Long-medium-short term nested scheduling method for water-wind-solar complementary power generation system

Abstract

The application provides a long-medium-short term nested scheduling method of a water-wind-solar complementary power generation system, and belongs to the technical field of mixed energy scheduling. The application proposes a water-wind-solar complementary power generation system scheduling target and constraint condition considering the comprehensive scheduling demands of flood control, power generation, ecology and navigation, establishes a long-medium-short term nested scheduling model of the water-wind-solar complementary power generation system, and proposes a long-medium-short term nested scheduling model solving method based on a "transmission-feedback" mechanism to obtain a long-term, medium-term and short-term scheduling scheme of the water-wind-solar complementary power generation system. The scheduling model can consider different scheduling demands under different scheduling time scales, and compared with a pure power generation scheduling model of the water-wind-solar complementary power generation system, the scheduling model also considers the scheduling demands of flood control, ecology and navigation of the water power, so that the calculated scheduling scheme is more in line with the actual scheduling demands, and the scheduling scheme has important significance for the operation and decision of the water-wind-solar complementary power generation system.

Description

Technical Field

[0001] This invention belongs to the field of hybrid energy system scheduling technology, and more specifically, relates to a long-term, medium-term, and short-term nested scheduling method for a hydro-wind-solar hybrid power generation system. Background Technology

[0002] Currently, the commonly used technologies in the industry are as follows:

[0003] Hybrid energy system dispatch is a technology that considers the joint operation of multiple energy subsystems such as hydropower, wind power, or photovoltaic power generation. Hydro-wind-solar hybrid power generation systems are currently the most important typical hybrid system in hybrid energy system dispatch. Obtaining dispatch schemes for hydro-wind-solar hybrid power generation systems is of great significance for their operation and decision-making.

[0004] Hydro-wind-solar hybrid power generation systems can be categorized into long-term, medium-term, and short-term dispatch based on their dispatch timescales. Long-term and medium-term dispatch models primarily aim to increase the system's total power generation, improve the power generation guarantee rate, and reduce power generation costs. These models typically utilize the seasonal distribution and complementary characteristics of runoff, wind resources, and solar radiation resources to enhance the total power generation of the hybrid system over a longer timescale, ensuring the system's long-term operational efficiency. Compared to medium- and long-term dispatch models, wind and solar power exhibit more significant randomness, volatility, and intermittency in short-term operation. Short-term dispatch models for hydro-wind-solar hybrid power generation systems primarily aim to smooth out fluctuations in wind and solar power output, promote the absorption of wind and solar power, and reduce the instability impact of wind and solar renewable energy grid connection on the power grid through peak-shaving dispatch. Short-term dispatch models for hydro-wind-solar hybrid power generation systems typically leverage the excellent regulating capabilities of hydropower to maximize efficiency and meet peak-shaving requirements while ensuring stable system operation.

[0005] The problem with existing technology is:

[0006] Currently, the technology of hydro-wind-solar hybrid power generation systems mainly focuses on increasing power generation, smoothing fluctuations in wind and solar power output, and improving grid stability. These dispatch requirements are all power generation dispatch requirements. However, hydropower energy requires different dispatch tasks during different dispatch periods, such as flood season and ecological dispatch period, focusing on flood control, fish spawning, etc., while also needing to consider dispatch needs for navigation throughout the year. Existing hydro-wind-solar hybrid power generation system technologies do not simultaneously consider the dispatch tasks of hydropower itself in flood control, ecological protection, and navigation, and have given insufficient consideration to the transmission and feedback mechanisms at the long-term, medium-term, and short-term time scales, as well as at different time scales. As a result, the obtained hydro-wind-solar hybrid power generation system dispatch schemes cannot well meet actual dispatch needs.

[0007] The difficulty in solving the above technical problems:

[0008] When hydropower is combined with new energy sources such as wind and solar, power generation dispatching needs to consider objectives such as power generation and grid matching. Simultaneously, the overall power generation target competes with demands for flood control, ecological protection, and navigation at different times, increasing the difficulty of coordinating power generation needs with other dispatching requirements. Therefore, how to simultaneously consider the dispatching needs of hydropower-wind-solar hybrid power generation systems in terms of flood control, power generation, ecological protection, and navigation, and construct a nested dispatching model with long, medium, and short-term considerations, is the challenge this invention aims to address. Summary of the Invention

[0009] To address the existing technical problems, the main objective of this invention is to provide a long-term, medium-term, and short-term nested scheduling method for a hydro-wind-solar hybrid power generation system. By adopting this invention, long-term, medium-term, and short-term scheduling schemes for a hydro-wind-solar hybrid power generation system that meet the needs of flood control, power generation, ecological restoration, and navigation scheduling can be obtained.

[0010] To achieve the above-mentioned technical features, the objective of this invention is as follows: a long-term, medium-term, and short-term nested scheduling method for a hydro-wind-solar hybrid power generation system. This method considers the comprehensive scheduling needs of flood control, power generation, ecology, and navigation, proposes scheduling objectives and constraints for the hydro-wind-solar hybrid power generation system, establishes a long-term, medium-term, and short-term nested scheduling model for the system, proposes a solution method for the long-term, medium-term, and short-term nested scheduling model based on a "transmission-feedback" mechanism, and obtains long-term, medium-term, and short-term scheduling schemes for the hydro-wind-solar hybrid power generation system.

[0011] Preferably, the scheduling method specifically includes the following steps:

[0012] S1 collects basic attribute data, scheduling characteristic curve data, hydrological data, and power output data of each power station in the hydro-wind-solar hybrid power generation system;

[0013] S2 analyzes the scheduling requirements of the hydro-wind-solar hybrid power generation system from four aspects: flood control, power generation, ecology and navigation, and obtains the scheduling objectives and constraints.

[0014] S3. Considering that the hydro-wind-solar hybrid power generation system focuses on different scheduling needs at different time scales in the long term, medium term and short term, a long-term, medium-term and short-term nested scheduling model is established by combining different scheduling objectives and constraints.

[0015] S4 proposes a "transmission-feedback" mechanism and model solution method for a nested scheduling model with long, medium and short time scales to ensure the consistency of hydropower station water level scheduling process at different time scales;

[0016] S5, based on the long-term, medium-term and short-term nested scheduling model and its solution method, obtains the long-term, medium-term and short-term scheduling schemes for the hydro-wind-solar hybrid power generation system.

[0017] Preferably, in step S2, the scheduling needs for flood control, power generation, ecology, and shipping are considered in the form of scheduling objectives or constraints as follows:

[0018] (a) Flood control requirements:

[0019] Flood control requirements are considered using constraints:

[0020]

[0021] In the formula, and These are the numbers representing flood control constraints, which respectively represent flood control constraints on downstream areas, upstream areas, and the reservoir itself. and Representing the first i The downstream flood protection targets are in the first t Flow rate and water level for each time period and These are the corresponding safe flow rate and safe water level; It is the first i The reservoir in the first t The upstream water level at each time period, and These are its upper and lower limits under flood control constraints;

[0022] (b) Power generation demand:

[0023] The power generation dispatch of a multi-energy complementary system of hydropower, wind power, and solar power needs to consider the demands of the three subsystems of hydropower, wind power, and solar power, as well as the grid load. In the medium to long term, the goal is to maximize power generation, while in the short term, the goal is to minimize the standard deviation of surplus load.

[0024]

[0025] For a multi-energy complementary system involving a cascade reservoir group, the hydropower, wind power, and photovoltaic power stations that package their output are considered as a group of multi-energy complementary systems. In the above formula... G Indicates a total of G A multi-energy complementary system integrating water, wind, and solar power; and These are the numbers of the power generation dispatch targets, representing the targets of maximizing power generation and minimizing the standard deviation of surplus load, respectively. This is the total power generation of the entire hydro-wind-solar multi-energy complementary system. It is the first g The group of water-wind-solar multi-energy complementary systems in the first t Total output over a period of time; , , Representing the first g The first in the group of water, wind and solar multi-energy complementary system iThe first hydropower station, j The first wind power station, the first k The photovoltaic power station is at the first t The effort exerted during each period; and These represent the duration of the scheduling period and the number of scheduling periods, respectively. It is the first g Standard deviation of surplus load in a multi-energy complementary system of water, wind and solar power; and They are the first g The group of water-wind-solar multi-energy complementary systems in the first t The remaining load and load for each time period yes The average value;

[0026] The dispatching of hydropower, wind power, and solar power complementary systems needs to consider constraints on the capacity of hydropower, wind power, solar power, and transmission channels. Specific constraints include:

[0027] (1) Reservoir water level constraints:

[0028]

[0029] In the formula, This refers to the generation dispatch constraint number; all symbols with the same structure below represent generation dispatch constraint numbers. The meanings of the variables in the above formula are the same as those of the flood control dispatch constraints. The meaning is similar; when flood control and power generation scheduling needs are considered simultaneously in the scheduling model, the upper and lower limits of the water level are taken as the intersection of the two.

[0030] (2) Reservoir discharge flow constraints:

[0031]

[0032] In the formula, It is the first i The reservoir in the first t Outbound flow for each time period and These are its corresponding upper and lower limit constraints, and the outbound flow rate. Power generation flow and water discharge flow Composition, power generation flow Cannot exceed the maximum transmission capacity ;

[0033] (3) Hydropower output constraints:

[0034]

[0035] In the formula, It is the first i The reservoir in the first tThe effort exerted during each period, and These are its corresponding upper and lower limit constraints. Hydropower output is also constrained by the expected output curve and the NHQ curve.

[0036] (4) Water balance equation:

[0037]

[0038] In the formula, and They represent the first i The reservoir in the first t Storage capacity at the beginning and end of each time period; and These are the corresponding inbound and outbound flow rates. This represents water loss due to evaporation;

[0039] (5) Hydraulic connections between cascade hydropower stations:

[0040]

[0041] In the formula: It is the first i The inter-regional flow rate of each reservoir's upstream river section; It is the traffic of the upstream site. Reaching the first [location / stage] through river channel evolution i The flow rate of each reservoir at that time; Is with the first i A collection of upstream stations that are hydraulically connected to the reservoir. yes Element; The representative river channel evolution model specifically adopts the Muskingen model or the time-delay evolution model;

[0042] (6) Wind power constraints:

[0043]

[0044] In the formula, It is the first j The first wind power station in the t Wind speed at different times, and It refers to the entry and exit wind speeds; It is the first j The first wind power station in the t The power output during any given time period cannot exceed the installed capacity of the corresponding wind power station. ;

[0045] (7) Constraints on photovoltaic power generation:

[0046]

[0047] In the formula, and Representing the first k The output and installed capacity of each photovoltaic power station;

[0048] (8) Conveying channel capacity constraints:

[0049]

[0050] In the formula, and Representing the first g The packaged output and transmission channel capacity of the multi-energy complementary system of water, wind and solar power;

[0051] (c) Ecological requirements:

[0052] Ecological regulation mainly targets the ecological regulation needs of fish spawning, including ecological regulation for fish that lay adhesive eggs and ecological regulation for fish that lay drifting eggs.

[0053] (1) Ecological regulation requirements of fish species that lay adhesive eggs:

[0054] Consider the ecological regulation needs of fish species that lay adhesive eggs in the form of constraints:

[0055]

[0056] In the formula, to All are ecological constraint condition numbers; It is the first i The reservoir with ecological regulation tasks was in the first t Outbound flow for each time period and These are the upper and lower limits of ecological flow; It is the variation of the outflow rate. The absolute value, It is the maximum variation limit of the outflow; Downstream water level fluctuation The absolute value, This is the maximum fluctuation limit of the downstream water level; It is the first i Reservoirs with ecological regulation tasks are maintained. to The duration of ecological scheduling constraints It is the minimum time required to maintain it;

[0057] (2) Ecological regulation needs of fish species that lay drifting eggs:

[0058] Consider the ecological regulation needs of drifting-egg-laying fish in the form of constraints:

[0059]

[0060] In the formula, and It is the ecological constraint condition number; and They are the first i The moments when a reservoir with ecological regulation tasks begins to increase its discharge flow and ends its increased discharge flow; This is the initial increase in the discharge flow. It refers to the increase in the outflow rate; and Representing the i The reservoirs with ecological regulation tasks were respectively in the [number]th [year]. t The time period and the ( t -1) outflow rate over a specific time period;

[0061] (d) Shipping demand:

[0062] Shipping scheduling needs should be considered with the goal of maximizing average throughput.

[0063]

[0064]

[0065] In the formula: It is the navigation scheduling target number. It is the first i The first reservoir with navigation function is in t Air traffic rate for a given period of time It is the average air traffic rate; It refers to the number of reservoirs with navigation functions. It is the number of scheduling periods; for The outflow within the section is too small, which can easily lead to ships running aground, and the corresponding navigation rate is 0. The discharge flow within the specified range represents the optimal navigation flow, corresponding to a maximum navigation rate of 100%. As the discharge flow increases, the navigation rate gradually decreases. The discharge flow then exceeds the maximum navigation flow. At that time, the air traffic rate was 0.

[0066] Preferably, in S3, the established long-term, medium-term, and short-term nested scheduling model is as follows:

[0067] Considering that the hydro-wind-solar hybrid system needs to take into account different scheduling tasks in different scheduling periods throughout the year, long-term scheduling models, medium-term scheduling models, and short-term scheduling models are constructed according to different time scales:

[0068] (1) Long-term scheduling model:

[0069] The constructed long-term scheduling model takes an annual scheduling period and a monthly scheduling period, with the goal of maximizing power generation. It considers flood control and power generation scheduling constraints. The long-term scheduling model uses the upstream water level of each reservoir at the beginning and end of the month as the decision variable.

[0070] (2) Mid-term scheduling model:

[0071] The medium-term scheduling model uses a month as the scheduling period and a day as the scheduling time period, with the goals of maximizing power generation and navigation rate, and takes into account scheduling constraints of flood control, power generation and ecology.

[0072] Considering the 12 months of the year, the medium-term scheduling model needs to be run 12 times. Since power generation and navigation rate targets are variables with different dimensions, a weighted approach is used to transform the multi-objective optimization into a single objective: φ PG_O_1 + (1-φ) NA_O_1 requires dimensionless optimization of the power generation target. In the medium-term scheduling model, the power generation target value is normalized using the power generation data from the long-term scheduling model for the same month. However, the navigation rate target value is already within the range of 0 to 1, and the coefficient... φ It is the weight ratio between power generation target and navigation target. The medium-term scheduling model uses the upstream water level of each reservoir at the beginning and end of the day as the decision variable.

[0073] (3) Short-term scheduling model:

[0074] Using a daily scheduling period and an hourly scheduling time period, with the objectives of minimizing the standard deviation of surplus load and maximizing navigation rate, and considering constraints related to flood control, power generation, and ecological scheduling, and taking into account that there are 365 or 366 days in a year, the short-term scheduling model needs to be run 365 or 366 times. Based on this, a weighted method is then used to transform the multi-objective optimization into a single objective: [φ PG_O_2 - (1-φ) [NA_O_1], during optimization, the residual load standard deviation objective also needs to undergo dimensionless processing; in the short-term scheduling model, the residual load standard deviation is normalized using the daily average load value, and the coefficients are... φ It is the weight ratio of the surplus load standard deviation target to the navigation target. The short-term scheduling model uses the upstream water level of each reservoir at the beginning and end of each hour as the decision variable.

[0075] Preferably, in S4, the "transmission-feedback" mechanism and model solution method of the long-term, medium-term, and short-term nested scheduling model are as follows:

[0076] (1) The results of the long-term scheduling model are transmitted to the medium-term scheduling model as its water level boundary at the corresponding time. The medium-term scheduling model will try its best to meet the water level boundary transmitted by the long-term scheduling model. When it cannot meet the water level boundary, it will feed back the results of the medium-term scheduling model to the water level of the long-term scheduling model at the corresponding time. At the same time, the results of the medium-term scheduling model will be transmitted to the short-term scheduling model as its water level boundary at the corresponding time. The short-term scheduling model will try its best to meet the water level boundary transmitted by the medium-term scheduling model. When it cannot meet the water level boundary, it will feed back the results of the short-term scheduling model to the water level of the medium-term scheduling model at the corresponding time. The long-term, medium-term and short-term nested scheduling model will realize water level transmission in the order of long-term-medium-short-term, and will also realize water level feedback in the order of short-term-medium-long-term, so as to ensure the consistency of the hydropower station water level scheduling process at different time scales.

[0077] (2) The optimization scheduling model of the water, wind and solar multi-energy complementary system is a complex optimization problem with multiple constraints and multi-dimensional decision variables. The long-term scheduling model and the medium-term scheduling model are solved by dynamic programming and evolutionary algorithms. The short-term scheduling model is not suitable for dynamic programming because the residual load standard deviation objective makes the scheduling decision process have aftereffects. It is more suitable to use evolutionary algorithms to solve it.

[0078] Preferably, the computer program for solving the long-term, medium-term, and short-term nested scheduling model of the hydro-wind-solar hybrid power generation system is used to implement the long-term, medium-term, and short-term nested scheduling method of the hydro-wind-solar hybrid power generation system.

[0079] Preferably, in another aspect, the present invention provides a terminal, the terminal being equipped with at least a controller that implements the long-term, medium-term, and short-term nested scheduling algorithm of the hydro-wind-solar hybrid power generation system.

[0080] Preferably, in another aspect, the present invention provides a computer-readable storage medium including instructions that, when executed on a computer, cause the computer to perform the long-term, medium-term, and short-term nested scheduling method for a hydro-wind-solar hybrid power generation system.

[0081] Preferably, in another aspect, the present invention provides a control system for implementing the long-term, medium-term, and short-term nested scheduling method of the hydro-wind-solar hybrid power generation system.

[0082] Preferably, in another aspect, the present invention provides a hydro-wind-solar hybrid power generation system scheduling device equipped with the control system of the aforementioned long-term, medium-term, and short-term nested scheduling method for the hydro-wind-solar hybrid power generation system.

[0083] The present invention has the following beneficial effects:

[0084] 1. The model proposed in this invention takes into account the scheduling needs of flood control, power generation, ecology and navigation at the same time, and coordinates the relationship between the power generation scheduling of the hydro-wind-solar hybrid power generation system and the flood control, ecology and navigation scheduling tasks of the hydropower itself. The resulting scheduling scheme is more in line with the actual scheduling needs.

[0085] 2. The long-term, medium-term, and short-term nested scheduling model for hydropower-wind-solar hybrid power generation system established in this invention, as well as the "transmission-feedback" mechanism at different time scales, can ensure the consistency of the hydropower station's water level scheduling process at different time scales, so that the scheduling personnel will not encounter the problem of time scale fragmentation when switching between long-term, medium-term, and short-term scheduling schemes. Attached Figure Description

[0086] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0087] Figure 1 This is a flowchart of a long-term, medium-term, and short-term nested scheduling model for a hydro-wind-solar hybrid power generation system provided in an embodiment of the present invention.

[0088] Figure 2 This is a schematic diagram of the long, medium and short nested scheduling model structure and solution method provided in the embodiments of the present invention.

[0089] Figure 3 This is a schematic diagram of the research area of ​​the downstream hydro-wind-solar hybrid power generation system of the Jinsha River provided in an embodiment of the present invention.

[0090] Figure 4 The water level process of the long-term scheduling model results provided in this embodiment of the invention.

[0091] Figure 5 The results of the medium-term scheduling model (flood control demand) provided in this embodiment of the invention.

[0092] Figure 6 The results of the medium-term dispatch model (power generation demand) provided in this embodiment of the invention.

[0093] Figure 7 The mid-term scheduling model results (ecological requirements) provided in this embodiment of the invention.

[0094] Figure 8 The results of the medium-term scheduling model (shipping demand) provided in this embodiment of the invention.

[0095] Figure 9 The results of the short-term scheduling model provided in this embodiment of the invention. Detailed Implementation

[0096] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments, examples of which are shown in the drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0097] Example 1:

[0098] Please see Figure 1-9 This invention proposes scheduling objectives and constraints for a hydro-wind-solar hybrid power generation system, taking into account the comprehensive scheduling needs of flood control, power generation, ecology and navigation. It establishes a long-term, medium-term and short-term nested scheduling model for the hydro-wind-solar hybrid power generation system, proposes a solution method for the long-term, medium-term and short-term nested scheduling model based on the "transmission-feedback" mechanism, and obtains long-term, medium-term and short-term scheduling schemes for the hydro-wind-solar hybrid power generation system.

[0099] Appendix Figure 1 The diagram shows a nested scheduling model for a hydro-wind-solar hybrid power generation system, which includes the following steps:

[0100] S1 collects basic attribute data, scheduling characteristic curve data, hydrological data, and power output data of each power station in the hydro-wind-solar hybrid power generation system;

[0101] S2 analyzes the scheduling requirements of the hydro-wind-solar hybrid power generation system from four aspects: flood control, power generation, ecology and navigation, and obtains the scheduling objectives and constraints.

[0102] (a) Flood control requirements:

[0103] Flood control is a crucial task in reservoir management, typically encompassing downstream flood control, upstream flood control, and the reservoir's own flood control. Downstream flood control primarily prevents the water level or flow rate of downstream protected areas from exceeding safe levels or flows. Upstream flood control mainly reduces the impact of reservoir backwater on upstream protected areas. The reservoir's own flood control aims to prevent adverse effects of reservoir impoundment on the dam's structural safety. This invention considers flood control requirements through constraints:

[0104]

[0105] In the formula, and These are the numbers representing flood control constraints, which respectively represent flood control constraints on downstream areas, upstream areas, and the reservoir itself. and Representing the first i The downstream flood protection targets are in the first t Flow rate and water level for each time period and These are the corresponding safe flow rate and safe water level; It is the firsti The reservoir in the first t The upstream water level at each time period, and These are its upper and lower limits under flood control constraints;

[0106] (b) Power generation demand:

[0107] The power generation dispatch of a multi-energy complementary system involving hydropower, wind power, and solar power needs to consider the demands of three subsystems—hydropower, wind power, and solar power—as well as the grid load. This invention constructs a power generation dispatch model with the medium- to long-term objective of maximizing power generation and the short-term objective of minimizing the standard deviation of surplus load.

[0108]

[0109] For a multi-energy complementary system involving a cascade reservoir group, the hydropower, wind power, and photovoltaic power stations that package their output are considered as a group of multi-energy complementary systems. In the above formula... G Indicates a total of G A multi-energy complementary system integrating water, wind, and solar power; and These are the numbers of the power generation dispatch targets, representing the targets of maximizing power generation and minimizing the standard deviation of surplus load, respectively. This is the total power generation of the entire hydro-wind-solar multi-energy complementary system. It is the first g The group of water-wind-solar multi-energy complementary systems in the first t Total output over a period of time; , , Representing the first g The first in the group of water, wind and solar multi-energy complementary system i The first hydropower station, j The first wind power station, the first k The photovoltaic power station is at the first t The effort exerted during each period; and These represent the duration of the scheduling period and the number of scheduling periods, respectively. It is the first g Standard deviation of surplus load in a multi-energy complementary system of water, wind and solar power; and They are the first g The group of water-wind-solar multi-energy complementary systems in the first t The remaining load and load for each time period yes The average value;

[0110] The dispatching of hydropower, wind power, and solar power complementary systems needs to consider constraints on the capacity of hydropower, wind power, solar power, and transmission channels. Specific constraints include:

[0111] (1) Reservoir water level constraints:

[0112]

[0113] In the formula, This refers to the generation dispatch constraint number; all symbols with the same structure below represent generation dispatch constraint numbers. The meanings of the variables in the above formula are the same as those of the flood control dispatch constraints. The meaning is similar; when flood control and power generation scheduling needs are considered simultaneously in the scheduling model, the upper and lower limits of the water level are taken as the intersection of the two.

[0114] (2) Reservoir discharge flow constraints:

[0115]

[0116] In the formula, It is the first i The reservoir in the first t Outbound flow for each time period and These are its corresponding upper and lower limit constraints, and the outbound flow rate. Power generation flow and water discharge flow Composition, power generation flow Cannot exceed the maximum transmission capacity ;

[0117] (3) Hydropower output constraints:

[0118]

[0119] In the formula, It is the first i The reservoir in the first t The effort exerted during each period, and These are its corresponding upper and lower limit constraints. Hydropower output is also constrained by the expected output curve and the NHQ curve.

[0120] (4) Water balance equation:

[0121]

[0122] In the formula, and They represent the first i The reservoir in the first t Storage capacity at the beginning and end of each time period; and These are the corresponding inbound and outbound flow rates. This represents water loss due to evaporation;

[0123] (5) Hydraulic connections between cascade hydropower stations:

[0124]

[0125] In the formula: It is the first i The inter-regional flow rate of each reservoir's upstream river section; It is the traffic of the upstream site. Reaching the first [location / stage] through river channel evolution i The flow rate of each reservoir at that time; Is with the first i A collection of upstream stations that are hydraulically connected to the reservoir. yes Element; The representative river channel evolution model specifically adopts the Muskingen model or the time-delay evolution model;

[0126] (6) Wind power constraints:

[0127]

[0128] In the formula, It is the first j The first wind power station in the t Wind speed at different times, and It refers to the entry and exit wind speeds; It is the first j The first wind power station in the t The power output during any given time period cannot exceed the installed capacity of the corresponding wind power station. ;

[0129] (7) Constraints on photovoltaic power generation:

[0130]

[0131] In the formula, and Representing the first k The output and installed capacity of each photovoltaic power station;

[0132] (8) Conveying channel capacity constraints:

[0133]

[0134] In the formula, and Representing the first g The packaged output and transmission channel capacity of the multi-energy complementary system of water, wind and solar power;

[0135] (c) Ecological requirements:

[0136] Ecological regulation mainly targets the ecological regulation needs of fish spawning, including ecological regulation for fish that lay adhesive eggs and ecological regulation for fish that lay drifting eggs.

[0137] (1) Ecological regulation requirements of fish species that lay adhesive eggs:

[0138] For fish species such as carp and crucian carp that lay adhesive-shelled eggs, stable water flow and level are more conducive to spawning, which requires that the outflow from the reservoir and the fluctuation of the downstream water level not be too large. This invention considers the ecological regulation needs of fish species that lay adhesive-shelled eggs in the form of constraints:

[0139]

[0140] In the formula, to All are ecological constraint condition numbers; It is the first i The reservoir with ecological regulation tasks was in the first t Outbound flow for each time period and These are the upper and lower limits of ecological flow; It is the variation of the outflow rate. The absolute value, It is the maximum variation limit of the outflow; Downstream water level fluctuation The absolute value, This is the maximum fluctuation limit of the downstream water level; It is the first i Reservoirs with ecological regulation tasks are maintained. to The duration of ecological scheduling constraints It is the minimum time required to maintain it;

[0141] (2) Ecological regulation needs of fish species that lay drifting eggs:

[0142] For fish species such as black carp, grass carp, silver carp, and bighead carp that lay drifting eggs, flowing water conditions are more conducive to their spawning, requiring reservoirs to continuously increase their outflow over a period of time. This invention considers the ecological regulation needs of drifting-egg-laying fish in the form of constraints:

[0143]

[0144] In the formula, and It is the ecological constraint condition number; and They are the first i The moments when a reservoir with ecological regulation tasks begins to increase its discharge flow and ends its increased discharge flow; This is the initial increase in the discharge flow. It refers to the increase in the outflow rate; and Representing the iThe reservoirs with ecological regulation tasks were respectively in the [number]th [year]. t The time period and the ( t -1) outflow rate over a specific time period;

[0145] (d) Shipping demand:

[0146] For vessels navigating the waterway, excessive traffic flow can lead to instability, while insufficient flow can cause ships to run aground; both are detrimental to navigation. This invention addresses shipping scheduling needs by aiming to maximize average traffic flow.

[0147]

[0148]

[0149] In the formula: It is the navigation scheduling target number. It is the first i The first reservoir with navigation function is in t Air traffic rate for a given period of time It is the average air traffic rate; It refers to the number of reservoirs with navigation functions. It is the number of scheduling periods; for The outflow within the section is too small, which can easily lead to ships running aground, and the corresponding navigation rate is 0. The discharge flow within the specified range represents the optimal navigation flow, corresponding to a maximum navigation rate of 100%. As the discharge flow increases, the navigation rate gradually decreases. The discharge flow then exceeds the maximum navigation flow. At that time, the air traffic rate was 0.

[0150] S3. Considering the different scheduling needs of hydro-wind-solar hybrid power generation systems at different time scales (long-term, medium-term, and short-term), a nested scheduling model for long-term, medium-term, and short-term systems is established by combining different scheduling objectives and constraints, as shown in the attached figure. Figure 2 As shown;

[0151] (1) Long-term scheduling model:

[0152] The long-term scheduling model constructed in this invention uses an annual scheduling period and a monthly scheduling time period, with the objective of maximizing power generation (PG_O_1), and considers flood control (FC_C_1~FC_C_2) and power generation (PG_C_1~PG_C_10) scheduling constraints. The long-term scheduling model uses the upstream water levels of each reservoir at the beginning and end of the month as decision variables.

[0153] (2) Mid-term scheduling model:

[0154] The medium-term scheduling model constructed in this invention uses a month as the scheduling period and a day as the scheduling time period, with the objectives of maximizing power generation (PG_O_1) and maximizing navigation rate (NA_O_1), and considering scheduling constraints for flood control (FC_C_1~FC_C_2), power generation (PG_C_1~PG_C_10), and ecology (EC_C_1~EC_C_4, EC_C_5~EC_C_6). Considering 12 months in a year, the medium-term scheduling model needs to be run 12 times. Since the power generation target (PG_O_1) and the navigation rate target (NA_O_1) are variables with different dimensions, a weighted method is used to transform the multi-objective optimization into a single objective [φ]. PG_O_1 + (1-φ) During optimization of [NA_O_1], the power generation target (PG_O_1) needs to be dedimensionalized. This invention normalizes the power generation target value (monthly power generation) in the medium-term scheduling model using the power generation of the same month in the long-term scheduling model, while the navigation rate target value is already within the range of 0 to 1. (Coefficient) φ This refers to the weighted ratio between power generation and navigation objectives. The medium-term scheduling model uses the upstream water levels of each reservoir at the beginning and end of the day as decision variables.

[0155] (3) Short-term scheduling model:

[0156] The short-term scheduling model constructed in this invention uses a daily scheduling period and an hourly scheduling time period, with the objectives of minimizing the standard deviation of surplus load (PG_O_2) and maximizing navigation rate (NA_O_1), considering scheduling constraints for flood control (FC_C_1~FC_C_2), power generation (PG_C_1~PG_C_10), and ecology (EC_C_1~EC_C_4, EC_C_5~EC_C_6). Given that there are 365 or 366 days in a year, the short-term scheduling model needs to be run 365 or 366 times. Similar to the medium-term scheduling model, a weighted approach is used to transform multi-objective optimization into a single objective [φ]. PG_O_2 - (1-φ) During optimization of NA_O_1, the residual load standard deviation (PG_O_2) objective also needs to be dedimensionalized. This invention normalizes the residual load standard deviation using the daily average load value in the short-term scheduling model. (Coefficients) φ This represents the weighting ratio between the surplus load standard deviation target and the navigation target. The short-term scheduling model uses the upstream water levels of each reservoir at the beginning and end of each hour as decision variables.

[0157] S4 proposes a "transmission-feedback" mechanism and model solution method for a nested scheduling model with long, medium, and short timeframes to ensure the consistency of hydropower station water level scheduling across different time scales, as shown in the appendix. Figure 2 As shown;

[0158] (1) The results of the long-term scheduling model (the upstream water level of the reservoir at the beginning and end of the month) are transmitted to the medium-term scheduling model as its water level boundary at the corresponding time. The medium-term scheduling model will try its best to meet the water level boundary transmitted from the long-term scheduling model. However, when it cannot meet the water level boundary, it will feed back the results of the medium-term scheduling model to the water level of the long-term scheduling model at the corresponding time. At the same time, the results of the medium-term scheduling model (the upstream water level of the reservoir at the beginning and end of the day) are transmitted to the short-term scheduling model as its water level boundary at the corresponding time. The short-term scheduling model will try its best to meet the water level boundary transmitted from the medium-term scheduling model. However, when it cannot meet the water level boundary, it will feed back the results of the short-term scheduling model to the water level of the medium-term scheduling model at the corresponding time. The nested long-term, medium-term and short-term scheduling models will realize water level transmission in the order of long-term-medium-short-term, and will also realize water level feedback in the order of short-term-medium-long-term, so as to ensure the consistency of the hydropower station water level scheduling process at different time scales.

[0159] (2) The optimal scheduling model of a multi-energy complementary system of water, wind and solar is a complex optimization problem with multiple constraints and multi-dimensional decision variables. The long-term and medium-term scheduling models can be solved using dynamic programming and evolutionary algorithms. The short-term scheduling model is not suitable for dynamic programming because the objective of residual load standard deviation (PG_O_2) makes the scheduling decision process have aftereffects. Evolutionary algorithms are more suitable for solving it.

[0160] S5, based on the long-term, medium-term and short-term nested scheduling model and its solution method, obtains the long-term, medium-term and short-term scheduling schemes for the hydro-wind-solar hybrid power generation system.

[0161] Example 2:

[0162] The application of this invention will be further described below with reference to specific experiments.

[0163] This invention focuses on the hydro-wind-solar hybrid power generation system in the lower reaches of the Jinsha River, as shown in the appendix. Figure 3 As shown. The four hydropower stations in this area—Wudongde, Baihetan, Xiluodu, and Xiangjiaba—are denoted by symbols H1, H2, H3, and H4, respectively. The three downstream flood control sections—Lizhuang, Zhutuo, and Cuntan—are denoted by symbols R1, R2, and R3, respectively. The wind power stations surrounding the H1, H2, and H3 hydropower stations are denoted by W1, W2, and W3, respectively, and the photovoltaic power stations surrounding the H1, H2, and H3 hydropower stations are denoted by S1, S2, and S3, respectively. The implementation example is completed using data from 2020.

[0164] (1) Flood control needs of the research subjects:

[0165] In this embodiment, the downstream flood control protection targets are mainly three hydrological stations (R1, R2, and R3), whose flood control requirements are not exceeding the safe flow rate and safe water level, as shown in Table 1. For the reservoir's own flood control, it operates between the dead water level and the flood control limit level when not blocking floodwaters; and between the dead water level and the flood control high level when blocking floodwaters, as shown in Table 1.

[0166] Table 1 Basic Information of the Study Subjects

[0167]

[0168] (2) Power generation demand of the research object:

[0169] In this embodiment, the medium- and long-term power generation dispatch needs are based on maximizing power generation, while the short-term power generation dispatch needs are based on peak shaving needs. The basic information related to power generation is shown in Table 1.

[0170] (3) Ecological needs of the research subjects:

[0171] In this embodiment, the ecological regulation needs are mainly concentrated in reservoirs H1 and H4. The ecological regulation time for H1 is distributed from March to May, and the ecological regulation time for H4 is distributed from May to June. The ecological regulation needs of H1 include the ecological regulation needs of fish that lay adhesive and drifting eggs, while the ecological regulation needs of H4 are mainly for fish that drift and spawn. The detailed ecological regulation needs are shown in Table 2.

[0172] Table 2 Ecological scheduling needs of the research subjects

[0173]

[0174] (4) Shipping demand of the research object:

[0175] In this embodiment, only Reservoir H4 has navigation capability. The navigation flow rate of H4 is between 1200 m³ / s and 12000 m³ / s. The relationship between its navigation rate and the outflow rate is shown in the following formula:

[0176] ;

[0177] The results analysis for the embodiments completed using the model proposed in this invention is as follows:

[0178] (a) Analysis of long-term scheduling results:

[0179] Taking the hydro-wind-solar hybrid power generation system composed of H3, W3, and S3 as an example, the upstream water level scheduling process of its long-term scheduling model is as follows: Figure 4 As shown. Figure 4In the diagram, scale 0 represents the dispatch process of pure hydropower (excluding wind and solar power), scale 1 represents the installed capacity of wind and solar power equal to the data listed in Table 1, and scales 2-4 represent the installed capacity of wind and solar power 2 to 4 times the data listed in Table 1. The diagram shows that the combined operation of the hydro-wind-solar hybrid power generation system affects the operation of hydropower itself. As the scale of wind and solar power generation gradually expands, the upstream water level drop during the non-flood season gradually occurs earlier and the depth of the drop gradually increases, increasing power generation during the non-flood season. During the flood season, hydropower operates at full capacity independently, resulting in a large amount of wasted water. When hydropower participates in the combined operation of the hydro-wind-solar hybrid power generation system, the earlier water level drop reduces hydropower generation during the flood season, but promotes the absorption of wind and solar power during the flood season, thereby increasing the overall power generation of the hydro-wind-solar hybrid power generation system. This dispatch model analysis is consistent with theoretical understanding, verifying that the long-term dispatch model proposed in this invention is accurate and effective.

[0180] (b) Analysis of intermediate scheduling results:

[0181] The results of the medium-term scheduling model (flood control demand) are as follows: Figure 5 As shown, taking flood control section R1 as an example, the maximum annual flow rate of R1 is 23,300 m³ / s, which is lower than the safe flow rate of 51,000 m³ / s; the highest annual water level of R1 is 265.88 m, which is lower than the safe water level of 268.7 m. R1 meets the flood control requirements in terms of both flow rate and water level. The same applies to R2 and R3, verifying that the medium-term scheduling model proposed in this invention can meet the flood control scheduling requirements.

[0182] The results of the medium-term dispatch model (generation demand) are as follows: Figure 6 As shown, taking the hydro-wind-solar hybrid power generation system composed of H1, W1, and S1 as an example, hydropower, wind power, and photovoltaic power generation exhibit certain seasonal patterns throughout the year. The outflow of H1 during the flood season is greater than during the dry season, and the corresponding power output is also greater. The power output of W1 and S1 is greater in January-May and November-December than in other months, indicating that H1, W1, and S1 are seasonally complementary throughout the year. This is consistent with current theoretical understanding and verifies that the medium-term dispatch model proposed in this invention can meet the power generation dispatch requirements.

[0183] The results of the medium-term scheduling model (ecological demand) are as follows: Figure 7As shown, taking H1 as an example, the ecological regulation of H1 can be divided into ecological regulation for fish that lay adhesive eggs and those that lay drifting eggs. From March 20th to March 31st, the outflow of H1 was controlled within the range of 1420 m³ / s to 2220 m³ / s; the maximum flow fluctuation was 202 m³ / s, not exceeding the flow fluctuation limit (800 m³ / s); the maximum downstream water level fluctuation was 0.55 m, not exceeding the water level fluctuation limit (1.5 m); and the above ecological constraints were maintained for 10 days. The ecological regulation process analysis from April 1st to April 10th was similar to that from March 20th to March 31st. In summary, the regulation process calculated using the medium-term regulation model of this invention can meet the ecological regulation needs of fish that lay adhesive eggs. On May 13th, the outflow of H1 was 1200 m³ / s, and the outflow increased by 390 m³ / s each day for the next four days. This artificial water flow pulse is beneficial to the spawning of fish that lay drifting eggs. In summary, the medium-term scheduling model of this invention can meet the ecological scheduling needs of fish species that lay drifting eggs.

[0184] The results of the medium-term scheduling model (shipping demand) are as follows: Figure 8 As shown in the figure, the navigation rate of H4 during the flood season is lower than that during the non-flood season. This is because the large flow during the flood season has an adverse effect on the navigation stability of ships, which is consistent with theoretical understanding. This shows that the medium-term scheduling model of the present invention can meet the shipping scheduling needs.

[0185] (c) Short-term scheduling result analysis:

[0186] To analyze the short-term peak-shaving capacity of hydropower in a hydro-wind-solar hybrid power generation system, taking H1, W1, and S1 as examples, output stacking diagrams for typical days during the flood season and non-flood season are drawn, as follows: Figure 9 As shown, the standard deviation (std) of surplus load on typical days during the flood season and non-flood season are 0 and 23 (104kW), respectively, indicating that the hydro-wind-solar hybrid power generation system is more likely to meet grid demand during the flood season. This is because the output of W1 and S1 is lower during the flood season than during the non-flood season, and the impact of the flood season on hydropower is less than that during the non-flood season. The above results are consistent with theoretical understanding, indicating that the short-term dispatch model of this invention can meet the peak-shaving dispatch requirements.

[0187] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A long-medium-short term nested scheduling method for a water-wind-solar complementary power generation system, characterized in that, The scheduling method specifically comprises the following steps: S1, collecting basic attribute data, scheduling characteristic curve data, hydrology and wind and light power station output data of each power station of the water, wind and light complementary power generation system; S2, analyzing scheduling requirements of the water, wind and light complementary power generation system from four aspects of flood control, power generation, ecology and navigation to obtain scheduling targets and constraint conditions; S3, considering that the water, wind and light complementary power generation system focuses on different scheduling requirements under different time scales of long-term, medium-term and short-term, different scheduling targets and constraint conditions are combined to establish a long-term, medium-term and short-term nested scheduling model; S4, a "transmission-feedback" mechanism of the long-term, medium-term and short-term nested scheduling model and a model solving method are proposed to guarantee consistency of water level scheduling process of the hydropower station on different time scales; S5, long-term, medium-term and short-term scheduling schemes of the water, wind and light complementary power generation system are obtained based on the long-term, medium-term and short-term nested scheduling model and the solving method thereof; In step S3, the long-term, medium-term and short-term nested scheduling model is as follows: Considering that the water, wind and light complementary system needs to consider different scheduling tasks in different scheduling periods throughout the year, long-term, medium-term and short-term scheduling models are respectively constructed according to different time scales: (1) Long-term scheduling model: The constructed long-term scheduling model takes year as the scheduling period and month as the scheduling period, takes maximum power generation as the target, considers flood control and power generation scheduling constraints, and takes upstream water levels of each reservoir at the beginning and end of the month as the decision variable; (2) Medium-term scheduling model: The medium-term scheduling model takes month as the scheduling period and day as the scheduling period, takes maximum power generation and maximum navigation rate as the target, and considers the scheduling constraints of flood control, power generation and ecology; Considering that the mid-term scheduling model needs to run 12 times in a year, and the power generation target and the navigation rate target are variables of different dimensions, the multi-objective optimization is converted into a single objective by using the weight method: φ PG_O_1+(1-φ) NA_O_1, the power generation target needs to be de-dimensioned during optimization; in the mid-term scheduling model, the power generation target value is normalized by the power generation in the same month by the long-term scheduling model, while the navigation rate target value is already in the range of 0-1, and the coefficient φ is the weight ratio of the power generation target to the navigation target or the standard deviation of the residual load target to the navigation target. The mid-term scheduling model takes the upstream water level of each reservoir at the beginning and end of the day as the decision variable. (3) Short-term scheduling model: With day as scheduling period, hour as scheduling period, with minimum standard deviation of residual load and maximum navigation rate as target, considering flood control, power generation, ecological scheduling constraints, considering that there are 365 or 366 days in a year, the short-term scheduling model needs to run 365 or 366 times, based on this, the multi-objective optimization is changed into single objective by using weight method: [φ PG_O_2-(1-φ) NA_O_1], the residual load standard deviation target also needs to complete the dimensionless operation during optimization; in the short-term scheduling model, the residual load standard deviation is normalized by the daily average load value, and the short-term scheduling model takes the upstream water level of each reservoir at the beginning and end of each hour as the decision variable, and is the number of power generation scheduling targets, respectively representing the maximum power generation and the minimum standard deviation of residual load; is the navigation scheduling target number.

2. The long, medium and short term nested scheduling method of the water, wind and light complementary power generation system according to claim 1, characterized in that, In step S2, the scheduling requirements of flood control, power generation, ecology and navigation in the form of scheduling targets or constraint conditions are as follows: (a) Flood control requirement: The flood control requirement is considered in the form of constraint: In the formula, and are the numbers of flood control constraints, respectively representing the constraints on downstream flood control, upstream flood control and reservoir self-flood control; and respectively represent the flow and water level of the i th downstream flood control protection object in the t th time period, and are the corresponding safe flow and safe water level; is the upstream water level of the i th reservoir in the t th time period, and are the upper and lower limits thereof under the flood control constraints; (b) Power generation requirement: The power generation scheduling of the water, wind and light multi-energy complementary system needs to consider the requirements of water power, wind power, photovoltaic power generation and grid load, and the power generation scheduling model is constructed in the medium and long term with the maximum power generation as the target and in the short term with the minimum residual load standard deviation as the target: For a multi-energy complementary system involving a cascade reservoir group, the hydropower, wind power, and photovoltaic power stations that package their output are considered as a group of multi-energy complementary systems. In the above formula... G Indicates a total of G A multi-energy complementary system integrating water, wind, and solar power; This is the total power generation of the entire hydro-wind-solar multi-energy complementary system. It is the first g The group of water, wind and solar multi-energy complementary systems in the first t Total output over a period of time; , , Representing the first g The first in the group of water, wind and solar multi-energy complementary system i The first hydropower station, j The first wind power station, the first k The photovoltaic power station is at the first t The effort exerted during each period; and These represent the duration of the scheduling period and the number of scheduling periods, respectively. It is the first g Standard deviation of surplus load in a multi-energy complementary system of water, wind and solar power; and They are the first g The group of water, wind and solar multi-energy complementary systems in the first t The remaining load and load for each time period yes The average value; The power generation scheduling of the water, wind and light multi-energy complementary system needs to consider the constraints of water power, wind power, photovoltaic power generation and transmission channel capacity, and the specific constraints include: (1) Reservoir water level constraint: In the formula, is the number of power generation scheduling constraints, and the following symbols of the same structure represent the number of power generation scheduling constraints; the meanings of the variables in the above formula are the same as those of the flood control scheduling constraints ; when the flood control and power generation scheduling requirements are considered simultaneously in the scheduling model, the upper and lower limits of the water level are the intersection of the two. (2) Reservoir discharge flow constraint: In the formula, It is the first i The reservoir in the first t Outbound flow rate for each time period and These are its corresponding upper and lower limit constraints, and the outbound flow rate. Power generation flow and water discharge flow Composition, power generation flow Cannot exceed the maximum transmission capacity ; (3) Water power output constraint: wherein, is the output of the nth reservoir in the mth time period, i is the output of the nth reservoir in the mth time period, t is the output of the nth reservoir in the mth time period, and is its corresponding upper and lower bound constraints, the hydro power output is also subject to the forecasted output curve and the NHQ curve. (4) Water balance equation: wherein, and respectively represent the initial and final reservoir capacity of the i-th reservoir in the j-th time period; i and t respectively represent the initial and final reservoir capacity of the i-th reservoir in the j-th time period; and are the corresponding inflow and outflow of the i-th reservoir in the j-th time period, represents the evaporation water loss; (5) Hydraulic connection between cascade hydropower stations: In the formula: It is the first i The inter-regional flow rate of each reservoir's upstream river section; It is the traffic of the upstream site. Reaching the first [location / stage] through river channel evolution i The flow rate of each reservoir at that time; Is with the first i A collection of upstream stations that are hydraulically connected to the reservoir. yes Element; The representative river channel evolution model specifically adopts the Muskingen model or the time-delay evolution model; (6) Wind power constraint: wherein is the wind speed of the j th wind farm in the t th time period, and is the cut-in, cut-out wind speed; is the output of the j th wind farm in the t th time period, which cannot exceed the installed capacity of the corresponding wind farm ; (7) Photovoltaic power generation constraint: In the formula, and Representing the first k The output and installed capacity of each photovoltaic power station; (8) Transmission channel capacity constraint: wherein, and represent the packaged output of the water, wind, and landscape view multi-energy complementary system and the transportation channel capacity, respectively. g represent the packaged output of the water, wind, and landscape view multi-energy complementary system and the transportation channel capacity, respectively. (c) Ecological requirement: Ecological scheduling aims at the ecological scheduling requirements of fish spawning, including the ecological scheduling for fish spawning adhesive eggs and the ecological scheduling for fish spawning drift eggs; (1) Ecological scheduling requirement for fish spawning adhesive eggs: The ecological scheduling requirement for fish spawning adhesive eggs is considered in the form of constraint: wherein, to are ecological constraint numbers; and are ecological flow upper and lower limits; is the absolute value of the variation amplitude of the released flow, is the maximum variation amplitude limit of the released flow; is the absolute value of the variation amplitude of the downstream water level, is the maximum variation amplitude limit of the downstream water level; is the i th reservoir with an ecological regulation task maintains to the duration of the ecological regulation constraint, is the minimum required maintenance time;​​ (2) Ecological scheduling requirement for fish spawning drift eggs: The ecological scheduling requirement for fish spawning drift eggs is considered in the form of constraint: In the formula, and It is the ecological constraint condition number; and They are the first i The moments when a reservoir with ecological regulation tasks begins to increase its discharge flow and ends its increased discharge flow; This is the initial increase in the discharge flow. It refers to the increase in the outflow rate; (d) Navigation requirement: The navigation scheduling requirement is considered with the maximum average navigation rate as the target: In the formula: It is the first i The first reservoir with navigation function is in t Air traffic rate for a given time period It is the average air traffic rate; It refers to the number of reservoirs with navigation functions. It is the number of scheduling periods; for The downstream flow within the interval is too small, which can easily lead to ships running aground, and the corresponding navigation rate is 0. The discharge flow within the specified range represents the optimal navigation flow, corresponding to a maximum navigation rate of 100%. As the discharge flow increases, the navigation rate gradually decreases. The discharge flow then exceeds the maximum navigation flow. At that time, the air traffic rate was 0.

3. The long, medium and short term nested scheduling method of the water, wind and light complementary power generation system according to claim 1, characterized in that, In step S4, the long-medium-short term nested scheduling model "pass-feedback" mechanism and model solving method are as follows: (1) The result of the long-term scheduling model is passed to the medium-term scheduling model as its water level boundary at the corresponding time. The medium-term scheduling model will try to meet the water level boundary passed by the long-term scheduling model. When it cannot meet the water level boundary, it will feedback the result of the medium-term scheduling model to the water level of the long-term scheduling model at the corresponding time. At the same time, the result of the medium-term scheduling model is passed to the short-term scheduling model as its water level boundary at the corresponding time. The short-term scheduling model will try to meet the water level boundary passed by the medium-term scheduling model. When it cannot meet the water level boundary, it will feedback the result of the short-term scheduling model to the water level of the medium-term scheduling model at the corresponding time. The long-medium-short term nested scheduling model will realize water level passing in the order of long-term-medium-term-short-term, and also realize water level feedback in the order of short-term-medium-term-long-term, to ensure the consistency of the water level scheduling process of the hydropower station at different time scales; (2) The water, wind, and light multi-energy complementary system optimization scheduling model is a complex, multi-constrained, and multi-dimensional decision variable optimization problem. The long-term scheduling model and the medium-term scheduling model are solved by dynamic programming algorithm and evolutionary algorithm. The short-term scheduling model has aftereffect due to the residual load standard deviation objective, which makes the scheduling decision process unsuitable for dynamic programming algorithm and suitable for evolutionary algorithm.

4. A computer program product of a long, medium and short term nested scheduling model of a water, wind and light complementary power generation system, characterized in that, The computer program product of the long-medium-short term nested scheduling model of the water, wind, and light complementary power generation system is used to realize the long-medium-short term nested scheduling method of the water, wind, and light complementary power generation system according to any one of claims 1-3.

5. A terminal, characterized by comprising: The terminal at least carries a controller for realizing the long-medium-short term nested scheduling method of the water, wind, and light complementary power generation system according to any one of claims 1-3.

6. A computer readable storage medium comprising instructions which, when executed on a computer, cause the computer to perform the long-medium-short term nested scheduling method of the water, wind, and light complementary power generation system according to any one of claims 1-3.

7. A control system for realizing the long-medium-short term nested scheduling method of the water, wind, and light complementary power generation system according to any one of claims 1-3.

8. A water, wind, and light complementary power generation system scheduling device carrying the control system for realizing the long-medium-short term nested scheduling method of the water, wind, and light complementary power generation system according to claim 7.

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