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

Abstract

The invention provides a long-medium-short-term nested scheduling method for a water-wind-light complementary power generation system, and belongs to the technical field of hybrid energy scheduling. According to the invention, a scheduling target and constraint conditions of the water-wind-light complementary power generation system considering flood control, power generation, ecology and shipping comprehensive scheduling requirements are provided, a long-medium-short-term nested scheduling model of the water-wind-light complementary power generation system is established, and a long-medium-short-term nested scheduling model solving method based on a transmission-feedback mechanism is provided. And obtaining long-term, medium-term and short-term scheduling schemes of the water-wind-light complementary power generation system. The scheduling model can emphatically consider different scheduling requirements under different scheduling time scales, and compared with a pure power generation scheduling model of the water-wind-light complementary power generation system, the scheduling model also considers flood control, ecology and shipping scheduling requirements of water and electricity, so that the calculated scheduling scheme better meets the actual scheduling requirements, and the scheduling efficiency is improved. The method is of great significance to operation and decision-making of the water-wind-light complementary power generation system.

Description

Technical Field

[0001] The present invention belongs to the technical field of hybrid energy system scheduling, and more specifically, relates to a long-term, medium-term and short-term nested scheduling method for a water-wind-solar complementary power generation system. Background Art

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

[0003] Hybrid energy system scheduling is a technology that considers the combined operation of multiple energy subsystems such as hydropower, wind power or photovoltaic power generation. The water-wind-solar complementary power generation system is one of the most important typical hybrid systems in current hybrid energy system scheduling. Obtaining the scheduling scheme of the water-wind-solar complementary power generation system is of great significance for the operation and decision-making of the water-wind-solar complementary power generation system.

[0004] The water-wind-solar complementary power generation system can be divided into long-term scheduling, medium-term scheduling and short-term scheduling according to different scheduling time scales. The long-term and medium-term scheduling models of the water-wind-solar complementary power generation system mainly aim to improve the total power generation of the system, improve the power generation guarantee rate and reduce the power generation cost. The long-term and medium-term scheduling models usually utilize the seasonal distribution and complementary characteristics of runoff, wind resources and solar radiation resources to improve the total power generation of the complementary power generation system on a longer time scale and ensure the benefits of the power generation system in long-term operation. Compared with the medium- and long-term scheduling models, the randomness, volatility and intermittency of short-term operation of wind power and photovoltaic power generation are more significant. The short-term scheduling model of the water-wind-solar complementary power generation system is mainly to suppress the fluctuations of wind power and photovoltaic power generation output, promote the consumption of wind power and photovoltaic power generation, and reduce the instability impact on the power grid caused by the grid connection of new wind and solar energy through peak shaving scheduling. The short-term scheduling model of the water-wind-solar complementary power generation system usually utilizes the good regulation ability of hydropower to maximize the benefits and meet the peak shaving demand while ensuring the stable operation of the hybrid system.

[0005] Problems existing in the prior art are:

[0006] Currently, the technologies of the water-wind-solar complementary power generation system mainly focus on aspects such as how to increase power generation, suppress the fluctuations of wind power and photovoltaic power generation output, and improve the stability of the power grid. These scheduling requirements are all power generation scheduling requirements. However, hydropower energy needs to consider different focused scheduling tasks such as flood control and fish spawning in different scheduling periods such as the flood season and the ecological scheduling period, and also needs to consider scheduling requirements such as shipping throughout the year. The existing technologies of the water-wind-solar complementary power generation system do not simultaneously consider the own scheduling tasks of hydropower in aspects such as flood control, ecology and shipping, and consider less about the long-term, medium-term and short-term three time scales and the transfer and feedback mechanisms on different time scales, so that the obtained scheduling scheme of the water-wind-solar complementary power generation system cannot well meet the actual scheduling requirements.

[0007] Difficulties in solving the above technical problems:

[0008] When hydropower energy is operated in combination with new energy sources such as wind and light, power generation scheduling needs to consider target scenarios such as power generation volume and source-grid matching. At the same time, the overall power generation target has a competitive game relationship with demands such as flood control, ecology, and shipping in different periods, increasing the coordination difficulty between power generation demand and other scheduling demands. Therefore, how to consider the scheduling demands of the water-wind-light complementary power generation system in aspects such as flood control, power generation, ecology, and shipping simultaneously and construct a long-medium-short-term nested scheduling model is the difficulty to be solved by the present invention. Summary of the Invention

[0009] To solve the existing technical problems, the main object of the present invention is to provide a long-medium-short-term nested scheduling method for a water-wind-light complementary power generation system, by adopting which long-term, medium-term, and short-term scheduling schemes of the water-wind-light complementary power generation system that meet the scheduling demands of flood control, power generation, ecology, and shipping can be obtained.

[0010] To achieve the above technical features, the object of the present invention is realized as follows: A long-medium-short-term nested scheduling method for a water-wind-light complementary power generation system, the scheduling method considers the comprehensive scheduling demands of flood control, power generation, ecology, and shipping, proposes the scheduling objectives and constraint conditions of the water-wind-light complementary power generation system, establishes a long-medium-short-term nested scheduling model for the water-wind-light complementary power generation system, proposes a solution method for the long-medium-short-term nested scheduling model based on the "transfer-feedback" mechanism, and obtains long-term, medium-term, and short-term scheduling schemes of the water-wind-light complementary power generation system.

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

[0012] S1, collect the basic attribute data, scheduling characteristic curve data, hydrology, and output data of wind and light power stations of each power station in the water-wind-light complementary power generation system;

[0013] S2, analyze the scheduling demands of the water-wind-light complementary power generation system from four aspects of flood control, power generation, ecology, and shipping to obtain the scheduling objectives and constraint conditions;

[0014] S3, considering that the water-wind-light complementary power generation system focuses on different scheduling demands at different time scales of long term, medium term, and short term, combine different scheduling objectives and constraint conditions to establish a long-medium-short-term nested scheduling model;

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

[0016] S5, obtain long-term, medium-term, and short-term scheduling schemes of the water-wind-light complementary power generation system based on the long-medium-short-term nested scheduling model and its solution method.

[0017] Preferably, in S2, the scheduling requirements of 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] The flood control requirements are considered in the form of constraints:

[0020]

[0021] In the formula, FC_C_1 and FC_C_2 are the numbers of flood control constraints, representing the flood control constraints on the downstream, upstream, and the reservoir itself respectively; Q i,t and Z i,t represent the flow and water level of the i-th downstream flood control protection object in the t-th period respectively, and are the corresponding safe flow and safe water level; Zu i,t is the upstream water level of the i-th reservoir in the t-th period, and are its upper and lower limits under flood control constraints;

[0022] (b) Power generation requirements:

[0023] The power generation scheduling of the hydropower, wind power, and photovoltaic power multi-energy complementary system needs to consider the requirements of the hydropower, wind power, and photovoltaic power subsystems and the grid load. A power generation scheduling model is constructed with the maximum power generation as the goal in the medium and long term and the minimum residual load standard deviation as the goal in the short term:

[0024]

[0025] For the hydropower, wind power, and photovoltaic power multi-energy complementary system containing cascade reservoir groups, the hydropower, wind power, and photovoltaic power stations that output the output in a bundled manner are regarded as a group of hydropower, wind power, and photovoltaic power multi-energy complementary systems. In the above formula, G represents a total of G groups of hydropower, wind power, and photovoltaic power multi-energy complementary systems; PG_O_1 and PG_O_2 are the numbers of power generation scheduling objectives, representing the maximum power generation and the minimum residual load standard deviation objectives respectively; E(N g,t ) is the total power generation of the entire hydropower, wind power, and photovoltaic power multi-energy complementary system, N g,t is the total output of the g-th group of hydropower, wind power, and photovoltaic power multi-energy complementary systems in the t-th period; Nh g,i,t , Nw g,j,t , Ns g,k,t represent the outputs of the i-th hydropower station, the j-th wind power station, and the k-th photovoltaic power station in the g-th group of hydropower, wind power, and photovoltaic power multi-energy complementary systems in the t-th period respectively; Δt and T represent the scheduling period duration and the number of scheduling periods respectively; std(R g,t ) is the residual load standard deviation of the g-th group of hydropower, wind power, and photovoltaic power multi-energy complementary systems; R g,t and P g,tare the surplus load and load of the g-th group of water-wind-solar multi-energy complementary system in the t-th period, respectively, is R g,t the average value of;

[0026] The power generation scheduling of the water-wind-solar multi-energy complementary system needs to consider the constraint limitations in aspects such as hydropower, wind power, photovoltaic power generation, and transmission channel capacity. The specific constraints include:

[0027] (1) Reservoir water level constraint:

[0028]

[0029] In the formula, PG_C_1 is the number of the power generation scheduling constraint, and the symbols with the same structure below all represent the power generation scheduling constraint numbers; the meanings of the variables in the above formula are similar to those of the flood control scheduling constraint FC_C_2; when considering both flood control and power generation scheduling requirements in the scheduling model, the upper and lower limits of the water level take the intersection of the two;

[0030] (2) Reservoir discharge flow constraint:

[0031]

[0032] In the formula, Qo i,t is the discharge flow of the i-th reservoir in the t-th period, and are its corresponding upper and lower limit constraints. The discharge flow Qo i,t is composed of the power generation flow Qg i,t and the spillage flow Qa i,t . The power generation flow Qg i,t shall not exceed the full-load flow

[0033] (3) Hydropower output constraint:

[0034]

[0035] In the formula, Nh i,t is the output of the i-th reservoir in the t-th period, and are its corresponding upper and lower limit constraints. The hydropower output is also restricted by the predicted output curve and the NHQ curve;

[0036] (4) Water balance equation:

[0037] PG_C_5:V i,t+1 =V i,t +(Qi i,t -Qo i,t -Ev i,t )·Δt; (11)

[0038] In the formula, Vi,t and V i,t+1 represent the reservoir storage of the i-th reservoir at the beginning and end of the t-th period respectively; Qi i,t and Qo i,t are the corresponding inflow and outflow discharges, and Ev i,t represents the evaporation water loss;

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

[0040]

[0041] In the formula: Qitv i,t is the inter-basin flow of the upper reach of the i-th reservoir; is the upstream station discharge when it reaches the i-th reservoir through river routing; Φ i is the set of upstream stations hydraulically connected to the i-th reservoir, is an element of Φ i ; represents the river routing model, specifically the Muskingum model or the lag routing model;

[0042] (6) Wind power constraint:

[0043]

[0044] In the formula, v j,t is the wind speed of the j-th wind power station at the t-th period, and are the cut-in and cut-out wind speeds; Nw j,t is the output of the j-th wind power station at the t-th period, and it cannot exceed the installed capacity of the corresponding wind power station

[0045] (7) Photovoltaic power generation constraint:

[0046]

[0047] In the formula, Ns k,t and represent the output and installed capacity of the k-th photovoltaic power generation station respectively;

[0048] (8) Transmission channel capacity constraint:

[0049]

[0050] In the formula, N g,t and represent the bundled output and transmission channel capacity of the g-th water-wind-solar multi-energy complementary system respectively;

[0051] (c) Ecological requirements:

[0052] Ecological regulation mainly targets the ecological regulation requirements for fish spawning, including ecological regulation for fish species that lay adhesive and sinking eggs and ecological regulation for fish species that lay drifting eggs;

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

[0054] Consider the ecological regulation requirements for fish species that lay adhesive and sinking eggs in the form of constraint conditions:

[0055]

[0056] In the formula, EC_C_1 to EC_C_4 are all ecological constraint condition numbers; Qo i,t is the discharge of the i-th reservoir with ecological regulation tasks at the t-th time period, and are the upper and lower limits of the ecological flow; |ΔQo i,t | is the absolute value of the variation range of the discharged flow ΔQo i,t ; is the maximum variation range limit of the discharged flow; |ΔZd i,t | is the absolute value of the downstream water level variation range ΔZd i,t ; is the maximum downstream water level variation range limit; Du i is the duration for the i-th reservoir with ecological regulation tasks to maintain the ecological regulation constraints of EC_C_1 to EC_C_3, is the minimum duration that needs to be maintained;

[0057] (2) Ecological regulation requirements for fish species that lay drifting eggs:

[0058] Consider the ecological regulation requirements for fish species that lay drifting eggs in the form of constraint conditions:

[0059]

[0060] In the formula, EC_C_5 and EC_C_6 are ecological constraint condition numbers; and are the start time and end time for the i-th reservoir with ecological regulation tasks to increase the discharged flow respectively; is the initial flow rate for starting to increase the discharged flow, is the increase amplitude of the discharged flow; Qo i,t-1 and Qo i,t represent the discharged flows of the i-th reservoir with ecological regulation tasks at the t-th time period and the (t - 1)-th time period respectively;

[0061] (d) Shipping requirements:

[0062] Consider the shipping scheduling requirements with the goal of maximizing the average navigation rate:

[0063]

[0064] In the formula: NA_O_1 is the navigation scheduling target number, NR i,t is the navigation rate of the i-th reservoir with navigation tasks in the t-th period, is the average navigation rate; I is the number of reservoirs with navigation tasks, and T is the number of scheduling periods; for the downstream discharge within the interval, if the flow is too small, it is likely to cause the ship to run aground, and the corresponding navigation rate is 0; the downstream discharge within the interval is the most suitable navigation flow, and its corresponding maximum navigation rate is 100%. Then, as the downstream discharge increases, the navigation rate gradually decreases. When the downstream discharge exceeds the maximum navigation flow the navigation rate is 0.

[0065] Preferably, in the S3, the established long-term, medium-term and short-term nested scheduling model is as follows:

[0066] Considering that the water-wind-solar complementary system needs to consider different scheduling tasks in different scheduling periods throughout the year, a long-term scheduling model, a medium-term scheduling model and a short-term scheduling model are respectively constructed according to different time scales:

[0067] (1) Long-term scheduling model:

[0068] The established long-term scheduling model takes the year as the scheduling period and the month as the scheduling period, with the goal of maximizing the power generation, considering the flood control and power generation scheduling constraints. The long-term scheduling model takes the upstream water levels of each reservoir at the beginning and end of the month as decision variables;

[0069] (2) Medium-term scheduling model:

[0070] The medium-term scheduling model takes the month as the scheduling period and the day as the scheduling period, with the goals of maximizing the power generation and the navigation rate, considering the scheduling constraints of flood control, power generation and ecology;

[0071] Considering the 12 months of the whole year, the medium-term scheduling model needs to run 12 times. The power generation target and the navigation rate target are variables with different dimensions. Based on this, the multi-objective optimization is changed to a single objective by using the weight method: During optimization, it is necessary to perform a dimensionless operation on the power generation target; in the medium-term scheduling model, the power generation target value is normalized by the power generation of the long-term scheduling model in the same month, while the navigation rate target value is already in the range of 0-1, and the coefficient is the weight ratio of the power generation target to the navigation target. The medium-term scheduling model takes the upstream water levels of each reservoir at the beginning and end of the day as decision variables;

[0072] (3) Short-term scheduling model:

[0073] Taking a day as the scheduling period and an hour as the scheduling time interval, with the objectives of minimizing the standard deviation of residual load and maximizing the navigation rate, considering the constraints of 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 run 365 or 366 times. Based on this, the multi-objective optimization is then transformed into a single objective by using the weight method: During optimization, the standard deviation of residual load objective also needs to complete the operation of dimensionless. In the short-term scheduling model, the standard deviation of residual load is normalized by the daily average load value, and the coefficient is the weight ratio of the standard deviation of residual load objective to the navigation objective. The short-term scheduling model takes the upstream water levels of each reservoir at the beginning and end of each hour as decision variables.

[0074] Preferably, in S4, the "transfer - feedback" mechanism and model solution method of the long - medium - short - term nested scheduling model are as follows:

[0075] (1) The results of the long - term scheduling model are transferred to the medium - term scheduling model as its water level boundary at the corresponding moment. The medium - term scheduling model will try its best to meet the water level boundary transferred from the long - term scheduling model. When it cannot meet this water level boundary, it will feedback the results of the medium - term scheduling model to the water level of the long - term scheduling model at the corresponding moment. At the same time, the results of the medium - term scheduling model will be transferred to the short - term scheduling model as its water level boundary at the corresponding moment. The short - term scheduling model will try its best to meet the water level boundary transferred from the medium - term scheduling model. When it cannot meet this water level boundary, it will feedback the results of the short - term scheduling model to the water level of the medium - term scheduling model at the corresponding moment. The long - medium - short - term nested scheduling model will achieve water level transfer in the order of long - term - medium - term - short - term, and will also achieve water level feedback in the order of short - term - medium - term - long - term to ensure the consistency of the hydropower station water level scheduling process on different time scales;

[0076] (2) The optimal scheduling model of the water - wind - light 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 using dynamic programming - like algorithms and evolutionary algorithms. Due to the standard deviation of residual load objective in the short - term scheduling model, the scheduling decision - making process has after - effect and is not suitable for using dynamic programming - like algorithms for solution, but is suitable for using evolutionary algorithms for solution.

[0077] Preferably, the computer program for solving the long - medium - short - term nested scheduling model of the water - wind - light complementary power generation system is used to implement the long - medium - short - term nested scheduling method of a water - wind - light complementary power generation system according to any one of claims 1 to 5.

[0078] Preferably, on the other hand, the present invention provides a terminal, which is at least equipped with a controller for implementing the long-term, medium-term and short-term nested scheduling algorithm of the water-wind-solar complementary power generation system.

[0079] Preferably, on the other hand, the present invention provides a computer-readable storage medium, including instructions, which when running on a computer, cause the computer to execute the long-term, medium-term and short-term nested scheduling method of a water-wind-solar complementary power generation system.

[0080] Preferably, on the other hand, the present invention provides a control system for implementing the long-term, medium-term and short-term nested scheduling method of the water-wind-solar complementary power generation system.

[0081] Preferably, on the other hand, the present invention provides a water-wind-solar complementary power generation system scheduling device equipped with the control system implementing the long-term, medium-term and short-term nested scheduling method of the water-wind-solar complementary power generation system.

[0082] The present invention has the following beneficial effects:

[0083] 1. The model proposed by the present invention simultaneously considers the scheduling requirements in four aspects of flood control, power generation, ecology and shipping, coordinates the relationship between the power generation scheduling of the water-wind-solar complementary power generation system and the flood control, ecology and shipping scheduling tasks of hydropower itself, and the obtained scheduling scheme is more in line with the actual scheduling requirements.

[0084] 2. The long-term, medium-term and short-term nested scheduling model of the water-wind-solar complementary power generation system established by the present invention and the "transmission-feedback" mechanism at different time scales can ensure the consistency of the hydropower station water level scheduling process at different time scales, so that the scheduling personnel will not have the problem of time scale fragmentation when switching to use long-term, medium-term and short-term scheduling schemes. BRIEF DESCRIPTION OF THE DRAWINGS

[0085] The present invention will be further described below in conjunction with the drawings and embodiments.

[0086] Figure 1 It is a flowchart of a long-term, medium-term and short-term nested scheduling model of a water-wind-solar complementary power generation system provided by an embodiment of the present invention.

[0087] Figure 2 It is a schematic diagram of the structure and solution method of the long-term, medium-term and short-term nested scheduling model provided by an embodiment of the present invention.

[0088] Figure 3 It is a schematic diagram of the research area of the water-wind-solar complementary power generation system in the lower reaches of the Jinsha River provided by an embodiment of the present invention.

[0089] Figure 4 The water level process of the long-term scheduling model result provided by an embodiment of the present invention.

[0090] Figure 5The results of the medium-term scheduling model provided by the embodiments of the present invention (flood control demand).

[0091] Figure 6 The results of the medium-term scheduling model provided by the embodiments of the present invention (power generation demand).

[0092] Figure 7 The results of the medium-term scheduling model provided by the embodiments of the present invention (ecological demand).

[0093] Figure 8 The results of the medium-term scheduling model provided by the embodiments of the present invention (shipping demand).

[0094] Figure 9 The results of the short-term scheduling model provided by the embodiments of the present invention. Detailed implementation manners

[0095] The present invention will be further described in detail below with reference to the embodiments of the accompanying drawings. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals represent the same or similar elements or elements with the same or similar functions from beginning to end. The embodiments described below by referring to the accompanying drawings are exemplary and are intended to explain the present invention, and should not be construed as limiting the present invention.

[0096] Embodiment 1:

[0097] Please refer to Figures 1-9 , the present invention proposes the scheduling objectives and constraint conditions of the water-wind-solar complementary power generation system in consideration of the comprehensive scheduling requirements of flood control, power generation, ecology and shipping, establishes a long-term, medium-term and short-term nested scheduling model for the water-wind-solar complementary power generation system, proposes a solution method for the long-term, medium-term and short-term nested scheduling model based on the "transfer-feedback" mechanism, and obtains the long-term, medium-term and short-term scheduling schemes of the water-wind-solar complementary power generation system.

[0098] Attached Figure 1 Shown is the flow chart of the long-term, medium-term and short-term nested scheduling model of the water-wind-solar complementary power generation system, which specifically includes the following steps:

[0099] S1. Collect the basic attribute data, scheduling characteristic curve data, hydrology and the output data of the wind and solar power stations of each power station in the water-wind-solar complementary power generation system;

[0100] S2. Analyze the scheduling requirements of the water-wind-solar complementary power generation system from four aspects of flood control, power generation, ecology and shipping to obtain the scheduling objectives and constraint conditions;

[0101] (a) Flood control demand:

[0102] Flood control is an important task in reservoir operation, which usually includes flood control for the downstream, flood control for the upstream, and flood control for the reservoir itself. Flood control for the downstream mainly aims to prevent the water level or flow rate of the downstream protected object from exceeding the safety water level or safety flow rate. Flood control for the upstream mainly aims to reduce the impact of reservoir backwater on the upstream protected object. Flood control for the reservoir itself is to avoid the adverse impact of reservoir water storage on the structural safety of the dam. The present invention considers flood control requirements in the form of constraints:

[0103]

[0104] In the formula, FC_C_1 and FC_C_2 are the numbers of flood control constraints, representing flood control constraints for the downstream, upstream, and the reservoir itself respectively; Q i,t and Z i,t represent the flow rate and water level of the i-th downstream flood control protected object at the t-th time period respectively, and are the corresponding safety flow rate and safety water level; Zu i,t is the upstream water level of the i-th reservoir at the t-th time period, and are its upper and lower limits under flood control constraints;

[0105] (b) Power generation demand:

[0106] The power generation scheduling of the water-wind-solar multi-energy complementary system needs to consider the requirements of three subsystems of hydropower, wind power, and photovoltaic power generation and the power grid load. The present invention constructs a power generation scheduling model with the maximum power generation as the goal in the medium and long term and the minimum residual load standard deviation as the goal in the short term:

[0107]

[0108] For a water-wind-solar multi-energy complementary system containing a cascade reservoir group, the hydropower, wind power, and photovoltaic power generation power stations that output the power in a bundled manner are regarded as a group of water-wind-solar multi-energy complementary systems. In the above formula, G represents a total of G groups of water-wind-solar multi-energy complementary systems; PG_O_1 and PG_O_2 are the numbers of power generation scheduling goals, representing the goals of maximum power generation and minimum residual load standard deviation respectively; E(N g,t ) is the total power generation of the entire water-wind-solar multi-energy complementary system, N g,t is the total output of the g-th group of water-wind-solar multi-energy complementary systems at the t-th time period; Nh g,i,t , Nw g,j,t , Ns g,k,t represent the outputs of the i-th hydropower station, the j-th wind power station, and the k-th photovoltaic power generation station in the g-th group of water-wind-solar multi-energy complementary systems at the t-th time period respectively; Δt and T represent the scheduling period duration and the number of scheduling periods respectively; std(R g,t ) is the residual load standard deviation of the g-th group of water-wind-solar multi-energy complementary systems; R g,t and Pg,t are the remaining load and load of the g - th group of water - wind - solar multi - energy complementary system in the t - th period respectively, is R g,t the average value of;

[0109] The power generation scheduling of the water - wind - solar multi - energy complementary system needs to consider the constraints of hydropower, wind power, photovoltaic power generation and transmission channel capacity. The specific constraints include:

[0110] (1) Reservoir water level constraint:

[0111]

[0112] In the formula, PG_C_1 is the number of the power generation scheduling constraint. Symbols with the same structure below all represent the power generation scheduling constraint numbers; the meanings of each variable in the above formula are similar to those of the flood control scheduling constraint FC_C_2; when considering both flood control and power generation scheduling requirements in the scheduling model, the upper and lower limits of the water level take the intersection of the two;

[0113] (2) Reservoir discharge flow constraint:

[0114]

[0115] In the formula, Qo i,t is the discharge flow of the i - th reservoir in the t - th period, and are its corresponding upper and lower limit constraints. The discharge flow Qo i,t is composed of the power generation flow Qg i,t and the spillage flow Qa i,t . The power generation flow Qg i,t shall not exceed the full - load flow

[0116] (3) Hydropower output constraint:

[0117]

[0118] In the formula, Nh i,t is the output of the i - th reservoir in the t - th period, and are its corresponding upper and lower limit constraints. The hydropower output is also restricted by the expected output curve and the NHQ curve;

[0119] (4) Water balance equation:

[0120] PG_C_5:V i,t+1 =V i,t +(Qi i,t -Qo i,t -Ev i,t )·Δt; (11)

[0121] wherein, V i,t and V i,t+1 respectively represent the reservoir capacities of the i-th reservoir at the beginning and end of the t-th period; Qi i,t and Qo i,t are the corresponding inflow and outflow discharges, and Ev i,t represents the evaporation water loss;

[0122] (5) Hydraulic connection between cascade hydropower stations:

[0123]

[0124] wherein: Qitv i,t is the inter-basin flow of the upper reach of the i-th reservoir; is the flow at the upstream station when it reaches the i-th reservoir through river routing; Φ i is the set of upstream stations hydraulically connected to the i-th reservoir, is an element of Φ i ; represents the river routing model, specifically the Muskingum model or the lag time routing model;

[0125] (6) Wind power constraint:

[0126]

[0127] wherein, v j,t is the wind speed of the j-th wind power station at the t-th period, and are the cut-in and cut-out wind speeds; Nw j,t is the output of the j-th wind power station at the t-th period, and it cannot exceed the installed capacity of the corresponding wind power station

[0128] (7) Photovoltaic power generation constraint:

[0129]

[0130] wherein, Ns k,t and respectively represent the output and installed capacity of the k-th photovoltaic power generation station;

[0131] (8) Transmission channel capacity constraint:

[0132]

[0133] wherein, N g,t and respectively represent the packaged output and transmission channel capacity of the g-th water-wind-solar multi-energy complementary system;

[0134] (c) Ecological requirements:

[0135] Ecological regulation mainly targets the ecological regulation requirements for fish spawning, including ecological regulation for fish that lay adhesive-sinking eggs and ecological regulation for fish that lay drifting eggs;

[0136] (1) Ecological regulation requirements for fish that lay adhesive-sinking eggs:

[0137] For fish such as carp and crucian carp that lay adhesive-sinking eggs, stable water flow and water level are more conducive to their spawning, which requires that the discharge from the reservoir and the variation range of the downstream water level should not be too large. The present invention considers the ecological regulation requirements for fish that lay adhesive-sinking eggs in the form of constraint conditions:

[0138]

[0139] In the formula, EC_C_1 to EC_C_4 are all ecological constraint condition numbers; Qo i,t is the discharge from the i-th reservoir with ecological regulation tasks in the t-th period, and are the upper and lower limits of the ecological flow; |ΔQo i,t | is the absolute value of the discharge variation range ΔQo i,t , is the maximum discharge variation range limit; |ΔZd i,t | is the absolute value of the downstream water level variation range ΔZd i,t , is the maximum downstream water level variation range limit; Du i is the duration for the i-th reservoir with ecological regulation tasks to maintain the ecological regulation constraints of EC_C_1 to EC_C_3, is the minimum duration that needs to be maintained;

[0140] (2) Ecological regulation requirements for fish that lay drifting eggs:

[0141] For fish such as black carp, grass carp, silver carp, and bighead carp that lay drifting eggs, flowing water conditions are more conducive to their spawning, which requires the reservoir to continuously increase the discharge over a period of time. The present invention considers the ecological regulation requirements for fish that lay drifting eggs in the form of constraint conditions:

[0142]

[0143] In the formula, EC_C_5 and EC_C_6 are ecological constraint condition numbers; and are the start time and end time for the i-th reservoir with ecological regulation tasks to increase the discharge respectively; is the initial discharge at the start of increasing the discharge, is the increase in the downstream discharge; Qo i,t-1 and Qo i,t represent the downstream discharges of the i-th reservoir with ecological scheduling tasks in the t-th period and the (t - 1)-th period respectively;

[0144] (d) Shipping demand:

[0145] For navigating ships, too large a flow rate will cause unstable ship navigation, and too small a flow rate will cause ships to run aground, both of which are not conducive to navigation. The present invention considers the shipping scheduling demand with the maximum average navigation rate as the goal:

[0146]

[0147] In the formula: NA_O_1 is the navigation scheduling target number, NR i,t is the navigation rate of the i-th reservoir with navigation tasks in the t-th period, is the average navigation rate; I is the number of reservoirs with navigation tasks, and T is the number of scheduling periods; For the downstream discharge within the interval, too small a flow rate is likely to cause ships to run aground, and its corresponding navigation rate is 0; the downstream discharge within the interval is the most suitable navigation flow rate, and its corresponding maximum navigation rate is 100%, and then as the downstream discharge increases, the navigation rate gradually decreases. When the downstream discharge exceeds the maximum navigation flow rate the navigation rate is 0.

[0148] S3. Considering that the water-wind-solar complementary power generation system focuses on different scheduling demands at different time scales of long term, medium term, and short term, different scheduling objectives and constraint conditions are combined to establish a long-medium-short term nested scheduling model, as shown in the appendix Figure 2 as follows;

[0149] (1) Long-term scheduling model:

[0150] The long-term scheduling model constructed by the present invention takes the year as the scheduling period and the month as the scheduling period, with the maximum power generation (PG_O_1) as the goal, and considers the flood control (FC_C_1~FC_C_2) and power generation (PG_C_1~PG_C_10) scheduling constraints. The long-term scheduling model takes the upstream water levels of each reservoir at the beginning and end of the month as decision variables.

[0151] (2) Medium-term scheduling model:

[0152] The medium-term scheduling model constructed by the present invention takes the month as the scheduling period and the day as the scheduling period, with the maximum power generation

[0153] Taking the maximum power generation (PG_O_1) and navigation rate (NA_O_1) as the goals, considering the flood control (FC_C_1~FC_C_2), power generation (PG_C_1~PG_C_10), and ecological (EC_C_1~EC_C_4, EC_C_5~EC_C_6) scheduling constraints. Considering 12 months in a year, the medium-term scheduling model needs to run 12 times. The power generation target (PG_O_1) and the navigation rate target (NA_O_1) are variables with different dimensions. Therefore, when using the weight method to transform multi-objective optimization into a single objective During optimization, it is necessary to perform a dimensionless operation on the power generation target (PG_O_1). In the medium-term scheduling model of the present invention, the power generation target value (monthly power generation) is normalized by the power generation in the same month of the long-term scheduling model, while the navigation rate target value is already within the range of 0 to 1. The coefficient is the weight ratio of the power generation target to the navigation target. The medium-term scheduling model uses the upstream water levels of each reservoir at the beginning and end of the day as decision variables.

[0154] (3) Short-term scheduling model:

[0155] The short-term scheduling model constructed by the present invention uses a day as the scheduling period and an hour as the scheduling time slot, taking the minimum standard deviation of remaining load (PG_O_2) and the maximum navigation rate (NA_O_1) as the goals, considering the flood control (FC_C_1~FC_C_2), power generation (PG_C_1~PG_C_10), and ecological (EC_C_1~EC_C_4, EC_C_5~EC_C_6) scheduling constraints. Considering 365 or 366 days in a year, the short-term scheduling model needs to run 365 or 366 times. Similar to the medium-term scheduling model, when using the weight method to transform multi-objective optimization into a single objective During optimization, the target of the standard deviation of remaining load (PG_O_2) also needs to complete the dimensionless operation. In the short-term scheduling model of the present invention, the standard deviation of remaining load is normalized by the daily average load value. The coefficient is the weight ratio of the standard deviation of remaining load target to 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.

[0156] S4. Propose the "transfer - feedback" mechanism and model solution method of the long-term, medium-term, and short-term nested scheduling model to ensure the consistency of the hydropower station water level scheduling process on different time scales, as shown in the appendix Figure 2 as follows;

[0157] (1) The results of the long-term scheduling model (the upstream water levels of the reservoir at the beginning and end of the month) are transmitted to the medium-term scheduling model as the water level boundaries at the corresponding moments. The medium-term scheduling model will try its best to meet the water level boundaries transmitted by the long-term scheduling model. However, when it cannot meet these boundaries, the results of the medium-term scheduling model will be fed back to the water levels of the long-term scheduling model at the corresponding moments. At the same time, the results of the medium-term scheduling model (the upstream water levels of the reservoir at the beginning and end of the day) will be transmitted to the short-term scheduling model as the water level boundaries at the corresponding moments. The short-term scheduling model will try its best to meet the water level boundaries transmitted by the medium-term scheduling model. However, when it cannot meet these boundaries, the results of the short-term scheduling model will be fed back to the water levels of the medium-term scheduling model at the corresponding moments. The long-medium-short nested scheduling model will achieve water level transmission in the order of long-term - medium-term - short-term, and will also achieve water level feedback in the order of short-term - medium-term - long-term to ensure the consistency of the hydropower station water level scheduling process at different time scales.

[0158] (2) The optimal scheduling model of the water-wind-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 can be solved using dynamic programming algorithms and evolutionary algorithms. Due to the aftereffect of the scheduling decision process caused by the residual load standard deviation (PG_O_2) target in the short-term scheduling model, it is not suitable to use dynamic programming algorithms for solution and is suitable to use evolutionary algorithms for solution.

[0159] S5. Based on the long-medium-short nested scheduling model and its solution method, long-term, medium-term, and short-term scheduling plans for the water-wind-solar complementary power generation system are obtained.

[0160] Example 2:

[0161] The application of the present invention will be further described below in combination with specific experiments.

[0162] The present invention takes the water-wind-solar complementary power generation system in the lower reaches of the Jinsha River as the object, as shown in the appendix Figure 3 As shown. The four hydropower stations in this area, Wudongde, Baihetan, Xiluodu, and Xiangjiaba, are represented by the symbols H1, H2, H3, and H4 respectively, and the three downstream flood control sections, Lizhuang, Zhutuo, and Cuntan, are represented by the symbols R1, R2, and R3 respectively. The wind power stations around the three hydropower stations H1, H2, and H3 are represented by W1, W2, and W3 respectively, and the photovoltaic power stations around the three hydropower stations H1, H2, and H3 are represented by S1, S2, and S3 respectively. The example is completed using the data in 2020.

[0163] (1) Flood control requirements of the research object:

[0164] In this embodiment, the downstream flood control protection targets are mainly three hydrological stations (R1, R2, and R3), and their flood control requirements are not exceeding the safe flow rate and the safe water level. The specific values are shown in Table 1. For the flood control of the reservoir itself, when there is no flood interception, it operates between the dead water level and the flood limit water level; when intercepting floods, it operates between the dead water level and the flood control high water level, and the specific values are shown in Table 1.

[0165] Table 1 Basic information of the research object

[0166]

[0167] (2) Power generation demand of the research object:

[0168] In this embodiment, for medium- and long-term power generation scheduling requirements, the maximum power generation is considered, and for short-term power generation scheduling requirements, the peak shaving demand is considered. The basic information related to power generation is shown in Table 1.

[0169] (3) Ecological demand of the research object:

[0170] In this embodiment, the ecological scheduling requirements are mainly concentrated in Reservoirs H1 and H4. Among them, the ecological scheduling time of H1 is distributed from March to May, and the ecological scheduling time of H4 is distributed from May to June. The ecological scheduling requirements of H1 include the ecological scheduling requirements for fish that produce adhesive demersal eggs and drifting eggs, while the ecological scheduling requirements of H4 are mainly for fish that spawn by drifting. The detailed ecological scheduling requirements are shown in Table 2.

[0171] Table 2 Ecological scheduling requirements of the research object

[0172]

[0173] (4) Shipping demand of the research object:

[0174] In this embodiment, only Reservoir H4 has the navigation function. The navigation flow rate of H4 is between 1200 m 3 / s and 12000 m 3 / s. The relationship between its navigation rate and the discharged flow rate is shown in the following formula:

[0175]

[0176] The result analysis of the embodiment completed by using the model proposed in the present invention is as follows:

[0177] (a) Long-term scheduling result analysis:

[0178] Taking the hydro-wind-solar complementary power generation system composed of H3, W3, and S3 as an example, the reservoir upstream water level scheduling process of its long-term scheduling model is as Figure 4 shown. Figure 4The medium-scale of 0 represents the scheduling process of pure hydropower (without considering wind power and photovoltaic power generation). The scale of 1 means that the installed capacities of wind power and photovoltaic power are equal to the data listed in Table 1. The scales of 2-4 indicate that the installed capacities of wind power and photovoltaic power are 2 to 4 times that of the data listed in Table 1. It can be analyzed from the figure that the combined operation of the hydropower-wind-solar complementary power generation system has an impact on the operation mode of hydropower itself. As the scales of wind power and photovoltaic power generation gradually expand, the time for the upstream water level of hydropower to drop during the non-flood season gradually advances, and the depth of the water level drop also gradually increases, increasing the power generation during the non-flood season. During the flood season, when hydropower operates independently, it is already operating at full load, resulting in a large amount of water being abandoned. When hydropower participates in the combined operation of the hydropower-wind-solar complementary power generation system, due to the early reduction of the water level, the hydropower generation during the flood season is reduced, but it promotes the consumption of wind power and photovoltaic power during the flood season, thus increasing the power generation of the entire hydropower-wind-solar complementary power generation system. The analysis of the results of this scheduling model is consistent with the theoretical understanding, verifying that the long-term scheduling model proposed by the present invention is accurate and effective.

[0179] (b) Analysis of medium-term scheduling results:

[0180] The results of the medium-term scheduling model (flood control requirements) are as Figure 5 shown, taking the flood control control section R1 as an example. The maximum flow rate of R1 throughout the year is 23300 m 3 / s, which is lower than the safe flow rate of 51000 m 3 / s; the highest water level of R1 throughout the year is 265.88 m, which is lower than the safe water level of 268.7 m. Whether from the perspective of flow rate or water level, R1 meets the flood control requirements. The same applies to R2 and R3, verifying that the medium-term scheduling model proposed by the present invention can meet the flood control scheduling requirements.

[0181] The results of the medium-term scheduling model (power generation requirements) are as Figure 6 shown, taking the hydropower-wind-solar complementary power generation system composed of H1, W1 and S1 as an example. The hydropower, wind power and photovoltaic power generation show certain seasonal patterns throughout the year. The discharge flow of H1 during the flood season is greater than that during the dry season, and the corresponding output is also greater than that during the dry season; the outputs of W1 and S1 from January to May and from November to December are greater than those in other months, indicating that H1, W1 and S1 are seasonally complementary throughout the year, which is consistent with the current theoretical understanding, verifying that the medium-term scheduling model proposed by the present invention can meet the power generation scheduling requirements.

[0182] The results of the medium-term scheduling model (ecological requirements) are as Figure 7 shown, taking H1 as an example. The ecological scheduling of H1 can be divided into the ecological scheduling of fish that produce sticky demersal eggs and fish that produce drifting eggs. Figure 7 (a) is the ecological scheduling process of H1 for fish that produce sticky demersal eggs. From March 20th to March 31st, the discharge flow of H1 is controlled at 1420 m 3 / s to 2220 m 3within the range of / s; the maximum flow rate variation is 202 m 3 / s, not exceeding the flow rate variation limit value (800 m 3 / s); the maximum downstream water level variation is 0.55 m, not exceeding the water level variation limit value (1.5 m); at the same time, the above ecological constraints were maintained for 10 days. The analysis of the ecological regulation process from April 1 to April 10 is similar to that from March 20 to March 31. In summary, the regulation process calculated by the medium-term regulation model of the present invention can meet the ecological regulation requirements of fish species that produce sticky and sinking eggs. The ecological regulation process for fish species that produce drifting eggs, H1, is as Figure 7 (b) shown. On May 13, the discharge flow of H1 was 1200 m 3 / s, and the discharge flow increased by 390 m 3 / s day by day in the following 4 days. This artificial water flow pulse is beneficial to the spawning of fish species that produce drifting eggs. In summary, the medium-term regulation model of the present invention can meet the ecological regulation requirements of fish species that produce drifting eggs.

[0183] The results of the medium-term regulation model (shipping demand) are as Figure 8 shown. It can be seen from the figure that the navigation rate of H4 during the flood season is lower than that during the non-flood season. This is because the large flow rate during the flood season has an adverse impact on the navigation stability of ships, which is consistent with the theoretical understanding, indicating that the medium-term regulation model of the present invention can meet the shipping regulation requirements.

[0184] (c) Analysis of short-term regulation results:

[0185] To analyze the short-term peak shaving ability of hydropower in the water-wind-solar complementary power generation system, taking H1, W1, and S1 as examples, the output stack diagrams of typical days during the flood season and non-flood season are respectively drawn, as Figure 9 shown. The standard deviations (std) of the remaining load during typical days in the flood season and non-flood season are 0 and 23 (104 kW) respectively, indicating that the water-wind-solar complementary power generation system is more likely to meet the grid demand during the flood season. This is because the output of W1 and S1 during the flood season is less than that 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 result analysis is consistent with the theoretical understanding, indicating that the short-term regulation model of the present invention can meet the peak shaving regulation requirements.

[0186] Although the embodiments of the present invention have been shown and described, those skilled in the art can understand that various changes, modifications, substitutions, and deformations can be made to these embodiments without departing from the principles and purposes of the present invention. The scope of the present invention is defined by the claims and their equivalents.

Claims

1. A long, medium and short-term nested scheduling method for a water-wind-solar complementary power generation system, characterized in that, The described scheduling method takes into account the comprehensive scheduling requirements of flood control, power generation, ecology, and shipping, proposes the scheduling objectives and constraint conditions for the water-wind-solar complementary power generation system, establishes a long-, medium-, and short-term nested scheduling model for the water-wind-solar complementary power generation system, proposes a solution method for the long-, medium-, and short-term nested scheduling model based on the "transfer-feedback" mechanism, and obtains the long-term, medium-term, and short-term scheduling plans for the water-wind-solar complementary power generation system.

2. The medium- and long-term nested scheduling method for a water-wind-solar hybrid power generation system according to claim 1, wherein, The described scheduling method specifically includes the following steps: S1. Collect the basic attribute data, scheduling characteristic curve data, hydrology, and output data of wind and solar power stations of the water-wind-solar complementary power generation system; S2. Analyze the scheduling requirements of the water-wind-solar complementary power generation system from four aspects: flood control, power generation, ecology, and shipping, and obtain the scheduling objectives and constraint conditions; S3. Considering that the water-wind-solar complementary power generation system focuses on different scheduling requirements at different time scales of long term, medium term, and short term, combine different scheduling objectives and constraint conditions to establish a long-, medium-, and short-term nested scheduling model; S4. Propose the "transfer-feedback" mechanism and model solution method for the long-, medium-, and short-term nested scheduling model to ensure the consistency of the hydropower station water level scheduling process at different time scales; S5. Based on the long-, medium-, and short-term nested scheduling model and its solution method, obtain the long-term, medium-term, and short-term scheduling plans for the water-wind-solar complementary power generation system.

3. A long, medium and short-term nested scheduling method for a water-wind-solar complementary power generation system according to claim 1, characterized in that, In the above S2, the scheduling requirements of flood control, power generation, ecology, and shipping are considered in the form of scheduling objectives or constraint conditions as follows: (a) Flood control requirements: Consider the flood control requirements in the form of constraints: In the formula, FC_C_1 and FC_C_2 are the numbers of flood control constraint conditions, representing the flood control constraints on the downstream, upstream and the reservoir itself respectively; Q i,t and Z i,t represent the flow rate and water level of the i-th downstream flood control protection object at the t-th time period respectively, and are the corresponding safe flow rate and safe water level; Zu i,t is the upstream water level of the i-th reservoir at the t-th time period, and are its upper and lower limits under flood control constraints; (b) Power generation requirements: The power generation scheduling of the water-wind-solar multi-energy complementary system needs to consider the requirements of three subsystems of hydropower, wind power, and photovoltaic power generation and the grid load. A power generation scheduling model is constructed with the maximum power generation as the objective in the medium and long term and the minimum residual load standard deviation as the objective in the short term: For a water-wind-solar multi-energy complementary system containing cascade reservoirs, the hydropower, wind power, and photovoltaic power generation power stations that output the combined output are regarded as a group of water-wind-solar multi-energy complementary systems. In the above formula, G represents a total of G groups of water-wind-solar multi-energy complementary systems; PG_O_1 and PG_O_2 are the numbers of the power generation scheduling objectives, representing the maximum power generation and the minimum standard deviation of the remaining load objectives respectively; E(N g,t ) is the total power generation of the entire water-wind-solar multi-energy complementary system, and N g,t is the total output of the g-th group of water-wind-solar multi-energy complementary systems in the t-th time period; Nh g,i,t , Nw g,j,t , Ns g,k,t represent the outputs of the i-th hydropower station, the j-th wind power station, and the k-th photovoltaic power station in the g-th group of water-wind-solar multi-energy complementary systems in the t-th time period respectively; Δt and T represent the duration of the scheduling time period and the number of scheduling time periods respectively; std(R g,t ) is the standard deviation of the remaining load of the g-th group of water-wind-solar multi-energy complementary systems; R g,t and P g,t are the remaining load and load of the g-th group of water-wind-solar multi-energy complementary systems in the t-th time period respectively, is the average value of R g,t ; The power generation scheduling of the water-wind-solar multi-energy complementary system needs to consider the constraint limitations of hydropower, wind power, photovoltaic power generation, and transmission channel capacity. The specific constraints include: (1) Reservoir water level constraint: In the formula, PG_C_1 is the number of the power generation scheduling constraint, and the symbols with the same structure below all represent the power generation scheduling constraint numbers; the meanings of the variables in the above formula are similar to those of the flood control scheduling constraint FC_C_2; when considering the flood control and power generation scheduling requirements simultaneously in the scheduling model, the upper and lower limits of the water level take the intersection of the two; (2) Reservoir discharge flow constraint: where, Qo i,t is the outflow of the i-th reservoir in the t-th period, and are its corresponding upper and lower bound constraints. The outflow Qo i,t consists of the power generation flow Qg i,t and the spill flow Qa i,t . The power generation flow Qg i,t shall not exceed the full-load flow (3) Hydropower output constraint: where, Nh i,t is the output of the i-th reservoir in the t-th period, and are its corresponding upper and lower bound constraints. The hydropower output is also constrained by the forecast output curve and the NHQ curve; (4) Water volume balance equation: PG_C_5:V i,t+1 = V i,t +(Qi i,t - Qo i,t - Ev i,t )·Δt;(11) where V i,t and V i,t+1 represent the reservoir storage at the beginning and end of the i-th reservoir in the t-th period, respectively; Qi i,t and Qo i,t are the corresponding inflow and outflow discharges, and Ev i,t represents the evaporation water loss; (5) Hydraulic connection between cascade hydropower stations: Where: Qitv i,t is the lateral inflow of the upper reach of the i-th reservoir; is the flow at the upstream station when the flow reaches the i-th reservoir through river routing; Φ i is the set of upstream stations hydraulically connected to the i-th reservoir, is an element of Φ i ; represents the river routing model, specifically the Muskingum model or the lag routing model; (6) Wind power constraint: where, v j,t is the wind speed of the j-th wind power station at the t-th time period, and are the cut-in and cut-out wind speeds; Nw j,t is the output of the j-th wind power station at the t-th time period, which cannot exceed the installed capacity of the corresponding wind power station (7) Photovoltaic power generation constraint: where Ns k,t and represent the output and installed capacity of the k-th photovoltaic power station, respectively; (8) Transmission channel capacity constraint: Where N g,t and represent the packaged output and transmission channel capacity of the gth group of water-wind-solar multi-energy complementary systems, respectively; (c) Ecological requirements: Ecological scheduling mainly aims at the ecological scheduling requirements for fish spawning, including ecological scheduling for fish laying adhesive-sinking eggs and ecological scheduling for fish laying drifting eggs; (1) Ecological scheduling requirements for fish laying adhesive-sinking eggs: Consider the ecological scheduling requirements for fish laying adhesive-sinking eggs in the form of constraint conditions: where EC_C_1 to EC_C_4 are all ecological constraint condition numbers; Qo i,t is the outflow discharge of the i-th reservoir with ecological scheduling tasks in the t-th period, and are the upper and lower limits of ecological flow; |ΔQo i,t | is the absolute value of the variation range of the outflow discharge ΔQo i,t ; is the maximum variation range limit of the outflow discharge; |ΔZd i,t | is the absolute value of the downstream water level variation range ΔZd i,t ; is the maximum variation range limit of the downstream water level; Du i is the duration for the i-th reservoir with ecological scheduling tasks to maintain the ecological scheduling constraints of EC_C_1 to EC_C_3, is the minimum required duration. (2) Ecological scheduling requirements for fish laying drifting eggs: Consider the ecological scheduling requirements for fish laying drifting eggs in the form of constraint conditions: Wherein, EC_C_5 and EC_C_6 are ecological constraint condition numbers; and are the start time and end time for the i-th reservoir with ecological scheduling tasks to start increasing the discharged flow, respectively; is the initial flow rate at the start of increasing the discharged flow, is the increase amplitude of the discharged flow; Qo i,t-1 and Qo i,t represent the discharged flows of the i-th reservoir with ecological scheduling tasks in the t-th period and the (t - 1)-th period, respectively; (d) Shipping requirements: Consider the shipping scheduling requirements with the maximum average navigation rate as the objective: Where: NA_O_1 is the navigation scheduling target number, NR i,t is the navigation rate of the i-th reservoir with navigation tasks in the t-th period, is the average navigation rate; I is the number of reservoirs with navigation tasks, and T is the number of scheduling periods; For the downstream discharge within the range, if the flow is too small, it is easy to cause ships to run aground, and its corresponding navigation rate is 0; the downstream discharge within the range is the most suitable navigation flow, and its corresponding maximum navigation rate is 100%. Then, as the downstream discharge increases, the navigation rate gradually decreases. When the downstream discharge exceeds the maximum navigation flow the navigation rate is 0.

4. The long-term, medium-term and short-term nested scheduling method for a water-wind-solar hybrid power generation system according to claim 3, characterized in that, In the above S3, the established long-, medium-, and short-term nested scheduling model is as follows: Considering that different scheduling tasks need to be considered for the water-wind-solar complementary system during 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 the year as the scheduling period and the month as the scheduling time interval, aims to maximize the power generation, and considers the flood control and power generation 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; (2) Medium-term scheduling model: The medium-term scheduling model takes the month as the scheduling period and the day as the scheduling time interval, aims to maximize the power generation and the navigation rate, and considers the scheduling constraints of flood control, power generation, and ecology; Considering the 12 months in a year, the medium-term scheduling model needs to run 12 times. The power generation target and the navigation rate target are variables with different dimensions. Based on this, the multi-objective optimization is changed into a single objective by using the weighting method: During optimization, it is necessary to dimensionless the power generation target; in the medium-term scheduling model, the power generation target value is normalized by the power generation in the same month of the long-term scheduling model, while the navigation rate target value is already in the range of 0 to 1. The coefficient is the weight ratio of the power generation target to the navigation target. The medium-term scheduling model uses the upstream water levels of each reservoir at the beginning and end of the day as decision variables; (3) Short-term scheduling model: Taking the day as the scheduling period and the hour as the scheduling time period, with the goal of minimizing the standard deviation of the remaining load and maximizing the navigation rate, considering the constraints of flood control, power generation, and ecological scheduling, and 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 then transformed into a single objective using the weighting method: The operation of dimensionless processing for the standard deviation of the remaining load target is also required during optimization; in the short-term scheduling model, the standard deviation of the remaining load is normalized using the daily average load value, and the coefficient is the weight ratio of the standard deviation of the remaining load target to 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.

5. The long, medium and short-term nested scheduling method for a water-wind-solar complementary power generation system according to claim 3, characterized in that, In S4, the "transfer-feedback" mechanism and model solution method of the long-medium-short-term nested scheduling model are as follows: (1) The results of the long-term scheduling model are transferred to the medium-term scheduling model as its water level boundary at the corresponding moment. The medium-term scheduling model will try its best to meet the water level boundary transferred by the long-term scheduling model. When it cannot meet this water level boundary, the results of the medium-term scheduling model will be fed back to the water level of the long-term scheduling model at the corresponding moment; at the same time, the results of the medium-term scheduling model will be transferred to the short-term scheduling model as its water level boundary at the corresponding moment. The short-term scheduling model will try its best to meet the water level boundary transferred by the medium-term scheduling model. When it cannot meet this water level boundary, the results of the short-term scheduling model will be fed back to the water level of the medium-term scheduling model at the corresponding moment; the long-medium-short-term nested scheduling model will achieve water level transfer in the order of long-term-medium-term-short-term, and will also achieve water level feedback in the order of short-term-medium-term-long-term to ensure the consistency of the hydropower station water level scheduling process on different time scales; (2) The optimal scheduling model of the water-wind-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 using dynamic programming algorithms and evolutionary algorithms. Due to the aftereffect of the scheduling decision process caused by the residual load standard deviation target, the short-term scheduling model is not suitable for using dynamic programming algorithms for solution and is suitable for using evolutionary algorithms for solution.

6. A computer program for solving a long, medium and short-term nested scheduling model of a water-wind-solar complementary power generation system, characterized in that, The computer program for solving the long-medium-short-term nested scheduling model of the water-wind-solar complementary power generation system is used to implement the long-medium-short-term nested scheduling method of a water-wind-solar complementary power generation system according to any one of claims 1 to 5.

7. A terminal, characterized in that, The terminal is at least equipped with a controller that implements the long-medium-short-term nested scheduling algorithm of the water-wind-solar complementary power generation system according to any one of claims 1 to 5.

8. A computer-readable storage medium, including instructions, which when running on a computer, cause the computer to execute the long-medium-short-term nested scheduling method of a water-wind-solar complementary power generation system according to any one of claims 1-5.

9. A control system for implementing the long-medium-short-term nested scheduling method of the water-wind-solar complementary power generation system according to any one of claims 1-5.

10. A water-wind-solar complementary power generation system scheduling device equipped with the control system of the long-medium-short-term nested scheduling method of the water-wind-solar complementary power generation system according to claim 9.

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