate in the calculation each time, and through the inequality transformation of the curtailment constraint, the range of the curtailment decision variable is relaxed, giving it a larger search space. The scheduling results show that the annual power generation of the present invention can be increased by 116 million kW·h on average. In addition, the method for transforming and processing the water discharge constraint proposed by the present invention effectively curbs the phenomenon of discharging water when the reservoir is not full, not only reducing the amount of discharged water, but also better meeting the operation requirements of actual hydropower projects.

[0255] (3) Generate the scheduling schemes for the coordinated operation of cascade hydropower and photovoltaic with the improved solution algorithm and the conventional solution algorithm;

[0256] Using the improved solution algorithm and the conventional solution algorithm to solve the medium- and long-term optimal scheduling model for the coordinated operation of cascade hydropower stations and photovoltaic power stations, two scheduling schemes can be obtained. Among them. The scheduling results of the No. 1 hydropower station and the No. 2 hydropower station in a certain year are as shown in the appendix Figure 3 and the appendix Figure 4 as shown.

[0257] From the appendix Figure 3 it can be seen that: under the conventional solution algorithm, the No. 1 hydropower station had two problems of discharging water when not full in the 21st and 26th weeks. Note that the highest allowable water level during these two periods of discharging water is the flood control limit water level (3040 m), and the annual cumulative discharged water is 795 million m 3 . While under the improved solution algorithm, the water level when discharging water is located at the highest allowable water level during the period and the annual cumulative discharged water is only 685 million m 3 .

[0258] From the appendix Figure 4 it can be seen that: under the conventional solution algorithm, the No. 2 hydropower station had 7 problems of discharging water when not full in the 32nd and 38th weeks. Note that the highest allowable water level during these 7 periods of discharging water is the normal storage water level (2895 m), and the annual cumulative discharged water is 3.669 billion m 3 . While under the improved solution algorithm, the water level when discharging water is located at the highest allowable water level during the period and the annual cumulative discharged water is only 3.629 billion m 3 .

[0259] The above is only the preferred implementation manner of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A mid- to long-term optimal scheduling modeling and solution method for the coordinated operation of cascade hydropower stations and photovoltaic power stations, characterized in that: The following steps are involved: Step S1, obtaining basic data of the scheduling model; the basic data of the scheduling model includes basic data of cascade hydropower stations in the river basin and basic data of photovoltaic power stations; The basic data of the cascade hydropower stations in the river basin include basic information of each hydropower station, inflow runoff of each hydropower station in the simulated operation year, operation constraint information of each hydropower station and topological relationship of each hydropower station; The basic information of the hydropower station, including the installed capacity of the hydropower station, the characteristic water level of the hydropower station and the characteristic curve of the hydropower station; The photovoltaic power station basic data includes photovoltaic power station installed capacity information and photovoltaic power station output data in multiple historical years; Step S2, establishing a photovoltaic output scenario construction model: The photovoltaic output scenario construction model is used to input photovoltaic output data of photovoltaic power stations in multiple historical years when a scheduling period and a scheduling time period scale are given, and to perform photovoltaic output scenario analysis to obtain a typical photovoltaic output scenario set; each typical photovoltaic output scenario in the typical photovoltaic output scenario set has a scenario photovoltaic output sequence and a scenario probability; Step S3, establishing a medium- and long-term optimization scheduling model for the coordinated operation of cascade hydropower stations and photovoltaic power stations: Taking the basic information of each hydropower station, the inflow runoff in the operation simulation year, the topological relationship of each hydropower station, the scene photovoltaic output sequence and scene probability of the typical photovoltaic output scene constructed by the photovoltaic output scene construction model as input, taking the maximum total power generation of the cascade hydropower stations and photovoltaic power stations in the basin as the objective function, comprehensively considering the operation constraint information of each hydropower station, the characteristic curves of each hydropower station processed by linearization technology, the water abandonment constraint and the light abandonment constraint processed by conversion, taking the water level of each hydropower station, the hydropower output, and the actual output of the photovoltaic power station in each scheduling period of the operation simulation year as output, a medium- and long-term optimization scheduling model for the coordinated operation of cascade hydropower stations and photovoltaic power stations is established; Step S4, using a two-stage nested coupling solution method to solve the medium- and long-term optimization scheduling model of the coordinated operation of the cascade hydropower station and the photovoltaic power station, to obtain the water level, hydropower output and actual output of the photovoltaic power station in the coordinated operation of the simulation year.

2. A mid- to long-term optimal scheduling modeling and solution method for the coordinated operation of cascade hydropower stations and photovoltaic power stations according to claim 1, characterized in that: The characteristic curves of the hydropower station include a water level-storage capacity curve, a downstream flow-tail water level curve, an output-head-flow curve and a flow-head loss curve of each of the hydropower stations; The operation constraint information of the hydropower station includes the following operation constraint information of each hydropower station in each dispatching period: water balance constraint, water level constraint, water level amplitude constraint, downstream flow constraint, net head constraint and output constraint, as well as the transmission line channel capacity limitation when the hydropower station and the photovoltaic power station operate in coordination; The topological relationship of the hydropower stations is determined according to the hydraulic connection between the hydropower stations in the cascade hydropower stations in the river basin.

3. A mid- and long-term optimization scheduling modeling and solution method for the coordinated operation of cascade hydropower stations and photovoltaic power stations according to claim 1, characterized in that: In step S2, the operation mode of the photovoltaic output scenario construction model is: Step S21, obtaining photovoltaic output hourly data of the photovoltaic power station in multiple historical years; Step S22: according to the currently given dispatch period scale, divide the photovoltaic output hourly data of all historical years into scheduling period; According to the scheduling period The scale of the photovoltaic power station in each dispatch period The photovoltaic output hourly data is analyzed and processed to obtain the photovoltaic power station in each dispatch period. Each scheduling period Photovoltaic output , thus the photovoltaic power station is obtained in each dispatch period Photovoltaic output sequence ,in, , The number of scheduling periods included in each scheduling period; Step S23, using clustering algorithm to Photovoltaic output sequence in a dispatch period Perform cluster analysis and extraction to obtain Clusters are formed, each cluster represents a typical photovoltaic output scenario, and the photovoltaic output sequence of the cluster center of each cluster is used as the photovoltaic output sequence of the corresponding typical photovoltaic output scenario, thereby obtaining A set of typical photovoltaic output scenarios for typical photovoltaic output scenarios; According to the number of photovoltaic output sequences and the total number of photovoltaic output sequences contained in the cluster corresponding to each typical photovoltaic output scenario The ratio of is used to determine the scenario probability of each typical PV output scenario.

4. A mid- to long-term optimal scheduling modeling and solution method for the coordinated operation of cascade hydropower stations and photovoltaic power stations according to claim 3, characterized in that: The photovoltaic power station is Each scheduling period Photovoltaic output , specifically, the photovoltaic power station during the dispatch period The average photovoltaic output is calculated by formula (1): （1） in: Represents the photovoltaic power station during the dispatch period Scheduling period No. Hours of output; For each scheduling period The number of hours you have.

5. A mid- and long-term optimal scheduling modeling and solution method for the coordinated operation of cascade hydropower stations and photovoltaic power stations according to claim 3, characterized in that: Step S23 is specifically as follows: Step S231, determine the initial cluster center: In the photovoltaic output sequence In, randomly select The photovoltaic output sequence is used as the initial cluster center , ;in, , Represents cluster center During each scheduling period The photovoltaic output sequence; Represents the randomly selected photovoltaic output sequence In each scheduling period The photovoltaic output sequence; ; Step S232, using formula (2), calculate each photovoltaic output sequence To cluster center The Euclidean distance : （2） Step S233, update the cluster center: According to each photovoltaic output sequence To cluster center The Euclidean distance , each photovoltaic output sequence Assign to the nearest cluster center , complete a clustering, and classify the photovoltaic output sequence Divide into clusters; For each newly obtained cluster, use formula (3) to calculate the new cluster center : （3） in: Representative clustering The number of PV output sequences included; Representatives are assigned to clusters All photovoltaic output sequence combinations; Step S234, determining the final cluster center of each cluster: if , , then the cluster center As clustering The final cluster center of , the cluster center As the initial cluster center , return to step S232; Step S235, photovoltaic output sequence Clustering clusters, each cluster Representative typical photovoltaic output scenario , whose cluster centers The photovoltaic output sequence is used as the corresponding typical photovoltaic output scenario The photovoltaic output sequence of the scenario; Step S236, using formula (4), obtain each typical photovoltaic output scenario The probability of the scene : （4） This step is finished.

6. A mid- to long-term optimal scheduling modeling and solution method for the coordinated operation of cascade hydropower stations and photovoltaic power stations according to claim 1, characterized in that: In step S3, the objective function of the medium- and long-term optimization scheduling model for the coordinated operation of the cascade hydropower station and the photovoltaic power station is: （5） in: Represents the total power generation of cascade hydropower stations and photovoltaic power stations; Representing hydropower station During the scheduling period The average output of Representative scheduling period Length of time; The number of hydropower stations in the basin; Representative typical photovoltaic output scenario During the scheduling period Output of photovoltaic power station; Representative typical photovoltaic output scenario During the scheduling period of photovoltaic power stations abandoned electricity; , represents the number of typical photovoltaic output scenarios in the typical photovoltaic output scenario set; Representative typical photovoltaic output scenario The probability of the scenario; The medium- and long-term optimal dispatching model for the coordinated operation of cascade hydropower stations and photovoltaic power stations includes the following constraints: (a) Water balance constraints of hydropower stations: （6） in: , Hydropower Station During the scheduling period The water storage capacity of the initial and final hydropower stations; For hydropower station During the scheduling period Inbound flow; For hydropower station During the scheduling period Outbound flow; (b) Water level constraints of hydropower stations: （7） in: For hydropower station During the scheduling period Initial water level; For hydropower station During the scheduling period The lower limit of the water level allowed at the initial moment; For hydropower station During the scheduling period The upper limit of water level allowed at the end of the day; (c) Constraints on outflow from hydropower stations: （8） in: For hydropower station During the scheduling period The downstream flow rate; and Hydropower Station During the scheduling period The lower and upper limits of the discharge flow rate; and Hydropower Station During the scheduling period The power generation flow and abandoned water flow; (d) Water level constraints at the beginning and end of the hydropower station dispatch period: （9） in: For hydropower station The starting water level at the beginning of the dispatch period; For hydropower station The initial water level in dispatch period 1 of the dispatch period; For hydropower station During the scheduling period The final water level; and , respectively hydropower station During the scheduling period The lower and upper limits of the final water level; (e) Hydropower station output constraints: （10） in: and , respectively hydropower station During the scheduling period The lower and upper limits of the average output; (f) Constraints on water level fluctuation of hydropower stations: （11） in: For hydropower station During the scheduling period Initial water level; is the upper limit of water level fluctuation; (g) Head constraints of hydropower stations: （12） in: For hydropower station During the scheduling period The upstream and downstream water level difference; and , respectively hydropower station During the scheduling period The minimum and maximum allowable values ​​of the upstream and downstream water level differences; Calculated by formula (13): （13） in: For hydropower station During the scheduling period Initial water level; For hydropower station During the scheduling period downstream water level; For hydropower station During the scheduling period Head loss; (h) Transmission line channel capacity constraints: （14） in: It is the maximum output that can be delivered by the transmission line channel; For the outbound line channel during the dispatch period of electricity delivered; (i) Constraints on photovoltaic power curtailment: When the cascade hydropower station and the photovoltaic power station work together to transmit electricity through the transmission line channel, and the capacity of the transmission line channel is exceeded, the photovoltaic power is abandoned first to meet the capacity constraint of the transmission line channel. Formula (15) is used to determine the typical photovoltaic output scenario: During the scheduling period of photovoltaic power plants have abandoned electricity : （15） in: It is the maximum output that can be delivered by the transmission line channel.

7. A mid- to long-term optimal scheduling modeling and solution method for the coordinated operation of cascade hydropower stations and photovoltaic power stations according to claim 1, characterized in that: In step S3, in the medium- and long-term optimization scheduling model for the coordinated operation of the cascade hydropower station and the photovoltaic power station, the characteristic curves of each hydropower station processed by the linearization technology refer to the linearization processing of the characteristic curves of the hydropower station, wherein the linearization processing principles of the hydropower station water level-storage capacity curve, the downstream flow-tail water level curve and the flow-head loss curve are the same; For the water level-storage capacity curve of the hydropower station, formula (16) is used for linearization: （16） in: is the total number of storage capacity intervals; It is a 0-1 integer variable, representing a hydropower station During the scheduling period Storage capacity An indicator variable indicating whether the storage capacity is in the cth storage capacity interval. Indicates storage capacity It is in the cth storage capacity interval; For hydropower station During the scheduling period The storage capacity value in the cth storage capacity interval; is the right endpoint value of hydropower station i in the cth storage capacity interval; For hydropower station At the right end point of the c-1 storage capacity interval, that is, the hydropower station The left endpoint value of the cth storage capacity interval; For hydropower station During the scheduling period Initial water level; For hydropower station The right endpoint value of the c-1th water level interval; is the right endpoint value of hydropower station i in the cth water level interval; The output-head-flow curve is linearized using formula (17): （17） in: is the total number of discrete values ​​of the hydraulic head; is the total number of discrete values ​​of power generation flow; For hydropower station The first power generation flow in the output function discrete values; ; For hydropower station The first water head in the output function discrete values; ; For hydropower station In the output-head-flow curve, ) corresponding output value; For hydropower station During the scheduling period The head and power generation flow correspond to ( ) is the weight value of the vertex of the subdivided triangle interval formed.

8. A mid- to long-term optimal scheduling modeling and solution method for the coordinated operation of cascade hydropower stations and photovoltaic power stations according to claim 1, characterized in that: In step S3, in the medium- and long-term optimal dispatching model for the coordinated operation of the cascade hydropower station and the photovoltaic power station, the water abandonment constraint and the photovoltaic abandonment constraint of the conversion processing include: converting the hydropower station water abandonment constraint into a hydropower station water abandonment inequality constraint group and converting the photovoltaic power abandonment constraint into a photovoltaic power abandonment inequality constraint group; The transformation of the hydropower station abandonment constraint into a hydropower station abandonment inequality constraint group is specifically: （18） in: It is a 0-1 integer variable, representing a hydropower station During the scheduling period The indicator variable of the water abandonment status; , indicating a hydroelectric power station During the scheduling period No waste water; , indicating a hydroelectric power station During the scheduling period There is water abandonment; It is essentially a sufficiently large constant; It is essentially a very small constant; is the set of positive real numbers; For hydropower station During the scheduling period The amount of abandoned water flow; For hydropower station During the scheduling period Initial water level; For hydropower station During the scheduling period The lower limit of the water level allowed at the initial moment; For hydropower station During the scheduling period The upper limit of water level allowed at the end of the day; The photovoltaic power curtailment constraint is converted into a photovoltaic power curtailment inequality constraint group, specifically: ；（19） in: Typical photovoltaic output scenario During the scheduling period of photovoltaic power stations have abandoned electricity.

9. A mid- to long-term optimal scheduling modeling and solution method for the coordinated operation of cascade hydropower stations and photovoltaic power stations according to claim 1, characterized in that: Step S4 specifically includes: Step S41, first stage solution: Step S411, set the scheduling period to the operation simulation year, the scheduling period for the moon; Step S412, analyzing and processing the inflow runoff of the hydropower station in the operation simulation year to obtain the monthly runoff data of the hydropower station in the operation simulation year; Step S413, analyzing and processing the photovoltaic output data of the photovoltaic power station in each historical year, obtaining the monthly photovoltaic output data of the photovoltaic power station in each historical year, and inputting the data into the photovoltaic output scenario construction model, the photovoltaic output scenario construction model outputs a typical photovoltaic output scenario set; each typical photovoltaic output scenario in the typical photovoltaic output scenario set is a monthly sequence of scene photovoltaic output and a scene probability; Step S414, setting the control water level of each hydropower station at the beginning and end of the scheduling period, inputting the monthly runoff data of the hydropower station in the operation simulation year, the monthly sequence of scene photovoltaic output of each typical photovoltaic output scene and the scene probability into the medium- and long-term optimization scheduling model of the coordinated operation of the cascade hydropower station and the photovoltaic power station, and using a mixed integer linear programming solution algorithm to solve the monthly scale water level process of each hydropower station in the operation simulation year; Step S42, second stage solution: Step S421, set the scheduling period to be monthly, and the scheduling period For time scale ; Among them, the time scale The length of time is less than the monthly scale; Step S422, analyzing and processing the inflow runoff of the hydropower station in the operation simulation year to obtain the time scale of the hydropower station in the operation simulation year Runoff data; Step S423: Analyze and process the photovoltaic output data of the photovoltaic power station in each historical year to obtain the time scale of the photovoltaic power station in each historical year. The photovoltaic output data is input into the photovoltaic output scenario construction model, and the photovoltaic output scenario construction model outputs a typical photovoltaic output scenario set; each typical photovoltaic output scenario in the typical photovoltaic output scenario set is a scenario photovoltaic output time scale Sequence and scenario probabilities; Step S424, recursively solve the problem month by month, and for the first moon, , the solution is: Set each hydropower station in The controlled water levels at the beginning and end of the monthly dispatch period are the monthly scale water levels of the hydropower station in the corresponding month obtained by solving step S414; The hydropower station is simulated in the operation of the time scale of the year Runoff data, scenario PV output per typical PV output scenario, time scale The sequence and scenario probability are input into the medium- and long-term optimization scheduling model for the coordinated operation of the cascade hydropower stations and photovoltaic power stations, and the mixed integer linear programming algorithm is used to solve the first Monthly time scale Water level process; After obtaining the first Monthly time scale After the water level process, recurse backward to the +1 month, and repeat this process repeatedly until all the months of the simulation year are solved; Step S425, the time scale of the hydropower station in the simulated year is calculated. Water level process, forming the time scale of the hydropower station Water level sequence; Based on the hydropower station time scale Water level sequence, calculate the time scale of the hydropower station Hydropower output and actual output of photovoltaic power stations.

10. A mid- to long-term optimal scheduling modeling and solution method for the coordinated operation of cascade hydropower stations and photovoltaic power stations according to claim 9, characterized in that: Time scale The time scale is ten days, weeks or days.

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